EFFECTS OF BUFFER SALTS, TEMPERATURE

AND pH ON FOOD WASTE COMPOSTING

A Thesis

Submitted to the Faculty of Graduate Studies and Research

in Partial Fulfillment of the Requirements

for the degree of

Master of Applied Science

in Environmental Systems Engineering

University of Regina

by

Sheng Li

Regina, Saskatchewan

January, 2012

Copyright 2012: Sheng. Li

EFFECTS OF BUFFER SALTS, TEMPERATURE

AND pH ON FOOD WASTE COMPOSTING

A Thesis

Submitted to the Faculty of Graduate Studies and Research

in Partial Fulfillment of the Requirements

for the degree of

Master of Applied Science

in Environmental Systems Engineering

University of Regina

by

Sheng Li

Regina, Saskatchewan

January, 2012

Copyright 2012: Sheng. Li

I 1 Library and Archives Canada

Published Heritage Branch

395 Wellington Street Ottawa ON KlA ON4 Canada

NOTICE:

The author has granted a non-exclusive license allowing Library and Archives Canada to reproduce, publish, archive, preserve, conserve, communicate to the public by telecommunication or on the Internet, loan, distrbute and sell theses worldwide, for commercial or non-commercial purposes, in microform, paper, electronic and/or any other formats.

The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.

Bibliotheque et Archives Canada

Direction du Patrimoine de ('edition

395, rue Wellington Ottawa ON K1A ON4 Canada

Your file Votre reference

ISBN: 978-0-494-88499-7

Our file Notre reference

ISBN: 978-0-494-88499-7

AVIS:

L'auteur a accorde une licence non exclusive permettant a la Bibliotheque et Archives Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par telecommunication ou par ('Internet, preter, distribuer et vendre des theses partout dans le monde, a des fins commerciales ou autres, sur support microforme, papier, electronique et/ou autres formats.

L'auteur conserve la propriete du droit d'auteur et des droits moraux qui protege cette these. Ni la these ni des extraits substantiels de celle-ci ne doivent etre imprimes ou autrement reproduits sans son autorisation.

In compliance with the Canadian Privacy Act some supporting forms may have been removed from this thesis.

While these forms may be included in the document page count, their removal does not represent any loss of content from the thesis.

Canada.

Conformement a la loi canadienne sur la protection de la vie privee, quelques formulaires secondaires ont ete enleves de cette these.

Bien que ces formulaires aient inclus dans la pagination, it n'y aura aucun contenu manquant.

Library and Archives Canada

Published Heritage Branch

Bibliotheque et Archives Canada

Direction du Patrimoine de I'edition

395 Wellington Street Ottawa ON K1A0N4 Canada

395, rue Wellington Ottawa ON K1A 0N4 Canada

Your file Votre reference

ISBN: 978-0-494-88499-7

Our file Notre reference

ISBN: 978-0-494-88499-7

NOTICE:

The author has granted a non­exclusive license allowing Library and Archives Canada to reproduce, publish, archive, preserve, conserve, communicate to the public by telecommunication or on the Internet, loan, distrbute and sell theses worldwide, for commercial or non­commercial purposes, in microform, paper, electronic and/or any other formats.

AVIS:

L'auteur a accorde une licence non exclusive permettant a la Bibliotheque et Archives Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par telecommunication ou par I'lnternet, preter, distribuer et vendre des theses partout dans le monde, a des fins commerciales ou autres, sur support microforme, papier, electronique et/ou autres formats.

The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.

L'auteur conserve la propriete du droit d'auteur et des droits moraux qui protege cette these. Ni la these ni des extraits substantiels de celle-ci ne doivent etre imprimes ou autrement reproduits sans son autorisation.

In compliance with the Canadian Privacy Act some supporting forms may have been removed from this thesis.

While these forms may be included in the document page count, their removal does not represent any loss of content from the thesis.

Conformement a la loi canadienne sur la protection de la vie privee, quelques formulaires secondaires ont ete enleves de cette these.

Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant.

Canada

UNIVERSITY OF REGINA

FACULTY OF GRADUATE STUDIES AND RESEARCH

SUPERVISORY AND EXAMINING COMMITTEE

Sheng Li, candidate for the degree of Master of Applied Science in Environmental Systems Engineering, has presented a thesis titled, Effects of Buffer Salts, Temperature and pH on Food Waste Composting, in an oral examination held on December 22, 2011. The following committee members have found the thesis acceptable in form and content, and that the candidate demonstrated satisfactory knowledge of the subject material.

External Examiner: Dr. David deMontigny, Process Systems Engineering

Supervisor: Dr. Guo H. Huang, Environmental Systems Engineering

Committee Member: Dr. Fanhua Zeng, Petroleum Systems Engineering

Committee Member: Dr. Stephanie Young, Environmental Systems Engineering

Chair of Defense: Dr. Boting Yang, Department of Computer Science

*Not present at defense

UNIVERSITY OF REGINA

FACULTY OF GRADUATE STUDIES AND RESEARCH

SUPERVISORY AND EXAMINING COMMITTEE

Sheng Li, candidate for the degree of Master of Applied Science in Environmental Systems Engineering, has presented a thesis titled, Effects of Buffer Salts, Temperature andpH on Food Waste Composting, in an oral examination held on December 22, 2011. The following committee members have found the thesis acceptable in form and content, and that the candidate demonstrated satisfactory knowledge of the subject material.

External Examiner: Dr. David deMontigny, Process Systems Engineering

Supervisor: Dr. Guo H. Huang, Environmental Systems Engineering

Committee Member: Dr. Fanhua Zeng, Petroleum Systems Engineering

Committee Member: Dr. Stephanie Young, Environmental Systems Engineering

Chair of Defense: Dr. Boting Yang, Department of Computer Science

*Not present at defense

ABSTRACT

In this thesis research, the effects of buffer salts, temperature and pH on food-waste

composting under various conditions were examined. Three factors regarding the

composting experiments were investigated via ten runs (including three control runs

and three parallel runs) of bench-scale in-vessel composting experiments. The

research can be divided into two sections:

In the first section, several buffer salts with initially different initial pH have been

utilized as the pH control amendments. Three runs with K2HPO4/MgSO4,

KH2PO4/MgSO4 and NaAc added, were compared with the control run without

anything added. The effects of the three agents upon pH, microbial activity and

ammonia release were then evaluated. Nitrogen transformation within composting

composition was also analyzed. The results showed the alkaline additives (NaAc and

K2HPO4/MgSO4) had a positive influence upon the degradation of organic material

while acidic additive (ICH2PO4/MgSO4) severely reduced the pH value and resulted in

reduced microorganism activity. With the assistance of MgSO4, phosphates could be

transferred to struvite, which further reduced the ammonia emission. However, the

results of nitrogen transformation showed NI-13-N only accounted for approximately

10% of total nitrogen and thus stimulating microorganism activity in order to

decompose more organic nitrogen into inorganic components was more critical than

ABSTRACT

In this thesis research, the effects of buffer salts, temperature and pH on food-waste

composting under various conditions were examined. Three factors regarding the

composting experiments were investigated via ten runs (including three control runs

and three parallel runs) of bench-scale in-vessel composting experiments. The

research can be divided into two sections:

In the first section, several buffer salts with initially different initial pH have been

utilized as the pH control amendments. Three runs with K^HPOVMgSO,!,

KH2PC>4/MgS04 and NaAc added, were compared with the control run without

anything added. The effects of the three agents upon pH, microbial activity and

ammonia release were then evaluated. Nitrogen transformation within composting

composition was also analyzed. The results showed the alkaline additives (NaAc and

K2HPC>4/MgS04) had a positive influence upon the degradation of organic material

while acidic additive (KftPCVMgSC^) severely reduced the pH value and resulted in

reduced microorganism activity. With the assistance of MgSC>4, phosphates could be

transferred to struvite, which further reduced the ammonia emission. However, the

results of nitrogen transformation showed NH3-N only accounted for approximately

10% of total nitrogen and thus stimulating microorganism activity in order to

decompose more organic nitrogen into inorganic components was more critical than

i

preventing ammonia emission. Due to this circumstance, K2HPO4/MgSO4 was the

best amendment to the investigated experiments.

In the second part of the research, a two-term modified Monod model was proposed to

quantify the combined effects of temperature and pH upon the growth of mesophiles

and thermophiles, simultaneously, during the composting process. Cross-validation

was also employed to improve the model's accuracy. Six runs of food waste

composting (two with the addition of water, two with heating conditions and the

remaining two as control runs) through bench-scale in-vessel reactors were

constructed to demonstrate the performance of the proposed model. The simulation

results indicated the modified Monod function exhibited superiority in a higher

coefficient of determination, a smaller SSE and provided more information regarding

mesophiles and thermophiles, respectively, when compared with the original model.

Meanwhile, the thermophiles had a larger maximum degradation rate and a larger

half-saturation constant than mesophiles, which revealed that thermophiles played a

major role in decomposing organic matter within the investigated composting

processes. Thermophiles were more sensitive to the changes in pH, which implied that

the initial stage was more critical with respect to thermophiles when the produced

organic acid exhibited the potentially inhibitory effects.

ii

preventing ammonia emission. Due to this circumstance, K.2HP04/MgS04 was the

best amendment to the investigated experiments.

In the second part of the research, a two-term modified Monod model was proposed to

quantify the combined effects of temperature and pH upon the growth of mesophiles

and thermophiles, simultaneously, during the composting process. Cross-validation

was also employed to improve the model's accuracy. Six runs of food waste

composting (two with the addition of water, two with heating conditions and the

remaining two as control runs) through bench-scale in-vessel reactors were

constructed to demonstrate the performance of the proposed model. The simulation

results indicated the modified Monod function exhibited superiority in a higher

coefficient of determination, a smaller SSE and provided more information regarding

mesophiles and thermophiles, respectively, when compared with the original model.

Meanwhile, the thermophiles had a larger maximum degradation rate and a larger

half-saturation constant than mesophiles, which revealed that thermophiles played a

major role in decomposing organic matter within the investigated composting

processes. Thermophiles were more sensitive to the changes in pH, which implied that

the initial stage was more critical with respect to thermophiles when the produced

organic acid exhibited the potentially inhibitory effects.

ii

ACKNOWLEDGEMENTS

First, I would like to express my sincere thanks to my supervisor, Dr. Gordon Huang,

for his exceptional instructions, guidance, and patience which were influencing

factors in the completion of this research. His philosophy will inspire me in every

aspect of my life and the experience in University of Regina will be forever engraved

in my mind.

I am very grateful to the members of the composting group, Dr. Baiyu Zhang, Dr. Hui

Yu, Dr. Yupeng Lin, Dr Xiaosheng Qin, Xueling Sun, Kuang Peng and Kai An who

designed the experiment and collected the data. I also extend my grateful

acknowledgement to Chunjiang An, Jia Wei, Yao Yao, Shan Zhao and Wei Sun for

their unselfish assistance during my research. Their suggestions heightened the results

of this project.

I am grateful to the Faculty of Graduate Studies and Research at the University of

Regina, for providing research scholarships for my graduate study.

Finally, I would like to thank my parents, Jinjie Li and Xiangyang Li, and my family

members, Xiangjun Li and Zhiwu Liang, for their understanding and encouragement

over the past few years. I could not present this thesis without their loving support.

iii

ACKNOWLEDGEMENTS

First, 1 would like to express my sincere thanks to my supervisor, Dr. Gordon Huang,

for his exceptional instructions, guidance, and patience which were influencing

factors in the completion of this research. His philosophy will inspire me in every

aspect of my life and the experience in University of Regina will be forever engraved

in my mind.

I am very grateful to the members of the composting group, Dr. Baiyu Zhang, Dr. Hui

Yu, Dr. Yupeng Lin, Dr Xiaosheng Qin, Xueling Sun, Kuang Peng and Kai An who

designed the experiment and collected the data. I also extend my grateful

acknowledgement to Chunjiang An, Jia Wei, Yao Yao, Shan Zhao and Wei Sun for

their unselfish assistance during my research. Their suggestions heightened the results

of this project.

I am grateful to the Faculty of Graduate Studies and Research at the University of

Regina, for providing research scholarships for my graduate study.

Finally, I would like to thank my parents, Jinjie Li and Xiangyang Li, and my family

members, Xiangjun Li and Zhiwu Liang, for their understanding and encouragement

over the past few years. I could not present this thesis without their loving support.

iii

TABLE OF CONTENTS

ABSTRACT i

ACKNOWLEDGEMENTS iii

LIST OF FIGURES viii

LIST OF TABLES xiii

CHAPTER 1 INTRODUCTION 1

1.1 Background 1

1.2 Challenges to Food Waste Composting 2

1.3 Objective 4

1.4 Thesis Organization 5

CHAPTER 2 LITERATURE REVIEW 7

2.1 Application of Composting 9

2.1.1 Composting of Farming/Agriculture Waste 9

2.1.2 Composting of Municipal Solid Waste 11

2.1.3 Composting of Food Waste 12

2.2 Factors Affecting Composting Reaction Rate and Products Quality 13

2.2.1 Temperature 14

2.2.2 pH 19

2.2.3 Moisture Content 27

iv

TABLE OF CONTENTS

ABSTRACT - i

ACKNOWLEDGEMENTS ui

LIST OF FIGURES viii

LIST OF TABLES xiii

CHAPTER 1 INTRODUCTION 1

1.1 Background 1

1.2 Challenges to Food Waste Composting 2

1.3 Objective 4

1.4 Thesis Organization 5

CHAPTER 2 LITERATURE REVIEW 7

2.1 Application of Composting 9

2.1.1 Composting of Farming/Agriculture Waste 9

2.1.2 Composting of Municipal Solid Waste 11

2.1.3 Composting of Food Waste 12

2.2 Factors Affecting Composting Reaction Rate and Products Quality 13

2.2.1 Temperature 14

2.2.2 pH 19

2.2.3 Moisture Content 27

iv

2.2.4 Oxygen 28

2.2.5 Nitrogen Content 29

2.3 Modeling of the Composting Process 31

2.4 Literature Review Summary 35

CHAPTER 3 MATERIALS AND METHOD 38

3.1 Overview of Experimental Approaches 38

3.2 Composting Materials 38

3.3 In-Vessel Composting System 39

3.4 Turning and Sampling 50

3.5 Physical and Chemical Analysis 50

3.5.1 Weight and Volume 51

3.5.2 Moisture Content 53

3.5.3 Ash and Organic Content 53

3.5.4 Oxygen Uptake Rate (OUR) 54

3.5.5 Aqueous Ammonium (NH4+-N) Concentration 55

3.5.6 Gaseous Ammonia (NI-I3-N) Concentration 58

3.5.7 Carbon-Nitrogen (C/N) Ratio 58

3.5.8 Microorganism Colony Counting 59

3.6 Statistical Analysis 61

3.6.1 One-way ANOVA 61

3.6.2 Leave-one-out Cross-validation 62

2.2.4 Oxygen 28

2.2.5 Nitrogen Content 29

2.3 Modeling of the Composting Process 31

2.4 Literature Review Summary 35

CHAPTER 3 MATERIALS AND METHOD 38

3.1 Overview of Experimental Approaches 38

3.2 Composting Materials 38

3.3 In-Vessel Composting System 39

3.4 Turning and Sampling 50

3.5 Physical and Chemical Analysis 50

3.5.1 Weight and Volume 51

3.5.2 Moisture Content 53

3.5.3 Ash and Organic Content 53

3.5.4 Oxygen Uptake Rate (OUR) 54

3.5.5 Aqueous Ammonium (NH/-N) Concentration 55

3.5.6 Gaseous Ammonia (NH3-N) Concentration 58

3.5.7 Carbon-Nitrogen (C/N) Ratio 58

3.5.8 Microorganism Colony Counting 59

3.6 Statistical Analysis 61

3.6.1 One-way ANOVA 61

3.6.2 Leave-one-out Cross-validation 62

v

CHAPTER 4 EFFECTS OF BUFFER SALTS 64

4.1 Variations in Physicochemical and Microbiological Parameters 64

4.1.1 Temperature 64

4.1.2 pH 67

4.2 Change in Microbial Activities 72

4.2.1 Daily and Cumulative Oxygen Uptake 72

4.2.2 Percentage of Organic Matter Degradation 79

4.2.3 Changes in Ammonia Loss 83

4.3 Nitrogen Mass Balance in Composting System 91

4.4 Summary 97

CHAPTER 5 El}'} ECTS OF TEMPERATURE AND pH 98

5.1 Kinetics Model Development 98

5.1.1 Development of a Two-Term Monod Equation with Correction Factors of

Temperature and pH 98

5.1.2 Non-Linear Regression 102

5.2 State-Variable Profiles 102

5.3 Model Calibration 110

5.4 Test of Model Performance 114

5.5 Discussion 135

5.5.1 Comparison with Original Monod Function 135

5.5.2 Environmental Factors of Microorganism Growth 148

vi

CHAPTER 4 EFFECTS OF BUFFER SALTS 64

4.1 Variations in Physicochemical and Microbiological Parameters 64

4.1.1 Temperature 64

4.1.2 pH 67

4.2 Change in Microbial Activities 72

4.2.1 Daily and Cumulative Oxygen Uptake 72

4.2.2 Percentage of Organic Matter Degradation 79

4.2.3 Changes in Ammonia Loss 83

4.3 Nitrogen Mass Balance in Composting System 91

4.4 Summary 97

CHAPTER 5 EFFECTS OF TEMPERATURE AND pH 98

5.1 Kinetics Model Development 98

5.1.1 Development of a Two-Term Monod Equation with Correction Factors of

Temperature and pH 98

5.1.2 Non-Linear Regression 102

5.2 State-Variable Profiles 102

5.3 Model Calibration 110

5.4 Test of Model Performance 114

5.5 Discussion 135

5.5.1 Comparison with Original Monod Function 135

5.5.2 Environmental Factors of Microorganism Growth 148

vi

5.6 Summary 149

CHAPTER 6 CONCLUSIONS 151

6.1 Summary 151

6.2 Contributions 152

6.3 Recommendations for Future Studies 154

REFERENCE 156

vii

5.6 Summary 149

CHAPTER 6 CONCLUSIONS 151

6.1 Summary 151

6.2 Contributions 152

6.3 Recommendations for Future Studies 154

REFERENCE 156

vii

LIST OF FIGURES

Figure 3.1 Schematic diagram of the composting system 44

Figure 3.2 Schematic diagram of the in-vessel reactor 45

Figure 3.3 Picture of the in-vessel composting reactor 46

Figure 3.4 Picture of the in-vessel composting reactor with insulation layer 47

Figure 3.5 Detailed procedure of sample testing 52

Figure 4.1 Temperature profiles during the composting process 65

Figure 4.2 pH profiles during the composting process 68

Figure 4.3a Temporal variations of 0 2 uptake rate and cumulative 0 2 uptake

during composting for Run A 73

Figure 4.3b Temporal variations of 02 uptake rate and cumulative 0 2 uptake

during composting for Run B 74

Figure 4.3c Temporal variations of 0 2 uptake rate and cumulative 0 2 uptake

during composting for Run C 75

Figure 4.3d Temporal variations of 0 2 uptake rate and cumulative 0 2 uptake

during composting for Run D 76

Figure 4.4 Degradation rate profiles during the compostingprocess 80

Figure 4.5a Temporal variations of ammonia loss rate and cumulative ammonia

loss in Run A 85

Figure 4.5b Temporal variations of ammonia loss rate and cumulative ammonia

loss in Run B 86

viii

LIST OF FIGURES

Figure 3.1 Schematic diagram of the composting system 44

Figure 3.2 Schematic diagram of the in-vessel reactor 45

Figure 3.3 Picture of the in-vessel composting reactor 46

Figure 3.4 Picture of the in-vessel composting reactor with insulation layer 47

Figure 3.5 Detailed procedure of sample testing 52

Figure 4.1 Temperature profiles during the composting process 65

Figure 4.2 pH profiles during the composting process 68

Figure 4.3a Temporal variations of O2 uptake rate and cumulative O2 uptake

during composting for Run A 73

Figure 4.3b Temporal variations of O2 uptake rate and cumulative O2 uptake

during composting for Run B 74

Figure 4.3c Temporal variations of O2 uptake rate and cumulative O2 uptake

during composting for Run C 75

Figure 4.3d Temporal variations of O2 uptake rate and cumulative O2 uptake

during composting for Run D 76

Figure 4.4 Degradation rate profiles during the compostingprocess 80

Figure 4.5a Temporal variations of ammonia loss rate and cumulative ammonia

loss in Run A 85

Figure 4.5b Temporal variations of ammonia loss rate and cumulative ammonia

loss in Run B 86

Vlll

Figure 4.5c Temporal variations of ammonia loss rate and cumulative ammonia

loss in Run C 87

Figure 4.5d Temporal variations of ammonia loss rate and cumulative ammonia

loss in Run D 88

Figure 4.6a Evolution of different nitrogen forms during composting (Run A) 93

Figure 4.6b Evolution of different nitrogen forms during composting (Run B) 94

Figure 4.6c Evolution of different nitrogen forms during composting (Run C) 95

Figure 4.6d Evolution of different nitrogen forms during composting (Run D) 96

Figure 5.1a Temperature profiles for Run E during the composting process ....104

Figure 5.1b Temperature profiles of Run F during the composting process 105

Figure 5.1c Temperature profiles of Run G during the composting process 106

Figure 5.2a pH profiles for Run E during the composting process 107

Figure 5.2b pH profiles for Run E during the composting process 108

Figure 5.2c pH profiles of Run G during the composting process 109

Figure 5.3a Degradation rate profiles by model prediction and experiment

measurement for Run El 115

Figure 5.3b Degradation rate profiles by model prediction and experiment

measurement for Run E2 116

Figure 5.3c Degradation rate profiles by model prediction and experiment

measurement for Run Fl 117

Figure 5.3d Degradation rate profiles by model prediction and experiment

ix

Figure 4.5c Temporal variations of ammonia loss rate and cumulative ammonia

loss in Run C 87

Figure 4.5d Temporal variations of ammonia loss rate and cumulative ammonia

loss in Run D 88

Figure 4.6a Evolution of different nitrogen forms during composting (Run A) ..93

Figure 4.6b Evolution of different nitrogen forms during composting (Run B)..94

Figure 4.6c Evolution of different nitrogen forms during composting (Run C)..95

Figure 4.6d Evolution of different nitrogen forms during composting (Run D) .96

Figure 5.1a Temperature profiles for Run E during the composting process .... 104

Figure 5.1b Temperature profiles of Run F during the composting process 105

Figure 5.1c Temperature profiles of Run G during the composting process 106

Figure 5.2a pH profiles for Run E during the composting process 107

Figure 5.2b pH profiles for Run E during the composting process 108

Figure 5.2c pH profiles of Run G during the composting process 109

Figure 5.3a Degradation rate profiles by model prediction and experiment

measurement for Run El 115

Figure 5.3b Degradation rate profiles by model prediction and experiment

measurement for Run E2 116

Figure 5.3c Degradation rate profiles by model prediction and experiment

measurement for Run F1 117

Figure 5.3d Degradation rate profiles by model prediction and experiment

IX

measurement for Run F2 118

Figure 5.3e Degradation rate profiles by model prediction and experiment

measurement for Run G1 119

Figure 5.3f Degradation rate profiles by model prediction and experiment

measurement for Run G2 120

Figure 5.4a Temporal variations of mesophilic and thermophilic degradation rate

for Run El 121

Figure 5.4b Temporal variations of mesophilic and thermophilic degradation rate

for Run E2 122

Figure 5 Ac Temporal variations of mesophilic and thermophilic degradation rate

for Run Fl 123

Figure 5.4d Temporal variations of mesophilic and thermophilic degradation rate

for Run F2 124

Figure 5.4e Temporal variations of mesophilic and thermophilic degradation rate

for Run G1 125

Figure 5.4f Temporal variations of mesophilic and thermophilic degradation rate

for Run G2 126

Figure 5.5a Normal probability plot of residues in modified Monod equation (Run

El) 129

Figure 5.5b Normal probability plot of residues in modified Monod equation (Run

E2) 130

x

measurement for Run F2 118

Figure 5.3e Degradation rate profiles by model prediction and experiment

measurement for Run G1 119

Figure 5.3f Degradation rate profiles by model prediction and experiment

measurement for Run G2 120

Figure 5.4a Temporal variations of mesophilic and thermophilic degradation rate

for Run El 121

Figure 5.4b Temporal variations of mesophilic and thermophilic degradation rate

for Run E2 122

Figure 5.4c Temporal variations of mesophilic and thermophilic degradation rate

for Run F1 123

Figure 5.4d Temporal variations of mesophilic and thermophilic degradation rate

for Run F2 124

Figure 5,4e Temporal variations of mesophilic and thermophilic degradation rate

for Run G1 125

Figure 5.4f Temporal variations of mesophilic and thermophilic degradation rate

for Run G2 126

Figure 5.5a Normal probability plot of residues in modified Monod equation (Run

El) 129

Figure 5.5b Normal probability plot of residues in modified Monod equation (Run

E2) 130

Figure 5.5c Normal probability plot of residues in modified Monod equation (Run

F l ) 131

Figure 5.5d Normal probability plot of residues in modified Monod equation (Run

F2) 132

Figure 5.5e Normal probability plot of residues in modified Monod equation (Run

Gl) 133

Figure 5.5f Normal probability plot of residues in modified Monod equation (Run

G2) 134

Figure 5.6a Comparison between predicted and observed degradation rate (Run

El) 136

Figure 5.6b Comparison between predicted and observed degradation rate (Run

E2) 137

Figure 5.6c Comparison between predicted and observed degradation rate (Run

Fl) 138

Figure 5.6d Comparison between predicted and observed degradation rate (Run

F2) 139

Figure 5.6e Comparison between predicted and observed degradation rate (Run

G I ) 140

Figure 5.6f Comparison between predicted and observed degradation rate (Run

G2) 141

Figure 5.7a Degradation rate profiles by original Monod model prediction and

xi

Figure 5.5c Normal probability plot of residues in modified Monod equation (Run

Fl) 131

Figure 5.5d Normal probability plot of residues in modified Monod equation (Run

F2) 132

Figure 5.5e Normal probability plot of residues in modified Monod equation (Run

Gl) 133

Figure 5.5f Normal probability plot of residues in modified Monod equation (Run

G2) 134

Figure 5.6a Comparison between predicted and observed degradation rate (Run

El) 136

Figure 5.6b Comparison between predicted and observed degradation rate (Run

E2) 137

Figure 5.6c Comparison between predicted and observed degradation rate (Run

Fl) 138

Figure 5.6d Comparison between predicted and observed degradation rate (Run

F2) 139

Figure 5.6e Comparison between predicted and observed degradation rate (Run

Gl) 140

Figure 5.6f Comparison between predicted and observed degradation rate (Run

G2) 141

Figure 5.7a Degradation rate profiles by original Monod model prediction and

XI

experiment measurement for Run El 142

Figure 5.7b Degradation rate profiles by original Monod model prediction and

experiment measurement for Run E2 143

Figure 5.7c Degradation rate profiles by original Monod model prediction and

experiment measurement for Run Fl 144

Figure 5.7d Degradation rate profiles by original Monod model prediction and

experiment measurement for Run F2 145

Figure 5.7e Degradation rate profiles by original Monod model prediction and

experiment measurement for Run G1 146

Figure 5.7f Degradation rate profiles by original Monod model prediction and

experiment measurement for Run G2 147

xii

experiment measurement for Run El 142

Figure 5.7b Degradation rate profiles by original Monod model prediction and

experiment measurement for Run E2 143

Figure 5.7c Degradation rate profiles by original Monod model prediction and

experiment measurement for Run F1 144

Figure 5.7d Degradation rate profiles by original Monod model prediction and

experiment measurement for Rim F2 145

Figure 5.7e Degradation rate profiles by original Monod model prediction and

experiment measurement for Run G1 146

Figure 5.7f Degradation rate profiles by original Monod model prediction and

experiment measurement for Run G2 147

xii

LIST OF TABLES

Table 3.1 Synthetic substrate composition in food waste composting system 40

Table 3.2 Physical and chemical properties of the initial composting material 41

Table 3.3 Composition of amendments in Experiment I 42

Table 3.4 Experiment conditions in Experiment II 43

Table 4.1 Peak value of oxygen uptake rate and final value of cumulative oxygen

uptake for the four runs 78

Table 4.2 Comparison between fmal organic matters and cumulative 0 2 uptake in

composting system 82

Table 4.3 Peak value of oxygen uptake rate and fmal value of cumulative oxygen

uptake for the four runs 89

Table 5.1 Coefficients obtained from modified and original Monod models.... 111

Table 5.2 Coefficients of determination values for six runs using cross-validation

results. 128

LIST OF TABLES

Table 3.1 Synthetic substrate composition in food waste composting system ....40

Table 3.2 Physical and chemical properties of the initial composting material...41

Table 3.3 Composition of amendments in Experiment I 42

Table 3.4 Experiment conditions in Experiment II 43

Table 4.1 Peak value of oxygen uptake rate and final value of cumulative oxygen

uptake for the four runs 78

Table 4.2 Comparison between final organic matters and cumulative O2 uptake in

composting system 82

Table 4.3 Peak value of oxygen uptake rate and final value of cumulative oxygen

uptake for the four runs 89

Table 5.1 Coefficients obtained from modified and original Monod models.... 111

Table 5.2 Coefficients of determination values for six runs using cross-validation

results 128

xiii

CHAPTER 1

INTRODUCTION

1.1 Background

Concern over Municipal Solid Waste (MSW) has attracted increased attention across

Canada due to economic progress and the enhancement of living standards

(Environment Canada, 2010). As a typical consumer society, Canada is now facing a

more severe environmental threat due to the production of large amounts of MSW. An

increasing in MSW can cause various severe environment problems, including surface

and groundwater contamination, air pollution and Greenhouse Gas (GHG) emissions

(Abu Qdais and Hamoda, 2004). Over 25 million tons of GHG emissions were related

to MSW in 2003, which accounts for 3.5% of Canada's total annual GHG emissions.

The majority of the GHG emissions were uncaptured methane due to MSW disposal

in landfills (Environment Canada, 2003).

Furthermore, as the heaviest component of MSW after recyclable ingredients are

removed, food waste must be given special consideration due to its characteristics of

high moisture content and high organic compounds (McDonnell, 1999). Too much

moisture will cause the leachate problems in a landfill, which in turn can threaten the

soil and groundwater in landfill and create further energy consumption during the

incineration process; many organic matters will produce unpleasant odors when food

waste is treated according to traditional disposal methods. Moreover, it is of

1

CHAPTER 1

INTRODUCTION

1.1 Background

Concern over Municipal Solid Waste (MSW) has attracted increased attention across

Canada due to economic progress and the enhancement of living standards

(Environment Canada, 2010). As a typical consumer society, Canada is now facing a

more severe environmental threat due to the production of large amounts of MSW. An

increasing in MSW can cause various severe environment problems, including surface

and groundwater contamination, air pollution and Greenhouse Gas (GHG) emissions

(Abu Qdais and Hamoda, 2004). Over 25 million tons of GHG emissions were related

to MSW in 2003, which accounts for 3.5% of Canada's total annual GHG emissions.

The majority of the GHG emissions were uncaptured methane due to MSW disposal

in landfills (Environment Canada, 2003).

Furthermore, as the heaviest component of MSW after recyclable ingredients are

removed, food waste must be given special consideration due to its characteristics of

high moisture content and high organic compounds (McDonnell, 1999). Too much

moisture will cause the leachate problems in a landfill, which in turn can threaten the

soil and groundwater in landfill and create further energy consumption during the

incineration process; many organic matters will produce unpleasant odors when food

waste is treated according to traditional disposal methods. Moreover, it is of

1

socio-economic and environment significance since the organic portion of food waste

can be restored and reused (Lin, 2006). As a result, it is advantageous to fmd an

attractive alternative with which to recycle and conserve food waste.

Composting is considered one of the most suitable approaches to food waste recycling.

It is defined as biodegradation process within a moist, warm and aerated environment,

in which the organic portion of solid waste can be decomposed into sanitary,

nuisance-free and humus like materials (An, 2006; Gray et al., 1971; C. Golueke,

1977; Wilson and Dalmat, 1986; Finstein and Morris, 1975; R.T. Haug, 1993).

Compared to traditional land filling without energy recovery, composting is useful for

avoiding GHGs emissions and reducing huge amounts of organic waste. Moreover,

successful compost products composting can be utilized as soil conditioner and

fertilizer. Another benefit is the low requirement of capital investment. As a result,

composting is an effective alternative in MSW management with which to divert

organic waste from the landfill or incineration. According to the Composting Council

of Canada, composting has also been widely employed in treating sewage sludge,

agricultural waste and contaminated soils (Antler, 2000). The wide applications of

compost indicate a growing market, which provides many benefits to the Canadian

society and the Canadian composting industry.

1.2 Challenges to Food Waste Composting

In order to obtain high quality compost products, it is necessary to understand the

2

socio-economic and environment significance since the organic portion of food waste

can be restored and reused (Lin, 2006). As a result, it is advantageous to find an

attractive alternative with which to recycle and conserve food waste.

Composting is considered one of the most suitable approaches to food waste recycling.

It is defined as biodegradation process within a moist, warm and aerated environment,

in which the organic portion of solid waste can be decomposed into sanitary,

nuisance-free and humus like materials (An, 2006; Gray et al., 1971; C. Golueke,

1977; Wilson and Dalmat, 1986; Finstein and Morris, 1975; R.T. Haug, 1993).

Compared to traditional land filling without energy recovery, composting is useful for

avoiding GHGs emissions and reducing huge amounts of organic waste. Moreover,

successful compost products composting can be utilized as soil conditioner and

fertilizer. Another benefit is the low requirement of capital investment. As a result,

composting is an effective alternative in MSW management with which to divert

organic waste from the landfill or incineration. According to the Composting Council

of Canada, composting has also been widely employed in treating sewage sludge,

agricultural waste and contaminated soils (Antler, 2000). The wide applications of

compost indicate a growing market, which provides many benefits to the Canadian

society and the Canadian composting industry.

1.2 Challenges to Food Waste Composting

In order to obtain high quality compost products, it is necessary to understand the

2

mechanism of the food waste composting. Although the concepts for composting

different substrates are very similar, the features of food waste composting still offer a

unique challenge, since existing knowledge is inadequate for supporting a successful

food waste composting (Lei and Vander-Gheynst, 2000). Although increased attention

has been drawn to the subject of food waste composting in the past decade, an

efficient composting performance is difficult to achieve. An in-depth look and

effective control at composting systems are also desired. The challenges of food waste

can be investigated via an experimental approach and a modeling approach.

In the experimental aspect, there are two major limitations which restrict the

acceleration of the composting process and the improvement of compost quality. One

limitation is acidification during the initial phase due to the generation of short-chain

organic acids (Lei and Vander-Gheynst, 2000). It has been proven that low pH during

the initial phase would severely inhibit the composting process (Beck-Friis et al.,

2003; Nakasaki et al., 1993; Sundberg and Jonsson, 2005). The second limitation is

volatile nitrogen loss in the form of ammonia in the thermophilic stage with a high pH

level (Groenestein and Van Faassen, 1996). Ammonia is generated from the

decomposition of organic nitrogen material by the deamination process (Y. Liang et

al., 2006). Since nitrogen is a rather important nutrient and the key factor in

maintaining compost quality, the loss of nitrogen not only slows down the speed of

organic matter biodegradation in a composting process but also worsens the compost

fertility (Hu et al., 2007). Simultaneously, NH3 produces an unpleasant odor and

3

mechanism of the food waste composting. Although the concepts for composting

different substrates are very similar, the features of food waste composting still offer a

unique challenge, since existing knowledge is inadequate for supporting a successful

food waste composting (Lei and Vander-Gheynst, 2000). Although increased attention

has been drawn to the subject of food waste composting in the past decade, an

efficient composting performance is difficult to achieve. An in-depth look and

effective control at composting systems are also desired. The challenges of food waste

can be investigated via an experimental approach and a modeling approach.

In the experimental aspect, there are two major limitations which restrict the

acceleration of the composting process and the improvement of compost quality. One

limitation is acidification during the initial phase due to the generation of short-chain

organic acids (Lei and Vander-Gheynst, 2000). It has been proven that low pH during

the initial phase would severely inhibit the composting process (Beck-Friis et al.,

2003; Nakasaki et al., 1993; Sundberg and Jonsson, 2005). The second limitation is

volatile nitrogen loss in the form of ammonia in the thermophilic stage with a high pH

level (Groenestein and Van Faassen, 1996). Ammonia is generated from the

decomposition of organic nitrogen material by the deamination process (Y. Liang et

al., 2006). Since nitrogen is a rather important nutrient and the key factor in

maintaining compost quality, the loss of nitrogen not only slows down the speed of

organic matter biodegradation in a composting process but also worsens the compost

fertility (Hu et al., 2007). Simultaneously, NH3 produces an unpleasant odor and

3

harms the environmental atmosphere. Moreover, interaction between the two

limitations makes the situation more complicated: if alkaline additives are used, it

would to be beneficial to inhibit pH drop in the beginning but more nitrogen loss

would be caused; in comparison, acid additives are suitable for ammonia absorption

but do not work with respect to organic acid when composting starts.

In the modeling aspect, many efforts have been undertaken in developing kinetics

models for microbiological reactions during the composting processes. A majority of

researchers have employed the Monod equation to simulate microorganism growth

(Monod, 1949). Predominantly based on the Monod equation and its modifications,

various kinetics models have been reported (Isik and Sponza, 2005; Castillo et al.,

1999). Limitations in the previous models could be found wherein only the

concentration of overall microorganisms was used instead of taking into consideration

different kinds of microorganism separately (Richard and Walker, 2006). In fact,

composting is an extremely complicated and dynamic process. The successions of

microorganism populations need to be closely considered. Meanwhile, few studies

have been conducted to quantitatively investigate the interaction effects between pH

and temperature on microbial growth during the composting process.

1.3 Objective

As an extension of previous research (Sun, 2006; An, 2006; Yu and Huang, 2009), the

objective of this particular research is to examine the effects of buffer salts,

4

harms the environmental atmosphere. Moreover, interaction between the two

limitations makes the situation more complicated: if alkaline additives are used, it

would to be beneficial to inhibit pH drop in the beginning but more nitrogen loss

would be caused; in comparison, acid additives are suitable for ammonia absorption

but do not work with respect to organic acid when composting starts.

In the modeling aspect, many efforts have been undertaken in developing kinetics

models for microbiological reactions during the composting processes. A majority of

researchers have employed the Monod equation to simulate microorganism growth

(Monod, 1949). Predominantly based on the Monod equation and its modifications,

various kinetics models have been reported (Isik and Sponza, 2005; Castillo et al.,

1999). Limitations in the previous models could be found wherein only the

concentration of overall microorganisms was used instead of taking into consideration

different kinds of microorganism separately (Richard and Walker, 2006). In fact,

composting is an extremely complicated and dynamic process. The successions of

microorganism populations need to be closely considered. Meanwhile, few studies

have been conducted to quantitatively investigate the interaction effects between pH

and temperature on microbial growth during the composting process.

13 Objective

As an extension of previous research (Sun, 2006; An, 2006; Yu and Huang, 2009), the

objective of this particular research is to examine the effects of buffer salts,

4

temperature and pH on food waste composting, using bench-scale in-vessel

composting systems. Improvements to both the experimental and modeling

approaches are undertaken. The objectives entail the following:

(i) To investigate the effects of buffer salts within different stages of the composting

process. Three types of buffer salts (K2HPO4/MgSO4, KH2PO4/MgSO4, and

NaAc) will be utilized as a novel category of pH amendments to investigate their

influences on the composting process. Moreover, nitrogen mass balance will be

considered to investigate the conflicts between multiple factors.

(ii) To develop a novel kinetics model to simulate the combined effects of pH and

temperature on mesophilic and thermophilic microorganisms, as well as organic

matter degradation in a food waste composting. Then, the kinetics parameters for

mesophiles and thermophiles will be calibrated, respectively. A comparison

between the modified model and the original Monod equation will be undertaken.

And the interactions between temperature and pH will be analyzed based on the

model results.

1.4 Thesis Organization

This thesis consists of five chapters. Chapter 2 reviews previous research regarding

waste composting, the factors affecting composting, and the models for microbiology

in composting systems. The review provides insight with which to understand how the

effects of buffer salts, pH and temperature influence the composting process and

5

temperature and pH on food waste composting, using bench-scale in-vessel

composting systems. Improvements to both the experimental and modeling

approaches are undertaken. The objectives entail the following:

(i) To investigate the effects of buffer salts within different stages of the composting

process. Three types of buffer salts (K2HP(VMgS04, Kt^PCVMgSO^ and

NaAc) will be utilized as a novel category of pH amendments to investigate their

influences on the composting process. Moreover, nitrogen mass balance will be

considered to investigate the conflicts between multiple factors.

(ii) To develop a novel kinetics model to simulate the combined effects of pH and

temperature on mesophilic and thermophilic microorganisms, as well as organic

matter degradation in a food waste composting. Then, the kinetics parameters for

mesophiles and thermophiles will be calibrated, respectively. A comparison

between the modified model and the original Monod equation will be undertaken.

And the interactions between temperature and pH will be analyzed based on the

model results.

1.4 Thesis Organization

This thesis consists of five chapters. Chapter 2 reviews previous research regarding

waste composting, the factors affecting composting, and the models for microbiology

in composting systems. The review provides insight with which to understand how the

effects of buffer salts, pH and temperature influence the composting process and

5

resulting products. Chapter 3 describes the composting system apparatus and

substrates, experimental procedures, and parameter measurements. The concepts and

methodologies of statistical testing and cross validation are also included. Chapter 4

focuses on the effects of buffer salts via graphical results of various parameters. The

findings are also interpreted and discussed. Chapter 5 illustrated the effects of pH and

temperature through establishing a modified Monod function. The model performance

will be testified and the results of this model will be introduced. Chapter 6 presents

the conclusion of this research.

6

resulting products. Chapter 3 describes the composting system apparatus and

substrates, experimental procedures, and parameter measurements. The concepts and

methodologies of statistical testing and cross validation are also included. Chapter 4

focuses on the effects of buffer salts via graphical results of various parameters. The

findings are also interpreted and discussed. Chapter 5 illustrated the effects of pH and

temperature through establishing a modified Monod function. The model performance

will be testified and the results of this model will be introduced. Chapter 6 presents

the conclusion of this research.

6

CHAPTER 2

LITERATURE REVIEW

Waste management has become a critical problem in research and everyday practice

due to rising concerns regarding environmental pollution and resource conservation

(Brewer, 2001). Many solid waste management experts believe a single, simple

solution is inadequate to meet the requirements of waste management. However, an

integrated approach with multiple combined techniques combined would be valuable

to a wide range of pollution concerns (Uif, 1998; Fromme, 1999). It is well

recognized that a successful comprehensive strategy involves the four key elements

applied in a hierarchical manner (Lin, 2006): a) reduction in the volume and toxicity

of solid waste; b) recycling the useful portion of solid waste as much as possible; c)

recovery of energy from the combustion systems coupled with the best available

pollution control technology; and d) utilization of landfills with adequate

environmental control methods.

Composting can meet these multiple objectives through an integrated approach, which

contain odor reduction, nutrients recycling and waste management (R. T. Haug, 1980,

1986; Miller, 1989; Hansen et al., 1992; Albert et al., 2002; Park et al., 2004). It is

defined as a biochemical process with the presence of various microorganisms, in

which biodegradable organic wastes can be transferred to nuisance-free, humus-like

and sanitary materials, which can be used as soil fertilizer and conditioner (Gray et al.,

7

CHAPTER 2

LITERATURE REVIEW

Waste management has become a critical problem in research and everyday practice

due to rising concerns regarding environmental pollution and resource conservation

(Brewer, 2001). Many solid waste management experts believe a single, simple

solution is inadequate to meet the requirements of waste management. However, an

integrated approach with multiple combined techniques combined would be valuable

to a wide range of pollution concerns (Uif, 1998; Fromme, 1999). It is well

recognized that a successful comprehensive strategy involves the four key elements

applied in a hierarchical manner (Lin, 2006): a) reduction in the volume and toxicity

of solid waste; b) recycling the useful portion of solid waste as much as possible; c)

recovery of energy from the combustion systems coupled with the best available

pollution control technology; and d) utilization of landfills with adequate

environmental control methods.

Composting can meet these multiple objectives through an integrated approach, which

contain odor reduction, nutrients recycling and waste management (R. T. Haug, 1980,

1986; Miller, 1989; Hansen et al., 1992; Albert et al., 2002; Park et al., 2004). It is

defined as a biochemical process with the presence of various microorganisms, in

which biodegradable organic wastes can be transferred to nuisance-free, humus-like

and sanitary materials, which can be used as soil fertilizer and conditioner (Gray et al.,

7

1971; C. G. Golueke, 1973; Nakasaki et al., 1985; Wilson and Dalmat, 1986; Nergo et

al., 1999; de Bertoldi et al., 1983; Buchannan and Gliessman, 1991; Garcia et al.,

1992; Schlegel, 1992). The use of composting products has been proved of its ability

to sustain the quality and quantity of crop production levels similar to crop treated

with chemical fertilizers (Finstein and Morris, 1975). Composted organic waste is

able to supply nutrients to plants in a balanced way, providing high yields with a low

risk of groundwater and soil contamination. Thus composting offers the potential of

producing useful products from the organic fraction of solid waste. It assists in

decomposing degradable material, decreasing volume, weight and water content, and

killing pathogenic microorganisms, thus producing stabilized process residue

(Godden, 1983; Hoitink and Keener, 1993; Finstein and Hogan, 1992; Szmidt and

Fox, 2001).

Composting is essential to solid waste recycling and pollution reduction by avoiding

the transformation of possible waste materials into a series of toxic products (J. E.

Campbell, 1990; Sequi, 1996; Ryckeboer and J., 2003). It has been thousands of years

since the composting of organic wastes was used as a fanning technique(Jacobowitz

and Steenhuis, 1984). However, the modern composting technique can be thought as

an accelerated and controlled decomposition process with human interference

(Finstein and Morris, 1975; Solano et al., 2001a). Since sustainability considerations

are major driving forces within composting technologies, improvements in

8

1971; C. G. Golueke, 1973; Nakasaki et al., 1985; Wilson and Dalmat, 1986; Nergo et

al., 1999; de Bertoldi et al., 1983; Buchannan and Gliessman, 1991; Garcia et al.,

1992; Schlegel, 1992). The use of composting products has been proved of its ability

to sustain the quality and quantity of crop production levels similar to crop treated

with chemical fertilizers (Finstein and Morris, 1975). Composted organic waste is

able to supply nutrients to plants in a balanced way, providing high yields with a low

risk of groundwater and soil contamination. Thus composting offers the potential of

producing useful products from the organic fraction of solid waste. It assists in

decomposing degradable material, decreasing volume, weight and water content, and

killing pathogenic microorganisms, thus producing stabilized process residue

(Godden, 1983; Hoitink and Keener, 1993; Finstein and Hogan, 1992; Szmidt and

Fox, 2001).

Composting is essential to solid waste recycling and pollution reduction by avoiding

the transformation of possible waste materials into a series of toxic products (J. E.

Campbell, 1990; Sequi, 1996; Ryckeboer and J., 2003). It has been thousands of years

since the composting of organic wastes was used as a farming technique(Jacobowitz

and Steenhuis, 1984). However, the modem composting technique can be thought as

an accelerated and controlled decomposition process with human interference

(Finstein and Morris, 1975; Solano et al., 2001a). Since sustainability considerations

are major driving forces within composting technologies, improvements in

8

composting processes will assist in promoting the efficiency and economic viability,

and ultimately contribute to agricultural and societal sustainability (Molla et al., 2005)

2.1 Application of Composting

2.1.1 Composting of Farming/Agriculture Waste

Composting was originally intended to treat farming waste such as livestock manure

and crop residues (Zucconi et al., 1981). Due to the large amounts of organic

materials within the waste, the results showed only sufficiently bio-stabilized

composting products could be applied to soil. The application of fresh organic

materials into soil might cause severe damage to plant growth. Due to anaerobic

conditions created within soil, nitrogen starvation and toxic metabolites such as

ammonia and organic acids will be produced (Mathur, 1991). Sufficiently matured

products were also recommended to control the spread of pathogens, phytotoxic

substances and unpleasant odors (Edwards and Daniel, 1992; Hansen et al., 1993;

Tiquia and Tam, 1998).

Manure is usually rich in nitrogen content, while crop residue is a good carbon source.

Extensive work has been conducted to mix and co-compost the two types of common

fanning waste. Appropriate conditions with respect to the compost carbon/nitrogen

ratio and moisture content can be achieved by choosing a favourable ratio of manure

and crops. The co-compost method has been widely recognized for treating

9

composting processes will assist in promoting the efficiency and economic viability,

and ultimately contribute to agricultural and societal sustainability (Molla et al., 2005)

2.1 Application of Composting

2.1.1 Composting of Farming/Agriculture Waste

Composting was originally intended to treat farming waste such as livestock manure

and crop residues (Zucconi et al., 1981). Due to the large amounts of organic

materials within the waste, the results showed only sufficiently bio-stabilized

composting products could be applied to soil. The application of fresh organic

materials into soil might cause severe damage to plant growth. Due to anaerobic

conditions created within soil, nitrogen starvation and toxic metabolites such as

ammonia and organic acids will be produced (Mathur, 1991). Sufficiently matured

products were also recommended to control the spread of pathogens, phytotoxic

substances and unpleasant odors (Edwards and Daniel, 1992; Hansen et al., 1993;

Tiquia and Tam, 1998).

Manure is usually rich in nitrogen content, while crop residue is a good carbon source.

Extensive work has been conducted to mix and co-compost the two types of common

fanning waste. Appropriate conditions with respect to the compost carbon/nitrogen

ratio and moisture content can be achieved by choosing a favourable ratio of manure

and crops. The co-compost method has been widely recognized for treating

9

farming/agriculture waste (Genevini et al., 1997; Tam and Tiquia, 1999; Filippi et al.,

2002). Pare et al. (1998) studied the composting process of animal manure and

shredded paper in order to analysis the transformation of carbon and nitrogen. In the

beginning, NH4+-N was initially decreased, and an increase of NC03-N was observed

towards the end of the process. Added nitrogen would lead to an inhibition of carbon

mineralization. Tiquia and Tam (2000) conducted research on the life cycle of

nitrogen during the composting of chicken litter, which is a mixture of chicken

manure, wood shavings, waste feed and feathers. The results showed a large portion

of nitrogen existed in the form of organic matter. The loss of nitrogen exceeded half

the initial nitrogen mass of the pile, which was predominated by the volatilization of

ammonia when the temperature and pH were both very high.

Farming waste is always accumulated into a heap-on-site. However, this condition is

not suitable for composting because an anaerobic process will occur. In this situation

and chosen from several popular methods, the windrow and aerated static-pile

methods become the most appropriate methods for on farm composting (de Bertoldi et

al., 1985). The windrow method is to heap the raw material into a long triangular pile

of a particular size with a thickness of 20 to 30 cm from the surface, depending on the

diffusion aeration (Haga, 1998). Periodical turning is required to avoid anaerobic

conditions inside the pile. Regular turning can not only help provide aeration within

the pile, but can also homogenize the composting substrates and re-establish the

porosity media to enlarge the active surface area (Cekmecelioglu, 2004). However,

10

farming/agriculture waste (Genevini et al., 1997; Tam and Tiquia, 1999; Filippi et al.,

2002). Pare et al. (1998) studied the composting process of animal manure and

shredded paper in order to analysis the transformation of carbon and nitrogen. In the

beginning, NH^-N was initially decreased, and an increase of N03-N was observed

towards the end of the process. Added nitrogen would lead to an inhibition of carbon

mineralization. Tiquia and Tam (2000) conducted research on the life cycle of

nitrogen during the composting of chicken litter, which is a mixture of chicken

manure, wood shavings, waste feed and feathers. The results showed a large portion

of nitrogen existed in the form of organic matter. The loss of nitrogen exceeded half

the initial nitrogen mass of the pile, which was predominated by the volatilization of

ammonia when the temperature and pH were both very high.

Farming waste is always accumulated into a heap-on-site. However, this condition is

not suitable for composting because an anaerobic process will occur. In this situation

and chosen from several popular methods, the windrow and aerated static-pile

methods become the most appropriate methods for on farm composting (de Bertoldi et

al., 1985). The windrow method is to heap the raw material into a long triangular pile

of a particular size with a thickness of 20 to 30 cm from the surface, depending on the

diffusion aeration (Haga, 1998). Periodical turning is required to avoid anaerobic

conditions inside the pile. Regular turning can not only help provide aeration within

the pile, but can also homogenize the composting substrates and re-establish the

porosity media to enlarge the active surface area (Cekmecelioglu, 2004). However,

sufficient aeration in the static pile method is generally achieved by heaping the

mixed raw materials onto a base containing perforated pipes, which supplies air from

a fan-blower to the heap (Solano et al., 2001b).

2.1.2 Composting of Municipal Solid Waste

With the continuous growth of human population and communities scale, traditional

solid waste disposal approaches such as landfills and incineration, can hardly meet the

demand of increasing solid waste generation and the aim of sustainable municipal

solid waste (MSW) management, due to their capacity limitations and the possibilities

of environmental pollution. In some regions with high population densities such as

Europe and Asia, a shortage of land for waste disposal has become a significant issue.

In order to reduce the amount of solid waste, composting the biodegradable fraction

of municipal solid waste has become a widely accepted approach (Gellens et al., 1995;

Yoshida et al., 2001). The techniques used in MSW composting are mostly derived

from fanning waste composting. Other than windrow and aerated static pile methods,

an in-vessel composting system can be used occasionally. It refers to a composting

reaction occurring in enclosed reactors. Generally, by using metal tanks or concrete

bunkers, this method can provide better control over aeration, temperature and

moisture in composting materials (Donahue et al., 1998; Seymour et al., 2001;

Cekmecelioglu, 2004). Also, an in-vessel composting system can handle more

11

sufficient aeration in the static pile method is generally achieved by heaping the

mixed raw materials onto a base containing perforated pipes, which supplies air from

a fan-blower to the heap (Solano et al., 2001b).

2.1.2 Composting of Municipal Solid Waste

With the continuous growth of human population and communities scale, traditional

solid waste disposal approaches such as landfills and incineration, can hardly meet the

demand of increasing solid waste generation and the aim of sustainable municipal

solid waste (MSW) management, due to their capacity limitations and the possibilities

of environmental pollution. In some regions with high population densities such as

Europe and Asia, a shortage of land for waste disposal has become a significant issue.

In order to reduce the amount of solid waste, composting the biodegradable fraction

of municipal solid waste has become a widely accepted approach (Gellens et al., 1995;

Yoshida et al., 2001). The techniques used in MSW composting are mostly derived

from farming waste composting. Other than windrow and aerated static pile methods,

an in-vessel composting system can be used occasionally. It refers to a composting

reaction occurring in enclosed reactors. Generally, by using metal tanks or concrete

bunkers, this method can provide better control over aeration, temperature and

moisture in composting materials (Donahue et al., 1998; Seymour et al., 2001;

Cekmecelioglu, 2004). Also, an in-vessel composting system can handle more

11

composting substrates than windrows and static aerated piles when a space is similar,

which makes it particularly suitable for urban areas.

2.13 Composting of Food Waste

Food-service establishments and urban households have generated increasing amounts

of food waste, giving municipal authorities significant concerns. Food waste is also a

major part of municipal solid waste. Because of the unique characteristics of food

waste, (very damp and highly degradable), many undesirable environmental issues

can arise during its storage, collection and transportation (Choi and Park, 1998; Seo et

al., 2004; Smars et al., 2002; Svensson et al., 2004; Chang et al., 2006). Up to now,

traditional solid waste management approaches such as landfill and incineration are

still used to treat a major portion of food waste. However, the high moisture content

of food waste may retard burning in incinerators and may reduce the efficiency of

waste-to-energy plants. Moreover, high moisture content may also generate large

amounts of leachate in landfills, which is a major reason for soil and groundwater

pollution (Walker, 2008). Also a large percentage of organic matters may be

transformed into methane gas, adding to the accumulation of greenhouse gases in the

atmosphere (Kjeldsen and Fischer, 1995; Boltze and de Freitas, 1997). Therefore,

composting has garnered more and more attention with respect to dealing with food

waste (Kwon and Lee, 2004; McDonnell, 1993, 1999; Namkoong et al., 1999). It is

believed that a dominantly aerobic environment will mitigate produced methane and

12

composting substrates than windrows and static aerated piles when a space is similar,

which makes it particularly suitable for urban areas.

2.1.3 Composting of Food Waste

Food-service establishments and urban households have generated increasing amounts

of food waste, giving municipal authorities significant concerns. Food waste is also a

major part of municipal solid waste. Because of the unique characteristics of food

waste, (very damp and highly degradable), many undesirable environmental issues

can arise during its storage, collection and transportation (Choi and Park, 1998; Seo et

al., 2004; Smlrs et al., 2002; Svensson et al., 2004; Chang et al., 2006). Up to now,

traditional solid waste management approaches such as landfill and incineration are

still used to treat a major portion of food waste. However, the high moisture content

of food waste may retard burning in incinerators and may reduce the efficiency of

waste-to-energy plants. Moreover, high moisture content may also generate large

amounts of leachate in landfills, which is a major reason for soil and groundwater

pollution (Walker, 2008). Also a large percentage of organic matters may be

transformed into methane gas, adding to the accumulation of greenhouse gases in the

atmosphere (Kjeldsen and Fischer, 1995; Boltze and de Freitas, 1997). Therefore,

composting has garnered more and more attention with respect to dealing with food

waste (Kwon and Lee, 2004; McDonnell, 1993, 1999; Namkoong et al., 1999). It is

believed that a dominantly aerobic environment will mitigate produced methane and

12

leaching acidic substances, and thus, aerobic composting is accepted as an

environment friendly alternative for the handling of food waste (Droffiner and Brinton,

1995; Seymour et al., 2001; Elwell et al., 1996; Donahue et al., 1998; Laos et al.,

1998; Faucette et al., 2001; Tomati et al., 2001; Filippi et al., 2002; Das et al., 2003;

Koivula et al., 2004).

Donahue et al. (1998) demonstrated a successful food waste composting process with

sawdust and mulch chips added into an in-vessel system within 14 days. Chang et al.

(2006) designed a reactor in laboratory scale to study the effects of the operational

conditions regarding the composting process by using a synthetic food waste made

from dog food. The results showed the synthetic food could be composted within 4

days, and the final products could pass maturity tests, which is a MCDA

(Multi-Criteria Decision Analysis) system. The optimal operating conditions were 1.6

L air/kg (dry solid) per minute of air suction rate, 32% of mass with seeding and 50%

of time with agitation. There are also several studies regarding the obtaining of a high

decomposition rate and stabilized end products by optimizing food-waste composting

operations.

2.2 Factors Affecting Composting Reaction Rate and Products Quality

Since composting is a biochemical process, aerobic breakdown of solid organic matter

by microorganisms is a crucial step in composting (Defilkx et al., 1990). However,

biodegradation is a sum of a series of complex metabolic processes and

13

leaching acidic substances, and thus, aerobic composting is accepted as an

environment friendly alternative for the handling of food waste (Droffiner and Brinton,

1995; Seymour et al., 2001; Elwell et al., 1996; Donahue et al., 1998; Laos et al.,

1998; Faucette et al., 2001; Tomati et al., 2001; Filippi et al., 2002; Das et al., 2003;

Koivula et al., 2004).

Donahue et al. (1998) demonstrated a successful food waste composting process with

sawdust and mulch chips added into an in-vessel system within 14 days. Chang et al.

(2006) designed a reactor in laboratory scale to study the effects of the operational

conditions regarding the composting process by using a synthetic food waste made

from dog food. The results showed the synthetic food could be composted within 4

days, and the final products could pass maturity tests, which is a MCDA

(Multi-Criteria Decision Analysis) system. The optimal operating conditions were 1.6

L air/kg (dry solid) per minute of air suction rate, 32% of mass with seeding and 50%

of time with agitation. There are also several studies regarding the obtaining of a high

decomposition rate and stabilized end products by optimizing food-waste composting

operations.

2.2 Factors Affecting Composting Reaction Rate and Products Quality

Since composting is a biochemical process, aerobic breakdown of solid organic matter

by microorganisms is a crucial step in composting (Derikx et al., 1990). However,

biodegradation is a sum of a series of complex metabolic processes and

13

transformations by a large mixed population of microorganisms, rather than a single

and simple unitary process. The precise chemical changes and microbial species

involved in the composting process will vary according to the composition of

raw-materials. Even in the same process, the entire system will also change

dynamically and reflect the change of environmental factors (Yoshida et al., 2001).

There are two groups of main factors in a composting process, operational conditions,

which include temperature, moisture content, pH level, and aeration rate; the

substrate-related parameters, which include the C/N ratio, particle size and nutrient

content (Bishop and Godfrey, 1983). These parameters will affect the progress of

composting, and may change continuously along with time. It is worth noting that all

the parameters are not independent but have some interactions and correlations. As a

result, these parameters should be appropriately controlled to achieve compost

maturity.

2.2.1 Temperature

2.2.1.1 Temperature Profiles in Food Waste Composting

The temperature of the system is the function of accumulated heat generated by

microbial reaction, heat brought out in the system by leachate and emitted gases and

the heat capacity of the compost and reactor. Its evolution is an indicator of metabolic

activities during the composting process (MacGregor et al., 1981). Consequently, the

temperature acts as a convenient and direct parameter with which to determine the

14

transformations by a large mixed population of microorganisms, rather than a single

and simple unitary process. The precise chemical changes and microbial species

involved in the composting process will vary according to the composition of

raw-materials. Even in the same process, the entire system will also change

dynamically and reflect the change of environmental factors (Yoshida et al., 2001).

There are two groups of main factors in a composting process, operational conditions,

which include temperature, moisture content, pH level, and aeration rate; the

substrate-related parameters, which include the C/N ratio, particle size and nutrient

content (Bishop and Godfrey, 1983). These parameters will affect the progress of

composting, and may change continuously along with time. It is worth noting that all

the parameters are not independent but have some interactions and correlations. As a

result, these parameters should be appropriately controlled to achieve compost

maturity.

2.2.1 Temperature

2.2.1.1 Temperature Profiles in Food Waste Composting

The temperature of the system is the function of accumulated heat generated by

microbial reaction, heat brought out in the system by leachate and emitted gases and

the heat capacity of the compost and reactor. Its evolution is an indicator of metabolic

activities during the composting process (MacGregor et al., 1981). Consequently, the

temperature acts as a convenient and direct parameter with which to determine the

14

phase of the composting process. Effective ways to control temperature are beneficial

for pathogen deduction, respiration rate optimization, moisture removal, and compost

stabilization (Soares et al., 1995; Lin, 2006). As a result, a number of studies have

addressed heat transport in the composting process (Finger et al., 1976; Characklis

and Gujer, 1979; Kishimoto et al., 1987; Bach et al., 1985; Nakasaki, 1987; Tiquia

and Tam, 2000; Pare et al., 1998).

A typical composting is normally an exothermal process, in which energy will be

released and temperature will rise when degradable organic materials break-down

during the process. This biological metabolism process will also produce carbon

dioxide and water (Tiquia and Tam, 1998; Bari and Koening, 2001; Ryckeboer et al.,

2003a). Composting is self-insulated, which means the temperature only decreases

according to the evaporated water being carried through ventilated air (R.T. Haug,

1993; An, 2006). Normally, the temperature in the system initially rises to 60-70 °C

during the process then cools down slowly to ambient temperature (Fu, 2004).

2.2.1.2 Microorganism Activities under Different Temperatures

Generally, the temperature exhibits major correlation with the types and species of

dominant microorganisms. Correspondingly, the composting process can be divided

into three stages based on temperature evolution, which are mesophilic, thermophilic

and cooling stages (Gray et al., 1971). Although there is no clear-cut threshold

between mesophilic and thermophilic stages, Miller (1996) considered a temperature

15

phase of the composting process. Effective ways to control temperature are beneficial

for pathogen deduction, respiration rate optimization, moisture removal, and compost

stabilization (Soares et al., 1995; Lin, 2006). As a result, a number of studies have

addressed heat transport in the composting process (Finger et al., 1976; Characklis

and Gujer, 1979; Kishimoto et al., 1987; Bach et al., 1985; Nakasaki, 1987; Tiquia

and Tam, 2000; Pare et al., 1998).

A typical composting is normally an exothermal process, in which energy will be

released and temperature will rise when degradable organic materials break-down

during the process. This biological metabolism process will also produce carbon

dioxide and water (Tiquia and Tam, 1998; Bari and Koening, 2001; Ryckeboer et al.,

2003a). Composting is self-insulated, which means the temperature only decreases

according to the evaporated water being carried through ventilated air (R.T. Haug,

1993; An, 2006). Normally, the temperature in the system initially rises to 60-70 °C

during the process then cools down slowly to ambient temperature (Fu, 2004).

2.2.1.2 Microorganism Activities under Different Temperatures

Generally, the temperature exhibits major correlation with the types and species of

dominant microorganisms. Correspondingly, the composting process can be divided

into three stages based on temperature evolution, which are mesophilic, thermophilic

and cooling stages (Gray et al., 1971). Although there is no clear-cut threshold

between mesophilic and thermophilic stages, Miller (1996) considered a temperature

15

below 40 °C as mesophilic, while a temperature between 45 °C and 70 °C as

thennophilic; Haug (1991) considered a temperature between 60 °C to 80 °C as

thermophilic, which is case sensitive. Although the combined activities of many

individual microorganisms are involved (Cheung, 2008), the most commonly used

classification of microorganisms in composting is based on their optimum

temperature, so that two classes of microorganism can be considered regarding to

different temperatures which they grow under: mesophilic microorganisms (also

known as mesophiles) and thermophilic microorganisms (also known as thermophiles)

(Swatek, 1967). Mesophiles have optimal temperature between 20.0 to 35.0 °C. The

miminum and maximum tolerable temperatures for survival are 10.0 °C and 45.0 °C,

respectively (Lyles, 1969). Thermophiles are best adapted to a temperature between

50.0 °C and 60.0 °C. The tolerance level for thermophiles is from 35.0 °C to 85.0 °C

(Lyles, 1969; Henis, 1987; Lester and Birkett, 1999). A temperature outside the

adaptive range will limit microbial activities (Ryckeboer et al., 2003b). On the other

hand, different species and types of microorganisms have their own optimum

temperature. It has been reported that fungi is an important group in the early

mesophilic stage, while bacteria dominants the thermophilic phase (Klamer and Bath,

1998; Strom, 1985).

Different microbial communities predominate at various stages: the most typical stage

in composting system is the early acidic phase. Mesophilic microorganisms are the

major active species which break down the readily degradable matters such as sugar,

16

below 40 °C as mesophilic, while a temperature between 45 °C and 70 °C as

thermophilic; Haug (1991) considered a temperature between 60 °C to 80 °C as

thermophilic, which is case sensitive. Although the combined activities of many

individual microorganisms are involved (Cheung, 2008), the most commonly used

classification of microorganisms in composting is based on their optimum

temperature, so that two classes of microorganism can be considered regarding to

different temperatures which they grow under: mesophilic microorganisms (also

known as mesophiles) and thermophilic microorganisms (also known as thermophiles)

(Swatek, 1967). Mesophiles have optimal temperature between 20.0 to 35.0 °C. The

miminum and maximum tolerable temperatures for survival are 10.0 °C and 45.0 °C,

respectively (Lyles, 1969). Thermophiles are best adapted to a temperature between

50.0 °C and 60.0 °C. The tolerance level for thermophiles is from 35.0 °C to 85.0 °C

(Lyles, 1969; Henis, 1987; Lester and Birkett, 1999). A temperature outside the

adaptive range will limit microbial activities (Ryckeboer et al., 2003b). On the other

hand, different species and types of microorganisms have their own optimum

temperature. It has been reported that fungi is an important group in the early

mesophilic stage, while bacteria dominants the thermophilic phase (Klamer and BMth,

1998; Strom, 1985).

Different microbial communities predominate at various stages: the most typical stage

in composting system is the early acidic phase. Mesophilic microorganisms are the

major active species which break down the readily degradable matters such as sugar,

starches, lipids, fats and proteins because they are easily hydrolyzed or have a low

molecular weight, and generated heat will shift the process to the thermophilic stage

(Nakasaki et al., 1985; Fogarty and Tuovinen, 1991). Several studies exist regarding

the decrease in pH decreasing during the initial stage of composting because of the

organic acids intermediates (Finstein and Morris, 1975). De Bertoldi et al. (1983)

testified the formed organic acids are a result of the breakdown of sugar and fats by

bacteria. Bacteria is less tolerant to low pH conditions than fungi (Sundberg et al.,

2004) although the optimum pH for bacteria is neutral. A low pH condition will be

inhibitory to microorganisms, which may lead to inactive or suppressed microbial

activity. The low degradation rate can be reflected from a low mesophilic temperature,

low oxygen consumption, and low carbon-dioxide emission rates (Kubota and

Kakasaki, 1991; Robertsson, 2002).

When most of the readily degradable matters are depleted, including organic acids,

the pH rises towards the neutral and alkaline range (Miller, 1996; Ryckeboer et al.,

2003a). With the temperature rising, thermophilic microorganisms begin to dominate

the process due to the suitable temperature in the system. In this stage, organic

decomposition rate reaches its maximum and temperature is maintained at

approximately 70 °C, because thermophilic microorganisms have a higher efficiency

with which to degrade nutrients. At this stage, moderate resistant degradable matters

such as celluloses, hemicelluloses and chitin, and highly resistant degradable matters

like lignin and lignocelluloses are degraded (Stentiford, 1993).

17

starches, lipids, fats and proteins because they are easily hydrolyzed or have a low

molecular weight, and generated heat will shift the process to the thermophilic stage

(Nakasaki et al., 1985; Fogarty and Tuovinen, 1991). Several studies exist regarding

the decrease in pH decreasing during the initial stage of composting because of the

organic acids intermediates (Finstein and Morris, 1975). De Bertoldi et al. (1983)

testified the formed organic acids are a result of the breakdown of sugar and fats by

bacteria. Bacteria is less tolerant to low pH conditions than fungi (Sundberg et al.,

2004) although the optimum pH for bacteria is neutral. A low pH condition will be

inhibitory to microorganisms, which may lead to inactive or suppressed microbial

activity. The low degradation rate can be reflected from a low mesophilic temperature,

low oxygen consumption, and low carbon-dioxide emission rates (Kubota and

Kakasaki, 1991; Robertsson, 2002).

When most of the readily degradable matters are depleted, including organic acids,

the pH rises towards the neutral and alkaline range (Miller, 1996; Ryckeboer et al.,

2003a). With the temperature rising, thermophilic microorganisms begin to dominate

the process due to the suitable temperature in the system. In this stage, organic

decomposition rate reaches its maximum and temperature is maintained at

approximately 70 °C, because thermophilic microorganisms have a higher efficiency

with which to degrade nutrients. At this stage, moderate resistant degradable matters

such as celluloses, hemicelluloses and chitin, and highly resistant degradable matters

like lignin and lignocelluloses are degraded (Stentiford, 1993).

Once the majority of the substrates have been consumed, so that there is not enough

metabolic heat remains to maintain thermophilic temperature, the rate of heat

generation cannot meet that of heat loss. The cooling stage takes place and the

temperature arrives at ambient temperature. The activities of microorganisms also

slow down. (Waksman et al., 1939). When the composting reaches the cooling phase,

the thermophile population decreases and mesophlies begin to re-colonize the

compost materials to help move the compost into maturity, which is called a

resumption of mesophiles (Poincelot, 1974; Strom, 1985; Fogarty and Tuovinen, 1991;

Herrmann and Shann, 1997). However, the activity of mesophiles is extremely low

because residues are highly resistant to decomposition. At last, the fmal composting

products become stable with extremely low microbial activity in the residue.

Temperature influences not only the dominant microorganisms and their population,

but also the rate and pathway of decomposition. Waksman et al. (1939) pointed out

that the maximum degradation rate occurred at 50 °C in a batch system with horse

manure; Jens and Regan (1973a) reported that the highest degradation rate can be

found at 55 °C in a continuous feeding system; Strom (1985) determined the optimum

temperature for a thermophilic continuous process to be 60 °C; and Cathcart et al.

(1986) showed maximum CO2 generation was realized at a temperature of 55 °C in a

batch composting;

18

Once the majority of the substrates have been consumed, so that there is not enough

metabolic heat remains to maintain thermophilic temperature, the rate of heat

generation cannot meet that of heat loss. The cooling stage takes place and the

temperature arrives at ambient temperature. The activities of microorganisms also

slow down. (Waksman et al., 1939). When the composting reaches the cooling phase,

the thermophile population decreases and mesophlies begin to re-colonize the

compost materials to help move the compost into maturity, which is called a

resumption of mesophiles (Poincelot, 1974; Strom, 1985; Fogarty and Tuovinen, 1991;

Herrmann and Shann, 1997). However, the activity of mesophiles is extremely low

because residues are highly resistant to decomposition. At last, the final composting

products become stable with extremely low microbial activity in the residue.

Temperature influences not only the dominant microorganisms and their population,

but also the rate and pathway of decomposition. Waksman et al. (1939) pointed out

that the maximum degradation rate occurred at 50 °C in a batch system with horse

manure; Jeris and Regan (1973a) reported that the highest degradation rate can be

found at 55 °C in a continuous feeding system; Strom (1985) determined the optimum

temperature for a thermophilic continuous process to be 60 °C; and Cathcart et al.

(1986) showed maximum CO2 generation was realized at a temperature of 55 °C in a

batch composting;

18

Controlling temperature is also a useful tool with respect to sanitation. The

thermophilic temperature reached during the composting process can reduce pathogen

concentration dramatically and provide a safe compost product (Cooney and Wise,

1975). As a result, attaining and maintaining an appropriate temperature for a specific

period of time is necessary for consistent pathogen destruction. Moreover, a faster and

more complete composting can be achieved if optimal temperature is maintained in

the thermophilic range. On the other hand, an extremely high temperature will

deactivate proteins and biological activity will decline as the population dies, thus,

severely slowing the composting process. In summary, maintaining a consistent

temperature may provide for faster and more complete composting, and pathogen

destruction and stable fmal compost products. Variations in material, composting

methods, aeration rates, substrate composition and other factors could affect optimal

composting (Hall, 1998).

2.2.2 pH

2.2.2.1 pH Profile in Food Waste Composting

Generally, pH has been used as an index to evaluate the composting process and

control the reaction rate (Nakasaki et al., 1993; VanderGheynst and Lei, 2000;

Nakasaki et al., 1992). In a typical composting process, pH usually drops during the

initial stage, but rises rapidly to alkaline when the thermophilic stage begins. The

generated short-chain organic acids and released ammonia appear to contribute a great

deal to the pH variation time, leading to an initial pH' drop to approximately 5.0

19

Controlling temperature is also a useful tool with respect to sanitation. The

thermophilic temperature reached during the composting process can reduce pathogen

concentration dramatically and provide a safe compost product (Cooney and Wise,

1975). As a result, attaining and maintaining an appropriate temperature for a specific

period of time is necessary for consistent pathogen destruction. Moreover, a faster and

more complete composting can be achieved if optimal temperature is maintained in

the thermophilic range. On the other hand, an extremely high temperature will

deactivate proteins and biological activity will decline as the population dies, thus,

severely slowing the composting process. In summary, maintaining a consistent

temperature may provide for faster and more complete composting, and pathogen

destruction and stable final compost products. Variations in material, composting

methods, aeration rates, substrate composition and other factors could affect optimal

composting (Hall, 1998).

2.2.2 pH

2.2.2.1 pH Profile in Food Waste Composting

Generally, pH has been used as an index to evaluate the composting process and

control the reaction rate (Nakasaki et al., 1993; VanderGheynst and Lei, 2000;

Nakasaki et al., 1992). In a typical composting process, pH usually drops during the

initial stage, but rises rapidly to alkaline when the thermophilic stage begins. The

generated short-chain organic acids and released ammonia appear to contribute a great

deal to the pH variation time, leading to an initial pH' drop to approximately 5.0

19

during the mesophilic stage, and a pH rising in the thermophilic stage to

approximately 8.0 (Conghos et al., 2003; Sundberg and Jonsson, 2005)

The organic acids, which are mainly lactic and acetic acids, were generated from the

decomposition of easily degradable organic matters, such as sugar, starch and fat

(Poincelot, 1974; Mathur, 1991; Beck-Friis et al., 2001; R.T. Haug, 1993; Kirchmann

and Widen, 1994; Venglovsky et al., 2005). This acid-producing process has been

observed during the storage and collection of substrates, as well as at the beginning of

batch-scale composting (Eklind and Kirchmann, 2000; Day et al., 1998; Beck-Friis et

al., 2003). It is commonly accepted that organic acids are generated by a microbial

product of anaerobic metabolism (Beck-Friis et al., 2001). In real systems, if the

oxygen diffusion is not sufficient, oxygen in some parts of the composting system can

be exhausted and short-chain organic acids can still be found even when the compost

off-gas has a high oxygen concentration (Beck-Friis et al., 2003). Consequently, the

anaerobic condition can hardly be avoided.

It has been well-recognized that organic acids are the reason for low pH in the initial

phase of composting. Because their un-dissociated forms can pass through cell

membranes and cause an imbalance of protons, the short-chain organic acids are toxic

to microorganisms when the pH is less than 5 (Erickson and Fung, 1988). On the

other hand, at the end of the mesophilic stage when the pH level rises up to 6, the

short-chain organic acids (dissociated forms) become readily decomposable

20

during the mesophilic stage, and a pH rising in the thermophilic stage to

approximately 8.0 (Conghos et al., 2003; Sundberg and Jonsson, 2005)

The organic acids, which are mainly lactic and acetic acids, were generated from the

decomposition of easily degradable organic matters, such as sugar, starch and fat

(Poincelot, 1974; Mathur, 1991; Beck-Friis et al., 2001; R.T. Haug, 1993; Kirchmann

and Widen, 1994; Venglovsky et al., 2005). This acid-producing process has been

observed during the storage and collection of substrates, as well as at the beginning of

batch-scale composting (Eklind and Kirchmann, 2000; Day et al., 1998; Beck-Friis et

al., 2003). It is commonly accepted that organic acids are generated by a microbial

product of anaerobic metabolism (Beck-Friis et al., 2001). In real systems, if the

oxygen diffusion is not sufficient, oxygen in some parts of the composting system can

be exhausted and short-chain organic acids can still be found even when the compost

off-gas has a high oxygen concentration (Beck-Friis et al., 2003). Consequently, the

anaerobic condition can hardly be avoided.

It has been well-recognized that organic acids are the reason for low pH in the initial

phase of composting. Because their un-dissociated forms can pass through cell

membranes and cause an imbalance of protons, the short-chain organic acids are toxic

to microorganisms when the pH is less than 5 (Erickson and Fung, 1988). On the

other hand, at the end of the mesophilic stage when the pH level rises up to 6, the

short-chain organic acids (dissociated forms) become readily decomposable

(Beck-Friis et al., 2003; Brinton, 1998). Due to this situation, the decarboxylation

process of organic anions occurs which assimilated one proton per molecule to

produce aldehyde and carbon dioxide (Barakzai and Mengel, 1993; Yan et al., 1996).

In a successful composting, the acids are decomposed and thus, the pH level is

accordingly increased to 8 from the mesophilic to the thermophilic stage (Day et al.,

1998; Jeong and Hwang, 2005). The ammonification and mineralization of organic

nitrogen through microbial activities also contribute to a shift in pH (Finstein and

Mon-is, 1975; Bishop and Godfrey, 1983; Mahimairaja et al., 1994; Thambirajah et al.,

1995). During the later stage, nitrification can help to neutralize the pH (Tiquia and

Tam, 1998; Venglovsky et al., 2005). The reaction of decarboxylation and

ammonification/mineralization processes can be found as follows:

R-000- +H+ -* R- CHO +CO2

R- NH2 + H20 + H+ + HCO3- -NH4> + +HCO3- +R-OH

Various species of microorganisms have different optimal pH levels. For example, the

optimal pH level for most of bacteria is from 6.0 to 7.5, while that for fungi is 5.5 to

8.5 (C. G. Golueke, 1973). Bertoldi et al. (1983) suggested that the optimum pH

levels for composting were between 5.5 and 8.0; Nakasaki et al. (1993) discussed the

pH dependency of microbial activities and found the optimal pH within a range of 7

to 8.

2.2.2.2 pH Problems in Food Waste Composting

21

(Beck-Friis et al., 2003; Brinton, 1998). Due to this situation, the decarboxylation

process of organic anions occurs which assimilated one proton per molecule to

produce aldehyde and carbon dioxide (Barakzai and Mengel, 1993; Yan et al., 1996).

In a successful composting, the acids are decomposed and thus, the pH level is

accordingly increased to 8 from the mesophilic to the thermophilic stage (Day et al.,

1998; Jeong and Hwang, 2005). The ammonification and mineralization of organic

nitrogen through microbial activities also contribute to a shift in pH (Finstein and

Morris, 1975; Bishop and Godfrey, 1983; Mahimairaja et al., 1994; Thambirajah et al.,

1995). During the later stage, nitrification can help to neutralize the pH (Tiquia and

Tam, 1998; Venglovsky et al., 2005). The reaction of decarboxylation and

ammonification/mineralization processes can be found as follows:

R-COO~ +H+ ->R-CH0+C02

R-NH2+ H2O+H++HCO~ -> NH;+HCO; +R-OH

Various species of microorganisms have different optimal pH levels. For example, the

optimal pH level for most of bacteria is from 6.0 to 7.5, while that for fungi is 5.5 to

8.5 (C. G. Golueke, 1973). Bertoldi et al. (1983) suggested that the optimum pH

levels for composting were between 5.5 and 8.0; Nakasaki et al. (1993) discussed the

pH dependency of microbial activities and found the optimal pH within a range of 7

to 8.

2.2.2.2 pH Problems in Food Waste Composting

21

Due to the large quantity of short-chain organic acids produced in the initial stage of

food-waste composting, the pH level in the materials can hardly stay in the optimum

range for composting (Beck-Friis et al., 2001; Smars et al., 2002), which will in turn

cause less viable microbial activities so as to hinder the transformation from

mesophilic to thermophilic conditions (Day et al., 1998; Schloss and Walker, 2000;

Weppen, 2001; Cathcart et al., 1986). Several studies have explained that this

phenomenon was due to a low pH in the materials, as well as the toxicity of

short-chain organic acids. Today, this problem has been further analyzed by

examining both temperature and pH levels (Smars et al., 2002; Sundberg et al., 2004).

Beck-Friss et al. (2001) found a very low respiration activity occurred in the material

if the temperature was regulated at 55 °C and the pH to 5.1-5.5. Smars et al. (2004)

stated the initial phase of the low pH would be substantially prolonged if the

temperature was above 40 °C. Nevertheless, the length of the initial phase can be

reduced if the temperature has been kept under 40 °C until the acids disappear.

Sundberg et al. (2002) tested the combined effects of temperature (from 36 to 46 °C)

and pH (from 4.6 to 9.2) on the respiration rate during the early stage of composting.

The results showed microorganism activity would be severely inhibited at 46 °C and a

pH of less than 6, which means that combination of low pH and high temperature is

detrimental to the composting process. One hypothesis with respect to this lag phase

with low microorganism activity is that the microorganism can withstand only one

extreme environment, high temperature or low pH, but not both simultaneously

(Storm, 1985). Another possible explanation is the existence of two types of

22

Due to the large quantity of short-chain organic acids produced in the initial stage of

food-waste composting, the pH level in the materials can hardly stay in the optimum

range for composting (Beck-Friis et al., 2001; Sm&rs et al., 2002), which will in turn

cause less viable microbial activities so as to hinder the transformation from

mesophilic to thermophilic conditions (Day et al., 1998; Schloss and Walker, 2000;

Weppen, 2001; Cathcart et al., 1986). Several studies have explained that this

phenomenon was due to a low pH in the materials, as well as the toxicity of

short-chain organic acids. Today, this problem has been further analyzed by

examining both temperature and pH levels (Sm&rs et al., 2002; Sundberg et al., 2004).

Beck-Friss et al. (2001) found a very low respiration activity occurred in the material

if the temperature was regulated at 55 °C and the pH to 5.1-5.5. Sm&rs et al. (2004)

stated the initial phase of the low pH would be substantially prolonged if the

temperature was above 40 °C. Nevertheless, the length of the initial phase can be

reduced if the temperature has been kept under 40 °C until the acids disappear.

Sundberg et al. (2002) tested the combined effects of temperature (from 36 to 46 °C)

and pH (from 4.6 to 9.2) on the respiration rate during the early stage of composting.

The results showed microorganism activity would be severely inhibited at 46 °C and a

pH of less than 6, which means that combination of low pH and high temperature is

detrimental to the composting process. One hypothesis with respect to this lag phase

with low microorganism activity is that the microorganism can withstand only one

extreme environment, high temperature or low pH, but not both simultaneously

(Storm, 1985). Another possible explanation is the existence of two types of

microorganisms, a mesophilic acid-tolerant community and a thermophilic

community which does not tolerate an acid environment. This assumption was

supported by the fact that fungi require a more acidic and colder environment than

bacteria do (Atlas and Bartha, 1998).

In the food waste composting process, temperature often rises quickly to a

thermophile level. This unfavourable condition with high temperature and low pH is

easily formed due to the good self-insulating features of the composting materials

(R.T. Haug, 1993). Therefore, the question of how to avoid this low-activity period is

a primary problem requiring a solution in food waste composting (Kwon and Lee,

2004). Meanwhile, the NH3 emission is also closely related to the pH level and the

effects of the pH variation on ammonia emission are also of concern (Groenestein and

Van Faassen, 1996; Y. Liang et al., 2006).

Unlike manure waste composting with a high degradation rate for ammonium, urea

and other organic nitrogen, and the emission of NH3 during food waste composting

shows a substantial difference (de Bertoldi et al., 1985; Tam and Tiquia, 1999). Thus,

the experience of manure-waste composting cannot be simply adapted to food waste

composting. Although the large amount of short-chain organic acids, generated in the

initial stage, can help restrain the emission of NH3, ammonia is emitted in gaseous

form when the composting process shifts to the thermophiles stage according to the

23

microorganisms, a mesophilic acid-tolerant community and a thermophilic

community which does not tolerate an acid environment. This assumption was

supported by the fact that fungi require a more acidic and colder environment than

bacteria do (Atlas and Bartha, 1998).

In the food waste composting process, temperature often rises quickly to a

thermophile level. This unfavourable condition with high temperature and low pH is

easily formed due to the good self-insulating features of the composting materials

(R.T. Haug, 1993). Therefore, the question of how to avoid this low-activity period is

a primary problem requiring a solution in food waste composting (Kwon and Lee,

2004). Meanwhile, the NH3 emission is also closely related to the pH level and the

effects of the pH variation on ammonia emission are also of concern (Groenestein and

Van Faassen, 1996; Y. Liang et al., 2006).

Unlike manure waste composting with a high degradation rate for ammonium, urea

and other organic nitrogen, and the emission of NH3 during food waste composting

shows a substantial difference (de Bertoldi et al., 1985; Tam and Tiquia, 1999). Thus,

the experience of manure-waste composting cannot be simply adapted to food waste

composting. Although the large amount of short-chain organic acids, generated in the

initial stage, can help restrain the emission of NH3, ammonia is emitted in gaseous

form when the composting process shifts to the thermophiles stage according to the

23

evolving factors such as temperature and pH (Nakasaki et al., 1993; Beck-Friis et al.,

2001).

2.2.2.3 PH Control Methods in Food Waste Composting

Many studies have been carried out in terms of pH control strategies in the

composting process. Basically, the control methods can be divided into two groups:

alkaline-material addition and nutrient adjustment (An, 2006). SmArs (2002)

developed the temperature process control strategy but it required more energy to cool

down the composting materials in order to keep the temperature under 40 °C until the

pH level rises to 5.5. As a result, this strategy is not suitable for large-scale or

industrial composting facilities. Two order strategies will be presented in this section.

Alkaline material addition: Alkaline materials can be divided into two groups: strong

alkaline materials and weak alkaline materials. Lime is one of most commonly used

strong alkaline materials. Nakasaki et al. (1993) used lime to prevent pH level from

dropping to the acidic range in laboratory-scale composting of garbage under

well-controlled experimental conditions. The results showed that pH control can assist

in enhancing the degradation rate when compared to that without pH control. Wong

and Fang (2000) have used a series of experiments to evaluate the effects of lime on

the composting of sewage sludge. In the tests, added lime at a high level of 1.63%

could raise the pH to 7.3 to 9.2 in composting materials. The pH rose to more than 9.0

in the beginning and gradually fell to 6.9. As for the run with added lime at a low

level of 0.63%, no inhibition effects on microbial and enzymes were observed when

24

evolving factors such as temperature and pH (Nakasaki et al., 1993; Beck-Friis et al.,

2001).

2.2.2.3 PH Control Methods in Food Waste Composting

Many studies have been carried out in terms of pH control strategies in the

composting process. Basically, the control methods can be divided into two groups:

alkaline-material addition and nutrient adjustment (An, 2006). SmSrs (2002)

developed the temperature process control strategy but it required more energy to cool

down the composting materials in order to keep the temperature under 40 °C until the

pH level rises to 5.5. As a result, this strategy is not suitable for large-scale or

industrial composting facilities. Two order strategies will be presented in this section.

Alkaline material addition: Alkaline materials can be divided into two groups: strong

alkaline materials and weak alkaline materials. Lime is one of most commonly used

strong alkaline materials. Nakasaki et al. (1993) used lime to prevent pH level from

dropping to the acidic range in laboratory-scale composting of garbage under

well-controlled experimental conditions. The results showed that pH control can assist

in enhancing the degradation rate when compared to that without pH control. Wong

and Fang (2000) have used a series of experiments to evaluate the effects of lime on

the composting of sewage sludge. In the tests, added lime at a high level of 1.63%

could raise the pH to 7.3 to 9.2 in composting materials. The pH rose to more than 9.0

in the beginning and gradually fell to 6.9. As for the run with added lime at a low

level of 0.63%, no inhibition effects on microbial and enzymes were observed when

compared with the test without any amendment. On the other hand, the thennophilic

stage was prolonged when lime was added. Sasaki et al. (2003) also used lime to

control pH in a waste oil composting treatment. The treated test could hold a pH level

of around 6 to 7. In the test without the lime addition, the pH dropped to lower than 3

after the first few days and remained at that level until the end of testing. The oil

degradation efficiency could be enhanced to 83.4% in the pH-controlled test, which

was approximately 60% more than uncontrolled test. Sodium hydroxide (NaOH) is

also widely used to adjust pH in laboratory scale composting (VanderGheynst and Lei,

2000). Due to high costs, strong alkaline additives are not suitable for a large-scale

composting application. Another reason is that a strong alkaline additive will become

totally ionized in the substrates. So that the strong base must be added gradually to

composting materials to neutralize the continually generated acids, to maintain pH at a

suitable range and to prevent a high pH level, which needs to be more labor intensive

(Sasaki et al., 2003).

Weak alkaline materials such as coal ash and wood ash residues can be used to

replace of strong alkaline in order to prevent a drop in pH during composting

(Nakasaki et al., 1993; A. G. Campbell et al., 1997). Wood ash is an alkaline material

with a pH typically between 8 to 13 and an amendment to control the pH level at 5.7

to 7.6 in yard waste and bio-solid composting (A. G. Campbell et al., 1997). Coal ash

residues which contain coal fly ash and bottom ash (also known as lagoon ash) are

• major wastes from burnt coal in electricity power stations and rich. with CaO, MgO

25

compared with the test without any amendment. On the other hand, the thermophilic

stage was prolonged when lime was added. Sasaki et al. (2003) also used lime to

control pH in a waste oil composting treatment. The treated test could hold a pH level

of around 6 to 7. In the test without the lime addition, the pH dropped to lower than 3

after the first few days and remained at that level until the end of testing. The oil

degradation efficiency could be enhanced to 83.4% in the pH-controlled test, which

was approximately 60% more than uncontrolled test. Sodium hydroxide (NaOH) is

also widely used to adjust pH in laboratory scale composting (VanderGheynst and Lei,

2000). Due to high costs, strong alkaline additives are not suitable for a large-scale

composting application. Another reason is that a strong alkaline additive will become

totally ionized in the substrates. So that the strong base must be added gradually to

composting materials to neutralize the continually generated acids, to maintain pH at a

suitable range and to prevent a high pH level, which needs to be more labor intensive

(Sasaki et al., 2003).

Weak alkaline materials such as coal ash and wood ash residues can be used to

replace of strong alkaline in order to prevent a drop in pH during composting

(Nakasaki et al., 1993; A. G. Campbell et al., 1997). Wood ash is an alkaline material

with a pH typically between 8 to 13 and an amendment to control the pH level at 5.7

to 7.6 in yard waste and bio-solid composting (A. G. Campbell et al., 1997). Coal ash

residues which contain coal fly ash and bottom ash (also known as lagoon ash) are

major wastes from burnt coal in electricity power stations and rich, with CaO, MgO

and silicate contents. They are believed to have a large alkaline buffering capacity.

Based on results from Wong et al. (1995) and Suzuki et al. (2004), the coal fly ash

and bottom ash have a pH level of 8.87 to 12.2 and 9.8 to 11.4, respectively. Suzuki et

al. (2004) used a statistical method to evaluate the correlation between the addition of

ash and the pH level during the co-composting of wood chips waste with coal ash and

volcanic ash. There was a positive correlation between added coal bottom ash and the

pH levels. However, coal fly ash appears to be not significantly related to the pH

levels. Wong et al. (1995) investigated the effects of different amounts of coal ash

residues with microbial sludge added. The additions of fly ash and bottom ash both

caused an increase of 4.5 to 7.2 in the pH level of the sludge. Thus, the bottom ash

would be a better suited co-composting candidate because of the smaller pH drop and

high CO2 evolution. Fang et al. (1999) studied nutrient transformation with the

addition of the coal fly ash during the sewage sludge composting. The coal fly ash

amendment wouldn't cause a significant nitrogen loss but would inhibit the activities

of nitrifying bacteria and nitrogen-fixing bacteria.

Nutrient adjustment: Because of the high organic content in food wastes such as

carbohydrate and fat, it can be easily transformed into acids to create a low pH in the

initial stage (An, 2006):

C 6 H i 20 6 2C 2 H 4 (OH)C00" + 2H+

But the produced H+ can be consumed when amine is mineralized (Hanajima et al.,

2001):

26

and silicate contents. They are believed to have a large alkaline buffering capacity.

Based on results from Wong et al. (1995) and Suzuki et al. (2004), the coal fly ash

and bottom ash have a pH level of 8.87 to 12.2 and 9.8 to 11.4, respectively. Suzuki et

al. (2004) used a statistical method to evaluate the correlation between the addition of

ash and the pH level during the co-composting of wood chips waste with coal ash and

volcanic ash. There was a positive correlation between added coal bottom ash and the

pH levels. However, coal fly ash appears to be not significantly related to the pH

levels. Wong et al. (1995) investigated the effects of different amounts of coal ash

residues with microbial sludge added. The additions of fly ash and bottom ash both

caused an increase of 4.5 to 7.2 in the pH level of the sludge. Thus, the bottom ash

would be a better suited co-composting candidate because of the smaller pH drop and

high CO2 evolution. Fang et al. (1999) studied nutrient transformation with the

addition of the coal fly ash during the sewage sludge composting. The coal fly ash

amendment wouldn't cause a significant nitrogen loss but would inhibit the activities

of nitrifying bacteria and nitrogen-fixing bacteria.

Nutrient adjustment: Because of the high organic content in food wastes such as

carbohydrate and fat, it can be easily transformed into acids to create a low pH in the

initial stage (An, 2006):

C6Ht206 -• 2C2Ha(OH)COO~ + 2/T

But the produced H+ can be consumed when amine is mineralized (Hanajima et al.,

2001):

26

R— NH2 + H20 + H+ +HCO3 -+ NH4+ +HCO3 + R— OH

Thus, an additives with high organic nitrogen could be used as a neutralization

method for pH control (Sasaki et al., 2003). Sasaki et al. have used two nitrogen

sources, urea and ammonium sulfate in the composting of waste oil. With an addition

of urea, the pH was increased to 8.5 and maintained around 6 without the use of other

manual pH controls, while ammonium sulfate could not prevent the pH from dropping

during the test.

Soybean residues such as waste from soya drinks and tofu (a popular food in Asian

countries), have also been used to amend food waste composting due to their high

nitrogen contents. Nakasaki et al. (1992) successfully composted soybean residues

with two inoculum sources; Hanajima et al. (2001) promoted the efficiency of

composing of cattle wastes and rice straw with the amendment of tofu residues. Wong

et al. (2001) investigated the effects of turning frequency in co-composting of soybean

residues and leaves.

2.2.3 Moisture Content

Water is a critical physical and ecological factor in a composting system (Miller, 1989;

Baker and Allmaras, 1990). Acceptable efficiency of microbial stabilization requires

sufficient moisture in the solid waste (Dirksen and Dasberg, 1993; Iriarte and Ciria,

2001). 50% to 70% is the most satisfactory range of moisture content for

27

R-NH2+ H2O+H++ HCO; -> NH; + HCO;+R-OH

Thus, an additives with high organic nitrogen could be used as a neutralization

method for pH control (Sasaki et al., 2003). Sasaki et al. have used two nitrogen

sources, urea and ammonium sulfate in the composting of waste oil. With an addition

of urea, the pH was increased to 8.5 and maintained around 6 without the use of other

manual pH controls, while ammonium sulfate could not prevent the pH from dropping

during the test.

Soybean residues such as waste from soya drinks and tofii (a popular food in Asian

countries), have also been used to amend food waste composting due to their high

nitrogen contents. Nakasaki et al. (1992) successfully composted soybean residues

with two inoculum sources; Hanajima et al. (2001) promoted the efficiency of

composing of cattle wastes and rice straw with the amendment of tofu residues. Wong

et al. (2001) investigated the effects of turning frequency in co-composting of soybean

residues and leaves.

2.2.3 Moisture Content

Water is a critical physical and ecological factor in a composting system (Miller, 1989;

Baker and Allmaras, 1990). Acceptable efficiency of microbial stabilization requires

sufficient moisture in the solid waste (Dirksen and Dasberg, 1993; Iriarte and Ciria,

2001). 50% to 70% is the most satisfactory range of moisture content for

27

composting and should be maintained at an active microbial reaction (Polprasert,

1989; Robinson and Stentiford, 1993; Zhang, 2000). Water is both a products and

reactants of microbial reactions. Moisture also affects substrate porosity and gas

diffusivity. Moisture in compost can be increased by a respiration process and

decreased by an evaporation process, as well as energy loss (Stentiford, 1996).

Microbial activity may be greatly slowed due to shortage of water if the moisture

content is below 30%. At this time, additional water may help to restart the process

(Bakshi et al., 1987; Stentiford, 1996; Nakasaki et al., 2004; Nakasaki et al., 2005).

However, excessive moisture content will fill the interspace between the organic mass

and restrict aeration, resulting in odors and a significant decrease in the temperature

and rate of decomposition (Iriarte and Ciria, 2001; Jeris and Regan, 1973b; Inbar,

1990). Although the optimum moisture level varies according to materials and species

of microorganisms, it is clear that maintaining a moderate moisture content (greater

than 30%) but avoiding excessive water (greater than 70%) is essential to maintain

effective degradation (Zhang, 2000; Jeris and Regan, 1973b).

2.2.4 Oxygen

Many researchers consider oxygen consumption and carbon dioxide levels as

feedback parameters of microorganism activities (Jeris and Regan, 1973a, 1973c;

Nakasaki and Shoda, 1987; deBertoldi et al., 1988; Vinci et al., 1996; Bodelier and

Laanbroek, 1997; Richard et al., 1999). It was generally recognized that a level of

28

composting and should be maintained at an active microbial reaction (Polprasert,

1989; Robinson and Stentiford, 1993; Zhang, 2000). Water is both a products and

reactants of microbial reactions. Moisture also affects substrate porosity and gas

diffusivity. Moisture in compost can be increased by a respiration process and

decreased by an evaporation process, as well as energy loss (Stentiford, 1996).

Microbial activity may be greatly slowed due to shortage of water if the moisture

content is below 30%. At this time, additional water may help to restart the process

(Bakshi et al., 1987; Stentiford, 1996; Nakasaki et al., 2004; Nakasaki et al., 2005).

However, excessive moisture content will fill the interspace between the organic mass

and restrict aeration, resulting in odors and a significant decrease in the temperature

and rate of decomposition (Iriarte and Ciria, 2001; Jeris and Regan, 1973b; Inbar,

1990). Although the optimum moisture level varies according to materials and species

of microorganisms, it is clear that maintaining a moderate moisture content (greater

than 30%) but avoiding excessive water (greater than 70%) is essential to maintain

effective degradation (Zhang, 2000; Jeris and Regan, 1973b).

2.2.4 Oxygen

Many researchers consider oxygen consumption and carbon dioxide levels as

feedback parameters of microorganism activities (Jeris and Regan, 1973a, 1973c;

Nakasaki and Shoda, 1987; deBertoldi et al., 1988; Vinci et al., 1996; Bodelier and

Laanbroek, 1997; Richard et al., 1999). It was generally recognized that a level of

28

oxygen less than 5% might limit aerobic activities (R.T. Haug, 1993). Moreover, the

interaction between moisture content and oxygen availability is important (Jeris and

Regan, 1973b). Excessive moisture in the compost can reduce the free air space

within the pile, which inhibits air distribution among the substrates and limits oxygen

availability and the degradation rate (Wong and Fang, 2000). On the other hand, the

oxygen level is, for the most part, decided by the aeration rate. Excessive aeration

rates will draw more evaporated water out of the system, which causes a decrease in

moisture content. If the moisture content is too low as previously discussed, the

degradation rate would also be inhibited (Wong et al., 1995; Suzuki et al., 2004;

Wong et al., 2001).

2.2.5 Nitrogen Content

Since compost has long been used as soil fertilizer, nitrogen (N) has as an essential

role in all living cells and is the most valuable part from an agricultural standpoint

(Wilson, 1989). The nitrogen transformation process has also been accepted as a

maturity and stability index for composting (Bernal et al., 1996; Fu, 2004). A loss of

nitrogen not only slows down the speed of organic matter biodegradation in the

composting process but also worsens the fertility of its products (Hu et al., 2007).

However, nitrogen-related reactions in a composting process are quite complicated.

Basically, the principle procedures governing transformation between different

species of nitrogen contain mineralization, volatilization, nitrification, immobilization

29

oxygen less than 5% might limit aerobic activities (R.T. Haug, 1993). Moreover, the

interaction between moisture content and oxygen availability is important (Jeris and

Regan, 1973b). Excessive moisture in the compost can reduce the free air space

within the pile, which inhibits air distribution among the substrates and limits oxygen

availability and the degradation rate (Wong and Fang, 2000). On the other hand, the

oxygen level is, for the most part, decided by the aeration rate. Excessive aeration

rates will draw more evaporated water out of the system, which causes a decrease in

moisture content. If the moisture content is too low as previously discussed, the

degradation rate would also be inhibited (Wong et al., 1995; Suzuki et al., 2004;

Wong et al., 2001).

2.2.5 Nitrogen Content

Since compost has long been used as soil fertilizer, nitrogen (N) has as an essential

role in all living cells and is the most valuable part from an agricultural standpoint

(Wilson, 1989). The nitrogen transformation process has also been accepted as a

maturity and stability index for composting (Bernal et al., 1996; Fu, 2004). A loss of

nitrogen not only slows down the speed of organic matter biodegradation in the

composting process but also worsens the fertility of its products (Hu et al., 2007).

However, nitrogen-related reactions in a composting process are quite complicated.

Basically, the principle procedures governing transformation between different

species of nitrogen contain mineralization, volatilization, nitrification, immobilization

29

and denitrification (Hellebrand, 1998). Mineralization, also known as ammonification,

is a process which transfers nitrogen content in the organic form (R-NH2) to inorganic

ammonium (NI14+), in an acidic environment. Volatilization is the conversion of

ammonium (NH4+), released in the form of ammonia gas (NH3) when the pH is

greater than 7. Nitrification and denitrification are the processes which transfer the

nitrogen element to NO2IN03", N20/N2 and cause nitrogen loss in the form of a gas

(Czepiel et al., 1996). However, NH3 is the dominant gas being emitted (Witter and

Lopez-Real, 1987; Beck-Friis et al., 2001)

The C/N ratio of the initial composting raw-materials could be the fatal reason which

affected the nitrogen loss in the composting process (de Bertoldi et al., 1980; Brink,

1995). In a composting system with a high C/N ratio, nitrogen becomes the restriction

in microorganism growth thus leads to nitrogen immobilization (assimilation). On the

other hand, carbon-limited substrates with a low C/N ratio will emit excess nitrogen

(Kirchmann and Witter, 1989; Sikora, 1999). In real world applications of composting,

the substrates can be either nitrogen-rich materials (such as grass chippings) or with

low nitrogen content (such as cellulose) (Kayragian and Tchobanogious, 1992). A

C/N ratio maintained at 15 to 30 was considered optimal for composting (R.T. Haug,

1993). It has also been verified that a lower initial C/N ratio will cause a promoted

biodegradation rate (Huang et al., 2004; Abu Qdais and Hamoda, 2004; Michel et al.,

2004).

30

and denitrification (Hellebrand, 1998). Mineralization, also known as anunonification,

is a process which transfers nitrogen content in the organic form (R-NH2) to inorganic

ammonium (NHt+), in an acidic environment. Volatilization is the conversion of

ammonium (NHt+), released in the form of ammonia gas (NH3) when the pH is

greater than 7. Nitrification and denitrification are the processes which transfer the

nitrogen element to NO2VNO3", N2O/N2 and cause nitrogen loss in the form of a gas

(Czepiel et al., 1996). However, NH3 is the dominant gas being emitted (Witter and

Lopez-Real, 1987; Beck-Friis et al., 2001)

The C/N ratio of the initial composting raw-materials could be the fatal reason which

affected the nitrogen loss in the composting process (de Bertoldi et al., 1980; Brink,

1995). In a composting system with a high C/N ratio, nitrogen becomes the restriction

in microorganism growth thus leads to nitrogen immobilization (assimilation). On the

other hand, carbon-limited substrates with a low C/N ratio will emit excess nitrogen

(Kirchmann and Witter, 1989; Sikora, 1999). In real world applications of composting,

the substrates can be either nitrogen-rich materials (such as grass chippings) or with

low nitrogen content (such as cellulose) (Kayragian and Tchobanogious, 1992). A

C/N ratio maintained at 15 to 30 was considered optimal for composting (R.T. Haug,

1993). It has also been verified that a lower initial C/N ratio will cause a promoted

biodegradation rate (Huang et al., 2004; Abu Qdais and Hamoda, 2004; Michel et al.,

2004).

30

Some pH control amendments also affect the nitrogen transfonnation process (Sun,

2006). In Fang et al.'s experiments, a 35% level of coal fly ash amendment could

significantly inhibit decomposition activity so that the concentration of organic

nitrogen and total nitrogen increased along with the composting process. The coal fly

ash also caused obvious NH4+-N during the thermophilic phase. Moreover, added coal

fly ash would also lower the amount of soluble NO3-N content, indicating a reduced

nitrification process (Fang and Wong, 1999). On the other hand, although coal bottom

ash has been accepted as a suitable pH control amendment, the effects of coal bottom

ash on nitrogen transfonnation have seldom been reported. To avoid nitrogen

emission, many measures have been taken into consideration. For example, high

carbon-containing substrates (sawdust and straw), physical adsorbents (clay, zeolite,

and basalt) and chemical absorbents (hydrogen phosphate) have been introduced

(Witter and Lopez-Real, 1988; Jeong and Hwang, 2005; Jeong and Kim, 2001).

2.3 Modeling of the Composting Process

Previously, many studies have been undertaken to conduct modeling simulation of

composting processes (Whang and Meenaghan, 1980; Heijnen and Roele, 1981;

Hamelers, 2001; Marugg, 1993; Hauhs, 1996; Mysliwiec et al., 2001; Nelson et al.,

2003; Trefry and Franzmann, 2003; Johannessen et al., 2005; Stombaugh and Nokes,

1996). Jeris and Regan (1973a, b, c) considered multiple factors of the composting

process in their models. Haug (1993) discussed the possibilities of using different

dynamic models to simulate composting systems. Many proposed models have been

31

Some pH control amendments also affect the nitrogen transformation process (Sun,

2006). In Fang et al.'s experiments, a 35% level of coal fly ash amendment could

significantly inhibit decomposition activity so that the concentration of organic

nitrogen and total nitrogen increased along with the composting process. The coal fly

ash also caused obvious NH/-N during the thermophilic phase. Moreover, added coal

fly ash would also lower the amount of soluble NO3-N content, indicating a reduced

nitrification process (Fang and Wong, 1999). On the other hand, although coal bottom

ash has been accepted as a suitable pH control amendment, the effects of coal bottom

ash on nitrogen transformation have seldom been reported. To avoid nitrogen

emission, many measures have been taken into consideration. For example, high

carbon-containing substrates (sawdust and straw), physical adsorbents (clay, zeolite,

and basalt) and chemical absorbents (hydrogen phosphate) have been introduced

(Witter and Lopez-Real, 1988; Jeong and Hwang, 2005; Jeong and Kim, 2001).

2.3 Modeling of the Composting Process

Previously, many studies have been undertaken to conduct modeling simulation of

composting processes (Whang and Meenaghan, 1980; Heijnen and Roele, 1981;

Hamelers, 2001; Marugg, 1993; Hauhs, 1996; Mysliwiec et al., 2001; Nelson et al.,

2003; Trefiy and Franzmann, 2003; Johannessen et al., 2005; Stombaugh and Nokes,

1996). Jeris and Regan (1973a, b, c) considered multiple factors of the composting

process in their models. Haug (1993) discussed the possibilities of using different

dynamic models to simulate composting systems. Many proposed models have been

verified through lab-scale experiments (Finger et al., 1976; Suler and Finstein, 1977;

Beck, 1979). Nowadays, the main stream of models will consider parameter such as

temperature, moisture, oxygen content, carbon dioxide, energy content and substrate

composition (Lin, 2006; R.T. Haug, 1993; R. T. Haug, 1996; Richard, 1997;

VanderGheynst et al., 1996; VanderGheynst, 1997; K. Ekinci, 2001).

Various portions of the composting process have been simulated, such as respiratory

activities under the effects of many physical, chemical and biological variables (Beck,

1984; Ljung and Glad, 1994; Nobuyuki et al., 2001; Das and Keener, 1996).

Moreover, the interactions, or multiple dimensional interactions among different

parameters must be taken into consideration. For example, the air flow rate will affect

temperature in two ways - input of cold ambient air causes a reduction temperature

and a prompted reaction rate, due to sufficient oxygen causes an increase in

temperature. Air flow will cause the evaporation of more water content. Therefore,

aeration rate, temperature and moisture are interrelated (R.T. Haug, 1993; R. T. Haug,

1996; Bari and Koening, 2001; Richard, 1997; Van Lier et al., 1994). To investigate

the energy flow in the modeling, normal conduction, convection and phase change

would be considered but conduction can frequently be negligible, especially in forced

air systems with high aeration rate (MacGregor et al., 1981; Trefry and Franzmann,

2003; Hall, 1998; R. Smith and Eilers, 1980)

32

verified through lab-scale experiments (Finger et al., 1976; Suler and Finstein, 1977;

Beck, 1979). Nowadays, the main stream of models will consider parameter such as

temperature, moisture, oxygen content, carbon dioxide, energy content and substrate

composition (Lin, 2006; R.T. Haug, 1993; R. T. Haug, 1996; Richard, 1997;

VanderGheynst et al., 1996; VanderGheynst, 1997; K. Ekinci, 2001).

Various portions of the composting process have been simulated, such as respiratory

activities under the effects of many physical, chemical and biological variables (Beck,

1984; Ljung and Glad, 1994; Nobuyuki et al., 2001; Das and Keener, 1996).

Moreover, the interactions, or multiple dimensional interactions among different

parameters must be taken into consideration. For example, the air flow rate will affect

temperature in two ways - input of cold ambient air causes a reduction temperature

and a prompted reaction rate, due to sufficient oxygen causes an increase in

temperature. Air flow will cause the evaporation of more water content. Therefore,

aeration rate, temperature and moisture are interrelated (R.T. Haug, 1993; R. T. Haug,

1996; Bari and Koening, 2001; Richard, 1997; Van Lier et al., 1994). To investigate

the energy flow in the modeling, normal conduction, convection and phase change

would be considered but conduction can frequently be negligible, especially in forced

air systems with high aeration rate (MacGregor et al., 1981; Trefry and Franzmann,

2003; Hall, 1998; R. Smith and Eilers, 1980)

32

Smith and Eilers (1980) proposed a two dimensional fmite difference model to

simulate windrow bio-solid composting with forced aeration conditions. The spatial

and temporal solutions of airflow, substrate decomposition and heat generation were

studied. The model was based on the assumption that flow patterns and quantities of

air were independent of time. There were several corresponding experiments to

validate the accuracy of the model in order to predict dry matter, volatile matter and

moisture content. Nakasaki (1987) developed a model to describe the process of

composting of sewage sludge cake composting. Mass and heat balance in a complete

batch reactor were the basis of this model. Carbon dioxide release and volatile

transformation were considered and the system's temperature and water content were

calculated. Three experiments also supported the validity of the model.

A continuous feed complete mix reactor was invented by Haug (1993), in which the

composting mass was schematically represented by gas, liquid and solid phases.

Many models have focused on this type of composting and organic matter degradation

was treated as a first-order reaction with respect to substrate concentration in these

models. The reaction constants in this reaction were correlated to the temperature.

Correlation factors with respect to suboptimal values of air filled volume fraction,

moisture content and oxygen concentration in aerated gas were taken into

consideration. Van Tier et al. (1994) developed a mathematical model of the

composting process based on mass and heat transfer in phase-II composting.

Continuous simulation and modeling were conducted via time-dependent analysis so.

33

Smith and Eilers (1980) proposed a two dimensional finite difference model to

simulate windrow bio-solid composting with forced aeration conditions. The spatial

and temporal solutions of airflow, substrate decomposition and heat generation were

studied. The model was based on the assumption that flow patterns and quantities of

air were independent of time. There were several corresponding experiments to

validate the accuracy of the model in order to predict dry matter, volatile matter and

moisture content. Nakasaki (1987) developed a model to describe the process of

composting of sewage sludge cake composting. Mass and heat balance in a complete

batch reactor were the basis of this model. Carbon dioxide release and volatile

transformation were considered and the system's temperature and water content were

calculated. Three experiments also supported the validity of the model.

A continuous feed complete mix reactor was invented by Haug (1993), in which the

composting mass was schematically represented by gas, liquid and solid phases.

Many models have focused on this type of composting and organic matter degradation

was treated as a first-order reaction with respect to substrate concentration in these

models. The reaction constants in this reaction were correlated to the temperature.

Correlation factors with respect to suboptimal values of air filled volume fraction,

moisture content and oxygen concentration in aerated gas were taken into

consideration. Van lier et al. (1994) developed a mathematical model of the

composting process based on mass and heat transfer in phase-II composting.

Continuous simulation and modeling were conducted via time-dependent analysis so

differential equations could be solved. The substrates were stratified because of their

different densities and porosities. The simulation results contain a time series of

oxygen demand, water and dry matter weight losses, temperature in various layers,

and heat loss through the tunnel wall of the container. The experimental data was used

to validate the model.

Stombaugh and Nokes (1996) designed a dynamic composting model to describe

microbial growth and death kinetics. Organic matter degradation rates were obtained

by establishing a stoichiometric conversion and parameterizing reasonable factors. To

test its validity, the model was compared to a lab scale experiment which composted a

mixture of cracked corn and palletized corncobs. The results showed that the model

was able to predict temperature fluctuation, the oxygen uptake rate, and moisture

exchange and substrate degradation for the input mixture which was readily

compostable.

Several related studies regarding developing kinetics models for microbiology

reactions models during the composting process exist. Hall (1998) developed a

numerical model which contained substrate, oxygen, moisture and energy as four

primary parameters. The changes in variables on system dynamics were investigated.

Hamelers (2001) constructed a particle-level mathematical model based on biofilm

theory. Polymeric substrates, microbial biomasses, oxygen and water concentration

were treated as state variables. First-order-kinetic and Mechaelis-Menten

34

differential equations could be solved. The substrates were stratified because of their

different densities and porosities. The simulation results contain a time series of

oxygen demand, water and dry matter weight losses, temperature in various layers,

and heat loss through the tunnel wall of the container. The experimental data was used

to validate the model.

Stombaugh and Nokes (1996) designed a dynamic composting model to describe

microbial growth and death kinetics. Organic matter degradation rates were obtained

by establishing a stoichiometric conversion and parameterizing reasonable factors. To

test its validity, the model was compared to a lab scale experiment which composted a

mixture of cracked corn and palletized corncobs. The results showed that the model

was able to predict temperature fluctuation, the oxygen uptake rate, and moisture

exchange and substrate degradation for the input mixture which was readily

compostable.

Several related studies regarding developing kinetics models for microbiology

reactions models during the composting process exist. Hall (1998) developed a

numerical model which contained substrate, oxygen, moisture and energy as four

primary parameters. The changes in variables on system dynamics were investigated.

Hamelers (2001) constructed a particle-level mathematical model based on biofilm

theory. Polymeric substrates, microbial biomasses, oxygen and water concentration

were treated as state variables. First-order-kinetic and Mechaelis-Menten

34

multiple-substrate-dependent rates have been used to model reaction rates. Nakhla et

al. (2006) have developed a modified Monod model including first-order hydrolysis of

particulate organic matter. Other than these kinetics, the Monod equation is the most

widely used equation (Monod, 1949; Xi et al., 2005; Petric and Selimbasic, 2008). Xi

et al. (2005) used two-stage Monod kinetics to analysis composting processes under

different limitative conditions. I. Petric and V. Selimbasic (2008) simulated aerobic

composting processes using a derivative form of the Monod equation with integrated

mass and heat transfer. Based predominantly on the Monod equation and its

modification, various kinetics models have been reported (Isik and Sponza, 2005;

Castillo et al., 1999; Nakhla et al., 2006; Bhunia and Ghangrekar, 2008). Furthermore,

many researchers have worked on comparing different kinetics models. The effects of

Monod, the Grau second-order and the Haldane kinetic model were compared through

linear and non-linear regression by Bhunia and Ghangrekar (2008). However,

limitations in previous models could be found only concentration of overall

microorganisms was used instead of considering different kinds separately (Richard

and Walker, 2006). Meanwhile, few studies have been conducted to quantitatively

investigate the interaction effects between pH and temperature on microbial growth

during the composting process.

2.4 Literature Review Summary

Composting has been recognized as a cost-effective alternative for treating food waste.

In the past decades, many research efforts have been undertaken to contribute to our

35

multiple-substrate-dependent rates have been used to model reaction rates. Nakhla et

al. (2006) have developed a modified Monod model including first-order hydrolysis of

particulate organic matter. Other than these kinetics, the Monod equation is the most

widely used equation (Monod, 1949; Xi et al., 2005; Petric and Selimbasic, 2008). Xi

et al. (2005) used two-stage Monod kinetics to analysis composting processes under

different limitative conditions. I. Petric and V. Selimbasic (2008) simulated aerobic

composting processes using a derivative form of the Monod equation with integrated

mass and heat transfer. Based predominantly on the Monod equation and its

modification, various kinetics models have been reported (Isik and Sponza, 2005;

Castillo et al., 1999; Nakhla et al., 2006; Bhunia and Ghangrekar, 2008). Furthermore,

many researchers have worked on comparing different kinetics models. The effects of

Monod, the Grau second-order and the Haldane kinetic model were compared through

linear and non-linear regression by Bhunia and Ghangrekar (2008). However,

limitations in previous models could be found only concentration of overall

microorganisms was used instead of considering different kinds separately (Richard

and Walker, 2006). Meanwhile, few studies have been conducted to quantitatively

investigate the interaction effects between pH and temperature on microbial growth

during the composting process.

2.4 Literature Review Summary

Composting has been recognized as a cost-effective alternative for treating food waste.

In the past decades, many research efforts have been undertaken to contribute to our

understanding of the composting process, technology development and modeling

simulation, which have proven to be essential in achieving the successful operation of

food waste composting. However, due to the distinctive features of food waste,

traditional solid waste disposal methods are no longer suitable, in-depth studies are

still lacking with respect to improving system efficiency and reliability:

(i) The literature review indicates complicated interactions of pH with other

parameters in the composting system and different stages of pH evolvement are

mutually related. However, the strategy of pH control is not straightforward:

keeping pH neither dropping in the initial stage to promote microorganism

activity nor rising too high at the thermophilic stage to prevent a loss of ammonia.

Few studies were conducted to meet the objective successfully with regards to the

conflict. As a result, a new category of amendment which has different effects on

pH in the composting system at different stages is desired.

(ii) A composting system is extremely complex and dynamic, or even case specific.

Many types of microorganisms exist and various reactions take place in the

system so their temporal variations must be taken into consideration. Previous

models which consider overall microbial concentration cannot depict the system

precisely and accurately. Consequently, previous empirical parameters, such as

kinetics parameters, optimal value of temperature, and pH are case-specific. Thus,

the development of a modelling system that .incorporates both mesophilic and

36

understanding of the composting process, technology development and modeling

simulation, which have proven to be essential in achieving the successful operation of

food waste composting. However, due to the distinctive features of food waste,

traditional solid waste disposal methods are no longer suitable, in-depth studies are

still lacking with respect to improving system efficiency and reliability:

(i) The literature review indicates complicated interactions of pH with other

parameters in the composting system and different stages of pH evolvement are

mutually related. However, the strategy of pH control is not straightforward:

keeping pH neither dropping in the initial stage to promote microorganism

activity nor rising too high at the thermophilic stage to prevent a loss of ammonia.

Few studies were conducted to meet the objective successfully with regards to the

conflict. As a result, a new category of amendment which has different effects on

pH in the composting system at different stages is desired.

(ii) A composting system is extremely complex and dynamic, or even case specific.

Many types of microorganisms exist and various reactions take place in the

system so their temporal variations must be taken into consideration. Previous

models which consider overall microbial concentration cannot depict the system

precisely and accurately. Consequently, previous empirical parameters, such as

kinetics parameters, optimal value of temperature, and pH are case-specific. Thus,

the development of a modelling system that .incorporates both mesophilic and

36

thermophilic microorganisms is desired. Such a model could provide parameters

for mesophiles and thermophiles, respectively. Moreover, this method would be

able to deal with the interactive effects of temperature and pH on the two types of

microorganisms, which is beneficial when it comes to understanding regarding

the mechanics of different microbes under different temperatures and pH

conditions.

37

thermophilic microorganisms is desired. Such a model could provide parameters

for mesophiles and thermophiles, respectively. Moreover, this method would be

able to deal with the interactive effects of temperature and pH on the two types of

microorganisms, which is beneficial when it comes to understanding regarding

the mechanics of different microbes under different temperatures and pH

conditions.

37

CHAPTER 3

MATERIALS AND METHOD

3.1 Overview of Experimental Approaches

In order to fulfill the objectives of this study, ten individual runs (A, B, C, D, El, E2,

Fl, F2, G1 and G2) were carried out with specifically designed amendments or

approaches under two different experiments. Experiment I was intended to analyze

the effects of buffer salts in composting. Three types of buffer salts (K2HPO4/MgSO4,

KH2PO4/MgSO4 and NaAc) with different initial pH were added into Runs A, B and

C, respectively. Experiment II aimed to modify the Monod equation in order to reflect

thermophile and mesophile activity, respectively. To create different external

conditions, water was added into Run E and temperature control was conducted at

Run F. The composting reaction took place under fully aerated (3.00 L/min)

conditions. Small samples were extracted from a compost reactor to undertake a

physical and a chemical test, in order to investigate microbial growth and activity.

Numbers of populations of microorganisms were counted to reflect the degree of

microorganism growth.

3.2 Composting Materials

To control the potential variability of substrate characteristics, a synthetic food waste

with better uniformity was preferred to real restaurant or cafeteria wasted (Schwab et

al., 1994). In this study, in order to obtain sufficient nutrients and a proper C/N ratio

38

CHAPTER 3

MATERIALS AND METHOD

3.1 Overview of Experimental Approaches

In order to fulfill the objectives of this study, ten individual runs (A, B, C, D, El, E2,

Fl, F2, G1 and G2) were carried out with specifically designed amendments or

approaches under two different experiments. Experiment I was intended to analyze

the effects of buffer salts in composting. Three types of buffer salts (K^HPO-j/MgSO^

KH2P04/MgSC>4 and NaAc) with different initial pH were added into Runs A, B and

C, respectively. Experiment II aimed to modify the Monod equation in order to reflect

thermophile and mesophile activity, respectively. To create different external

conditions, water was added into Run E and temperature control was conducted at

Run F. The composting reaction took place under fully aerated (3.00 L/min)

conditions. Small samples were extracted from a compost reactor to undertake a

physical and a chemical test, in order to investigate microbial growth and activity.

Numbers of populations of microorganisms were counted to reflect the degree of

microorganism growth.

3.2 Composting Materials

To control the potential variability of substrate characteristics, a synthetic food waste

with better uniformity was preferred to real restaurant or cafeteria wasted (Schwab et

al., 1994). In this study, in order to obtain sufficient nutrients and a proper C/N ratio

38

and moisture, a mixture of potato, carrot, ground pork, steamed rice, cooked soybean

and leaves was utilized as synthetic food waste. All food products were brought from

a local grocery store and pretreated prior to the experiment. The soybeans and rice

were steamed, and carrots and potatoes were peeled and chopped into pieces with a

diameter of approximately 5 mm. All food materials were ground into small pieces

and thoroughly mixed manually before being loaded into the compost reactor. (R.T.

Haug, 1993; Richard et al., 2002). The substrate composition and initial

characteristics of the raw materials can be found in Table 3.1 and Table 3.2,

respectively (Cheung, 2008). In Experiment I, K2HPO4, KH2PO4, NaAc with the same

amount of substance was added into Runs A, B and C as three different types of pH

amendment. To achieve a better performance of phosphate, MgSO4 was added into

Runs A and B with a mole ratio of 2:1 to phosphate buffer salts. The detailed

mole-based amounts of amendments are listed in Table 3.3. In Experiment II, different

conditions were adopted to test microbial activity. As a result, water was added into

Runs Fl and F2; the temperature of Runs GI and G2 was controlled between 50 °C

and 60 °C. Detailed external conditions can be found on Table 3.4.

3.3 In-Vessel Composting System

Figure 3.1shows a schematic diagram of the experimental system of this experiment.

The main reactor was assembled in three parts: an upper part, a bottom part and a

cylindrical body. The reactor was designed and manufactured as presented by Figure

3.2 to Figure 3.4. The main body of the reactor was made of acrylic tube with a

39

and moisture, a mixture of potato, carrot, ground pork, steamed rice, cooked soybean

and leaves was utilized as synthetic food waste. All food products were brought from

a local grocery store and pretreated prior to the experiment. The soybeans and rice

were steamed, and carrots and potatoes were peeled and chopped into pieces with a

diameter of approximately 5 mm. All food materials were ground into small pieces

and thoroughly mixed manually before being loaded into the compost reactor. (R.T.

Haug, 1993; Richard et al., 2002). The substrate composition and initial

characteristics of the raw materials can be found in Table 3.1 and Table 3.2,

respectively (Cheung, 2008). In Experiment I, K2HPO4, KH2PO4, NaAc with the same

amount of substance was added into Runs A, B and C as three different types of pH

amendment. To achieve a better performance of phosphate, MgSC>4 was added into

Runs A and B with a mole ratio of 2:1 to phosphate buffer salts. The detailed

mole-based amounts of amendments are listed in Table 3.3. In Experiment II, different

conditions were adopted to test microbial activity. As a result, water was added into

Runs F1 and F2; the temperature of Runs G1 and G2 was controlled between 50 °C

and 60 °C. Detailed external conditions can be found on Table 3.4.

3 J In-Vessel Composting System

Figure 3.1 shows a schematic diagram of the experimental system of this experiment.

The main reactor was assembled in three parts: an upper part, a bottom part and a

cylindrical body. The reactor was designed and manufactured as presented by Figure

3.2 to Figure 3.4. The main body of the reactor was made of acrylic tube with a

Table 3.1 Synthetic substrate composition in food waste composting system

Composition Amount (kg) Mass Percentage

Potato 1.27 12.7%

Rice 2.01 2.0%

Carrot 1.96 19.6%

Leaves 0.46 4.6%

Pork meat 0.35 3.5%

Soy bean 1.96 19.6%

Seed soil 2.00 20%

Total weight 10.00 100%

40

Table 3.1 Synthetic substrate composition in food waste composting system

Composition Amount (kg) Mass Percentage

Potato 1.27 12.7%

Rice 2.01 2.0%

Carrot 1.96 19.6%

Leaves 0.46 4.6%

Pork meat 0.35 3.5%

Soybean 1.96 19.6%

Seed soil 2.00 20%

Total weight 10.00 100%

40

Table 3.2 Physical and chemical properties of the initial composting material

Parameter value

pH 5.99-6.06

Moisture Content (%) 64.0-67.0

C/N Ratio 18.1-20.6

Organic Content in Dry Solids(%) 92.0-97.0

Ash Content (%) 3.00-8.00

41

Table 3.2 Physical and chemical properties of the initial composting material

Parameter value

pH 5.99-6.06

Moisture Content (%) 64.0-67.0

C/N Ratio 18.1-20.6

Organic Content in Dry Solids(%) 92.0-97.0

Ash Content (%) 3.00-8.00

41

Table 3.3 Composition of amendments in Experiment I

Amendment(g) Run A Run B Run C Run D

K2HPO4 195.00 0 0 0

KH2PO4 0 152.00 0 0

MgSO4 276.00 276.00 0 0

NaAc 0 0 100.00 0

42

Table 3.3 Composition of amendments in Experiment I

Amendment(g) Run A Run B Run C Run D

K2HPO4 195.00 0 0 0

KH2PO4 0 152.00 0 0

MgS04 276.00 276.00 0 0

NaAc 0 0 100.00 0

42

Table 3.4 Experiment conditions in Experiment II

Condition Run E Run F Run G

Water as solvent (kg)

Temperature

0

Self-heating

0.70

Self-heating

0.70

Heating to keep T

between 50 °C and 60 °C

43

Table 3.4 Experiment conditions in Experiment II

Condition Run E Run F Run G

Water as solvent (kg) 0 0.70 0.70

Heating to keep T Temperature Self-heating Self-heating

between 50 °C and 60 °C

43

Exhaust Gas Row

insulator

Composting

Matenais

Upper

Thermocouple

Lower

Thermocouple t t t

1 X

Leachate Overflow

02 Moridor

Effluent Air

Desiccabr Condensate Monacan Trap

Air Flow Meter Vacuum Air Fresh Air Input

Pump

Figure 3.1 Schematic diagram of the composting system

44

Exhaust Gas Flow

Condenser

Upper

Thermocouple

Lower

Thermocouple

Desiccator Condensate AmmonteTrap Colector

Leachate Overflow Air Row Meter Vacuum Air Fresh Air Input

Pump

Figure 3.1 Schematic diagram of the composting system

44

Exhaust Gas Outlet

Thermocouple Poitit

Thermocouple Point

Pillars

Madan Distribution

Plate

Laicism Outlet

0-dng Vimi Tube with Tiny Holes

Sim"ling Port

0-ring Rubber Seal

Aeration Distribution Chamber

Compressed Mr Inkt

Figure 3.2 Schematic diagram of the in-vessel reactor

45

Gas Outlet

Thermocouple Point

Thennoooaple Point

Sampling Pact

Pillars

Aenuoo Distribution

Plate

T

O-ring Rubber Seal

Lcachate Outlet

O-ring Vinyl Tube with Tiny Hole«

Aeration Distribution Chamber

Compressed Air Inlet

Figure 3.2 Schematic diagram of the in-vessel reactor

45

Figure 3.3 Picture of the in-vessel composting reactor

46

Figure 3.3 Picture of the in-vessel composting reactor

Figure 3.4 Picture of the in-vessel composting reactor with insulation layer

47

Figure 3.4 Picture of the in-vessel composting reactor with insulation layer

47

12-inch inside diameter, an 18-inch height and a 0.25-inch wall thickness. The top and

bottom were made of acrylic plate with a thickness of 0.50 inch and a diameter of 16

inches. And the bottom was glued to the body while the top was open for the

convenience of feeding and cleaning. There were six pillars with screw caps evenly

distributed around the reactor to provide pressure and enhance the air tightness of the

system.

The space within the main body of the reactor could be divided into two parts - one

part for the composting reaction and the other for air distribution. The composting

reaction section had a height of 16 inches and an approximate volume of 1 cubic foot.

The aeration distribution section was segmented by a holed acrylic plate of 0.25

inches in thickness and an attached mesh layer (0.10x0.10-inch pore size) on the plate.

The screened plate supported the compost substrate and prevented them from

dropping into the aeration distribution section. In order to ensure air flowed evenly

from the aeration distribution section to the composting reaction section, the aeration

distribution section was connected with a round vinyl air distribution pipe on the

surface, of which there were many tiny holes.

The bottom of the reactor had two holes, one being a compressed air inlet and the

other a leachate outlet. Air was diffused upward by a vacuum pump (M0A-P101-AA,

GAST Manufacturing Inc., MI). The inlet air would go through an airflow meter

(No.13 Shielded Flowmeter Gilmont instrument, IL) before reaching the aeration

48

12-inch inside diameter, an 18-inch height and a 0.25-inch wall thickness. The top and

bottom were made of acrylic plate with a thickness of 0.50 inch and a diameter of 16

inches. And the bottom was glued to the body while the top was open for the

convenience of feeding and cleaning. There were six pillars with screw caps evenly

distributed around the reactor to provide pressure and enhance the air tightness of the

system.

The space within the main body of the reactor could be divided into two parts - one

part for the composting reaction and the other for air distribution. The composting

reaction section had a height of 16 inches and an approximate volume of 1 cubic foot.

The aeration distribution section was segmented by a holed acrylic plate of 0.25

inches in thickness and an attached mesh layer (0.10x0.10-inch pore size) on the plate.

The screened plate supported the compost substrate and prevented them from

dropping into the aeration distribution section. In order to ensure air flowed evenly

from the aeration distribution section to the composting reaction section, the aeration

distribution section was connected with a round vinyl air distribution pipe on the

surface, of which there were many tiny holes.

The bottom of the reactor had two holes, one being a compressed air inlet and the

other a leachate outlet. Air was diffused upward by a vacuum pump (MOA-PIOI-AA,

GAST Manufacturing Inc., MI). The inlet air would go through an airflow meter

(No. 13 Shielded Flowmeter Gilmont instrument, IL) before reaching the aeration

distribution section so the aeration rate could be monitored and adjusted. There were

three holes on the top, one for compost sampling, the second for thermometer

insertion and the third for an air outlet. Two thermometers were tapped on a stainless

steel rod at different heights, inserted into the reactor and sealed with #1 rubber

stopper. The sampling port was designed with a 2.50-inch diameter and constantly

sealed with a #13 rubber stopper except samples were taken from the composting

system. An 0-ring with 0.25-inch thickness, 11-inch inside diameter and 13-inch

outside diameter was utilized to tighten the upper and body sections of the reactor

together. The gas outlet had a 1.00 inch opening for the release of exhaust gas. A tee

connector was linked with the gas outlet. One end of the tee was designed for gas

sampling when analysis was undertaken. It is usually closed during the composting

process and only opened when gas is tested. The other end of the tee was arranged for

gas absorption and discharge. A desiccator (a conical flask) was used to collect

evaporated water from the exhaust gas outlet through a plastic tube. Afterwards, a

condenser (Pryex 2560) would trap all remaining escaped moisture into a collector.

The dewatered gas will bubbled into a flask with 100 mL 0.20M sulfuric acid (H2SO4)

solution to absorb the ammonia gas. An oxygen monitor was then connected with the

acidic washing bottle to evaluate oxygen concentration. Outlet gas finally discharged

into the lab ventilation system through plastic tube.

The reactor was shielded by three layers of heat-insulating materials when the reactor

system was set up to minimize heat loss. The inner layer was heavy duty aluminum

49

distribution section so the aeration rate could be monitored and adjusted. There were

three holes on the top, one for compost sampling, the second for thermometer

insertion and the third for an air outlet. Two thermometers were tapped on a stainless

steel rod at different heights, inserted into the reactor and sealed with #1 rubber

stopper. The sampling port was designed with a 2.50-inch diameter and constantly

sealed with a #13 rubber stopper excqpt samples were taken from the composting

system. An O-ring with 0.25-inch thickness, 11-inch inside diameter and 13-inch

outside diameter was utilized to tighten the upper and body sections of the reactor

together. The gas outlet had a 1.00 inch opening for the release of exhaust gas. A tee

connector was linked with the gas outlet. One end of the tee was designed for gas

sampling when analysis was undertaken. It is usually closed during the composting

process and only opened when gas is tested. The other end of the tee was arranged for

gas absorption and discharge. A desiccator (a conical flask) was used to collect

evaporated water from the exhaust gas outlet through a plastic tube. Afterwards, a

condenser (Pryex 2560) would trap all remaining escaped moisture into a collector.

The dewatered gas will bubbled into a flask with 100 mL0.20M sulfuric acid (H2SO4)

solution to absorb the ammonia gas. An oxygen monitor was then connected with the

acidic washing bottle to evaluate oxygen concentration. Outlet gas finally discharged

into the lab ventilation system through plastic tube.

The reactor was shielded by three layers of heat-insulating materials when the reactor

system was set up to minimize heat loss. The inner layer was heavy duty aluminum

49

foil, which was used to reflect heat. The middle layer was a foam insulation material

with which to fill the interspaces between the reactor's surface and six pillars. An

insulator filled with fiberglass with a thickness of 3.50 inches was used as the outer

layer around the reactor. Similar insulators were applied on the top and bottom. Three

straps were buckled around the reactor to ensure the tightness of the insulating

materials. In the laboratory, a 20±1.00 °C room temperature was maintained during

the experiment.

3.4 Turning and Sampling

Turning the compost material with a shovel was undertaken a few times daily before

taking samples in order to homogenize the compost material inside the reactor.

Samples were collected every day to analyze pH, moisture content, ash content,

ammonium, C/N ratio and microorganism. Three samples of approximately 50 g were

collected, randomly, from the reactors after the turning is over. In order to achieve an

evenly-distributed, consistent and highly representative pooled sample, three samples

(regardless of moisture content) were then mixed in a 500 mL beaker. Then, the

pooled sample was divided into several small individual sub-samples by weight for

the parameter tests. Some samples were used to analyze more than one parameter.

3.5 Physical and Chemical Analysis

Physical, chemical and microbial parameters which included temperature, pH, oxygen

uptake rate (OUR), weight of compost material and reactor, moisture, ash and organic

50

foil, which was used to reflect heat. The middle layer was a foam insulation material

with which to fill the interspaces between the reactor's surface and six pillars. An

insulator filled with fiberglass with a thickness of 3.50 inches was used as the outer

layer around the reactor. Similar insulators were applied on the top and bottom. Three

straps were buckled around the reactor to ensure the tightness of the insulating

materials. In the laboratory, a 20±1.00 °C room temperature was maintained during

the experiment.

3.4 Turning and Sampling

Turning the compost material with a shovel was undertaken a few times daily before

taking samples in order to homogenize the compost material inside the reactor.

Samples were collected every day to analyze pH, moisture content, ash content,

ammonium, C/N ratio and microorganism. Three samples of approximately 50 g were

collected, randomly, from the reactors after the turning is over. In order to achieve an

evenly-distributed, consistent and highly representative pooled sample, three samples

(regardless of moisture content) were then mixed in a 500 mL beaker. Then, the

pooled sample was divided into several small individual sub-samples by weight for

the parameter tests. Some samples were used to analyze more than one parameter.

3.5 Physical and Chemical Analysis

Physical, chemical and microbial parameters which included temperature, pH, oxygen

uptake rate (OUR), weight of compost material and reactor, moisture, ash and organic

50

contents, gaseous ammonia and aqueous ammonium concentrations, carbon-nitrogen

ratio and microorganism colony counting were measured during the experiment to

evaluate the composting process. The analysis procedures and determinations of

specific parameters are given below (Figure 3.5)

3.5.1 Weight and Volume

The weight and volume of the remaining composting materials were measured daily.

To determine the mass of the decomposed compost material in a reactor ( Wcompost), the

total weight of both the reactor and compost substrate (Wcompost+reactor) on day i was

measured on an electronic scale. The weight of the empty reactor was 10.9 kg

(Wreactor):

(compost)i(k g) = W(compost+reactor)i (kg) — W reactor (kg)

= W (compatt+reactor)i (kg)-10.9

A ruler was used to measure the distance (hdistance) from the upper surface of the

composting material, 1 to the top of the reactor on a daily basis. Because the size of

the designed reactor was known (total height was 16 inches and inner diameter was 12

inches).

Knives! = -7-4 D (kW' hdistance) = 113x (1 6—hd!stance)

51

contents, gaseous ammonia and aqueous ammonium concentrations, carbon-nitrogen

ratio and microorganism colony counting were measured during the experiment to

evaluate the composting process. The analysis procedures and determinations of

specific parameters are given below (Figure 3.5)

3.5.1 Weight and Volume

The weight and volume of the remaining composting materials were measured daily.

To determine the mass of the decomposed compost material in a reactor (fVcompost), the

total weight of both the reactor and compost substrate (fVcomposl+reactor) on day i was

measured on an electronic scale. The weight of the empty reactor was 10.9 kg

(Wreactor)•

^(compost+reaetor)i ($8) -W^ikg)

A ruler was used to measure the distance (hdistance) from the upper surface of the

composting material, 1 to the top of the reactor on a daily basis. Because the size of

the designed reactor was known (total height was 16 inches and inner diameter was 12

inches).

Ko^s, = -Kisxmct) = 1 13x (16 —AdisaaJ

51

Put no not olsomplo Ma to rumor

Tskri 150g sompls saw lurning

5p, add 25 ra12111 10p. add 00 ml 5 10 KCI solution for 59. dry at105'CI 5 I" acidly and dry al OM% slosIturd g. add rol mist

for pH H144:11 lAcislunt Content 105'C for CN NaCt solution far Concentration Raw colony coating

I Mialum SW wing

OAOpm mentesio for ND, 4Calartallon

woo* al SOO V Ow Ash Conlost

Figure 3.5 Detailed procedure of sample testing

52

Put •» mat o< back to iwoor

>fer

StMkiyMddqii 100 *C for CM

5 g, (ky at 106 *C tot NaCtMMbnlor ootaiy umi^nj

Figure 3.5 Detailed procedure of sample testing

52

3.5.2 Moisture Content

Thermo-gravimetry was chosen to measure moisture content and ash content (Black,

1965). A crucible with a cover was weighted after it had been dried in the oven at 105

°C overnight to a constant weight (Wcrucibk). A 5.00 g wet sub-sample was placed in

the crucible and weighted (W wet-total) with the cover closed to reduced evaporation.

Later, the sample with the crucible was dried at 105 ±5 °C in the oven for over 4

hours. The sample was removed from the oven and placed into a desiccator until it

cooled to room temperature. The sample was then weighted with the cover closed

(Wary-wia0. The moisture content of the compost sample could be calculated using

Wcrucibk: Wwet-total and Why-total based on the following equation:

Moisturecontent = Wva —total —W dry—total x100%

Wwet—total —Wctucibk

After the Wthy-totai data was recorded, the cooled crucible with compost material was

retained for ash content analysis.

3.5.3 Ash and Organic Content

The ignition method was used to obtain the ash content data from the compost

samples (Black, 1965). After measurement the moisture content, the dry sample

(Wiry-total) was incinerated in a muffle-furnace at 550 °C for about 4 hours. Then it was

removed from the furnace and placed in a desiccator to cool. When the sample

reached room temperature, the fmal weight was recorded as W — final- Based on an

assumption that the inorganic matter remained unchanged during the food waste

53

3.5.2 Moisture Content

Thermo-gravimetry was chosen to measure moisture content and ash content (Black,

1965). A crucible with a cover was weighted after it had been dried in the oven at 105

°C overnight to a constant weight (fVcrucMe)- A 5.00 g wet sub-sample was placed in

the crucible and weighted (fVweMOta/) with the cover closed to reduced evaporation.

Later, the sample with the crucible was dried at 105 ±5 °C in the oven for over 4

hours. The sample was removed from the oven and placed into a desiccator until it

cooled to room temperature. The sample was then weighted with the cover closed

(Wdry-toiai)- The moisture content of the compost sample could be calculated using

Wcrucibie, WweMoml and Wdry-totai based on the following equation:

W -W Moisturecontent = ^-totai x

W -W yvet-total crucible

After the Wdry-wmi data was recorded, the cooled crucible with compost material was

retained for ash content analysis.

3.5.3 Ash and Organic Content

The ignition method was used to obtain the ash content data from the compost

samples (Black, 1965). After measurement the moisture content, the dry sample

(Wdry-tou!) was incinerated in a muffle-furnace at 550 °C for about 4 hours. Then it was

removed from the furnace and placed in a desiccator to cool. When the sample

reached room temperature, the final weight was recorded as Wf,nai. Based on an

assumption that the inorganic matter remained unchanged during the food waste

53

composting (Jeong and Kim, 2001; Stabnikova et al., 2005), ash and organic-matter

contents could be calculated using the following equations.

"a r"cible X100% AshContent; = Wfii —Wccfry-toial — Wcnicibk

Organic — matterContent =1— AshContent

—W = rby-total final W —W wet-total crucible

And the percentage of degradation (the loss of organic matter) could be calculated as

follows:

OMLoss. = -4 xi00% (1-4)4

Where Ai and Ao are the ash contents of ith day and the initial ash contents,

respectively.

3.5.4 Oxygen Uptake Rate (OUR)

M40 Multi-Gas Monitor (Industrial Scientific Corp., Oakdale, PA) was used to

monitor the oxygen concentration of exhaust gas. The OUR, a parameter describing

the amount of oxygen consumed by microorganisms, was the difference between inlet

and outlet concentrations of oxygen. First, an ambient 0 2 concentration was recorded

as 02,air (%). The monitor was then connected to the exhaust gas outlet of the

composting system. Data was read and recorded as 02,out (%) after 40 seconds to

attain a stabilized reading. At last, the oxygen uptake rate (OUR) could be calculated

using this equation:

54

composting (Jeong and Kim, 2001; Stabnikova et al., 2005), ash and organic-matter

contents could be calculated using the following equations.

W —W AshContent = x 100%

' w —w dry-total crucible

Organic - matterContent = 1 - AshContent

W - W _ "dry-total "final

W -W wet-total crucible

And the percentage of degradation (the loss of organic matter) could be calculated as

follows:

OMLoss, = A,~A° x 100% 0-4)4

Where A,• and Ao are the ash contents of ith day and the initial ash contents,

respectively.

3.5.4 Oxygen Uptake Rate (OUR)

M40 Multi-Gas Monitor (Industrial Scientific Corp., Oakdale, PA) was used to

monitor the oxygen concentration of exhaust gas. The OUR, a parameter describing

the amount of oxygen consumed by microorganisms, was the difference between inlet

and outlet concentrations of oxygen. First, an ambient O2 concentration was recorded

as Oi,air (%)- The monitor was then connected to the exhaust gas outlet of the

composting system. Data was read and recorded as 02,out (%) after 40 seconds to

attain a stabilized reading. At last, the oxygen uptake rate (OUR) could be calculated

using this equation:

54

— 0 OUR =

0 2•°". 24u1 X AerationRate (L/min)

100

Once every three hours, the OUR was measured.

3.5.5 Aqueous Ammonium (NI14+-N) Concentration

Aqueous ammonia in composting materials was extracted and transformed into a

liquid sample. The indophenol blue method was used to measure aqueous ammonium

concentrations (Keeney and Nelson, 1982). An ultraviolet wavelength spectrometer

(UV-2100 UNICO), equipped with a 1-cm light path was used as a detector.

3.5.5.1 Test Procedures

5 g of a composting sample ( Ws,,,,,pk) was weighed and placed into a 250 mL flask.

50.0 mL ( Vextract,) 2 M KC1 (ACS, EMD Chemicals INC., NJ) was added into the flask.

Then, the flask was shaken on a mechanical shaker for 1 hour to allow the

sample-KC1 suspension to settle. The suspension was then centrifuged for about 8 to

15 minutes (5000 rpm; Thermo Electronic Crop., Precision Durefuge 100) and filtered

via a vacuum filter system. The filtered liquid sample was stored below a temperature

of 2 to 4 °C. When testing, the sample was diluted by 10 times, 100 times using 2 M

KCl to prepare solutions with serial concentrations. Sample neutralization was

necessary if the pH was too acidic or basic.

3.5.5.2 Indophenol Blue Method

Phenol-nitroprusside reagent: 7 g of phenol and. 34 mg of sodium nitroprusside

55

OUR = 2'a,r x AerationRate (L/min) 100

Once every three hours, the OUR was measured.

3.5.5 Aqueous Ammonium (NH4+-N) Concentration

Aqueous ammonia in composting materials was extracted and transformed into a

liquid sample. The indophenol blue method was used to measure aqueous ammonium

concentrations (Keeney and Nelson, 1982). An ultraviolet wavelength spectrometer

(UV-2100 UNICO), equipped with a 1-cm light path was used as a detector.

3.5.5.1 Test Procedures

5 g of a composting sample (Wsampie) was weighed and placed into a 250 mL flask.

50.0 mL (Vextract) 2 M KC1 (ACS, EMD Chemicals INC., NJ) was added into the flask.

Then, the flask was shaken on a mechanical shaker for 1 hour to allow the

sample-KCl suspension to settle. The suspension was then centrifuged for about 8 to

15 minutes (5000 rpm; Thermo Electronic Crop., Precision Durefuge 100) and filtered

via a vacuum filter system. The filtered liquid sample was stored below a temperature

of 2 to 4 °C. When testing, the sample was diluted by 10 times, 100 times using 2 M

KC1 to prepare solutions with serial concentrations. Sample neutralization was

necessary if the pH was too acidic or basic.

3.5.5.2 Indophenol Blue Method

Phenol-nitroprusside reagent: 7 g of phenol and 34 mg of sodium nitroprusside

55

[disodium pentacyanonitrosylferrate, Na2Fe(CN)5N0.2H20] were dissolved into 80

mL of NH4+-free water. The solution was then diluted to final volume of 100 ml. The

solution was thoroughly mixed and stored in a dark-colored bottle in a refrigerator.

Buffered hypochlorite reagent: 1.480 g of sodium hydroxide (NaOH) was dissolved in

70 ml of NI-14+-free water. Next, 4.98 g of sodium monohydrogen phosphate

(Na2HPO4) and 20 ml of sodium hypochlorite (Na0C1) solution (5% w/w, VWR) was

added to the solution. Additional NaOH was used to adjust the pH to between the

range of 11.4 and 12.2 if necessary. The solution was then diluted to a final volume of

100 ml.

Ethylenediaminetetraacetic acid (EDTA) reagent: 6 g of ethylenediaminetetraacetic

acid disodium salt (EDTA disodium) was dissolved in 80 ml of deionized water. The

pH was adjusted to around 7. The solution was then diluted to a final volume of 100

ml and thoroughly mixing.

Standard ammonium (NITA+) solution: Immediately before usage, a 2 µg/ml standard

ammonium (NH4+) solution was prepared by pipetting: 0.4 ml standard ammonia

(NH4+) solution (1000 ppm, LabChem Inc. PA) into a 200 ml volumetric flask and

diluted into the scaled volume.

When testing, 3 ml of the diluted sample and 1 ml of the EDTA reagent were pipetted •

56

[disodium pentacyanonitrosylferrate, Na2Fe(CN)5N0.2H20] were dissolved into 80

mL of NH4+-free water. The solution was then diluted to final volume of 100 ml. The

solution was thoroughly mixed and stored in a dark-colored bottle in a refrigerator.

Buffered hypochlorite reagent: 1.480 g of sodium hydroxide (NaOH) was dissolved in

70 ml of NlV-free water. Next, 4.98 g of sodium monohydrogen phosphate

(Na2HPC>4) and 20 ml of sodium hypochlorite (NaOCl) solution (5% w/w, VWR) was

added to the solution. Additional NaOH was used to adjust the pH to between the

range of 11.4 and 12.2 if necessary. The solution was then diluted to a final volume of

100 ml.

Ethvlenediaminetetraacetic acid (EDTA) reagent: 6 g of ethylenediaminetetraacetic

acid disodium salt (EDTA disodium) was dissolved in 80 ml of deionized water. The

pH was adjusted to around 7. The solution was then diluted to a final volume of 100

ml and thoroughly mixing.

Standard ammonium (NH^) solution: Immediately before usage, a 2 |ig/ml standard

ammonium (NFLt+) solution was prepared by pipetting: 0.4 ml standard ammonia

(NH4+) solution (1000 ppm, LabChem Inc. PA) into a 200 ml volumetric flask and

diluted into the scaled volume.

When testing, 3 ml of the diluted sample and 1 ml of the EDTA reagent were pipetted

56

into a 25 ml volumetric flask, and thoroughly mixed. The flask contents were allowed

to stand for 1 minute. 2 ml of the phenol-nitroprusside reagent were added first to the

flask. Then, 4 ml of the buffered hypochlorite reagent was also added, into the flask.

The solution was immediately diluted to the scaled volume with NI-14+-free water and

mixed.

The flask was bathed in water (40 °C) for 30 minutes and then cooled to room

temperature. The blue-colored complex was transferred to a 10 mm cell and the

absorbance at a wavelength of 636 run was determined against a blank reagent

solution. The NH4+-N concentration was read by referring to the calibration curve.

The calibration curve can be derived as follows: 3 ml 2M KCl solution was added into

a series of 25-m1 volumetric flasks. 0, 1, 2, 3, 4, 5 and 6 ml of the 2 µg/ml NH4+-N

solution were then added to the series of flasks to obtain the solutions with NH4+-N

concentrations of 0, 2, 4, 6, 8, 10 and 12 pg of NH4+-N/ml. The absorbance of these

solutions were measured and plotted to a calibration curve of

concentration-versus-absorbance.

Because the accuracy range of the indophenol blue method was 0.05-20 pg/mL, using

the appropriate dilution ratio (D) of a sample must be chosen with its concentration (C

mg/1) to fall within this accuracy range. The NH4+-N concentration of the target

solution can be calculated as follows:

57

into a 25 ml volumetric flask, and thoroughly mixed. The flask contents were allowed

to stand for 1 minute. 2 ml of the phenol-nitroprusside reagent were added first to the

flask. Then, 4 ml of the buffered hypochlorite reagent was also added, into the flask.

The solution was immediately diluted to the scaled volume with NH4+-free water and

mixed.

The flask was bathed in water (40 °C) for 30 minutes and then cooled to room

temperature. The blue-colored complex was transferred to a 10 mm cell and the

absorbance at a wavelength of 636 nm was determined against a blank reagent

solution. The NHU+-N concentration was read by referring to the calibration curve.

The calibration curve can be derived as follows: 3 ml 2M KCl solution was added into

a series of 25-ml volumetric flasks. 0, 1,2, 3, 4, 5 and 6 ml of the 2 ng/ml NR(+-N

solution were then added to the series of flasks to obtain the solutions with NH4+-N

concentrations of 0, 2, 4, 6, 8, 10 and 12 jig of NH4+-N/ml. The absorbance of these

solutions were measured and plotted to a calibration curve of

concentration-versus-absorbance.

Because the accuracy range of the indophenol blue method was 0.05-20 ng/mL, using

the appropriate dilution ratio (D) of a sample must be chosen with its concentration (C

mg/1) to fall within this accuracy range. The NH^-N concentration of the target

solution can be calculated as follows:

C x D x10 (mg/kg dry compost) C

NN, -N =

1 — MOisturecontent

3.5.6 Gaseous Ammonia (N113-N) Concentration

A 500 mL wide mouth flask containing 100 mL 0.1 M H2SO4 was used to trap

ammonia (NH3) in the exhaust gas. After ammonia had been transformed into aqueous

ammonium in the liquid sample, the solution was collected daily and diluted to 500

mL. After a series of dilutions were made and neutralized, the NH4+-N concentration

in the collected solution was determined using the indophenol blue method which is

described in the Section 3.5.5. The trap was changed on a daily basis also.

3.5.7 Carbon-Nitrogen (C/N) Ratio

LECO TruSpec CN Determinator (LECO Corporation, St. Joseph, MI), which is a

combustion and gas composition analysis system, was used to determine the total

carbon and total nitrogen content. The mechanism is: when a sample was subjected to

two combustion processes at high temperature, formed combustion gases were

collected in the vessel. The gases were passed through a CO2 infrared detector to

measure the carbon content of the sample. NO„ was transformed into N2 by incoming

helium and N2 was determined as nitrogen content. The detailed procedure was as

follows:

5 g compost sample was weighted in a crucible and acidified, if necessary, to avoid

58

C x D C , = x lO (mg/kg dry compost)

NH, N | _ Moistvrecontent

3.5.6 Gaseous Ammonia (NH3-N) Concentration

A 500 mL wide mouth flask containing 100 mL 0.1 M H2SO4 was used to trap

ammonia (NH3) in the exhaust gas. After ammonia had been transformed into aqueous

ammonium in the liquid sample, the solution was collected daily and diluted to 500

mL. After a series of dilutions were made and neutralized, the NHL»+-N concentration

in the collected solution was determined using the indophenol blue method which is

described in the Section 3.5.5. The trap was changed on a daily basis also.

3.5.7 Carbon-Nitrogen (C/N) Ratio

LECO TruSpec CN Detenninator (LECO Corporation, St. Joseph, MI), which is a

combustion and gas composition analysis system, was used to determine the total

carbon and total nitrogen content. The mechanism is: when a sample was subjected to

two combustion processes at high temperature, formed combustion gases were

collected in the vessel. The gases were passed through a CO2 infrared detector to

measure the carbon content of the sample. NOx was transformed into N2 by incoming

helium and N2 was determined as nitrogen content. The detailed procedure was as

follows:

5 g compost sample was weighted in a crucible and acidified, if necessary, to avoid

58

possible NH4+ loss. The sample with the crucible was placed in the oven and dried at

105 °C for 4 hours. The dry sample was mashed to a homogeneous powder and stored

in a desiccator. When tested, weight 0.1500 g and 0.2000 g dry sample in a tin foil cup,

then twist and seal it into a capsule, and the sample mass was recorded.

When using the LECO TruSpec CN Determinator, the system check and leak check

must be completed. The blank sample was analyzed first, until a stabilized plateau of

reading was reached (typically±- 0.001%). After analyzing 5 additional blank samples,

the blank calibration could be settled. 10 EDTA standard samples were weighted with

the mass being between 0.1500 g and 0.2000 g, and capsulated into small tins. The

EDTA samples were analyzed and standard calibration was performed. The sample

capsules were placed in order and analyzed in the carousel auto-sampler. For the sake

of quality control, the EDTA sample was inserted in the order every 10 samples.

3.5.8 Microorganism Colony Counting

The number of microorganism colonies was counted using the spread plate counting

method (Ryckeboer et al., 2003b; Pascual et al., 2002). 10% strength tryptic soy broth

agar (TSA, BactoTm, Difco, pH=7.3 ± 0.2, 17.0 g/L of pancreatic digest of casein, 3.0

g/L of enzymatic digest of soybean meal, 2.5 g/L of dextrose, 5.0 g/L of NaC1, 2.5 g/L

of K2HPO4, and 20.0 g/L of agar powder), a nutrient-rich and general-purpose media

was used as the culture medium for total thermophilic and mesophilic microorganism.

59

possible NH4+ loss. The sample with the crucible was placed in the oven and dried at

105 °C for 4 hours. The dry sample was mashed to a homogeneous powder and stored

in a desiccator. When tested, weight 0.1500 g and 0.2000 g dry sample in a tin foil cup,

then twist and seal it into a capsule, and the sample mass was recorded.

When using the LECO TruSpec CN Determinator, the system check and leak check

must be completed. The blank sample was analyzed first, until a stabilized plateau of

reading was reached (typically±0.001%). After analyzing 5 additional blank samples,

the blank calibration could be settled. 10 EDTA standard samples were weighted with

the mass being between 0.1500 g and 0.2000 g, and capsulated into small tins. The

EDTA samples were analyzed and standard calibration was performed. The sample

capsules were placed in order and analyzed in the carousel auto-sampler. For the sake

of quality control, the EDTA sample was inserted in the order every 10 samples.

3.5.8 Microorganism Colony Counting

The number of microorganism colonies was counted using the spread plate counting

method (Ryckeboer et al., 2003b; Pascual et al., 2002). 10% strength tryptic soy broth

agar (TSA, Bacto™, Difco, pH=7.3 ±0.2, 17.0 g/L of pancreatic digest of casein, 3.0

g/L of enzymatic digest of soybean meal, 2.5 g/L of dextrose, 5.0 g/L of NaCl, 2.5 g/L

of K2HP04, and 20.0 g/L of agar powder), a nutrient-rich and general-purpose media

was used as the culture medium for total thermophilic and mesophilic microorganism.

59

The media solution was thorough mixed and then sterilized by autoclaving at 15 psi

for 15 minutes and cooled to around 50 °C. 15 mL of media solution was poured into

each Petri plate and was ready to be used when the agar solidified.

When conducting each test, a fresh weight of 10 g sample was added into a 250 mL

Erlenmeyer beaker with 90 mL of 0.85% sterile sodium chloride solution (Sigmam1).

The flask was capped, placed in a horizontal position on a mechanical shaker and

shaken at 200 rpm for 30 minutes in a water bath under a temperature of 20± 1 °C. A

certain amount of supernatant in the flask was transferred and diluted with 0.85%

sterilized sodium chloride solution to prepare tenfold serial dilutions with dilution

ratio from 10-2 to 10-1°.

Four dilutions adequately characterizing the microorganism were chosen from serial

solutions. 0.1 ml of each dilution was drawn and spread to a Petri plate which

contained the required medium. Each chosen dilution was extracted twice and

transferred to different Petri plate, using a sterilized spreader. The plates were

incubated at 25 °C to identify mesophilic microorganisms and at 55 °C to identify

thermophilic microorganisms. After 48 hours of incubation, all thermophiles were

enumerated. The mesophiles were counted after incubation on Day 7. The dilution

factor, which yielded 30 to 300 Colony-Forming Units (CFU) per plate was preferred

in order to enumerate the number of colonies. One extra sample was randomly

selected and incubated- for quality control so as to check data consistency. Then

60

The media solution was thorough mixed and then sterilized by autoclaving at 15 psi

for 15 minutes and cooled to around 50 °C. 15 mL of media solution was poured into

each Petri plate and was ready to be used when the agar solidified.

When conducting each test, a fresh weight of 10 g sample was added into a 250 mL

Erlenmeyer beaker with 90 mL of 0.85% sterile sodium chloride solution (Sigma™).

The flask was capped, placed in a horizontal position on a mechanical shaker and

shaken at 200 rpm for 30 minutes in a water bath under a temperature of 20 ± 1 °C. A

certain amount of supernatant in the flask was transferred and diluted with 0.85%

sterilized sodium chloride solution to prepare tenfold serial dilutions with dilution

ratio from 10"2 to 10"10.

Four dilutions adequately characterizing the microorganism were chosen from serial

solutions. 0.1 ml of each dilution was drawn and spread to a Petri plate which

contained the required medium. Each chosen dilution was extracted twice and

transferred to different Petri plate, using a sterilized spreader. The plates were

incubated at 25 °C to identify mesophilic microorganisms and at 55 °C to identify

thermophilic microorganisms. After 48 hours of incubation, all thermophiles were

enumerated. The mesophiles were counted after incubation on Day 7. The dilution

factor, which yielded 30 to 300 Colony-Forming Units (CFU) per plate was preferred

in order to enumerate the number of colonies. One extra sample was randomly

selected and incubated- for quality control so as to check data consistency. Then

colony count was calculated as follows (Sun, 2006):

logo Co/onycount

= NumberojColonyx DilutionFactor

x100 (CFU/g dry compost) 1— MoistureContent

100 in this equation indicated that 0.01g dry sample was used in the test (the 100 ml

solution contained 10 g of dry sample and 0.1 ml solution was drawn and tested).

Both microorganisms were measured once every two days.

3.6 Statistical Analysis

3.6.1 One-Way ANOVA

One-way Analysis of Variance (ANOVA) is a technique used to compared the means

of two or more samples using F distribution (Montgomery, 2001). In this study,

one-factor four-level ANOVA is used to investigate the effects of three types of buffer

salts to multiple objectives, such as temperature, pH and oxygen uptake. The ANOVA

tests the null hypothesis that four treatments share the same mean value at a specific

time t. The core idea of ANOVA is partitioning the total variability into its component

parts, so that sample variance, which is also called unexplained variance or

within-group variance (described as the sum of squares due to error) and treatment

variance, which is called explained variance or between-group variance (described as

the sum of squares between treatments) can be obtained based on these equations

below:

AB T„atmem = En, (E — i7)2 K 1

MSE = Eorg -T, (N —K)

61

colony count was calculated as follows (Sun, 2006):

• „ , „ NumberofColonyx DilutionFactor , . log,0 ColonyCount = x 100 (CFU/g dry compost)

1 - MoistureContent

100 in this equation indicated that O.Olg dry sample was used in the test (the 100 ml

solution contained 10 g of dry sample and 0.1 ml solution was drawn and tested).

Both microorganisms were measured once every two days.

3.6 Statistical Analysis

3.6.1 One-Way ANOVA

One-way Analysis of Variance (ANOVA) is a technique used to compared the means

of two or more samples using F distribution (Montgomery, 2001). In this study,

one-factor four-level ANOVA is used to investigate the effects of three types of buffer

salts to multiple objectives, such as temperature, pH and oxygen uptake. The ANOVA

tests the null hypothesis that four treatments share the same mean value at a specific

time t. The core idea of ANOVA is partitioning the total variability into its component

parts, so that sample variance, which is also called unexplained variance or

within-group variance (described as the sum of squares due to error) and treatment

variance, which is called explained variance or between-group variance (described as

the sum of squares between treatments) can be obtained based on these equations

below:

MSr^^nW'-Y)2/K-\

MSe = (X0 -Yi-Y /(N-K) i

61

Where Y. denotes the sample mean in the ith treatment, n, is the number of

replicates in the ith treatment, Y is the overall mean value of data, lc, is the jth

replicates in the ith treatment, K denotes the number of treatments and N is overall

sample size. Consequently, F test can be adopted to investigate whether the two types

of variability are significant or not:

F=MST„. ,IMSE

The F statistic follows the F-distribution with K-1, N-K degrees of freedom. Based on

given p value (c) :).05 in this research), the conclusion of which buffer salts can

significantly affect composting parameters can be made. If obtained p value is larger

than 0.05, which means treatment variance is insignificant, indicating no significant

differences existing with buffer salts added.

3.6.2 Leave-One-Out Cross-Validation

Cross-validation, also known as rotation estimation, is used to assess the accuracy of a

predictive model as it performs in practice (Picard and Cook, 1984). It is important to

guard against the testing hypothesis suggested by the data, which is also called "type

III errors" (hosteller, 1948). As the most common cross-validation method,

leave-one-out cross-validation was adopted herein by retaining the observed data from

a single run of six runs in the experiment as the validation run in order to test the

model, and the remaining five runs were retained as the training data in order to

obtain the kinetics parameters, specifically. To reduce variability, multiple rounds (6

62

Where Y, denotes the sample mean in the ith treatment, n, is the number of

replicates in the ith treatment, Y is the overall mean value of data, Yt] is the jth

replicates in the ith treatment, K denotes the number of treatments and N is overall

sample size. Consequently, F test can be adopted to investigate whether the two types

of variability are significant or not:

F = MSTrealmtn,lMSE

The F statistic follows the F-distribution with K-l, N-K degrees of freedom. Based on

given p value (p=0.05 in this research), the conclusion of which buffer salts can

significantly affect composting parameters can be made. If obtained p value is larger

than 0.05, which means treatment variance is insignificant, indicating no significant

differences existing with buffer salts added.

3.6.2 Leave-One-Out Cross-Validation

Cross-validation, also known as rotation estimation, is used to assess the accuracy of a

predictive model as it performs in practice (Picard and Cook, 1984). It is important to

guard against the testing hypothesis suggested by the data, which is also called "type

HI errors" (Mosteller, 1948). As the most common cross-validation method,

leave-one-out cross-validation was adopted herein by retaining the observed data from

a single run of six runs in the experiment as the validation run in order to test the

model, and the remaining five runs were retained as the training data in order to

obtain the kinetics parameters, specifically. To reduce variability, multiple rounds (6

62

times in this study) of cross-validation are performed, with each of the six runs used

exactly once as the validation data and five times as training data. Then, six groups of

parameters were averaged to produce a single group of estimations as the final result,

based on numbers of data in the training set each time

63

times in this study) of cross-validation are performed, with each of the six runs used

exactly once as the validation data and five times as training data. Then, six groups of

parameters were averaged to produce a single group of estimations as the final result,

based on numbers of data in the training set each time

63

CHAPTER 4

Eh '14 ECTS OF BUFFER SALTS

4.1 Variations in Physicochemical and Microbiological Parameters

4.1.1 Temperature

The temperature levels were the averages of the measured values for upper and lower

sampling points within the reactor. The temperature profiles in these four reactors are

illustrated in Figure 4.1. A rapid heating-generating process was observed, so that the

temperature of the composting materials of the four runs rose from ambient

temperature to 50 °C at Day 4. This embarked upon a resumption of thermophilic

microorganism activity. It is widely accepted that 50 °C is a threshold between the

mesophilic stage and thermophilic stage, meaning when the temperature rises above

50 °C, thermophiles dominate the biodegradation process in the composting system. It

is reasonable to believe the quick initial activation of microorganism activities is due

to the sufficient contents of easily utilizable organic materials for microorganisms in

the synthetic composting materials.

The temperature levels of the four runs continued to increase after Day 4. Meanwhile,

the mesophilic microorganisms were overcome by the thermophiles (Deniz, 2004).

Thereafter, the temperatures of the four runs were kept at a high level (within the

range of 55 °C and 65 °C) for more than 10 days, which may contribute the

64

CHAPTER 4

EFFECTS OF BUFFER SALTS

4.1 Variations in Physicochemical and Microbiological Parameters

4.1.1 Temperature

The temperature levels were the averages of the measured values for upper and lower

sampling points within the reactor. The temperature profiles in these four reactors are

illustrated in Figure 4.1. A rapid heating-generating process was observed, so that the

temperature of the composting materials of the four runs rose from ambient

temperature to 50 °C at Day 4. This embarked upon a resumption of thermophilic

microorganism activity. It is widely accepted that 50 °C is a threshold between the

mesophilic stage and thermophilic stage, meaning when the temperature rises above

50 °C, thermophiles dominate the biodegradation process in the composting system. It

is reasonable to believe the quick initial activation of microorganism activities is due

to the sufficient contents of easily utilizable organic materials for microorganisms in

the synthetic composting materials.

The temperature levels of the four runs continued to increase after Day 4. Meanwhile,

the mesophilic microorganisms were overcome by the thermophiles (Deniz, 2004).

Thereafter, the temperatures of the four runs were kept at a high level (within the

range of 55 °C and 65 °C) for more than 10 days, which may contribute the

64

0 5 10 15 Times (day)

20

Figure 4.1 Temperature profiles during the composting process

65

25

Run A

Run C

Run B

Run D

10 15 Times (day)

Figure 4.1 Temperature profiles during the composting process

65

elimination of pathogenic microorganisms (Joshua et al., 1998). As a result, no

unpleasant odor appeared (Rao Bhamidimarri and Pandey, 1996). Runs A and B had a

comparatively higher temperature (around 60 °C) during the thermophilic stage, while

Runs C and D had lower temperature (around 55 °C) but a longer-lasting thermophilic

stage. The temperature of Runs A and B did not decrease until Day 20, which was

obviously later than Runs C and D, while the temperature of Run D began to drop on

Day 15, which was the earliest of the four. The corresponding shorter thermophilic

stage and higher maximum temperature is due to a higher degradation rate with a

higher temperature but similar available organic matter. However, a temperature drop

could be found on Day 5 after reaching its first peak. This early temperature drop

could be the result of the retardation of composting caused by produced organic-acid

at the early stage. After the breakdown of organic matter, the rate of heat generation

became less than that of heat loss so the temperature decreased gradually and went

back to an ambient temperature. Finally, on the Day 25, the temperature of the four

runs dropped back to 20 °C, which suggested the end of the composting process. It is

clear that Run D without the buffer salt additive had the least thermophilic stage

duration, in addition to the shortest cooling phase. It was evident that NaAc,

ICH2PO4/MgSO4 and K2HPO4/MgSO4 could effectively stimulate and prolong the

metabolisms of microorganism during composting. The duration of the entire process

is much shorter than that other composting process as stated in previous literature,

which means the experimental design in this study is satisfactory.

66

elimination of pathogenic microorganisms (Joshua et al., 1998). As a result, no

unpleasant odor appeared (Rao Bhamidimarri and Pandey, 1996). Runs A and B had a

comparatively higher temperature (around 60 °C) during the thermophilic stage, while

Runs C and D had lower temperature (around 55 °C) but a longer-lasting thermophilic

stage. The temperature of Rims A and B did not decrease until Day 20, which was

obviously later than Runs C and D, while the temperature of Run D began to drop on

Day 15, which was the earliest of the four. The corresponding shorter thermophilic

stage and higher maximum temperature is due to a higher degradation rate with a

higher temperature but similar available organic matter. However, a temperature drop

could be found on Day 5 after reaching its first peak. This early temperature drop

could be the result of the retardation of composting caused by produced organic-acid

at the early stage. After the breakdown of organic matter, the rate of heat generation

became less than that of heat loss so the temperature decreased gradually and went

back to an ambient temperature. Finally, on the Day 25, the temperature of the four

runs dropped back to 20 °C, which suggested the end of the composting process. It is

clear that Run D without the buffer salt additive had the least thermophilic stage

duration, in addition to the shortest cooling phase. It was evident that NaAc,

KH2PC>4/MgS04 and K.2HP04/MgS04 could effectively stimulate and prolong the

metabolisms of microorganism during composting. The duration of the entire process

is much shorter than that other composting process as stated in previous literature,

which means the experimental design in this study is satisfactory.

66

4.1.2 pH

The pH profiles of the four experiments are shown in Figure 4.2, and the changes in

pH was significantly related to the temperature and followed a typical pattern of an

aerobic composting process, indicating a satisfactorily operated system. The initial pH

of the composting materials was rather acidic (around 6). In the first four days, Runs

C and D had identical composition; they presented exactly the same pH profiles as a

result of the Day 1 to Day 4 when NaAc was added into Run C, which showed our

experiments were under well-controlled conditions. Because K2HPO4 and KH2PO4

was added to Runs A and B at the beginning of the composting process, respectively,

they changed the initial pH of the composting material to 6.52 and 5.65, which were

the highest and lowest values of initial pH among the four runs.

The above phenomenon indicated a strong relationship to pH in composting materials

and the initial pH of buffer salts. Based on the references, K2HPO4 had an initial pH

(with a concentration of 0.1 mol/L) of 8.0, while KH2PO4 had an initial pH of 4.4. The

significant variance within the two similar phosphates was due to the amphoteric

characteristic of HP042- and H2PO4 , because HP042- and H2PO4 are anions with

ionizable protons, which can act as either weak acids or weak bases depending upon

the relative values of their K, and Kb constant. K2HPO4 has the following reactions

when dissolved in water:

K2HPO4 -+ 21C+ + HPO4'

HP042- + 1120 ++ H30 + +P043- K„(HP042)

67

4.1.2 pH

The pH profiles of the four experiments are shown in Figure 4.2, and the changes in

pH was significantly related to the temperature and followed a typical pattern of an

aerobic composting process, indicating a satisfactorily operated system. The initial pH

of the composting materials was rather acidic (around 6). In the first four days, Runs

C and D had identical composition; they presented exactly the same pH profiles as a

result of the Day 1 to Day 4 when NaAc was added into Run C, which showed our

experiments were under well-controlled conditions. Because K2HPO4 and KH2PO4

was added to Runs A and B at the beginning of the composting process, respectively,

they changed the initial pH of the composting material to 6.52 and 5.65, which were

the highest and lowest values of initial pH among the four runs.

The above phenomenon indicated a strong relationship to pH in composting materials

and the initial pH of buffer salts. Based on the references, K2HPO4 had an initial pH

(with a concentration of 0.1 mol/L) of 8.0, while KH2PO4 had an initial pH of 4.4. The

significant variance within the two similar phosphates was due to the amphoteric

characteristic of HPO42" and H2PO4" , because HPO42" and H2PO4' are anions with

ionizable protons, which can act as either weak acids or weak bases depending upon

the relative values of their K„ and Kb constant. K2HPO4 has the following reactions

when dissolved in water:

K2HPOT -» 2K* +HPO,2-

HPO;~ + H20 <-> HP* + PO/~ A.(HP042")

67

10

8

6

4 0 5 10 15

Times (day) 20

Figure 4.2 pH profiles during the composting process

68

25

10

8

a a

6

Run A Run C

Run B Run D

4 10 20 0 5 15 25

Times (day)

Figure 4.2 pH profiles during the composting process

68

11P042- + H20 4+ H2PO4- +011- Kb(HP042 )

IC,,(HP042 )= IC43(H3PO4)= 4.2 x10-13

Ka (HP042- ) / Ka 2 (H3PO4 ) 10-'4 / 6.3 x 1 0" = 1.6 x 10-7

Since ii,(HP042) is far smaller than Kb(HP042"), K2HPO4 is a stronger base than it is

an acid, so the K2HPO4 solution is basic.

KH2PO4 has similar reactions when dissolved in water:

KH2PO4 --> K+ + H2PO4

H2PO4 +1120 ++1130k +HP042- Ka(112PO4)

H2 PO4- + H2 0 4-> H3PO4 +0H" Kb(H2PO4)

Ka(H2PO4-)= Ka 2 (H3 PO4 ) = 6.3 x 10'

K,(H2PO4) = Kw / (H3PO4) = 10-14 / 7.2 x10-3 = 1.4 x10-' 2

Since Ka(H2PO4) is far larger than Kb(H2PO4) KH2PO4 is a stronger acid than it is a

base, so that the KH2PO4 solution is acidic.

As for NaAc added into Run C on Day 4, it is also basic in a solution because of the

contained basic anions. The reactions were as follows.

NaAc --÷Na+ + Ac-

Ac- +1120 44 HAc+011" 14(Ac")

Kb(Ac-)= Kw , Ic(HAc)= 10-14 /1.8xle =5.6x10-1°

Thus NaAc is a base in a water solution, with an initial pH of 8.87. One common

.characteristic. of these three additives was they can absorb H+ or Off into a solution to

69

HPO;~ + H20 H2PO*~ + OH' JrA(HP042")

Ka(HPO<2-) = Ka}(HiPO4) = 4.2xl0~li

KB(HP042~) = KW / KA2(H3POA) = 1(T"7 6.3x10"* = 1.6xl(T7

Since JTa(HP042") is far smaller than Aa(HP042"), K2HPO4 is a stronger base than it is

an acid, so the K2HPO4 solution is basic.

KH2PO4 has similar reactions when dissolved in water:

KH2POA -+K++H2PO4~

H2PO~ + H20<r^ H,0+ + HPOA2- Aro(H2P04")

H2PO~ +H20<+ H3PO; + OH~ tf6(H2P04")

KA(H2P04') = KA2(H3P04) = 6.3xl0"8

Kb(H2P04-) = Kw/Ka i(H3POA) = 10"14 /7.2xl(T3 = 1.4xl0"12

Since ifaOHhPCV) is far larger than ^(tfcPOO , KH2PO4 is a stronger acid than it is a

base, so that the KH2P04 solution is acidic.

As for NaAc added into Rim C on Day 4, it is also basic in a solution because of the

contained basic anions. The reactions were as follows.

NaAc —> Na+ + Ac'

Ac +H20<r* HAc+OH' Kb( Ac)

K b ( A c ) = K W / K t t ( H A c ) = 10"'4 /1.8 x 10"5 = 5.6 x 1(T10

Thus NaAc is a base in a water solution, with an initial pH of 8.87. One common

.characteristic of these three additives was they can absorb H+ or OH" into a solution to

69

resist change in ambient pH when acid or alkali is added or produced (Y. Liang et al.,

2006; Yu and Huang, 2009). HP042" and H2PO4 could both react with H+ or OR in

hydrolysis and ionization process. NaAc, in addition to produced HAc in a hydrolysis

process, could form a NaAc/HAc buffer solution and absorb both H+ and OR. Thus,

they were named "buffer salts" and are believed to be capable of maintaining a

constant pH value.

The pH dropped gradually from 6 to about 4.5 on the second day in all runs, and

stayed at this level in this stage, which could be explained by the generation of a large

amount of organic acids as the intermediate by-product of easily degradable organic

materials (Smars et al., 2002; Nakasaki et al., 1993; Beck-Friis et al., 2003). The

stabilization of pH during the initial acidic stage could be due to the following two

reasons: first, because of the properties of hydrolysis and ionization equilibriums of

buffer salts and produced organic acids, which are weak acids; second, microbial

activities would be inhibited in a low pH environment, which slowed the degradation

rate of organic acids. Only when the generation of short-chain organic acids was

slower than the degradation and evaporation of organic acids, did the pH levels begin

to rise (An, 2006). In the first four days, Runs C and D had identical compositions so

they presented exactly the same pH profile before NaAc was added into Run C, which

showed our experiments were under well-controlled conditions. The lowest pH was

4.51, 4.42, 4.70 and 4.73, respectively, in the four runs. A low pH was fairly

unsuitable for microorganism growth because low pH always coincided with a

70

resist change in ambient pH when acid or alkali is added or produced (Y. Liang et al.,

2006; Yu and Huang, 2009). HPO42" and H2PO4" could both react with H* or OH. in

hydrolysis and ionization process. NaAc, in addition to produced HAc in a hydrolysis

process, could form a NaAc/HAe buffer solution and absorb both H+ and OH". Thus,

they were named "buffer salts" and are believed to be capable of maintaining a

constant pH value.

The pH dropped gradually from 6 to about 4.5 on the second day in all runs, and

stayed at this level in this stage, which could be explained by the generation of a large

amount of organic acids as the intermediate by-product of easily degradable organic

materials (Sm&rs et al., 2002; Nakasaki et al., 1993; Beck-Friis et al., 2003). The

stabilization of pH during the initial acidic stage could be due to the following two

reasons: first, because of the properties of hydrolysis and ionization equilibriums of

buffer salts and produced organic acids, which are weak acids; second, microbial

activities would be inhibited in a low pH environment, which slowed the degradation

rate of organic acids. Only when the generation of short-chain organic acids was

slower than the degradation and evaporation of organic acids, did the pH levels begin

to rise (An, 2006). In the first four days, Runs C and D had identical compositions so

they presented exactly the same pH profile before NaAc was added into Rim C, which

showed our experiments were under well-controlled conditions. The lowest pH was

4.51, 4.42, 4.70 and 4.73, respectively, in the four runs. A low pH was fairly

unsuitable for microorganism growth because low pH always coincided with a

reduced microorganism population and limited nutrient availability (Lei and

Vander-Gheynst, 2000; Nakasaki et al., 1993; Sundberg et al., 2004). Furthermore,

low pH also hindered the shift from the mesophilic stage to the thermophilic stage

because thermophiles have less tolerance to an acidic environment.

After the lag phase, a pH-increasing phase was observed in the four runs. In this phase,

pH in all runs rose gradually to around 5.5 prior to day 12 and then ascended

dramatically into the alkaline range. As for Runs A and B, the quick rise of pH levels

was later than in Runs C and D, which was coherent with temperature profiles. In

Figure 4.1, Runs A and B experienced a slightly lower temperature before Day 15

during their thermophilic stage. During the fmal stage, when the composting

processes ended, the pH of all the four runs had risen to an alkaline range. During the

later stage, the raised pH levels in all runs were induced due to the production of

ammonia through ammonification and mineralization of organic nitrogen, resulting

fronunicrobial activities (Bishop and Godfrey, 1983). Runs C and B had a maximum

and minimum pH, which was 9.16 and 7.64 (p<0.05), respectively. It suggested that

amendments added at the beginning of the composting process would still influence

the fmal pH. Comparing Runs A and C with two acidic additives, K2HF'04/MgSO4

and NaAc, respectively, Run A had a significantly lower final pH than Run C and

almost the same value with the control run, Run D. This would indicate strong buffer

ability towards the rising of pH for K2HPO4.

71

reduced microorganism population and limited nutrient availability (Lei and

Vander-Gheynst, 2000; Nakasaki et al., 1993; Sundberg et al., 2004). Furthermore,

low pH also hindered the shift from the mesophilic stage to the thermophilic stage

because thermophiles have less tolerance to an acidic environment.

After the lag phase, a pH-increasing phase was observed in the four runs. In this phase,

pH in all runs rose gradually to around 5.5 prior to day 12 and then ascended

dramatically into the alkaline range. As for Runs A and B, the quick rise of pH levels

was later than in Runs C and D, which was coherent with temperature profiles. In

Figure 4.1, Runs A and B experienced a slightly lower temperature before Day 15

during their thermophilic stage. During the final stage, when the composting

processes ended, the pH of all the four runs had risen to an alkaline range. During the

later stage, the raised pH levels in all runs were induced due to the production of

ammonia through ammonification and mineralization of organic nitrogen, resulting

frommicrobial activities (Bishop and Godfrey, 1983). Runs C and B had a maximum

and minimum pH, which was 9.16 and 7.64 (p<0.05), respectively. It suggested that

amendments added at the beginning of the composting process would still influence

the final pH. Comparing Runs A and C with two acidic additives, K2HP(VMgS04

and NaAc, respectively, Run A had a significantly lower final pH than Run C and

almost the same value with the control ran, Run D. This would indicate strong buffer

ability towards the rising of pH for K2HPO4.

71

4.2 Change in Microbial Activities

4.2.1 Daily and Cumulative Oxygen Uptake

In composting research, the Carbon Dioxide (CO2) evolution rate and Oxygen (02)

Uptake Rate have been widely adopted to evaluate microbial activity and composting

efficiency (Fang et al., 1999; C. Liang et al., 2003; Yu and Huang, 2009). In this study,

0 2 uptake was used to evaluate microbial activity since 0 2 uptake directly reflects

microbial aerobic metabolism velocity (Iannotti et al.). Daily and cumulative 0 2

uptake profiles for the four runs are illustrated in Figure 4.3 (ds in the figure means

dry solid matters). Unlike the temperature and pH profiles, significant differences in

daily and cumulative 0 2 uptake profiles could be found in the four runs. In particular,

the daily 0 2 uptake of Run B, with the addition of KH2PO4/MgSO4, was much less

when compared with the other three runs. Run B had a daily 02 uptake value of less

than 2 mg/(g-ds*h) during the entire process, while the daily 02 uptake of the other 3

runs rose to 4-6 mg/(g-ds*h) after the acid inhibited phase and increased rapidly to

more than 10 mg/(g-ds*h) at the thermophilic stage. This phenomenon also testified to

the fact that the lower initial pH value did not suit microorganism growth (Hu et al.,

2007). In Run C, there was an obvious drop in the daily OUR on Day 4 with the

addition of NaAc, which reflects a short term of readaptation for microorganisms. The

profile of OUR rose again and secures the highest peak value.

72

4.2 Change in Microbial Activities

4.2.1 Daily and Cumulative Oxygen Uptake

In composting research, the Carbon Dioxide (CO2) evolution rate and Oxygen (O2)

Uptake Rate have been widely adopted to evaluate microbial activity and composting

efficiency (Fang et al., 1999; C. Liang et al., 2003; Yu and Huang, 2009). In this study,

O2 uptake was used to evaluate microbial activity since O2 uptake directly reflects

microbial aerobic metabolism velocity (Iannotti et al.). Daily and cumulative 02

uptake profiles for the four runs are illustrated in Figure 4.3 (ds in the figure means

dry solid matters). Unlike the temperature and pH profiles, significant differences in

daily and cumulative O2 uptake profiles could be found in the four runs. In particular,

the daily O2 uptake of Run B, with the addition of KI^POVMgSO,^ was much less

when compared with the other three runs. Run B had a daily O2 uptake value of less

than 2 mg/(g-ds*h) during the entire process, while the daily 02 uptake of the other 3

runs rose to 4-6 mg/(g-ds*h) after the acid inhibited phase and increased rapidly to

more than 10 mg/(g-ds*h) at the thermophilic stage. This phenomenon also testified to

the fact that the lower initial pH value did not suit microorganism growth (Hu et al.,

2007). In Run C, there was an obvious drop in the daily OUR on Day 4 with the

addition of NaAc, which reflects a short term of readaptation for microorganisms. The

profile of OUR rose again and secures the highest peak value.

72

Run A 14

2

0 0

--*-- Daily 02 uptake

— — — Cumulative 02 uptake

.0 ,e

0,...

al.

.. ...

e e

e

e

OW 4.110.

O. 01..

3500

- 3000

- 2500

- 2000

- 1500

- 1000

- 500

5 10 15 Times (day)

20 25 0

Figure 4.3a Temporal variations of 0 2 uptake rate and cumulative

02 uptake during composting for Run A

73

Cum

ulat

ive

02 u

ptak

e (m

g/g-

ds)

Run A

- Daily 02 uptake

• Cumulative 02 uptake

- 1500

- 1000

- 500

0 5 10 15 Times (day)

Figure 4.3a Temporal variations of O2 uptake rate and cumulative

O2 uptake during composting for Run A

73

Run B 14

2 -

o 0

—+— Daily 02 uptake

— — — Cumulative 02 uptake

i r 5 10 15

Times (day) 20

AM ••• ... •••

3500

- 3000

- 2500

- 2000

- 1500

- 1000

- 500

0 25

Figure 4.3b Temporal variations of 0 2 uptake rate and cumulative

0 2 uptake during composting for Run B

74

Cum

ulat

ive

02 u

ptak

e (m

g/g-

ds)

14

^ 12 a *

10 bl) 0* a 8

2 6 .£ a 4 <s ° 2

Run B r 3500

-•— Daily 02 uptake

- - Cumulative 02 uptake " 3000

- 2500

- 2000

- 1500

- 1000

• 5 0 0

- , , . n

0 5 10 15 20 25 Times (day)

Figure 4.3b Temporal variations of O2 uptake rate and cumulative

O2 uptake during composting for Run B

74

Run C 14

12 -

10 - 04

El 8

yr 6 -

t 4 - esi 0 2-

0

Daily 02 uptake

— — — Cumulative 02 uptake

I

1

• 11.

3500

- 3000

- 2500

- 2000

- 1500

- 1000

- 500

10 15 Times (day)

20 25 0

Figure 4.3c Temporal variations of 0 2 uptake rate and cumulative

0 2 uptake during composting for Run C

75

Cum

ulat

ive

02 u

ptak

e (m

g/g-

ds)

Run C 14

~ 12 ja * w .. V 10

OA

<w •** 2 6 J2

a 4

O o

3500 -•— Daily 02 uptake

— — Cumulative 02 uptake - 3000

- 2500

- 2000

- 500

10 15 Times (day)

Figure 4.3c Temporal variations of O2 uptake rate and cumulative

O2 uptake during composting for Run C

"? is a •w

OS

a 3 fS 0 1 9 a s <J

75

Run D 14

12 -.b

i io - to -a

E1 8 -

i 6 -

▪e s rs. 4 -o t'l 0 2 -

o 0

- -•— Daily 02 uptake

— — — Cumulative 02 uptake

3500

- 3000

- 2500

- 2000

- 1500

- 1000

- 500

I I I 0 10 15 20 25 Times (day)

Figure 4.3d Temporal variations of 0 2 uptake rate and cumulative

0 2 uptake during composting for Run D

76

Cum

ulat

ive

02 u

ptak

e (m

g/gd

s)

RunD

• Daily 02 uptake

• Cumulative 02 uptake

3500

- 3000 $

"S - 2500 a

M • 2000 -2 p< s

1500 O <u

V 1000 .2 9 a

- 500 u

10 15 Times (day)

I 20 25

Figure 4.3d Temporal variations of O2 uptake rate and cumulative

O2 uptake during composting for Run D

76

The peak value of the oxygen uptake rate and the fmal value of the cumulative oxygen

uptake can be found in Table 4.1 According the table, the OUR peak value of Run B

was 1.86 mg/(g.h), which is much lower than the other three runs with peak values

larger than 9 mg/(g.h). This phenomenon testified that acidic additive will severely

inhibit the aerobic process of microorganisms. The differences between Run A and

Run C with control Run D are also statistically significant (p<0.05), which indicates

an alkaline additive can promote microbial activity in an aerobic composting process.

The significant higher rates are due to the favorable effects of buffer agents which can

alleviate inhibition effects under low pH. All four runs have a peak value after Day 10.

However, when referring to the temperature profile, the temperature rapidly rose to

the thermophilic stage on Day 5. The possible reason for this inconsistency may be

due to inhibition of the combined effects of low pH and higher temperatures because

the pH did not rise to 6 quickly after the temperature rose (Sundberg et al., 2004). The

highest aerobic reaction rate can only be found when the pH in the system is alkaline.

When the pH exceeded 6 and continued to increase, microbial activity was also

significantly enhanced. The positive effects could be due to the presence of buffer

salts which can prevent pH from dropping extensively when the temperature rose in

the initial stage. The peak OUR of Run A appeared several days later than the other

three runs. The possible reason is K2HPO4 had better buffer ability than NaAc in the

alkaline range, which can slow down the trend of rising pH.

77

The peak value of the oxygen uptake rate and the final value of the cumulative oxygen

uptake can be found in Table 4.1 According the table, the OUR peak value of Run B

was 1.86 mg/(g.h), which is much lower than the other three runs with peak values

larger than 9 mg/(g.h). This phenomenon testified that acidic additive will severely

inhibit the aerobic process of microorganisms. The differences between Run A and

Run C with control Run D are also statistically significant (p<0.05), which indicates

an alkaline additive can promote microbial activity in an aerobic composting process.

The significant higher rates are due to the favorable effects of buffer agents which can

alleviate inhibition effects under low pH. All four runs have a peak value after Day 10.

However, when referring to the temperature profile, the temperature rapidly rose to

the thermophilic stage on Day 5. The possible reason for this inconsistency may be

due to inhibition of the combined effects of low pH and higher temperatures because

the pH did not rise to 6 quickly after the temperature rose (Sundberg et al., 2004). The

highest aerobic reaction rate can only be found when the pH in the system is alkaline.

When the pH exceeded 6 and continued to increase, microbial activity was also

significantly enhanced. The positive effects could be due to the presence of buffer

salts which can prevent pH from dropping extensively when the temperature rose in

the initial stage. The peak OUR of Run A appeared several days later than the other

three runs. The possible reason is K2HPO4 had better buffer ability than NaAc in the

alkaline range, which can slow down the trend of rising pH.

77

Table 4.1 Peak value of oxygen uptake rate and final value of cumulative oxygen

uptake for the four runs

peak value of OUR final cumulative

(mg/(g-ds*h)) oxygen uptake (mg/g)

Run A 10.02 (day 17)

Run B 1.82 (day 14)

Run C 12.40 (day 13)

Run D 9.06 (day 12)

3037

811

3209

2510

78

Table 4.1 Peak value of oxygen uptake rate and final value of cumulative oxygen

uptake for the four runs

peak value of OUR final cumulative

(mg/(g-ds*h)) oxygen uptake (mg/g)

Run A 10.02 (day 17) 3037

RunB 1.82 (day 14) 811

RunC 12.40 (day 13) 3209

RunD 9.06 (day 12) 2510

78

After the runs showed their peak 0 2 consumption velocity, the 02 uptake rate began to

decrease extensively because of the reduced amount of degradable materials available.

OUR began to drop for Runs D, C and A on Day 13, Day 15 and Day 19, respectively.

The same order and time points could also be reflected in temperature profiles, which

imply the major reason for the cooling phase is a deficiency of nutrients could be

utilized by the microorganism in order to produce enough heat. After Day 25, the

daily OUR in all runs dropped to lower than 1 mg/(g.h), indicating the compost was

stable (Wong and Fang, 2000).

As for cumulative 0 2 uptake, similar results could be found. Run B also had the

lowest final cumulative 0 2 uptake, while Runs A and C, with an alkaline additive

exhibited higher cumulative 0 2 uptake values than the control run. It was obvious that

alkaline buffer agents stimulated microbial aerobic reaction and acidic buffer agents

severely inhibited the aerobic metabolism of microorganisms. All the aforementioned

differences are statistically significant (p<0.05)

4.2.2 Percentage of Organic Matter Degradation

Figure 4.4 depicts the temporal variations of the percentage of organic material

degradation profiles for the four reactors. During the first 12 days, the four runs had

other three runs after Day 12, and there is a skyrocketing stage for Run A after Day

16.

79

After the runs showed their peak O2 consumption velocity, the O2 uptake rate began to

decrease extensively because of the reduced amount of degradable materials available.

OUR began to drop for Runs D, C and A on Day 13, Day 15 and Day 19, respectively.

The same order and time points could also be reflected in temperature profiles, which

imply the major reason for the cooling phase is a deficiency of nutrients could be

utilized by the microorganism in order to produce enough heat. After Day 25, the

daily OUR in all runs dropped to lower than 1 mg/(g.h), indicating the compost was

stable (Wong and Fang, 2000).

As for cumulative O2 uptake, similar results could be found. Run B also had the

lowest final cumulative O2 uptake, while Runs A and C, with an alkaline additive

exhibited higher cumulative O2 uptake values than the control run. It was obvious that

alkaline buffer agents stimulated microbial aerobic reaction and acidic buffer agents

severely inhibited the aerobic metabolism of microorganisms. All the aforementioned

differences are statistically significant (p<0.05)

4.2.2 Percentage of Organic Matter Degradation

Figure 4.4 depicts the temporal variations of the percentage of organic material

degradation profiles for the four reactors. During the first 12 days, the four runs had

other three runs after Day 12, and there is a skyrocketing stage for Run A after Day

16.

79

70

0 0 5 10 15

Times (day) 20

Figure 4.4 Degradation rate profiles during the compostingprocess

80

25

70

Run B Run A 60

Run C Run D

at 50

20

10

0 20 25 15 0 5 10

Times (day)

Figure 4.4 Degradation rate profiles during the compostingprocess

80

Finally, Run A had the highest percentage of organic matter degradation, while there

was no significant difference between Run B and Run D in this parameter. The total

organic degradation rate for the four runs can be found in Table 4.2 Comparison

between final organic matters and cumulative 0 2 uptake in composting system. When

compared with Run D, far more organic matter decomposed in Runs A and C, and the

significant improvement is due to the help of alkaline additives (p<0.05). On the other

hand, Run B with acid amendment, also led the control run in this parameter, but the

improvement is not significant (p>0.05). This result indicated that the addition of

alkaline may effectively increase the extent of total organic material degradation.

Table 4.2 also contains the ratio of degradation percentage and cumulative 0 2 uptake

of Runs A, B and C to those parameters of control run, Run D. When

comparing the ratios, Run B has a relatively lower aerobic coefficient, which means

large portions of organic matters in Run B were degraded in anaerobic pathways. Run

A had a higher percentage of organic matter degradation but a lower cumulative OUR

value than Run C, which implies the portion of aerobic reaction and anaerobic

reaction is different in different runs, which is due to different amendments being

added. Thus, the aerobic coefficient can be defined in this study, which is the

result of the ratio of cumulative 0 2 uptake, divided by the ratio of organic

matter degradation. Because the percentage of degradation reflects the overall

reaction rate by measuring the decreased quantity of reactants, which include both

81

Finally, Run A had the highest percentage of organic matter degradation, while there

was no significant difference between Run B and Run D in this parameter. The total

organic degradation rate for the four runs can be found in Table 4.2 Comparison

between final organic matters and cumulative O2 uptake in composting system. When

compared with Run D, far more organic matter decomposed in Runs A and C, and the

significant improvement is due to the help of alkaline additives (p<0. OS). On the other

hand, Run B with acid amendment, also led the control run in this parameter, but the

improvement is not significant (p>0.05). This result indicated that the addition of

alkaline may effectively increase the extent of total organic material degradation.

Table 4.2 also contains the ratio of degradation percentage and cumulative O2 uptake

of Runs A, B and C to those parameters of control run, Run D. When

comparing the ratios, Run B has a relatively lower aerobic coefficient, which means

large portions of organic matters in Run B were degraded in anaerobic pathways. Run

A had a higher percentage of organic matter degradation but a lower cumulative OUR

value than Run C, which implies the portion of aerobic reaction and anaerobic

reaction is different in different runs, which is due to different amendments being

added. Thus, the aerobic coefficient can be defined in this study, which is the

result of the ratio of cumulative O2 uptake, divided by the ratio of organic

matter degradation. Because the percentage of degradation reflects the overall

reaction rate by measuring the decreased quantity of reactants, which include both

Table 4.2 Comparison between final organic matters and cumulative 0 2 uptake in

composting system

Final percentage of OM

degradation ( % )

OM degradation ratio to

control run

Cumulative 0 2 uptake

ratio to control run

Aerobic

coefficient

Run A 58.2 1.20 1.21 1.01

Run B 49.2 1.01 0.32 0.32

Run C 54.4 1.12 1.18 1.14

Run D 48.7 1.00 1.00 1.00

82

Table 4.2 Comparison between final organic matters and cumulative O2 uptake in

composting system

Final percentage of OM OM degradation ratio to Cumulative O2 uptake Aerobic

degradation (%) control run ratio to control run coefficient

Run A 58.2 1.20 1.21 1.01

RunB 49.2 1.01 0.32 0.32

RunC 54.4 1.12 1.18 1.14

RunD 48.7 1.00 1.00 1.00

82

aerobic and anaerobic reactions, while the cumulative 0 2 uptake reflects only the

aerobic activity of the microorganisms. The higher the value of the aerobic coefficient,

the more easily aerobic decomposition will take place in the composting system. In

this study, ICH2PO4/MgSO4 can severely decrease the value and NaAc can promote

this value significantly, while K2HPO4/MgSO4 seems unable to change the type of

microbial reactions. Microbial anaerobic metabolism would cause incomplete

degradation of nutrients so a decreased maturity and the intermediate products such as

organic acids were detrimental to the microorganism itself. Although it is a critical

problem in the composting process, complete elimination of an anaerobic reaction

would be impossible in an actual composting system because many factors may

trigger anaerobic degradation such as too much moisture, deficient inlet oxygen,

uneven turning, etc.

4.23 Changes in Ammonia Loss

Nitrogen loss is an important parameter in composting research, because it is also

closely related to the composting reaction rate. Theoretically, the loss of nitrogen

element was mainly through gaseous emission. The amount of leachate was negligible

during the composting process. In gas phase, NH3 accounts for nearly 80% of the total

gaseous loss, (besides N20, N2 and NO„ compounds) (Hu et al., 2007). Based on this

theory, only the ammonia emission was measured in this study and accounted for

nitrogen loss.

83

aerobic and anaerobic reactions, while the cumulative O2 uptake reflects only the

aerobic activity of the microorganisms. The higher the value of the aerobic coefficient,

the more easily aerobic decomposition will take place in the composting system. In

this study, KH2PC>4/MgS04 can severely decrease the value and NaAc can promote

this value significantly, while K2HPC>4/MgS04 seems unable to change the type of

microbial reactions. Microbial anaerobic metabolism would cause incomplete

degradation of nutrients so a decreased maturity and the intermediate products such as

organic acids were detrimental to the microorganism itself. Although it is a critical

problem in the composting process, complete elimination of an anaerobic reaction

would be impossible in an actual composting system because many factors may

trigger anaerobic degradation such as too much moisture, deficient inlet oxygen,

uneven turning, etc.

4.23 Changes in Ammonia Loss

Nitrogen loss is an important parameter in composting research, because it is also

closely related to the composting reaction rate. Theoretically, the loss of nitrogen

element was mainly through gaseous emission. The amount of leachate was negligible

during the composting process. In gas phase, NH3 accounts for nearly 80% of the total

gaseous loss, (besides N2O, N2 and NOx compounds) (Hu et al., 2007). Based on this

theory, only the ammonia emission was measured in this study and accounted for

nitrogen loss.

83

Ammonia emission is affected by the following three factors: i), microbial reaction

activity, which is related to the amount of transformed inorganic nitrogen (mainly

ammonia) from organic matter; ii), pH, which decides the distribution of inorganic

nitrogen as a form of NH3- H2O and NH4+ in the aqueous phase. If NH3-1120 is

saturated in solvent, an ammonia emission can be observed; iii) anti-correlated to an

additive that would react with ammonia. However, it is necessary to realize the three

factors are not independent of each other. A change in one factor may cause variation

in other factors. For example, the buffer agent will change the pH in the system,

which also affects microbial activity; phosphates will absorb emitted ammonia, but

also stimulate microbial activity by providing nutrient.

Based on this theory, it is easy to understand the ammonia loss profile in this research.

Figure 4.5 depicted daily ammonia loss rates and cumulative ammonia loss in the four

runs, respectively. Both figures indicated that in the first 10 days, the ammonia

emission amount was negligible. There are two reasons for ammonia not emitting in

the beginning of the composting process. The first reason was microbial activity was

too low to produce enough inorganic nitrogen; the second reason was an acidic

environment in the starter could absorb more ammonia in the solution. When pH

arrived at the alkaline range, ammonia loss increased rapidly (K Ekinci et al., 2000).

In this stage, Runs C and D emitted ammonia earlier than Runs A and B. Table 4.3

also gives the peak daily ammonia loss and final cumulative amount of lost ammonia.

84

Ammonia emission is affected by the following three factors: i), microbial reaction

activity, which is related to the amount of transformed inorganic nitrogen (mainly

ammonia) from organic matter; ii), pH, which decides the distribution of inorganic

nitrogen as a form of NHa-HfeO and NH4+ in the aqueous phase. If NHh-HhO is

saturated in solvent, an ammonia emission can be observed; iii) anti-correlated to an

additive that would react with ammonia. However, it is necessary to realize the three

factors are not independent of each other. A change in one factor may cause variation

in other factors. For example, the buffer agent will change the pH in the system,

which also affects microbial activity; phosphates will absorb emitted ammonia, but

also stimulate microbial activity by providing nutrient.

Based on this theory, it is easy to understand the ammonia loss profile in this research.

Figure 4.5 depicted daily ammonia loss rates and cumulative ammonia loss in the four

runs, respectively. Both figures indicated that in the first 10 days, the ammonia

emission amount was negligible. There are two reasons for ammonia not emitting in

the beginning of the composting process. The first reason was microbial activity was

too low to produce enough inorganic nitrogen; the second reason was an acidic

environment in the starter could absorb more ammonia in the solution. When pH

arrived at the alkaline range, ammonia loss increased rapidly (K Ekinci et al., 2000).

In this stage, Runs C and D emitted ammonia earlier than Runs A and B. Table 4.3

also gives the peak daily ammonia loss and final cumulative amount of lost ammonia.

0 5 10 15 Times (day)

Run A

I i 0 25 20

Figure 4.5a Temporal variations of ammonia loss rate and

cumulative ammonia loss in Run A

85

Run A

Daily NH3 loss

• Cumulative NH3 loss

5 10 Times (day)

Figure 4.5a Temporal variations of ammonia loss rate and

cumulative ammonia loss in Run A

85

Run B

Am

mon

ia lo

ss r

ate

(mg/

h)

100

80 -

60 -

40 -

20 -

5 10 15 25 Times (day)

Figure 4.5b Temporal variations of ammonia loss rate and

cumulative ammonia loss in Run B

86

Cum

ulat

ive

Am

mon

ia lo

ss (

mg)

RunB 100 12000

- Daily NH3 loss

— Cumulative NH3 loss - 10000 £? 80

- 8000

- 6000 o OS

a o - 4000 «

< - 2000

25 15 20 10 5 Times (day)

Figure 4.5b Temporal variations of ammonia loss rate and

cumulative ammonia loss in Run B

86

Run C

Am

mon

ia l

oss

rate

(m

g/h)

100 - -•-- Daily NH3 loss

— — — Cumulative NH3 loss 80 -

60 -

40 -

20 -

• • ♦ •

0 5

I

I I I

I I

10 15 Times (day)

20

•

•

12000

- 10000 1

- 8000 as 'S

- 6000 a

- 4000 1= es

- 2000 0

25 0

Figure 4.5c Temporal variations of ammonia loss rate and

cumulative ammonia loss in Run C

87

Run C

Daily NH3 loss

• Cumulative NH3 loss

15 Times (day)

Figure 4.5c Temporal variations of ammonia loss rate and

cumulative ammonia loss in Run C

87

Run D 100

Daily NH3 loss

— — — Cumulative NH3 loss .174 80 -

60

.4 40 - os:1

a 20

12000

10000

- 8000 es

o

- 6000 El

- 4000 es

a - 2000

0 • • • , • 0 5 10 15 20 25

Times (day)

0

Figure 4.5d Temporal variations of ammonia loss rate and

cumulative ammonia loss in Run D

88

Run D

- Daily NH3 loss

• Cumulative NH3 loss

12000

10000

8000

- 6000

• 4000

- 2000

• • »

bt a, <n oi O

a 0 a 1

4> >

•o "3 a s

U

10 15 Times (day)

25

Figure 4.5d Temporal variations of ammonia loss rate and

cumulative ammonia loss in Run D

88

Table 4.3 Peak value of oxygen uptake rate and final value of cumulative oxygen

uptake for the four runs

peak value of ammonia fmal cumulative

loss rate (mg/h) ammonia loss (mg)

Run A 91.73 (day 20)

Run B 27.84 (day 18)

Run C 93.88 (day 14)

Run D 76.1 (day 14)

9605

2783

10821

8060

89

Table 4.3 Peak value of oxygen uptake rate and final value of cumulative

uptake for the four runs

peak value of ammonia final cumulative

loss rate (mg/h) ammonia loss (mg)

Run A 91.73 (day 20) 9605

RunB 27.84 (day 18) 2783

RunC 93.88 (day 14) 10821

Run D 76.1 (day 14) 8060

89

The daily peak of ammonia loss in Runs C and D appears on Day 14, while the daily

peak of ammonia loss in Runs A and B can be found on Day 17 and Day 18,

respectively. As for Run C, NaAc only changed the pH, while for Runs A and B,

because HP042" and H2PO4 could react with NH3 with the help of Mg2+, produced

struvite crystallized magnesium ammonium phosphate (MAP, MgNH4PO4.6H2O) to

stabilize the nitrogen element (Li et al., 2010). Only when the output rate of ammonia

by microbial was larger than absorption rate by absorbents, could ammonia emission

be detected. The reaction of struvite formation is shown below:

Mg + + NH4 + H2PO: + 6H 20 ---> MgNH4PO4 • 61120 + 2H+

When comparing Runs A and C in a cumulative 0 2 uptake rate and cumulative

ammonia release, some inconsistency could be found between the two alkaline

additives. Although Run A with ICH2PO4/MgSO4 had a higher ammonia loss and an

0 2 uptake rate better than Run C amended with NaAc, the two parameters are not

proportional. The final cumulative 0 2 uptake rate of Run A was 95% of the value of

Run C; while for the cumulative ammonia loss, the rate became 88%. This

phenomenon could be analyzed by the theory above. The OUR was proportional to

the reaction velocity while ammonia loss reflected the combined effects of the

reaction rate and the absorption of additive. K211130 4 and MgSO4 in Run A not only

affected the pH in the system, but could absorb ammonia by forming struvite, while

NaAc in Run C only changed the pH. That was why Run A had less of an ammonia

loss than expected. However, the difference between alkaline additives is relatively

90

The daily peak of ammonia loss in Runs C and D appears on Day 14, while the daily

peak of ammonia loss in Runs A and B can be found on Day 17 and Day 18,

respectively. As for Run C, NaAc only changed the pH, while for Runs A and B,

because HPO42" and H2PO4* could react with NH3 with the help of Mg2+, produced

struvite crystallized magnesium ammonium phosphate (MAP, MgNH4P04-6H20) to

stabilize the nitrogen element (Li et al., 2010). Only when the output rate of ammonia

by microbial was larger than absorption rate by absorbents, could ammonia emission

be detected. The reaction of struvite formation is shown below:

Mg2* + NH* + H2POR + 6H20 -» MgNH4P04 • 6H20+2H*

When comparing Runs A and C in a cumulative O2 uptake rate and cumulative

ammonia release, some inconsistency could be found between the two alkaline

additives. Although Run A with KH2PC>4/MgS04 had a higher ammonia loss and an

O2 uptake rate better than Run C amended with NaAc, the two parameters are not

proportional. The final cumulative O2 uptake rate of Run A was 95% of the value of

Run C; while for the cumulative ammonia loss, the rate became 88%. This

phenomenon could be analyzed by the theory above. The OUR was proportional to

the reaction velocity while ammonia loss reflected the combined effects of the

reaction rate and the absorption of additive. K2HPO4 and MgS04 in Run A not only

affected the pH in the system, but could absorb ammonia by forming struvite, while

NaAc in Run C only changed the pH. That was why Run A had less of an ammonia

loss than expected. However, the difference between alkaline additives is relatively

slight, which indicated the pH may still be the predominant factor in deciding the

amount of ammonia release. This was also reflected by the fmal amount of oxygen

uptake and ammonia loss in the four reactors. The order of the two parameters was the

same: Run C, Run A, Run D and Run B, from highest to lowest, corresponding to the

pH from greatest to lowest. Because the amendment in Run B was acidic, the

microbial reaction rate was severely affected, presenting the lowest ammonia release

due to limited ammonia being produced. In addition, by comparing the three factors in

Runs A and B, it could be found that struvite was more likely to be formed under an

alkaline environment. With respect to OUR, the value of Run B was 27% of the value

of Run A; while the ratio changed to 29% for the cumulative ammonia loss,. The

difference should result in less struvite being produced in Run B amended with acidic

amendment, M.-12Pa4.

In summary, it showed the factors, including the changing distribution of NH3•H2O

and NH4+ because of the pH and the absorbance of NH3 due to P0.43" and Mg2+, did

affect ammonia emission amount, but it could not be used to support the ratio of the

two effects because it was based upon a comparison not an accurate measurement. In

a real system, it is impossible to divide every factor and obtain the ratio of every

factor for the fmal result.

43 Nitrogen Mass Balance in Composting System

91

slight, which indicated the pH may still be the predominant factor in deciding the

amount of ammonia release. This was also reflected by the final amount of oxygen

uptake and ammonia loss in the four reactors. The order of the two parameters was the

same: Run C, Run A, Run D and Run B, from highest to lowest, corresponding to the

pH from greatest to lowest. Because the amendment in Run B was acidic, the

microbial reaction rate was severely affected, presenting the lowest ammonia release

due to limited ammonia being produced. In addition, by comparing the three factors in

Runs A and B, it could be found that struvite was more likely to be formed under an

alkaline environment. With respect to OUR, the value of Run B was 27% of the value

of Run A; while the ratio changed to 29% for the cumulative ammonia loss,. The

difference should result in less struvite being produced in Run B amended with acidic

amendment, KH2PO4.

In summary, it showed the factors, including the changing distribution of NH3-H20

and NH4+ because of the pH and the absorbance of NH3 due to PO43" and Mg2+, did

affect ammonia emission amount, but it could not be used to support the ratio of the

two effects because it was based upon a comparison not an accurate measurement. In

a real system, it is impossible to divide every factor and obtain the ratio of every

factor for the final result.

43 Nitrogen Mass Balance in Composting System

91

The above phenomenon states the conflict between a better aerobic degradation rate

under high pH and a less ammonia emission under low pH. In order to fully

investigate the optimum pH for a composting process, nitrogen mass balance in the

four runs is studied herein. Figure 4.6 shows the distribution of organic nitrogen.,

inorganic nitrogen in solution (NH4+-N) and emitted ammonia (NH3-N). The NH4+-N

containing nitrogen in both NH3•H2O and NH4+ and NH3-N which includes ammonia

nitrogen in exhaust gas. In these figures, NH3-N shared the same trend with ammonia

loss. Run B had a much less NH3—N component than the other 3 runs, but when

compared with other nitrogen component, NH3 only accounted for a small portion of

the total nitrogen element. Even the rate in Run C, with the highest ammonia emission,

was less than 15%. Unlike NH3-N, Organic N and NI14+-N were the major parts of

nitrogen in the composting system, and their timing distribution was different in the

four runs. Transformed inorganic N from organic matters is preferred because

inorganic N is more easily utilized by microorganisms. The organic N ratio in the final

product is 43%, 55%, 43% and 50% in Runs A, B, C and D, respectively. There is

obviously more inorganic nitrogen produced in Run A and Run C with alkaline

additives. Whereas, for Run B amended with acidic buffer salts, far more organic

nitrogen remains in the final composting products. The NH4-N percentages from the

four runs on Day 24 were 45%, 41%, 44% and 41%, respectively. Although the

difference was not as significant as that for organic nitrogen, Runs A and C retained

more inorganic nitrogen in the composting system despite further emitted ammonia.

Based on nitrogen mass balance data, alkaline additives in this research are beneficial

92

The above phenomenon states the conflict between a better aerobic degradation rate

under high pH and a less ammonia emission under low pH. In order to fully

investigate the optimum pH for a composting process, nitrogen mass balance in the

four runs is studied herein. Figure 4.6 shows the distribution of organic nitrogen,

inorganic nitrogen in solution (NH/-N) and emitted ammonia (NH3-N). The NHj+-N

containing nitrogen in both NH3-H2O and NKt+ and NH3-N which includes ammonia

nitrogen in exhaust gas. In these figures, NH3-N shared the same trend with ammonia

loss. Run B had a much less NH3-N component than the other 3 runs, but when

compared with other nitrogen component, NH3 only accounted for a small portion of

the total nitrogen element. Even the rate in Run C, with the highest ammonia emission,

was less than 15%. Unlike NH3-N, Organic N and NH4+-N were the major parts of

nitrogen in the composting system, and their timing distribution was different in the

four runs. Transformed inorganic N from organic matters is preferred because

inorganic N is more easily utilized by microorganisms. The organic N ratio in the final

product is 43%, 55%, 43% and 50% in Runs A, B, C and D, respectively. There is

obviously more inorganic nitrogen produced in Run A and Run C with alkaline

additives. Whereas, for Run B amended with acidic buffer salts, far more organic

nitrogen remains in the final composting products. The NH4-N percentages from the

four runs on Day 24 were 45%, 41%, 44% and 41%, respectively. Although the

difference was not as significant as that for organic nitrogen, Runs A and C retained

more inorganic nitrogen in the composting system despite further emitted ammonia.

Based on nitrogen mass balance data, alkaline additives in this research are beneficial

(A) 100

e 80

g 60

40 tu)

'1"-' 20

0

6

•

• • •

0

6

606

6

6

• • 6 •

5

6 6

• • • •

• • • • • .•. • • •. • • • • •

•

oi° r0

10 15

Time (day)

Organic N p NH4-N • NH3-N

I

•

•

o°°

20

• •

'1°

Figure 4.6a Evolution of different nitrogen forms during composting (Run A)

93

100

80 -

§ 60 a a 0 a 40 4> WD

1 20 -I

(A)

T m

> * * * 11 * * *

10 15 Time (day)

20

Q Organic N E3NH4-N INH3-N

Figure 4.6a Evolution of different nitrogen forms during composting (Run A)

93

(B)

100

80

,11 60

40

z 20

0

0 i /

11 • • • •

•

•

•

•

•

•

•

•

•

•

•

•

•

•

Pd

•• • •• .•• .•• •

w /PP

/ / 0

PdPd

0 0

0 •

0 5 10 15

Time (day)

Organic N 0 NH4-N ■ NH3-N

20

Figure 4.6b Evolution of different nitrogen forms during composting (Run B)

94

100

(B)

80

a 4) § 60 a a o ^ 4 0 V oe s

20 -

t * * * * * * *

* t * * t

10 Time (day)

15 20

a Organic N 0 NH4-N • NH3-N

Figure 4.6b Evolution of different nitrogen forms during composting (Run B)

94

e 4.6c Evolution o

f different nitrogen forms during com

posting (Run C

)

0

0

A

00

a

cn

O

01

O

Nitrogen Component (%) 42% 0) 03

0 0 0 0 0 8 1 I

r

. ........ --MEM

(C)

100

§ 60

« 40 S

O

OA £ s 20 0

0

5

10

20

15 T

ime (day)

• O

rgan

ic N

ED

NH

4-N • N

H3-N

Figure 4.6c E

volution of different nitrogen forms during com

posting (Run C

)

95

Figure 4.6d E

volution of different nitrogen forms during com

posting (Run D

)

0 Oct

n z

0 z

z

z

0

01

0

-24

O

Nitrogen component (%)

0 0 0

.......... ...... "Vq6.1104h.

IC • mai

\11:\1

101 1001 101 1•11011.71k:

gh.

Ne.16.1. ...... IE..... • •• •• ....

-a

100

a <U a o

a

a so o

o

(D)

a 4> an

I Z

*

* t * *

* I. 1

10

15

Tim

e (day)

20

B O

rgan

ic N

GJN

H4-N

•N

H3-N

Figure 4.6d E

volution of different nitrogen forms during com

posting (Run D

)

96

to microorganism activity, which transfers organic nitrogen into inorganic forms.

4.4 Summary

In this section, the effects of different buffer agents, NaAc, K2HPO4 and KH2PO4, as

amendments for food waste composting were examined under well-controlled

experimental conditions. The effects of buffer salts were found to closely relate to

their initial pH level. As for alkaline buffer agents, NaAc and K2HPO4 in this study, a

higher final pH, a temperature in the thermophilic phase and the oxygen uptake rate

were monitored indicating positive evidence of an improving composting process.

However, an enhanced ammonia loss was also shown in the runs with alkaline

amendment, which would lower product quality. As for the run with acidic buffer

agents amended, the microbial activities were severely hampered which made

KH2PO4 an unsuitable additive for composting processes. The effects of struvite,

formed by phosphates and MgSO4, were also investigated. The results indicated

struvite was not an effective method with which to prevent NH3 emission; pH

remained the dominant factor to control ammonia emission in this study.

The study results demonstrated that amendment K2HPO4 with MgSO4 is the optimal

choice among the three buffer agents tested in this study. Because amendment

K2HPO4/MgSO4 had an obvious improvement in composting parameters as buffer

agents like NaAc, it could successfully help to avoid the adverse effects of organic

acids during the initial stage but would cause less ammonia release than NaAc.

97

to microorganism activity, which transfers organic nitrogen into inorganic forms.

4.4 Summary

In this section, the effects of different buffer agents, NaAc, K2HPO4 and KH2PO4, as

amendments for food waste composting were examined under well-controlled

experimental conditions. The effects of buffer salts were found to closely relate to

their initial pH level. As for alkaline buffer agents, NaAc and K2HPO4 in this study, a

higher final pH, a temperature in the thermophilic phase and the oxygen uptake rate

were monitored indicating positive evidence of an improving composting process.

However, an enhanced ammonia loss was also shown in the runs with alkaline

amendment, which would lower product quality. As for the run with acidic buffer

agents amended, the microbial activities were severely hampered which made

KH2PO4 an unsuitable additive for composting processes. The effects of struvite,

formed by phosphates and MgSC>4, were also investigated. The results indicated

struvite was not an effective method with which to prevent NH3 emission; pH

remained the dominant factor to control ammonia emission in this study.

The study results demonstrated that amendment K2HP04 with MgSC>4 is the optimal

choice among the three buffer agents tested in this study. Because amendment

K2HP04/MgS04 had an obvious improvement in composting parameters as buffer

agents like NaAc, it could successfully help to avoid the adverse effects of organic

acids during the initial stage but would cause less ammonia release than NaAc.

97

CHAPTER 5

EFFECTS OF TEMPERATURE AND pH

5.1 Kinetics Model Development

5.1.1 Development of a Two-Term Monod Equation with Correction Factors of

Temperature and pH

With respect to a composting system without biomass recycle, Monod equation is

employed to express the rate of reaction as Equations (1) (Monod, 1949) :

dS max SX

dt Ks + S (5.1)

where the variables S and X indicate organic substrate (kg) and concentration of

microorganisms ( g I kgom ), respectively. The constant Pimu indicates maximum

specific degradation rate of organic substrate (day-I ) when microorganisms reach

their maximum activity, and K3 is called "half-saturation constant" (kg), which

indicates the concentration of organic substrate at one-half the maximum specific

substrate utilization rate.

Since /.1m,x is a measure of the overall efficiency of each composting process under

analysis, it is obviously dependent on the system design, type of material under

treatment, operational condition, and any other factors that may affect overall system

performance. As a result, p is the result of pm. multiplied by given environment

correction factors, which is a coefficient within 0 to 1 when environmental variables

98

CHAPTERS

EFFECTS OF TEMPERATURE AND pH

5.1 Kinetics Model Development

5.1.1 Development of a Two-Term Monod Equation with Correction Factors of

Temperature and pH

With respect to a composting system without biomass recycle, Monod equation is

employed to express the rate of reaction as Equations (1) (Monod, 1949) :

(5.1) dS M^SX dt Kt+S

where the variables S and X indicate organic substrate (kg) and concentration of

microorganisms ( g/kg0M ), respectively. The constant //max indicates maximum

specific degradation rate of organic substrate (day'1) when microorganisms reach

their maximum activity, and Ks is called "half-saturation constant" (kg), which

indicates the concentration of organic substrate at one-half the maximum specific

substrate utilization rate.

Since jumix is a measure of the overall efficiency of each composting process under

analysis, it is obviously dependent on the system design, type of material under

treatment, operational condition, and any other factors that may affect overall system

performance. As a result, //roai is the result of /Jmix multiplied by given environment

correction factors, which is a coefficient within 0 to 1 when environmental variables

98

are in a certain range. The value of 1 represents no prohibitive effects on

microorganisms, while correction factors close to 0 represents an inhibition so strong

that microorganisms lose their activity. Because one of the major objectives in this

paper is to discuss the effects of different environment factors on the activities of

mesophiles and thermophiles, it is reasonable to believe temperature and pH are

among the variables which have remarkable differences in affecting two types of

microorganisms, respectively (Baptista et al., 2010). Thus, only correction factors for

temperature and pH are introduced in Equation (2) while the effects of other

operational conditions are neglected.

ft =- 4u. x f (T)x f(pH),0 < f (T), f(pH) <1 (5.2)

where f(T) and f(pH) are correction functions to evaluate the effects of

temperature and pH (Lin et al., 2008; Petric and Selimbasic, 2008; Baptista et al.,

2010).

Extensive sets of equations in the literature to describe the dependency of the

composting rate on temperature and pH (R.T. Haug, 1993). Quadratic forms apprear

to have strong experimental support and wide acceptance by composting researchers

(Lin et al., 2008). Equations (3) and (4) describe the correction functions of

temperature and pH, respectively.

f(T). aT2 +br + c (5.3)

f(pH)=o(pH)2 + p(pH) + q (5.4)

where a, b, c, o, p and q are parameters to be estimated through the experimental data.

99

are in a certain range. The value of 1 represents no prohibitive effects on

microorganisms, while correction factors close to 0 represents an inhibition so strong

that microorganisms lose their activity. Because one of the major objectives in this

paper is to discuss the effects of different environment factors on the activities of

mesophiles and thermophiles, it is reasonable to believe temperature and pH are

among the variables which have remarkable differences in affecting two types of

microorganisms, respectively (Baptista et al., 2010). Thus, only correction factors for

temperature and pH are introduced in Equation (2) while the effects of other

operational conditions are neglected.

Mmax = x f(T) X f ( P H) , 0 < f (T ) , f ( pH) < 1 (5.2)

where f {T ) and f ( pH) are correction functions to evaluate the effects of

temperature and pH (Lin et al., 2008; Petric and Selimbasic, 2008; Baptista et al.,

2010).

Extensive sets of equations in the literature to describe the dependency of the

composting rate on temperature and pH (R.T. Haug, 1993). Quadratic forms apprear

to have strong experimental support and wide acceptance by composting researchers

(Lin et al., 2008). Equations (3) and (4) describe the correction functions of

temperature and pH, respectively.

f (T ) = aT 2 +bT+c (5.3)

f ( pH) = o (pHf +p(pH) + q (5.4)

where a, b, c, o, p and q are parameters to be estimated through the experimental data.

99

The transformation of quadratic functions in Equations (3) and (4) is designed for not

only directly assessing optimal temperature and pH, but decreasing the numbers of

parameters. As a result, the updated correlation factors for temperature and pH are

shown in Equation (5) and (6):

f (T) = 0.9

2 (Top, — + 1

( Topi — To .I )

Top: > 7 0.1

(5.5)

0.9 f (p11) = opH) _ (pin° OPH)„,,, (PHD' +1

(5.6)

(P11)0„, > (PH)0

where Too, and pH op, op, are the optimal temperature and pH values corresponding to

a maximum reaction rate for microorganisms ( f (Too) =1, f ((pH)„,,,) =1); To and

(pH)0 I are the inhibition values of temperature and pH in the quadratic function

when f (To ,) = 0.1 , f ((pH)01) = 0.1 . The constraints T op, > To , and

(p11)0„, > (PI00.1 ensure that the inhibition value is in the left side of the quadratic

functions. Because there is no clear-cut value with which to judge whether

microorganisms have lost their activity, a correction factor equal to 0.1 is considered

as the lower bound of microorganism activity in the model. It means the activity of

microorganisms can be neglected if environmental conditions are beyond this range.

In addition to the aforementioned considerations, since mesophilic and hemophilic

microorganisms might have different optimal kinetics parameters (Vogelaar et al.,

2003), Monod function has been modified into a two-term form as follows:

AS = PmakinesoSX meso PinakthermoSg thermo

+ S K„ h„,„0 + S

100

(5.7)

The transformation of quadratic functions in Equations (3) and (4) is designed for not

only directly assessing optimal temperature and pH, but decreasing the numbers of

parameters. As a result, the updated correlation factors for temperature and pH are

shown in Equation (5) and (6):

fCH = : 5- (7L - Tf +1 (Topl~T0i)2 Kop' (5.5)

Topt > T0,

f ( pH) = — T ( ( P H)OOI +1 (CpH) o p , - {pH\ A f K K P (5.6)

(PH) 0 P , > (pH\ ,

where Topl and pHopt are the optimal temperature and pH values corresponding to

a maximum reaction rate for microorganisms (/(7^,) = 1, /{{pH)opt) = 1); T0, and

(pH)0 l are the inhibition values of temperature and pH in the quadratic function

when f(T0i) = 0.1 , f((pH)0 l) = 0.1 . The constraints Topl > Tot and

(pH)opl > (pH)o, ensure that the inhibition value is in the left side of the quadratic

functions. Because there is no clear-cut value with which to judge whether

microorganisms have lost their activity, a correction factor equal to 0.1 is considered

as the lower bound of microorganism activity in the model. It means the activity of

microorganisms can be neglected if environmental conditions are beyond this range.

In addition to the aforementioned considerations, since mesophilic and hemophilic

microorganisms might have different optimal kinetics parameters (Vogelaar et al.,

2003), Monod function has been modified into a two-term form as follows:

Mwaj^mesomeso ^majLJhermo^^thermo /r -yx

K,,m,so + S Ks.,hermo + S

100

where AS indicate daily organic matter degradation rate; Al.,. , pm...therm. ,

and Ichermo are the maximum specific degradation rate and the

half-saturation coefficient for mesophilic and thermophilic microorganisms

respectively. It is worth mentioning that the modification of the Monod function is

based on the assumption that both mesophiles and thermophiles utilize the same

substrates in a composting system, so that the two terms in Equation (7) share the

same parameter S, which represents the substrate concentration.

By integrating the transformed correction factors of temperature and pH with the

composting kinetics models, a two-term modified Monod function is obtained and can

be used to consider the combined effects of different temperatures and pH conditions

on thermophilic and mesophilic microorganisms, as shown in Equation (8):

p

SO x

mm, 0.9 As _ ,MeS SX

(T — T)2 +1)x Ks,meso S )2 " 1'mej°

0.9 (— ff P l Oopt,meso —03HW +1)-i-

kkpliu iopt,meso — (P H )0.1,mesol

Pinax,therinoSX thermo 0.9 ( T optAtenno — TY +10 X

lch enno + S ( T opt,thenno —70.1,thermo)

0.9 E., .1 OPH)opt,thermo — (PH))` +

1opt,thernto (P H )0.1,thermo)

(5.8)

where T wn,meso TO.1,meso s (P H )opr,meso (P 1 00.1,meso (P H )opt,meso 9 T opi,thermo 7 0.1,thenno

(pH) op, ,thermo and ( pH) 0.1,thenno are optimal and the lowest allowed temperature and

pH values for mesophilic and thermophilic microorganisms.

101

where AS" indicate daily organic matter degradation rate; /u^ , H^4hermo ,

K, mao ^-sjhermo arc maximum specific degradation rate and the

half-saturation coefficient for mesophilic and thermophilic microorganisms

respectively. It is worth mentioning that the modification of the Monod function is

based on the assumption that both mesophiles and thermophiles utilize the same

substrates in a composting system, so that the two terms in Equation (7) share the

same parameter S, which represents the substrate concentration.

By integrating the transformed correction factors of temperature and pH with the

composting kinetics models, a two-term modified Monod function is obtained and can

be used to consider the combined effects of different temperatures and pH conditions

on thermophilic and mesophilic microorganisms, as shown in Equation (8):

A o _ ^wx,meao^^meso .,/• 0.9 {rf, ^ ^ — 7?lJ0P,,mes0~J ) +l)X

V opt,meso OA,meso *

("TTIK 7Si - W)2 +1>+ ( ( P H)op,.n,e,o 0.1,m„0) (5.8)

mixjhermo^^thermo r 0.9 /m + llx tr n ^ /rp rjrt -y2 ^ OptjkermO ' ^

sjhermo v optjhermo OAjhermo'

+') \\pti Jopf^ermo \P** /QAjhermo)

WllCTC ^opt,meso ' ^0.1 ,meso ' (PH)* ,meso ' *opt,meso ' ^optjhermo ' ^0.1 Jhermo 1

(PH)op,Aermo and G^Oo..,*—.«* optimal and the lowest allowed temperature and

pH values for mesophilic and thermophilic microorganisms.

101

5.1.2 Non-Linear Regression

In previous studies, the Lineweaver—Burk linear equation has been widely utilized to

linearize the single-term Monod equation (Lineweaver and Burk, 1934).

Unfortunately, linear transformations of a non-linear modeling equation alter the

errors and are always considered to have a low accuracy. Furthermore, linearization is

quite infeasible for some modified Monod equations or when several correction

factors exist (L. H. Smith et al., 1998). In order to achieve a fitting accuracy, the

Solver add-in function in Microsoft Excel was used in this non-linear data fitting

process. The model parameters were successfully estimated via trial and error analysis

to minimize the sum of the squared errors (SSE) in Equation (9):

SSE = E (Kb, —ntred )2 1=1

(5.9)

where k'b' is an observed value of parameter n in the ith observation, and nfred is

the predicted value of parameter n by the model corresponding to ith observation.

The best estimation of coefficients in Equation (8) was determined based on the

observed values for S , X „,no Xth o (pH) and T, together with the initial

experienced values located in the literature (Xi et al., 2005).

5.2 State-Variable Profiles

Figure 5.1 and Figure 5.2 show the temperature and pH profiles in the six runs.

According to these figures, all pairs of parallel samples shared the same trend in pH

and temperature profiles, which indicated our experiment was conducted under

102

5.1.2 Non-Linear Regression

In previous studies, the Lineweaver-Burk linear equation has been widely utilized to

linearize the single-term Monod equation (Lineweaver and Burk, 1934).

Unfortunately, linear transformations of a non-linear modeling equation alter the

errors and are always considered to have a low accuracy. Furthermore, linearization is

quite infeasible for some modified Monod equations or when several correction

factors exist (L. H. Smith et al., 1998). In order to achieve a fitting accuracy, the

Solver add-in function in Microsoft Excel was used in this non-linear data fitting

process. The model parameters were successfully estimated via trial and error analysis

to minimize the sum of the squared errors (SSE) in Equation (9):

SSE = YJ(nf -nr"? (5.9) 1=1

where n f ' is an observed value of parameter n in the ith observation, and nfr'd is

the predicted value of parameter n by the model corresponding to ith observation.

The best estimation of coefficients in Equation (8) was determined based on the

observed values for S, Xmao, Xthermo, (pH) and T, together with the initial

experienced values located in the literature (Xi et al., 2005).

5.2 State-Variable Profiles

Figure 5.1 and Figure 5.2 show the temperature and pH profiles in the six runs.

According to these figures, all pairs of parallel samples shared the same trend in pH

and temperature profiles, which indicated our experiment was conducted under

102

well-controlled conditions. The ambient temperature was within a very narrow range

(around 22 °C) during the composting period so that the effects of ambient

temperature could be neglected. The temperature in all runs showed a typical trend:

temperature rose to around 60 °C during the first 5 days, which embarked upon the

beginning of thermophilic composting. This phase would last 15 to 20 days, at which

time tThe temperature dropped to ambient. However, there were slight differences

among the runs. It was obvious that Rims G1 and G2 could maintain a stable

temperature between 50 °C to 60 °C due to external heating. Due to the large specific

heat capacity of the added water, Runs F1 and F2 experienced a variation in a slower

temperature rising process and a later temperature decreasing phase due to high

energy consumption. The temperature of Runs Fl and F2 was the lowest before the

6th day and began to drop on the 14th day, while the cooling phase for Runs El and

E2 started on the 10th day. Furthermore, it seemed that water only affected the time in

which to reach thermophilic composting but not the efficiency of composting, which

could be reflected from similar highest temperatures among Runs El, E2, Fl and F2.

The pH of all six runs also followed a typical composting pattern: the pH dropped to

4.5 in the first stage due to produced organic acid from easily degradable matters,

which was toxic to microorganisms and inhibitive to the reaction rate; after the 5th

day, the pH began to rise because of produced ammonia when starch, proteins etc.

were decomposed accompanied by producing ammonia; during the final stage, pH

103

well-controlled conditions. The ambient temperature was within a very narrow range

(around 22 °C) during the composting period so that the effects of ambient

temperature could be neglected. The temperature in all runs showed a typical trend:

temperature rose to around 60 °C during the first 5 days, which embarked upon the

beginning of thermophilic composting. This phase would last 15 to 20 days, at which

time tThe temperature dropped to ambient. However, there were slight differences

among the runs. It was obvious that Runs G1 and G2 could maintain a stable

temperature between 50 °C to 60 °C due to external heating. Due to the large specific

heat capacity of the added water, Runs F1 and F2 experienced a variation in a slower

temperature rising process and a later temperature decreasing phase due to high

energy consumption. The temperature of Runs F1 and F2 was the lowest before the

6th day and began to drop on the 14th day, while the cooling phase for Runs El and

E2 started on the 10th day. Furthermore, it seemed that water only affected the time in

which to reach thermophilic composting but not the efficiency of composting, which

could be reflected from similar highest temperatures among Runs El, E2, F1 and F2.

The pH of all six runs also followed a typical composting pattern: the pH dropped to

4.5 in the first stage due to produced organic acid from easily degradable matters,

which was toxic to microorganisms and inhibitive to the reaction rate; after the 5th

day, the pH began to rise because of produced ammonia when starch, proteins etc.

were decomposed accompanied by producing ammonia; during the final stage, pH

103

Run E 80

60

U

40 4,) a

20

0 5 10 15 Time (day)

20 25 30

Figure 5.1a Temperature profiles for Run E during the composting process

104

80 Run E

f —O-Run E1

1 15

Time (day)

—i—

20

-Run E2 —i— 25 10 30

Figure 5.1a Temperature profiles for Run E during the composting process

104

Run F 80

60

LA 1 40 0 0., a

20

—0— Run Fl -•— Run F2

0 5 10 15 Time (day)

20 25 30

Figure 5.1b Temperature profiles of Run F during the composting process

105

Run F

Run F2 Run F1

10 15 20 Time (day)

25 30

Figure 5.1b Temperature profiles of Run F during the composting process

105

Run G

0 5 10 15 Time (day)

Figure 5.1c Temperature profiles of Run G during the composting process

106

20 25 30

Run G 80

40 -

20 -

—o— Run G1

15 20 25 30 0 5 10 Time (day)

Figure 5.1c Temperature profiles of Run G during the composting process

106

Run E 10

= a.

8

4

0 5 10 15 20 25 30 Time (day)

Figure 5.2a pH profiles for Run E during the composting process

107

Run E

RunE1

0 5 10 15 20 25 30 Time (day)

Figure 5.2a pH profiles for Run E during the composting process

107

Run F

1

—0—Run Fl -+- Run F2

0 5 10 15 Time (day)

Figure 5.2b pH profiles for Run E during the composting process

108

20 25 30

Run F

-O—Run F1 Run F2

30 10 15 20 25 0 5 Time (day)

Figure 5.2b pH profiles for Run E during the composting process

108

Run G

0 5 10 15 Time (day)

20 25

Figure 5.2c pH profiles of Run G during the composting process

109

30

Run G 10

8

X o.

6

RunG2 -0-RunG1

4 0 5 10 15 20 25 30

Time (day)

Figure 5.2c pH profiles of Run G during the composting process

109

was stable around 8.5, which was a sign of a successful composting. For Runs El, E2,

Fl and F2, the change in pH from acidic to an alkaline environment coincided with

the change from mesophilic to thermophilic stage. This phenomenon was also

observed elsewhere (Beck-Friis et al., 2001). However, the pH of Runs Fl and F2,

was slightly higher on the Day 2 than that of Runs El and E2, which was due to the

added water served as a solvent to dilute organic acid. The pH of Runs Cl and C2

began to rise on the Day 8, which suggested a prolonged lag phase could occur based

on a condition of combined high temperature and low pH.

5.3 Model Calibration

Table 5.1 summarizes the values of the kinetics coefficients with respect to

mesophiles and thermophiles, as well as their 95% confidence intervals. Solutions i to

vi are best-fitted parameters obtained from six-time cross-validations. Solution vii,

which is treated as the fmal result of coefficients in this model, is the weighted mean

of the six result groups based on the sample size. In Solution vii, the maximum

specific growth rate for mesophilic and thermophilic microorganisms are 13.47 and

8.46 day, respectively. A higher specific growth rate implies that thermophilic

microorganisms have higher degradation ability when environmental conditions are

all optimized. This conclusion is also coherent with the fact that thermophilic

microorganisms are responsible for a large portion of degradation in a composting

system (Yu and Huang, 2009). As for half-saturation constants, Ki4„ , 3.64 is larger

than K, me o , 3.19. This result indicates inhibition effects on thermophilic

110

was stable around 8.5, which was a sign of a successful composting. For Runs El, E2,

F1 and F2, the change in pH from acidic to an alkaline environment coincided with

the change from mesophilic to thermophilic stage. This phenomenon was also

observed elsewhere (Beck-Friis et al., 2001). However, the pH of Runs F1 and F2,

was slightly higher on the Day 2 than that of Runs El and E2, which was due to the

added water served as a solvent to dilute organic acid. The pH of Runs CI and C2

began to rise on the Day 8, which suggested a prolonged lag phase could occur based

on a condition of combined high temperature and low pH.

5.3 Model Calibration

Table 5.1 summarizes the values of the kinetics coefficients with respect to

mesophiles and thermophiles, as well as their 95% confidence intervals. Solutions i to

vi are best-fitted parameters obtained from six-time cross-validations. Solution vii,

which is treated as the final result of coefficients in this model, is the weighted mean

of the six result groups based on the sample size. In Solution vii, the maximum

specific growth rate for mesophilic and thermophilic microorganisms are 13.47 and

8.46 day"1, respectively. A higher specific growth rate implies that thermophilic

microorganisms have higher degradation ability when environmental conditions are

all optimized. This conclusion is also coherent with the fact that thermophilic

microorganisms are responsible for a large portion of degradation in a composting

system (Yu and Huang, 2009). As for half-saturation constants, KiAermo, 3.64 is larger

than Ks meso ,3.19. This result indicates inhibition effects on thermophilic

110

Table 5.1 Coefficients obtained from modified and original Monod models.

Solution i to Solution vi are six sets of parameters obtained by two-term Monod

model with cross-validation method. Solution vii is averaged final result. 95%

confidence intervals of parameters in Solution vii are included also. Solution viii is

result generated by two-term Monod model without cross-validation method. Solution

ix is result derived from original Monod model.

Solution i ii iii iv v vi vii 95% CI viii ix

No. of Samp. 64 67 64 65 65 65 78 78

Pmax.theno 12 12.28 14.97 14.45 14.59 12.55 13.47 13.4713.129 13.58

9.70

7.13 9.3 8.27 8.66 7.65 8.36 8.24 8.2411.786 8.36

Ks.thermo 3.36 3.5 3.93 3.94 3.15 3.99 3.64 3.6410.836 3.99

3.36

2.5 3.01 3.89 3.61 2.84 3.32 3.19 3.1911.203 2.84

Topco,„,„. 56.99 51.59 50.58 51.05 52.4 51.17 52.28 52.2815.584 52.05

44.36

Tom. 41.65 39.77 41.69 41.66 41.61 42.11 41.41 41.4111.94 41.73

To Lthenno 16.00 15.93 17.83 17.97 18.56 17.68 17.32 173212376 17.49

19.00

To.1.0 18.25 14.61 18.3 18.26 18.12 18.42 17.64 17.6413.514 18.40

(1311)00 ammo 9.3 7.8 7.68 7.76 8.75 7.91 8.2 8.211.556 8.01

6.84

(pH),,,,. 6.46 6.38 6.49 6.48 6.24 6.56 6.43 6.4310.262 6.56

(pH)0 2.89 3.75 3.64 3.81 3.83 3.68 3.6 3.6010.834 3.56

4.08

(PH) o.1. 50 3.58 3.83 3.48 3.57 3.28 3.68 3.57 3.5710.435 3.68

SSE (104) 43 3.92 4.76 5.01 4.09 4.69 4.44 N/A 4.71 17.54

111

Table 5.1 Coefficients obtained from modified and original Monod models.

Solution i to Solution vi are six sets of parameters obtained by two-term Monod

model with cross-validation method. Solution vii is averaged final result. 95%

confidence intervals of parameters in Solution vii are included also. Solution viii is

result generated by two-term Monod model without cross-validation method. Solution

ix is result derived from original Monod model.

Solution i ii iii iv V vi vii 95% CI viii ix

No. of Samp. 64 67 64 65 65 65 \ \ 78 78

Mmax,thamo 12 12.28 14.97 14.45 14.59 12.55 13.47 13.47±3.129 13.58

9.70

Mmax,meso 7.13 9.3 8.27 8.66 7.65 8.36 8.24 8.24±1.786 8.36

^s.IhcniK) 3.36 3.5 3.93 3.94 3.15 3.99 3.64 3.64±0.836 3.99

3.36

^s.mcso 2.5 3.01 3.89 3.61 2.84 3.32 3.19 3.19±1.203 2.84

Topl.thentio 56.99 51.59 50.58 51.05 52.4 51.17 52.28 52.28±5.584 52.05

44.36

Topuneso 41.65 39.77 41.69 41.66 41.61 42.11 41.41 41.41±1.94 41.73

To.l.thenno 16.00 15.93 17.83 17.97 18.56 17.68 17.32 17J2±2.576 17.49

19.00

TO.IJIMO 18.25 14.61 18.3 18.26 18.12 18.42 17.64 17.64±3.514 18.40

(pH)opt.thenno 9.3 7.8 7.68 7.76 8.75 7.91 8.2 8.2±1.556 8.01

6.84

(pH) oprmeso 6.46 6.38 6.49 6.48 6.24 6.56 6.43 6.43*0.262 6.56

(pH)o | ttenjQ 2.89 3.75 3.64 3.81 3.83 3.68 3.6 3.60±0.834 3.56

4.08

(pH) 0.1 jncso 3.58 3.83 3.48 3.57 3.28 3.68 3.57 3.57±0.435 3.68

SSE (10"4) 4.5 3.92 4.76 5.01 4.09 4.69 4.44 N/A 4.71 17.54

I l l

microorganisms will be more severe than on mesophilic microorganisms, when the

substrate concentration is limited. Higher K, for thermophiles was also taken from

work obtained from other's (Borja et al., 1995).

Mesophiles and thermophiles reveal an obvious discrepancy of their optimized

temperature, (41.41 °C for mesophiles and 52.28 °C for thermophiles), compared with

a well-accepted mesophilic temperature (around 35 °C) and a thermophilic

temperature (around 55 °C). The results are not uncommon (Hashimoto, 1983).

However, the mesophilic temperature is a little higher than the literature stated which

may be caused by specific substrates and microorganisms in the experiments. This

deviation also reflects the importance of finding experiment-specific kinetics

parameters. The inhibitive low temperatures for both mesophilic and thermophilic

microorganisms are very close to each other, which are 17.32 °C and 17.64 °C

respectively. It was suggested if temperature in composting system falls below 17 °C,

both microorganisms are unable to grow properly. Based on the symmetrical

characteristic of quadratic function, it is easily deferred that the upper bounds of

activated temperatures for both mesophilic and thermophilic microorganisms are

65.50 °C and 87.24 °C, while in previous literature; the activity of microorganisms

will be impeded significantly if the temperature is 10 °C higher than its optimum

temperature. The inconsistent upper bounds of activated temperature are due to the

limitation of quadratic correction factors. Other expressions of correction factors with

which to solve this problem are desirable, but on the other hand, they would

112

microorganisms will be more severe than on mesophilic microorganisms, when the

substrate concentration is limited. Higher Ks for thermophiles was also taken from

work obtained from other's (Boija et al., 1995).

Mesophiles and thermophiles reveal an obvious discrepancy of their optimized

temperature, (41.41 °C for mesophiles and 52.28 °C for thermophiles), compared with

a well-accepted mesophilic temperature (around 35 °C) and a thermophilic

temperature (around 55 °C). The results are not uncommon (Hashimoto, 1983).

However, the mesophilic temperature is a little higher than the literature stated which

may be caused by specific substrates and microorganisms in the experiments. This

deviation also reflects the importance of finding experiment-specific kinetics

parameters. The inhibitive low temperatures for both mesophilic and thermophilic

microorganisms are very close to each other, which are 17.32 °C and 17.64 °C

respectively. It was suggested if temperature in composting system falls below 17 °C,

both microorganisms are unable to grow properly. Based on the symmetrical

characteristic of quadratic function, it is easily deferred that the upper bounds of

activated temperatures for both mesophilic and thermophilic microorganisms are

65.50 °C and 87.24 °C, while in previous literature; the activity of microorganisms

will be impeded significantly if the temperature is 10 °C higher than its optimum

temperature. The inconsistent upper bounds of activated temperature are due to the

limitation of quadratic correction factors. Other expressions of correction factors with

which to solve this problem are desirable, but on the other hand, they would

complicate the model by increasing the numbers of parameters.

With respect to the optimum pH for mesophiles and thermophiles, it has similar result

with temperature, which is 6.43 for mesophiles and 8.20 for thermophiles. It is

interesting to argue as to, whether thermophilic microorganisms, which are named by

their optimum temperature, have an optimum pH? It has been found that, except some

for a few acidothermophiles which live in sulfur-rich regions such as volcanoes, the

majority of thermophiles have an optimum pH in the alkaline range (Madigan et al.,

2006). This is also coherent with this study. Lower pH limits for mesophilic and

thermophilic microorganism activity are 3.57 and 3.60, respectively. Lower optimum

pH for mesophiles implies it has better adaptability to an acidic environment during

the initial stage of composting.

Advantages of cross-validation can be reflected when comparing the Sum of Squared

Error (SSE) among Solutions i to viii. Solution viii is obtained by conducting

regression using all six runs without the cross-validation process. The average SSE of

Solutions i to vi is demonstrated on Equation (10).

±SSEiSSE,,,, = 5 =5.39x10"

• (10)

The reason that average SSE is obtained from the total SSE in six runs divided by 5 is

every data has been used 5 times as validation data. The average SSE is close to the

SSE of Solution viii obtained without the cross-validation process. However,

113

complicate the model by increasing the numbers of parameters.

With respect to the optimum pH for mesophiles and thermophiles, it has similar result

with temperature, which is 6.43 for mesophiles and 8.20 for thermophiles. It is

interesting to argue as to, whether thermophilic microorganisms, which are named by

their optimum temperature, have an optimum pH? It has been found that, except some

for a few acidothermophiles which live in sulfur-rich regions such as volcanoes, the

majority of thermophiles have an optimum pH in the alkaline range (Madigan et al.,

2006). This is also coherent with this study. Lower pH limits for mesophilic and

thermophilic microorganism activity are 3.57 and 3.60, respectively. Lower optimum

pH for mesophiles implies it has better adaptability to an acidic environment during

the initial stage of composting.

Advantages of cross-validation can be reflected when comparing the Sum of Squared

Error (SSE) among Solutions i to viii. Solution viii is obtained by conducting

regression using all six runs without the cross-validation process. The average SSE of

Solutions i to vi is demonstrated on Equation (10).

ilSSE, =•& = 5.39x10-"

5 (10)

The reason that average SSE is obtained from the total SSE in six runs divided by 5 is

every data has been used 5 times as validation data. The average SSE is close to the

SSE of Solution viii obtained without the cross-validation process. However,

113

coefficients in solution vii, which is averaged from the previous six runs under

cross-validation, decrease SSE to 4.44x104 (around a 20% improvement). A

reasonable explanation would be that the real values of the kinetics parameters are in

the range formed by the previous six solutions so that the averaged parameters would

be closer to the real parameters.

5.4 Test of Model Performance

The fitting curves of the degradation rate are drawn using the obtained two-term

Monod model. Figure 5.3 show an agreement between the experimental data and the

simulation results in the degradation rate from Runs El to G2. The most obvious

feature in our experiment is the two peaks in Runs El, E2, F1 and F2, which might be

caused by the recovery of mesophilic activity when easily degradable nutrients were

depleted and the temperature dropped slightly. As for Runs G1 and G2, external

heating maintained temperatures between 50 °C to 60 °C. The second peak of the

degradation rate is not observed and the degradation rate decreased smoothly after it

reached the maximum value on Day 14.

Mesophilic and thermophlic reaction rates for six runs are clearly depicted

respectively in Figure 5.4 (from El to G2). In the beginning, mesophilic activity

accounted for a major portion of the overall reaction rate because the system's

temperature was close to the optimum mesophilic temperature. As the temperature

increased, thermophilic microorganisms were activated and played a predominant role

114

coefficients in solution vii, which is averaged from the previous six runs under

cross-validation, decrease SSE to 4.44x10"4 (around a 20% improvement). A

reasonable explanation would be that the real values of the kinetics parameters are in

the range formed by the previous six solutions so that the averaged parameters would

be closer to the real parameters.

5.4 Test of Model Performance

The fitting curves of the degradation rate are drawn using the obtained two-term

Monod model. Figure 5.3 show an agreement between the experimental data and the

simulation results in the degradation rate from Runs El to G2. The most obvious

feature in our experiment is the two peaks in Rims El, E2, F1 and F2, which might be

caused by the recovery of mesophilic activity when easily degradable nutrients were

depleted and the temperature dropped slightly. As for Runs G1 and G2, external

heating maintained temperatures between 50 °C to 60 °C. The second peak of the

degradation rate is not observed and the degradation rate decreased smoothly after it

reached the maximum value on Day 14.

Mesophilic and thermophlic reaction rates for six runs are clearly depicted

respectively in Figure 5.4 (from El to G2). In the beginning, mesophilic activity

accounted for a major portion of the overall reaction rate because the system's

temperature was close to the optimum mesophilic temperature. As the temperature

increased, thermophilic microorganisms were activated and played a predominant role

114

Run El 0.03

0 5 10 15 20 25

Time (day)

Figure 5.3a Degradation rate profiles by model prediction

and experiment measurement for Run El

115

30

Run El 0.03

Prodded Observed

« 0.02

•a

0

0 5 10 15 20 25 30

Time (day)

Figure 5.3a Degradation rate profiles by model prediction

and experiment measurement for Run El

115

Run E2 0.03

.:-.N

41 2 0.02 es L.. a 0 = se ..o at

CZ at 41 0.01

0 5 10 15 20

Time (day)

Figure 5.3b Degradation rate profiles by model prediction

and experiment measurement for Run E2

116

25

Run E2 0.03

Predicted Observed

0.02

0.01

09

J 0.02 2 a o S8 •o A

J 0.01

-r 10 15 20 25

Time (day)

Figure 5.3b Degradation rate profiles by model prediction

and experiment measurement for Run E2

116

Run Fl 0.03

—6— Predicted —NE— Observed

is' Ts 0.02

es i.. a o t= es ..es es Id

0

OA 41) 0.01

0 5 10 15 20 25 30

Time (day)

Figure 5.3c Degradation rate profiles by model prediction

and experiment measurement for Run Fl

117

Run F1 0.03

Predicted Observed

J 0.02

J; 0.01

25 0

30 10 15 20 0 5

Time (day)

Figure 5.3c Degradation rate profiles by model prediction

and experiment measurement for Run F1

117

Run F2 0.03

—40— Predicted —NE— Observed

0 5 10 15 20 Time (day)

Figure 5.3d Degradation rate profiles by model prediction

and experiment measurement for Run F2

118

25 30

Run F2 0.03

Predicted —Observed

g 0.02

2 0.01

0

0 5 10 15 20 25 30 Time (day)

Figure 5.3d Degradation rate profiles by model prediction

and experiment measurement for Run F2

118

Run G1 0.03

4",-• c, Sh es 0.02

.1

0 o = es 1 0.01 6 bs) A'

0 5 10 15 20 25

Time (day)

Figure 5.3e Degradation rate profiles by model prediction

and experiment measurement for Run G1

119

30

Run G1

Predicted hk-Observed

Time (day)

Figure 5.3e Degradation rate profiles by model prediction

and experiment measurement for Run G1

119

Run G2 0.03

—•— Predicted -4—Observed

0 5 10 15 20 25 30

Time (day)

Figure 5.3f Degradation rate profiles by model prediction

and experiment measurement for Run G2

120

Run G2 0.03

Predicted Observed

£ 0.02 -

1 0.01 OA

0 5 10 20 15 25 30

Time (day)

Figure 5.3f Degradation rate profiles by model prediction

and experiment measurement for Run G2

120

Run El

0 5 10 15 20 25

Time (day)

Figure 5.4a Temporal variations of mesophilic

and thermophilic degradation rate for Run El

121

30

Run El 0.03

Pre-thenmo

•t0.02

S0.01

oo

0

0 5 10 15 20 25 30

Time (day)

Figure 5.4a Temporal variations of mesophilic

and thermophilic degradation rate for Run El

121

Run E2

0 5 10 15

Time (day)

20

Figure 5.4b Temporal variations of mesophilic

and thermophilic degradation rate for Run E2

122

25

Run E2 0.03

Pre-thermo -o-Pre-meso

T 0.02

0

0 5 10 15 20 25

Time (day)

Figure 5.4b Temporal variations of mesophiiic

and thermophilic degradation rate for Run E2

122

Run Fl

0.03

4-. 0.02 a .a

E.

0 6- et

0 5 10 15 20 25

Time (day)

Figure 5.4c Temporal variations of mesophilic

and thermophilic degradation rate for Run Fl

30

Run F1 0.03

Pro-thermo -o-Pre-meso

"L.0.02

OA

0

15 20 25 30 0 5 10

Time (day)

Figure 5.4c Temporal variations of mesophilic

and thermophilic degradation rate for Run F1

4

123

Run F2

0 5 10 15 20 25

Time (day)

Figure 5.4d Temporal variations of mesophilic

and thermophilic degradation rate for Run F2

124

30

Run F2 0.03

Pre-thermo -o-Pre-meso

0.02 •a

30.01

0

20 25 30 10 15 0 5

Time (day)

Figure 5.4d Temporal variations of mesophilic

and thermophilic degradation rate for Run F2

124

Run G1

0 5 10 15 20 25

Time (day)

Figure 5.4e Temporal variations of mesophilic

and thermophilic degradation rate for Run G1

125

30

Run G1

0.03

Pre-thermo -o-Pre-meso

0.02 -

§0.01

20 25 30 10 15 0 5

Time (day)

Figure 5.4e Temporal variations of mesophilic

and thermophilic degradation rate for Run G1

125

Run G2

0 5 10 15 20 25

Time (day)

Figure 5.4f Temporal variations of mesophilic

and thermophilic degradation rate for Run G2

126

30

Run G2 0.03

Pi»4henno -o-Pre-meso

£0.02

€ 0.01

0

10 20 25 30 0 5 15

Time (day)

Figure 5.4f Temporal variations of mesophilic

and thermophilic degradation rate for Run G2

126

in the degradation in the thermophilic stage. After Day 15, mesophilic activity was

significantly prompted from 0.05 day' to above 0.1 day', again in Runs El to F2. On

the contrary, this phenomenon was not observed in Runs G1 and G2 under heating

conditions and the mesophilic rate was kept around 0.005 day'. All the analyses

demonstrate the significance of adopting the two-term Monod equation to depict

mesophilic and thermophilic activity respectively.

Table 5.2 records the coefficient of determination for all

parameters from Solution vii (R2). In non-linear regression,

original definition in Equation (11):

Ecy, so2 -- sS m.• 1— -- r =1 '

SS wi E (Y

where y, is the observed value under ith observation,

six runs based on the

R2 is calculated by its

(5.11)

is the mean observed data

and 5,, is the fitted value corresponding to y, . R2 varied from 0.66 to 0.87 in the six

runs, which implies the model is fitted well with the observed data.

Figure 5.5 is normal probability plots for residues for the six runs (from El to G2). In

These figures, basically all the plots form a straight line, except for one extreme low

value in Runs El and E2 individually. This phenomenon reveals good applicability of

the proposed model, although some plots deviate slightly from the straight line

(Montgomery, 2001). In Run GI, the distribution of residues is light tailed when

compared with normal distribution, which implied that residues are concise to their

127

in the degradation in the thermophilic stage. After Day 15, mesophilic activity was

significantly prompted from 0.05 day"1 to above 0.1 day"1, again in Runs El to F2. On

the contrary, this phenomenon was not observed in Runs G1 and G2 under heating

conditions and the mesophilic rate was kept around 0.005 day"1. All the analyses

demonstrate the significance of adopting the two-term Monod equation to depict

mesophilic and thermophilic activity respectively.

Table 5.2 records the coefficient of determination for all six runs based on the

parameters from Solution vii (R2). In non-linear regression, R2 is calculated by its

original definition in Equation (11):

w ZO'.-j,)2

= (5.11) ss„ ICk,-*)2

i

where y, is the observed value under ith observation, y, is the mean observed data

and y, is the fitted value corresponding to y,. R2 varied from 0.66 to 0.87 in the six

runs, which implies the model is fitted well with the observed data.

Figure 5.5 is normal probability plots for residues for the six runs (from El to G2). In

These figures, basically all the plots form a straight line, except for one extreme low

value in Runs El and E2 individually. This phenomenon reveals good applicability of

the proposed model, although some plots deviate slightly from the straight line

(Montgomery, 2001). In Run Gl, the distribution of residues is light tailed when

compared with normal distribution, which implied that residues are concise to their

127

Table 5.2 Coefficients of determination values for six runs using cross-validation results.

Solution vii Run Al Run A2 Run B1 Run B2 Run Cl Run C2

R2 0.87 0.66 0.86 0.84 0.68 0.79

128

Table 5.2 CoeflBcients of determination values for six runs using cross-validation results.

Solution vii RunAl Run A2 RunBl RunB2 Run CI RunC2

R2 0.87 0.66 0.86 0.84 0.68 0.79

128

Normal order statistic medians

Figure 5.5a Normal probability plot of residues

in modified Monod equation (Run El)

129

0.01

a 2 8 o.oo

£ "2 o

-0.01

0.00 0.20 0.40 0.60

Normal order statistic medians

0.80 1.00

Figure 5.5a Normal probability plot of residues

in modified Monod equation (Run El)

129

0.01

-0.01

(E2)

•

•

• • •

0.00 0.20 0.40 0.60 0.80

Normal order statistic medians

Figure 5.5b Normal probability plot of residues

in modified Monod equation (Run E2)

130

1.00

0.01

L i L. 4> "E o

-0.01 0.00 0.20 0.40 0.60 0.80

Normal order statistic medians

1.00

Figure 5.5b Normal probability plot of residues

in modified Monod equation (Run E2)

130

0.20 0.40 0.60 0.80

Normal order statistic medians

Figure 5.5c Normal probability plot of residues

in modified Monod equation (Run Fl)

131

1.00 0.00 0.20 0.40 0.60 0.80

Normal order statistic medians

1.00

Figure 5.5c Normal probability plot of residues

in modified Monod equation (Run Fl)

131

0.01

o.00

0

-0.01

On)

* * * * #

0.00 0.20 0.40 0.60 0.80

Normal order statistic medians

Figure 5.5d Normal probability plot of residues

in modified Monod equation (Run F2)

132

1.00 0.00 0.20 0.40 0.60 0.80

Normal order statistic medians

1.00

Figure 5.5d Normal probability plot of residues

in modified Monod equation (Run F2)

132

0.01

I 0.00 in

-0.01 0.00

(G1)

• * • * $ •

• *

•

•

0.20 0.40 0.60 0.80 Normal order statistic medians

Figure 5.5e Normal probability plot of residues

in modified Monod equation (Run G1)

133

1.00 0.00 0.20 0.40 0.60 0.80 Normal order statistic medians

Figure 5.5e Normal probability plot of residues

in modified Monod equation (Run Gl)

1.00

133

0.01 (G2)

0

-0.01 0.00 0.20 0.40 0.60 0.80

Normal order statistic medians

Figure 5.5f Normal probability plot of residues

in modified Monod equation (Run G2)

134

1.00

0.01

4> 3

'O

i

i fc. •H

o.oo -

-0.01 0.00 0.20 0.40 0.60 0.80 1.00

Normal order statistic medians

Figure 5.5f Normal probability plot of residues

in modified Monod equation (Run G2)

134

mean value with a relatively small variance. In Run F2, the distribution of residues

skews to the right when compared with normal distributions, which means the left

side of the distribution is light tailed while the right side is heavy tailed.

Figure 5.6 shows the relationships between the measured and observed degradation

rates for the six runs (from El to G2). Clearly, this two-term Monod equation can

predict the degradation rate quite successfully.

5.5 Discussion

5.5.1 Comparison with Original Monod Function

A comparison between the original and the modified Monod models' predictive

capacity can be found in Table 3.4. Solution ix contains kinetics parameters and

correction factors based on the original one-term Monod function. When compared

with solution vii without the cross-validation procedure, it is reasonable that •usurer

T optsingk and (PH)opiaingie fall between the interval of those parameters for

thermophilic and mesophilic. This is due to the one-term Monod function which only

accounts for the integrated effects of both types of microorganisms. The SSE from the

one-term Monod function (0.00178) is significantly larger than the value from

solution viii (0.00047) according to the modified two-term function proposed in this

paper. Simulated results from the original one-term Monod function are compared

with the observed degradation rate profiles for the six runs in Figure 5.7. The

135

mean value with a relatively small variance. In Run F2, the distribution of residues

skews to the right when compared with normal distributions, which means the left

side of the distribution is light tailed while the right side is heavy tailed.

Figure 5.6 shows the relationships between the measured and observed degradation

rates for the six runs (from El to G2). Clearly, this two-term Monod equation can

predict the degradation rate quite successfully.

5.5 Discussion

5.5.1 Comparison with Original Monod Function

A comparison between the original and the modified Monod models' predictive

capacity can be found in Table 3.4. Solution ix contains kinetics parameters and

correction factors based on the original one-term Monod function. When compared

with solution vii without the cross-validation procedure, it is reasonable that M)ingie >

Topumgie an<* (pH)op,Mngie foU between the interval of those parameters for

thermophilic and mesophilic. This is due to the one-term Monod function which only

accounts for the integrated effects of both types of microorganisms. The SSE from the

one-term Monod function (0.00178) is significantly larger than the value from

solution viii (0.00047) according to the modified two-term function proposed in this

paper. Simulated results from the original one-term Monod function are compared

with the observed degradation rate profiles for the six runs in Figure 5.7. The

135

0.03

X

0.01 0.02

Observed degradation rate (day-1)

Figure 5.6a Comparison between predicted

and observed degradation rate (Run E1)

136

0.03

0.03

X X

X. X

X

0.00 0.01 0.02

Observed degradation rate (day*1) 0.03

Figure 5.6a Comparison between predicted

and observed degradation rate (Run El)

136

0.03 (E2)

x 0.00 X

0.00

X

X

X

X

0.01 0.02 0.03

Observed degradation rate (day')

Figure 5.6b Comparison between predicted

and observed degradation rate (Run E2)

137

0.03 (E2)

•o

« 0.02 -

0.01

0.00 0.02 0.03 0.00 0.01

Observed degradation rate (day1)

Figure 5.6b Comparison between predicted

and observed degradation rate (Run E2)

137

Observed degradation rate (day')

Figure 5.6c Comparison between predicted

and observed degradation rate (Run Fl)

138

0.03 (Fl)

0.01

XX

0.00 0.00 0.01 0.02

Observed degradation rate (day1)

0.03

Figure 5.6c Comparison between predicted

and observed degradation rate (Run Fl)

138

0.03

0.00 0.00 0.01 0.02

Observed degradation rate (day-1)

Figure 5.6d Comparison between predicted

and observed degradation rate (Run F2)

139

0.03

0.03

0.00 0.00 0.01 0.02

Observed degradation rate (day1)

Figure 5.6d Comparison between predicted

and observed degradation rate (Run F2)

0.03

139

Observed degradation rate (day-1)

Figure 5.6e Comparison between predicted

and observed degradation rate (Run G1)

140

0.00 *-*-0.00 0.01 0.02

Observed degradation rate (day1)

0.03

Figure 5.6e Comparison between predicted

and observed degradation rate (Run Gl)

140

Pre

dict

ed d

egra

dati

on r

ate

(day

s)

0.03

0.02

0.01

0.01 0.02

Observed degradation rate (day')

Figure 5.6f Comparison between predicted

and observed degradation rate (Run G2)

141

0.03 0.00 0.01 0.02 0.03

Observed degradation rate (day1)

Figure 5.6f Comparison between predicted

and observed degradation rate (Run G2)

141

Run El

0.03

,-, -",0.02 41

4... 0 1. 0 0

41) 0.01 es I.

A

0 5 10 15 20 Time (day)

25

Figure 5.7a Degradation rate profiles by original Monod model

prediction and experiment measurement for Run El

142

30

Run £1 0.03

Predicted original

Observed

1*0.02

30.01

0

0 5 10 15 20 25 30 Time (day)

Figure 5.7a Degradation rate profiles by original Monod model

prediction and experiment measurement for Run El

142

Run E2

0 5 10 15 20

Time (day)

Figure 5.7b Degradation rate profiles by original Monod model

prediction and experiment measurement for Run E2

143

25

Run E2 0.03

Pretfctad original

Observed

0.02

0

20 0 5 10 15 25

Time (day)

Figure 5.7b Degradation rate profiles by original Monod model

prediction and experiment measurement for Run E2

143

Run Fl

Time (day)

Figure 5.7c Degradation rate profiles by original Monod model

prediction and experiment measurement for Run Fl

144

Run F1 0.03

Predicted original

Observed

a 0.02

5 0.01 u ©JO

25 0

30 0 5 10 15 20

Time (day)

Figure 5.7c Degradation rate profiles by original Monod model

prediction and experiment measurement for Run F1

144

Run F2

0.03

0 5 10 15 20 25

Time (day)

Figure 5.7d Degradation rate profiles by original Monod model

prediction and experiment measurement for Run F2

145

30

Run F2

Predicted cxiginal

Time (day)

Figure 5.7d Degradation rate profiles by original Monod model

prediction and experiment measurement for Run F2

145

Run G1

0 5 10 15 20 25

Time (day)

Figure 5.7e Degradation rate profiles by original Monod model

prediction and experiment measurement for Run GI

146

30

Run G1 0.03

Predicted original

Observed

2 0.02

5 0.01 eo

0

0 5 10 15 20 25 30

Time (day)

Figure 5.7e Degradation rate profiles by original Monod model

prediction and experiment measurement for Run G1

146

Run G2

0 5 10 15 20 25

Time (day)

Figure 5.7f Degradation rate profiles by original Monod model

prediction and experiment measurement for Run G2

147

30

Run G2 0.03

-•-Predicted original

Observed

2 0.02

3 0.01

0

30 10 15 20 25 0 5

Time (day)

Figure 5.7f Degradation rate profiles by original Monod model

prediction and experiment measurement for Run G2

147

simulation data deviates from the observed value significantly. Normally, the profile

with two peaks cannot be obtained, which is due to the limitation of original the

Monod function. Thus, it can be concluded the modified two-term Monod equation is

preferred over the original Monod model to simulate the composting system because

it is not only better fitted with observed data but is also able to provide maximum

degradation rate, an optimum temperature and pH for mesophilic and thermophilic

microorganisms, separately.

5.5.2 Environmental Factors of Microorganism Growth

Sm1rs pointed out microbial respiration in composting reactors was seriously

inhibited if the temperature increased while the substrate was still acidic (Snuirs et al.,

2002). But few studied have been conducted to quantitatively investigate the

interaction between pH and temperature. The combined effects of pH and temperature

can be analyzed using a product of two correlation factors under the premise that the

effects of temperature and pH are independent with each other. For example, two

typical environment conditions at the initial stage when organic acid is produced are

considered herein: i), lower pH with higher temperature (pH=5, temperature=50 °C)

and ii), and higher pH with lower temperature (pH=6, temperature=40 °C). The

multiplied mesophilic and thermophilic correction factors under two conditions are:

0.68 and 0.56 under condition i and 0.98 and 0.71 under condition ii. It is obvious that

both types of microorganisms are inhibited under condition i. According to condition

148

simulation data deviates from the observed value significantly. Normally, the profile

with two peaks cannot be obtained, which is due to the limitation of original the

Monod function. Thus, it can be concluded the modified two-term Monod equation is

preferred over the original Monod model to simulate the composting system because

it is not only better fitted with observed data but is also able to provide maximum

degradation rate, an optimum temperature and pH for mesophilic and thermophilic

microorganisms, separately.

5.5.2 Environmental Factors of Microorganism Growth

Sm&rs pointed out microbial respiration in composting reactors was seriously

inhibited if the temperature increased while the substrate was still acidic (Sm&rs et al.,

2002). But few studied have been conducted to quantitatively investigate the

interaction between pH and temperature. The combined effects of pH and temperature

can be analyzed using a product of two correlation factors under the premise that the

effects of temperature and pH are independent with each other. For example, two

typical environment conditions at the initial stage when organic acid is produced are

considered herein: i), lower pH with higher temperature (pH=5, temperature=50 °C)

and ii), and higher pH with lower temperature (pH=6, temperature=40 °C). The

multiplied mesophilic and thermophilic correction factors under two conditions are:

0.68 and 0.56 under condition i and 0.98 and 0.71 under condition ii. It is obvious that

both types of microorganisms are inhibited under condition i. According to condition

148

ii to condition i, mesophilic activity decreased by 31% but thermophilic activity only

dropped by 21%. Because of the low activity for mesophiles, the time it takes the

composting system to shift to the thermophilic stage is delayed, which is called the

"lag phase". It can be concluded that sufficient ventilation for cooling is critical to in

order to avoid the lag phase when organic acid is produced in the initial stage (Smirs

et al., 2002). On the other hand, thermophilic correction factors for temperature and

pH are: 0.89 and 0.79 under condition i along with 0.99 and 0.56 under condition ii.

This proves that thermophiles are more sensitive to the change in pH than to a change

in temperature. In other words, inhibition of low pH to thermophiles would be

dominant even though the temperature is close to its optimum range. All the results

above can be used to test the following hypothesis: microorganisms can only

withstand one extreme environmental factor, high temperature or low pH, but not both

simultaneously (Deacon, 1997). Last but not the least, it must be realized that

estimations of correction factors and their combinations are not very precise because:

1) interactions of pH, temperature and other factors exist in composting systems in

order to affect the performance of two types of microorganisms; 2) the quadratic form

is a rough approximation to evaluate the effects of temperature and pH.

5.6 Summary

In this section, a two-term modified Monod model has been proposed to

quantitatively depict the effects of temperature and pH on microorganism growth

simultaneously during the composting process. Six runs of food waste composting

149

ii to condition i, mesophilic activity decreased by 31% but thermophilic activity only

dropped by 21%. Because of the low activity for mesophiles, the time it takes the

composting system to shift to the thermophilic stage is delayed, which is called the

"lag phase". It can be concluded that sufficient ventilation for cooling is critical to in

order to avoid the lag phase when organic acid is produced in the initial stage (Sm&rs

et al., 2002). On the other hand, thermophilic correction factors for temperature and

pH are: 0.89 and 0.79 under condition i along with 0.99 and 0.56 under condition ii.

This proves that thermophiles are more sensitive to the change in pH than to a change

in temperature. In other words, inhibition of low pH to thermophiles would be

dominant even though the temperature is close to its optimum range. All the results

above can be used to test the following hypothesis: microorganisms can only

withstand one extreme environmental factor, high temperature or low pH, but not both

simultaneously (Deacon, 1997). Last but not the least, it must be realized that

estimations of correction factors and their combinations are not very precise because:

1) interactions of pH, temperature and other factors exist in composting systems in

order to affect the performance of two types of microorganisms; 2) the quadratic form

is a rough approximation to evaluate the effects of temperature and pH.

5.6 Summary

In this section, a two-term modified Monod model has been proposed to

quantitatively depict the effects of temperature and pH on microorganism growth

simultaneously during the composting process. Six runs of food waste composting

149

reaction through bench-scale reactors in a laboratory were constructed to demonstrate

the performance of the proposed model. The effects of the addition of water and

external heating were considered. The observed data has been used to validate the

proposed model by employing nonlinear regression method with cross-validation to

reduce errors. The results indicate coefficients of determination in the six runs vary

from 0.65-0.87, which reflects a good performance of the proposed model. When

compared with the original Monod function, the modified function exhibits its

superiority in both a smaller SSE value and more information being provided

regarding mesophiles and thermophiles, respectively. The optimum temperature for

mesophiles and thermophiles, in this study, is 41 °C and 52 °C, and the optimum pH is

6.2 and 8.2 for mesophiles and thermophiles, respectively, which are close to

well-accepted mesophilic temperature (around 35 °C) and thennophilic temperature

(around 55 °C).

150

reaction through bench-scale reactors in a laboratory were constructed to demonstrate

the performance of the proposed model. The effects of the addition of water and

external heating were considered. The observed data has been used to validate the

proposed model by employing nonlinear regression method with cross-validation to

reduce errors. The results indicate coefficients of determination in the six runs vary

from 0.65-0.87, which reflects a good performance of the proposed model. When

compared with the original Monod function, the modified function exhibits its

superiority in both a smaller SSE value and more information being provided

regarding mesophiles and thermophiles, respectively. The optimum temperature for

mesophiles and thermophiles, in this study, is 41 °C and 52 °C, and the optimum pH is

6.2 and 8.2 for mesophiles and thermophiles, respectively, which are close to

well-accepted mesophilic temperature (around 35 °C) and thermophilic temperature

(around 55 °C).

150

CHAPTER 6

CONCLUSIONS

6.1 Summary

In this thesis research, ten runs of bench-scale in-vessel food-waste composting

experiments were operated with the objective of investigating the effects of

buffer salts, temperature and pH on composting process. Meanwhile, a

modified two-term Monod equation was proposed to quantify the growth of

mesophiles and thermophiles simultaneously.

The first part of this research was to compare the effects of three types of buffer

salts (K2HPO4/MgSO4, KH2PO4/MgSO4 and NaAc) with different initial pH.

Temperature, pH, percentage of organic matter degradation, 0 2 uptake and NH3

emission were examined in this study. The results indicated that alkaline

additives (K2HPO4/MgSO4 and NaAc) could alleviate the negative influence of

organic acids in the initial stage, but would emit more ammonia, in

comparision, acidic amendment (KH2PO4/MgSO4) could hold more ammonia

in the system but would inhibit microorganism activity. Moreover, produced

Mg2+ and P043" could form struvite to prevent the ammonia from releasing.

Thus, K2HPO4/MgSO4 and proved to be the best buffer agent because it could

transfer more organic nitrogen into an inorganic form so as to retain more

nitrogen in the system.

151

CHAPTER 6

CONCLUSIONS

6.1 Summary

In this thesis research, ten runs of bench-scale in-vessel food-waste composting

experiments were operated with the objective of investigating the effects of

buffer salts, temperature and pH on composting process. Meanwhile, a

modified two-term Monod equation was proposed to quantify the growth of

mesophiles and thermophiles simultaneously.

The first part of this research was to compare the effects of three types of buffer

salts (K2HP04/MgS04, KH2P04/MgS04 and NaAc) with different initial pH.

Temperature, pH, percentage of organic matter degradation, 02 uptake and NH3

emission were examined in this study. The results indicated that alkaline

additives (K2HP04/MgS04 and NaAc) could alleviate the negative influence of

organic acids in the initial stage, but would emit more ammonia, in

comparision, acidic amendment (KH2P04/MgS04) could hold more ammonia

in the system but would inhibit microorganism activity. Moreover, produced

Mg2+ and PO43" could form struvite to prevent the ammonia from releasing.

Thus, K2HP04/MgS04 and proved to be the best buffer agent because it could

transfer more organic nitrogen into an inorganic form so as to retain more

nitrogen in the system.

151

The second part considered modification of the original Monod equation. A

modified two-term Monod equation was proposed to analyze the growth of

mesophiles and thermophiles, simultaneously. The effects of temperature and

pH were also taken into consideration. Unlike the original Monod equation

which provides an overall degradation rate and the half-saturation constant, the

new model could predict the parameters for both mesophiles and thermophiles.

The simulation results fitted well with the observed data, with coefficients of

determination being around 0.7. Thermophiles had a higher maximum

degradation rate (13.47 day"') than mesophiles, which implied thermophiles

played a major role during the investigated composting experiments. The

optimum temperature for mesophiles and thennophiles was 41 °C and 52 °C, and the

optimum pH was 6.2 and 8.2 for mesophiles and thermophiles respectively. Both

types of microorganisms would be inhibited under a low-pH, high-temperature

environment. However, thermophiles were more sensitive in the pH change, which

caused the lag phase during the initial stage of composting.

6.2 Contributions

This thesis provides experimental and modeling analyses regarding the effects

of buffer salts, temperature and pH on food waste composting processes. The

study produces the following achievements from the engineering perspective:

152

The second part considered modification of the original Monod equation. A

modified two-term Monod equation was proposed to analyze the growth of

mesophiles and thermophiles, simultaneously. The effects of temperature and

pH were also taken into consideration. Unlike the original Monod equation

which provides an overall degradation rate and the half-saturation constant, the

new model could predict the parameters for both mesophiles and thermophiles.

The simulation results fitted well with the observed data, with coefficients of

determination being around 0.7. Thermophiles had a higher maximum

degradation rate (13.47 day"1) than mesophiles, which implied thermophiles

played a major role during the investigated composting experiments. The

optimum temperature for mesophiles and thermophiles was 41 °C and 52 °C, and the

optimum pH was 6.2 and 8.2 for mesophiles and thermophiles respectively. Both

types of microorganisms would be inhibited under a low-pH, high-temperature

environment. However, thermophiles were more sensitive in the pH change, which

caused the lag phase during the initial stage of composting.

6.2 Contributions

This thesis provides experimental and modeling analyses regarding the effects

of buffer salts, temperature and pH on food waste composting processes. The

study produces the following achievements from the engineering perspective:

152

(i) Interactions between organic production in the initial stage and ammonia

release in the alkaline stage were investigated under different pH

conditions. A comprehensive understanding is helpful in selecting

pH-control amendments.

(ii) Three buffer salts with different initial pH were compared. K2HPO4/MgSO4

was selected as the best buffer salt based on an examination of nitrogen

transformation in the system.

(iii)The original Monod equation was modified into a two-term form, which

took into consideration the activity of mesophiles and thermophiles,

separately. Nonlinear regression and cross-validation were used to find

accurate parameters.

(iv) The effects of temperature and pH in mesophiles and thermophiles were

studied. The reasoning behind the lag phase was established based on the

model results.

The results of this research will benefit composting industry, end-users and

academic researchers in various aspects: the optimum temperature and pH for

mesophiles and thermophiles obtained in Chapter 5 will facilitate the process

control in composting plant; buffer salts have their potential to be employed in

153

(i) Interactions between organic production in the initial stage and ammonia

release in the alkaline stage were investigated under different pH

conditions. A comprehensive understanding is helpful in selecting

pH-control amendments.

(ii) Three buffer salts with different initial pH were compared. K2HP04/MgS04

was selected as the best buffer salt based on an examination of nitrogen

transformation in the system.

(iii)The original Monod equation was modified into a two-term form, which

took into consideration the activity of mesophiles and thermophiles,

separately. Nonlinear regression and cross-validation were used to find

accurate parameters.

(iv)The effects of temperature and pH in mesophiles and thermophiles were

studied. The reasoning behind the lag phase was established based on the

model results.

The results of this research will benefit composting industry, end-users and

academic researchers in various aspects: the optimum temperature and pH for

mesophiles and thermophiles obtained in Chapter 5 will facilitate the process

control in composting plant; buffer salts have their potential to be employed in

153

industry; the substrate ratio and the experiment set-up will guide the people

who want to conduct composting in their gardens or yards; the data obtained in

this study is also a precious reference for other researchers who focus on

composting.

63 Recommendations for Future Studies

(i) In this study, the population of microorganisms was examined via the

colony counting method, which mainly focused upon bacteria. It is desired

that advanced equipment can be used to detect not only the amount of

bacteria, but also the amounts of fungi and actinomyces in an accurate and

straightforward manner. In that case, a more comprehensive analysis of

composting microbiology can be provided, which in turn could benefit both

experimental and modeling work.

(ii) Three types of buffer agents are investigated in this study. Alkaline buffer

salts were proven to function but with less efficiency. In future studies,

different types of buffer agents (e.g. organic acids) can be considered. The

manipulation of the C/N ratio may also be a feasible way to avoid organic

acids production and ammonia emission.

(iii) In the developed kinetics model, temperature and pH are considered as two

state variables. It is desired that more factors such as moisture content and

154

industry; the substrate ratio and the experiment set-up will guide the people

who want to conduct composting in their gardens or yards; the data obtained in

this study is also a precious reference for other researchers who focus on

composting.

6.3 Recommendations for Future Studies

(i) In this study, the population of microorganisms was examined via the

colony counting method, which mainly focused upon bacteria. It is desired

that advanced equipment can be used to detect not only the amount of

bacteria, but also the amounts of fungi and actinomyces in an accurate and

straightforward manner. In that case, a more comprehensive analysis of

composting microbiology can be provided, which in turn could benefit both

experimental and modeling work.

(ii) Three types of buffer agents are investigated in this study. Alkaline buffer

salts were proven to function but with less efficiency. In future studies,

different types of buffer agents (e.g. organic acids) can be considered. The

manipulation of the C/N ratio may also be a feasible way to avoid organic

acids production and ammonia emission.

(iii) In the developed kinetics model, temperature and pH are considered as two

state variables. It is desired that more factors such as moisture content and

154

C/N ratio be involved to create a more applicable model. The

multi-dimensional interactions among these parameters could be

investigated. The empirical expression for the correction coefficients in the

model could be improved through additional experimental work, which

could bring about a more accurate prediction model.

155

C/N ratio be involved to create a more applicable model. The

multi-dimensional interactions among these parameters could be

investigated. The empirical expression for the correction coefficients in the

model could be improved through additional experimental work, which

could bring about a more accurate prediction model.

155

REFERENCE

Abu Qdais H. A., and Hamoda M. F. (2004) Enhancement of carbon and nitrogen

transformations during composting of municipal solid waste. Journal of

Environmental Science and Health Part A-Toxic/Hazardous substance &

environmental engineering, A39(2), 409-420.

Albert J. E., Hernandez M., and Soto Y. (2002) Process and structural upgrades -

improving a state-of-the-art composting facility. Biocycle, 43(5), 40-42.

An K. (2006). Amendment for pH control in food-waste composting. University of

Regina,

Antler S. (2000). Composting Grows Stronger.

Atlas R. M., and Bartha R. (1998). Microbial Ecology: fundamentals and applications:

Menlo Park,.

Bach D. B., Shoda M., and Kubota H. (1985) Composting reaction rate of sewage

sludge in an autothermal packed bed reactor. Journal of fermentation

technolog, 63(3), 271-278.

Baker J. M., and Allmaras R. R. (1990) System for automating and multiplexing soil

moisture measurement by time-domain reflectometry. Soil science society of

America journal, 54, 1-6.

Bakshi M., Gupta V. K., and Langar P. N. (1987) Effect of moisture level on the

chemical composition and nutritive value of fermented straw. Biological wasts,

21, 283-290.

156

REFERENCE

Abu Qdais H. A., and Hamoda M. F. (2004) Enhancement of carbon and nitrogen

transformations during composting of municipal solid waste. Journal of

Environmental Science and Health Part A-Toxic/Hazardous substance &

environmental engineering, A39(2), 409-420.

Albert J. E., Hernandez M., and Soto Y. (2002) Process and structural upgrades -

improving a state-of-the-art composting facility. Biocycle, 43(5), 40-42.

An K. (2006). Amendment for pH control in food-waste composting. University of

Regina,

Antler S. (2000). Composting Grows Stronger.

Atlas R. M., and Bartha R. (1998). Microbial Ecology: fundamentals and applications:

Menlo Park,.

Bach D. B., Shoda M., and Kubota H. (1985) Composting reaction rate of sewage

sludge in an autothermal packed bed reactor. Journal of fermentation

technolog, 63(3), 271-278.

Baker J. M., and Allmaras R. R. (1990) System for automating and multiplexing soil

moisture measurement by time-domain reflectometry. Soil science society of

America journal, 54,1-6.

Bakshi M., Gupta V. K., and Langar P. N. (1987) Effect of moisture level on the

chemical composition and nutritive value of fermented straw. Biological wasts,

21,283-290.

Baptista M., Antunes F., Gonsalves M. S., Morvan B., and Silveira A. (2010)

Composting kinetics in full-scale mechanical-biological treatment plants.

Waste Management, 30(10), 1908-1921.

Barakzai A., and Mengel K. (1993) Effect of microbial decomposition of mature

leaves on soil-pH. Zeitschrift Fur Pflanzenernahrung Und Bodenkunde, 156(1),

93-94.

Ban Q. H., and Koening A. (2001) Effect of air recirculation and reuse on

compostging of organic solid waste. Resources, Conservation and Recycling,

33(2), 93-111.

Beck-Friis B., SmArs S., Jonsson H., Eklind Y., and Kirchmann H. (2003) Composting

of source-separated household organics at different oxygen levels: gaining an

understanding of the emission dynamics. Compost Science and Utilization.,

11(1), 41-50.

Beck-Friis B., Smars S., Jonsson H., and Kirchmann H. (2001) SE—structures and

environment: gaseous emissions of carbon dioxide, ammonia and nitrous

oxide from organic household waste in a compost reactor under different

temperature regimes. Journal of Agricultural Engineering Research, 78(4),

423-430.

Beck M. B. (1979). Model structure identification from experimental data. In E.

Halfon (Ed.), Theoretical Systems Ecology: advances and case studies (pp.

259-298). London: Academic press.

Beck M. B. (1984). Understanding environmental systems. In J. Grassman, & G.

157

Baptista M., Antunes F., Gon^alves M. S., Morvan B., and Silveira A. (2010)

Composting kinetics in full-scale mechanical-biological treatment plants.

Waste Management, 30(10), 1908-1921.

Barakzai A., and Mengel K. (1993) Effect of microbial decomposition of mature

leaves on soil-pH. Zeitschrift Fur Pflanzenemahrung Und Bodenkunde, 156(1),

93-94.

Bari Q. H., and Koening A. (2001) Effect of air recirculation and reuse on

compostging of organic solid waste. Resources, Conservation and Recycling,

33(2), 93-111.

Beck-Friis B., Smars S., Jonsson H., Eklind Y., and Kirchmann H. (2003) Composting

of source-separated household organics at different oxygen levels: gaining an

understanding of the emission dynamics. Compost Science and Utilization.,

11(1), 41-50.

Beck-Friis B., Sm&rs S., Jonsson H., and Kirchmann H. (2001) SE—structures and

environment: gaseous emissions of carbon dioxide, ammonia and nitrous

oxide from organic household waste in a compost reactor under different

temperature regimes. Journal of Agricultural Engineering Research, 78(4),

423-430.

Beck M. B. (1979). Model structure identification from experimental data. In E.

Halfon (Ed.), Theoretical Systems Ecology: advances and case studies (pp.

259-298). London: Academic press.

Beck M. B, (1984). Understanding environmental systems. In J. Grassman, & G.

157

Straten (Eds.), Predictability and nonlinear modeling in natural sciences and

economics (pp. 294-311). Kluwer: Kluwer Academic Pulishers.

Bemal M. P., Navarro A. F., Roig A., Cegarra J., and Garcia D. (1996) Carbon and

nitrogen transformation during composting sweet sorghum bagasse. Biology

and Fertility of Soils, 22(1-2), 141-148.

Bhunia P., and Ghangrekar M. M. (2008) Analysis, evaluation, and optimization of

kinetic parameters for performance appraisal and design of UASB reactors.

Bioresource Technology, 99(7), 2132-2140.

Bishop P. L., and Godfrey C. (1983) Nitrogen transformation during sludge

composting. Biocycle, 24, 34-39.

Black C. A. (Ed.). (1965). methods of soil analysis, part 1, physical and mineralogical

properties. Madison: American Society of Agronomy.

Bodelier P. L., and Laanbroek H. J. (1997) Oxygen uptake kinetics of pseudomonas

chlororaphis grown in glucose- or glatamate- limited continuous cultures.

Archives microbiology, 167, 392-395.

Boltze U., and de Freitas M. H. (1997) Monitoring gas emissions from landfill sites.

Waste Management and Research, 15(5), 463-476.

Borja R., Martin A., Banks C. J., Alonso V., and Chica A. (1995) A kinetic study of

anaerobic digestion of olive mill wastewater at mesophilic and thermophilic

temperatures. Environmental Pollution, 88(1), 13-18.

Brewer L. J. (2001). Maturity and stability evaluation of composted yard debris.,

Oregon State University, Corvallis.

158

Straten (Eds.), Predictability and nonlinear modeling in natural sciences and

economics (pp. 294-311). Kluwer: Kluwer Academic Pulishers.

Bernal M. P., Navarro A. F., Roig A., Cegarra J., and Garcia D. (1996) Carbon and

nitrogen transformation during composting sweet sorghum bagasse. Biology

and Fertility of Soils, 22(1-2), 141-148.

Bhunia P., and Ghangrekar M. M. (2008) Analysis, evaluation, and optimization of

kinetic parameters for performance appraisal and design of UASB reactors.

Bioresource Technology, 99(7), 2132-2140.

Bishop P. L., and Godfrey C. (1983) Nitrogen transformation during sludge

composting. Biocycle, 24, 34-39.

Black C. A. (Ed.). (1965). methods of soil analysis, part 1, physical and mineralogical

properties. Madison: American Society of Agronomy.

Bodelier P. L., and Laanbroek H. J. (1997) Oxygen uptake kinetics of pseudomonas

chlororaphis grown in glucose- or glatamate- limited continuous cultures.

Archives microbiology, 167, 392-395.

Boltze U., and de Freitas M. H. (1997) Monitoring gas emissions from landfill sites.

Waste Management and Research, 15(5), 463-476.

Boija R., Martin A., Banks C. J., Alonso V., and Chica A. (1995) A kinetic study of

anaerobic digestion of olive mill wastewater at mesophilic and thermophilic

temperatures. Environmental Pollution, 88(1), 13-18.

Brewer L. J. (2001). Maturity and stability evaluation of composted yard debris.,

Oregon State University, Corvallis.

158

Brink N. (1995) Composting of Food Waste with Straw and other Carbon Sources for

Nitrogen Catching. Acta Agriculturae Scandinavica, Section B - Soil & Plant

Science, 45(2), 118-123.

Brinton W. F. (1998) Volatile organic acids in compost: production and odorant

aspects. Compost Science and Utilization, 6(1), 75-82.

Buchannan M., and Gliessman S. R. (1991) How compost fertilization affects

soil-nitrogen and crop yield. Biocycle, 32(12), 72-76.

Campbell A. G., Folk R. L., and Tripepi R. R. (1997) Wood ash as an amendment in

municipal sludge and yard waste composting process. Compost Science and

Utilization, 5(1), 62-73.

Campbell J. E. (1990) Dielectric properties and influence of conductivity in soils at

one to fifty megahertz. Soil science society of America journal, 54, 332-341.

Castillo A., Ilabres P., and Mata-Alvarez J. (1999) A kinetic study of a combined

anaerobic-aerobic system for treatment of domestic sewage. Water Research,

33(7), 1742-1747.

Cathcart T. P., Wheaton E W., and Brinsfield R. B. (1986) Optimizing variables

affecting composting of blue-crab scrap. Agricultural Waters, 15(4), 269-287.

Cekmecelioglu D. (2004). Fate of pathogenic microorganisms in food waste

composting. The pennsylvania state university, Pennsylvania.

Chang J. I., Tsai J. J., and Wu K. H. (2006) Thermophilic composting of food waste.

Bioresource Technology, 97(1), 116-122.

Characklis W. G., and Gujer W. (1979) Temperature dependency of microbial

159

Brink N. (1995) Composting of Food Waste with Straw and other Carbon Sources for

Nitrogen Catching. Acta Agriculturae Scandinavica, Section B - Soil & Plant

Science, 45(2), 118-123.

Brinton W. F. (1998) Volatile organic acids in compost: production and odorant

aspects. Compost Science and Utilization, 6(1), 75-82.

Buchannan M., and Gliessman S. R. (1991) How compost fertilization affects

soil-nitrogen and crop yield. Biocycle, 32(12), 72-76.

Campbell A. G., Folk R. L., and Tripepi R. R. (1997) Wood ash as an amendment in

municipal sludge and yard waste composting process. Compost Science and

Utilization, 5(1), 62-73.

Campbell J. E. (1990) Dielectric properties and influence of conductivity in soils at

one to fifty megahertz. Soil science society of America journal, 54, 332-341.

Castillo A., llabres R, and Mata-Alvarez J. (1999) A kinetic study of a combined

anaerobic-aerobic system for treatment of domestic sewage. Water Research,

33(7), 1742-1747.

Cathcart T. R, Wheaton F. W., and Brinsfield R. B. (1986) Optimizing variables

affecting composting of blue-crab scrap. Agricultural Waters, 15(4), 269-287.

Cekmecelioglu D. (2004). Fate of pathogenic microorganisms in food waste

composting. The Pennsylvania state university, Pennsylvania.

Chang J. I., Tsai J. J., and Wu K. H. (2006) Thermophilic composting of food waste.

Bioresource Technology, 97(1), 116-122.

Characklis W. G., and Gujer W. (1979) Temperature dependency of microbial

159

reactions. Progress water technology supply, 1, 111-130.

Cheung H. (2008). Bacterial Growth Inhibition During Composting of Food Waste:

Effects of Organic Acids. University of Regina,

Choi M. H., and Park Y. H. (1998) The inflence of yeast on thermophilic composting

of food waste. Letters in applied microbiology, 26, 175-178.

Conghos M. M., Aguirre M. E., and Santamaria R. M. (2003) Biodegradation of

sunflower hulls with different nitrogen sources under mesophilic and

thermophilic incubations. Biology and Fertility of Soils, 38(5), 282-287.

Cooney C. L., and Wise D. L. (1975) Therrnophilic anaerobic digestion of solid waste

for fuel gas production. Biotechnology and Bioengineering, 17, 1119-1135.

Czepiel P., Douglas E., Harriss R., and Call P. (1996) Measurements of N20 from

composted organic wastes. Environmental Science & Technology, 30,

2519-2525.

Das K. C., and Keener H. M. (1996). Process control based on dynamic properties in

composting: moisture and compaction considerations. In M. Debertoldi, B.

Lemmes, & T. Papi (Eds.), The science of composting (pp. 116-125). London:

Mackie Academic and professional.

Das K. C., Tolhier E. W., and Eiteman M. A. (2003) Comparison of synthetic and

natural bulking agents in food waste composting. Compost Science &

Utilization, 11(1), 27-35.

Day M., Krzymien M., SHaw K., Zaremba L., Wilson W. R., Botden C., et al. (1998)

An investigation of the chemical and physical changes occuring during

160

reactions. Progress water technology supply, 1,111 -130.

Cheung H. (2008). Bacterial Growth Inhibition During Composting of Food Waste:

Effects of Organic Acids. University of Regina,

Choi M. H., and Parte Y. H. (1998) The inflence of yeast on thermophilic composting

of food waste. Letters in applied microbiology, 26, 175-178.

Conghos M. M., Aguirre M. E., and Santamaria R. M. (2003) Biodegradation of

sunflower hulls with different nitrogen sources under mesophilic and

thermophilic incubations. Biology and Fertility of Soils, 38(5), 282-287.

Cooney C. L., and Wise D. L. (1975) Thermophilic anaerobic digestion of solid waste

for fuel gas production. Biotechnology and Bioengineering, 17, 1119-1135.

Czepiel P., Douglas E., Harriss R., and Call P. (1996) Measurements of N20 from

composted organic wastes. Environmental Science & Technology, 30,

2519-2525.

Das K. C., and Keener H. M. (1996). Process control based on dynamic properties in

composting: moisture and compaction considerations. In M. Debertoldi, B.

Lemmes, & T. Papi (Eds.), The science of composting (pp. 116-125). London:

Blackie Academic and professional.

Das K. C., Tollner E. W., and Eiteman M. A. (2003) Comparison of synthetic and

natural bulking agents in food waste composting. Compost Science &

Utilization, 11(1), 27-35.

Day M., Krzymien M., SHaw K., Zaremba L., Wilson W. R., Botden C., et al. (1998)

An investigation of the chemical and physical changes occuring during

160

commercial. Compost Science and Utilization, 6, 44-66.

de Bertoldi M., Citernesi U., and Gricelli M. (1980) Bulking agents in sludge

composting. COmpost science and land utilization, 21(1), 32-35.

de Bertoldi M., Vallini G., and Pera A. (1983) The biology of composting. Waste

Management and Research, 1, 157-176.

de Bertoldi M., Vallini G., and Zucconi F. (1985). technological aspects of composting

including modelling and microbiology. In Composting of agricultural and

other wastes. London: Elsevier applied science publishers.

Deacon J. W. (1997). Modern mycology (Vol. 7): Wiley-Blackwell.

deBertoldi M., Rutili A., Citterio B., and Civilini M. (1988) composting management

a new process control through 02 feedback. Waste Management and Research,

6, 239-259.

Deniz C. (2004). Fate of pathogenic microorgisms in food waste composting. the

Pennsylvania State University,

Derikx P. J. L., Camp H. J. M. 0. D., Drift C. V. D., Griensven L. J. L. D. V., and

vogel G. D. (1990) Biomass and biological activity during the production of

compos used as a substrate in the mushroom cultivation. Applied and

Environmental Microbiology, 56, 3029-3034.

Dirksen C., and Dasberg S. (1993) Improved calibration of time domain reflectometry

soil water content measurements. Soil science society of America journal,

57(258-264)

Donahue D. W., Chalmers J. A., and Storey J. A. (1998) Evaluation of in-vessel

161

commercial. Compost Science and Utilization, 6,44-66.

de Bertoldi M., Citernesi U., and Gricelli M. (1980) Bulking agents in sludge

composting. COmpost science and land utilization, 21(1), 32-35.

de Bertoldi M., Vallini G., and Pera A. (1983) The biology of composting. Waste

Management and Research, 1,157-176.

de Bertoldi M., Vallini G., and Zucconi F. (1985). technological aspects of composting

including modelling and microbiology. In Composting of agricultural and

other wastes. London: Elsevier applied science publishers.

Deacon J. W. (1997). Modern mycology (Vol. 7): Wiley-Blackwell.

deBertoldi M., Rutili A., Citterio B., and Civilini M. (1988) composting management

a new process control through 02 feedback. Waste Management and Research,

6, 239-259.

Deniz C. (2004). Fate of pathogenic microorgisms in food waste composting, the

Pennsylvania State University,

Derikx P. J. L., Camp H. J. M. O. D., Drift C. V. D., Griensven L. J. L. D. V., and

vogel G. D. (1990) Biomass and biological activity during the production of

compos used as a substrate in the mushroom cultivation. Applied and

Environmental Microbiology, 56, 3029-3034.

Dirksen C., and Dasberg S. (1993) Improved calibration of time domain reflectometry

soil water content measurements. Soil science society of America journal,

57(258-264)

Donahue D. W,, Chalmers J. A., and Storey J. A. (1998) Evaluation of in-vessel

composting of university postconsumer food wastes. Compost Science &

Utilization, 6(2), 75-81.

Droffiner M. L., and Brinton W F. (1995) Survival of E. coli and Salmonella

population in aerobic thermophilic composts as measured with DNA gene

probes. Zentralbl Hygiene, 197, 387-397.

Edwards D. R., and Daniel T. C. (1992) Environmental impacts of on-farm poultry

waste disposal, a review. Bioresource Technology, 41(1), 9-33.

Ekinci K. (2001). Theoretical and experimental studies on the effects of aeration

strategies on the composting process., the Ohio State University, Columbus.

Ekinci K., Keener H., and Elwell D. (2000) Composting Short Paper Fiber with

Broiler Litter and Additives Part I: Effects of Initial pH and Carbon/Nitrogen

Ratio On Ammonia Emission. Compost Science & Utilization, 8(2), 167-172.

Eklind Y., and Kirchmann H. (2000) Composting and storage of organic household

waste with different litter amendments. H: nitrogen turnover and losses.

Bioresource Technology, 74(2), 125-133.

Elwell D. L., Keener H. M., and Hansen R. C. (1996) Controlled high rate composting

of mixtures of food residuals, yard trimmings and chicken manure. Compost

Science & Utilization, 4(1), 6-15.

Erickson L. E., and Fung D. Y. C. (1988). Handbook on anaerobic fermentations. New

York.

Fang M., and Wong J. W. C. (1999) Effects of lime amendment on availability of

heavy metals and maturation in sewage sludge composting. Environmental

162

composting of university postconsumer food wastes. Compost Science &

Utilization, 6(2), 75-81.

Droffiner M. L., and Brinton W. F. (1995) Survival of E. coli and Salmonella

population in aerobic thermophilic composts as measured with DNA gene

probes. Zentralbl Hygiene, 197, 387-397.

Edwards D. R., and Daniel T. C. (1992) Environmental impacts of on-farm poultry

waste disposal, a review. Bioresource Technology, 41(1), 9-33.

Ekinci K. (2001). Theoretical and experimental studies on the effects of aeration

strategies on the composting process., the Ohio State University, Columbus.

Ekinci K., Keener H., and Elwell D. (2000) Composting Short Paper Fiber with

Broiler Litter and Additives Part I: Effects of Initial pH and Carbon/Nitrogen

Ratio On Ammonia Emission. Compost Science & Utilization, 8(2), 167-172.

Eklind Y., and Kirchmann H. (2000) Composting and storage of organic household

waste with different litter amendments. II: nitrogen turnover and losses.

Bioresource Technology, 74(2), 125-133.

Elwell D. L., Keener H. M., and Hansen R. C. (1996) Controlled high rate composting

of mixtures of food residuals, yard trimmings and chicken manure. Compost

Science & Utilization, 4(1), 6-15.

Erickson L. E., and Fung D. Y. C. (1988). Handbook on anaerobic fermentations. New

York.

Fang M., and Wong J. W. C. (1999) Effects of lime amendment on availability of

heavy metals and maturation in sewage sludge composting. Environmental

162

Pollution, 106(83-89)

Fang M., Wong J. W. C., Ma K. K., and Wong M. H. (1999) Co-composting of

sewage sludge and coal fly ash: nutrient transformations. Bioresource

Technology, 67(1), 19-24.

Faucette B., Das K. C., and Risse M. (2001) University tests in-vessel composting of

food residuals. Biocycle, 42(1), 68-70.

Filippi C., Bedini s., Levi-Minzi R., Cardelli R., and Saviozzi A. (2002)

Cocomposting of olive oil mill by-products: chemical and microbiological

evaluations. Compost Science & Utilization, 10(1), 63-71.

Finger S. M., Hatch R. T., and Regan T. M. (1976) Aerobic microbial growth in

semisolid matrices: heat and mass transfer limitation. . Biotechnology and

Bioengineering, 18, 1193-1218.

Finstein M. S., and Hogan J. A. Integration of composting process microbiology,

facility structure and decision-making. In Proceeding of the international

composting research symposium, 1992: Renaissance publications

Finstein M. S., and Moths M. L. (1975) Microbiology of Municipal Solid Waste

Composting. Applied Microbiology, 19, 113-151.

Fogarty A. M., and Tuovinen 0. H. (1991) Microbiological degradation of pestcides

in yard waste composting. Microbiological reviews, 55(2), 225-233.

Fromme W. R. (1999). Characterization of changes occurring in natural organic

matter during the composting of a synthetic compost and a municipal solid

waste. University of Cincinnati, Cincinnati.

163

Pollution, 106(83-89)

Fang M., Wong J. W. C., Ma K. K., and Wong M. H. (1999) Co-composting of

sewage sludge and coal fly ash: nutrient transformations. Bioresource

Technology, 67(1), 19-24.

Faucette B., Das K. C., and Risse M. (2001) University tests in-vessel composting of

food residuals. Biocycle, 42(1), 68-70.

Filippi C., Bedini s., Levi-Minzi R., Cardelli R., and Saviozzi A. (2002)

Cocomposting of olive oil mill by-products: chemical and microbiological

evaluations. Compost Science & Utilization, 10(1), 63-71.

Finger S. M., Hatch R. T., and Regan T. M. (1976) Aerobic microbial growth in

semisolid matrices: heat and mass transfer limitation. . Biotechnology and

Bioengineering, 18, 1193-1218.

Finstein M. S., and Hogan J. A. Integration of composting process microbiology,

facility structure and decision-making. In Proceeding of the international

composting research symposium, 1992: Renaissance publications

Finstein M. S., and Morris M. L. (1975) Microbiology of Municipal Solid Waste

Composting. Applied Microbiology, 19, 113-151.

Fogarty A. M., and Tuovinen O. H. (1991) Microbiological degradation of pestcides

in yard waste composting. Microbiological reviews, 55(2), 225-233.

Fromme W. R. (1999). Characterization of changes occurring in natural organic

matter during the composting of a synthetic compost and a municipal solid

waste. University of Cincinnati, Cincinnati.

Fu Z. (2004). A Fuzzy Inference System for Synthetic Evaluation of Compost

Maturity and Stability University of regina,

Garcia C., Hernandez T., and Costa F. (1992) crop growth impact composted vs

uncomposted organics. Biocycle, 33(11), 70-72.

Gellens V., Boelens J., and Verstraete W. (1995) Source separation, selective

collection and in reactor digestion of biowaste. Antonie van Leeuwenhoek, 67,

79-89.

Genevini P., Adani F., and Villa C. (1997) Rich hull degradation by co-composting

with dairy cattle slurry. Soil science and plant nutrition, 43(1), 135-147.

Godden B. (1983) Evolution of enzyme activities and microbial populations during

composting of cattle manure. European journal of applied microbiology and

biotechnology, 17, 306-310.

Golueke C. (1977). Biological Reclamation of Solid Wastes. Emmaus, Pennsylvania:

Rodale Press.

Golueke C. G. (1973). Composting: a study of the process and its principles. Emmaus:

Rodale Press.

Gray K. R., Sherman K., and Biddlestone A. J. (1971) A Review of Composting - Part

1. Process Biochemistry, 6, 32-36.

Groenestein C. M., and Van Faassen H. G. (1996) Volatilization of Ammonia, Nitrous

Oxide and Nitric Oxide in Deep-litter Systems for Fattening Pigs. Journal of

Agricultural Engineering Research, 65(4), 269-274.

Haga K. (1998) Animal waste problems and their solution from the technological

164

Fu Z. (2004). A Fuzzy Inference System for Synthetic Evaluation of Compost

Maturity and Stability University of regina,

Garcia C., Hernandez T., and Costa F. (1992) crop growth impact composted vs

uncomposted organics. Biocycle, 33(11), 70-72.

Gellens V., Boelens J., and Verstraete W. (1995) Source separation, selective

collection and in reactor digestion of biowaste. Antonie van Leeuwenhoek, 67,

79-89.

Genevini P., Adani F., and Villa C. (1997) Rich hull degradation by co-composting

with dairy cattle slurry. Soil science and plant nutrition, 43(1), 135-147.

Godden B. (1983) Evolution of enzyme activities and microbial populations during

composting of cattle manure. European journal of applied microbiology and

biotechnology, 17, 306-310.

Golueke C. (1977). Biological Reclamation of Solid Wastes. Emmaus, Pennsylvania:

Rodale Press.

Golueke C. G. (1973). Composting: a study of the process and its principles. Emmaus:

Rodale Press.

Gray K. R., Sherman K., and Biddlestone A. J. (1971) A Review of Composting - Part

1. Process Biochemistry, 6, 32-36.

Groenestein C. M., and Van Faassen H. G. (1996) Volatilization of Ammonia, Nitrous

Oxide and Nitric Oxide in Deep-litter Systems for Fattening Pigs. Journal of

Agricultural Engineering Research, 65(4), 269-274.

Haga K. (1998) Animal waste problems and their solution from the technological

164

point of view in Japan. Jarq-Japan agricultural research quarterly, 32(3),

203-210.

Hall S. C. (1998). Temperature feedback and control via aeration rate regulation in

biological composting systems., Cornell University, Ithaca.

Hamelers H. V. M. (2001). A mathematical model for composting kinetics.

Wageningen University, Wageningen.

Hanajima D., Kuroda K., and Haga K. (2001) Enhancement of the thermophilic stage

in cattle waste composting by addition of tofu residue. Bioresource

Technology, 78(2), 213-216.

Hansen R. C., Keener H. M., Marugg C., Dick C. A., and Hoitink H. A. J. (1992).

Composting of poultry manure. In H. A. J. Hoitink (Ed.), Science and

engineering of composting: design, environmental, microbiological and

utilization aspects, ed.

(pp. 131-153). Columbus: Renaissance Publications.

Hansen R. C., Keener H. M., Marugg C., Dick W. A., and Hoitink H. A. J. (1993).

Composting of poultry manure. In The science and engineering of

composting. Wooster: Ohio agricultural research and development center.

Hashimoto A. G. (1983) Conversion of straw—manure mixtures to methane at

mesophilic and thermophilic temperatures. Biotechnology and Bioengineering,

25(1), 185-200.

Haug R. T. (1980). Compost engineering: principles and practice. Ann Arbor: Ann

Arbor Science.

165

point of view in Japan. Jarq-Japan agricultural research quarterly, 32(3),

203-210.

Hall S. C. (1998). Temperature feedback and control via aeration rate regulation in

biological composting systems., Cornell University, Ithaca.

Hamelers H. V. M. (2001). A mathematical model for composting kinetics.

Wageningen University, Wageningen.

Hanajima D., Kuroda K., and Haga K. (2001) Enhancement of the thermophilic stage

in cattle waste composting by addition of tofii residue. Bioresource

Technology, 78(2), 213-216.

Hansen R. C., Keener H. M., Marugg C., Dick C. A., and Hoitink H. A. J. (1992).

Composting of poultry manure. In H. A. J. Hoitink (Ed.), Science and

engineering of composting: design, environmental, microbiological and

utilization aspects, ed.

(pp. 131-153). Columbus: Renaissance Publications.

Hansen R. C„ Keener H. M., Marugg C., Dick W. A., and Hoitink H. A. J. (1993).

Composting of poultry manure. In The science and engineering of

composting. Wooster: Ohio agricultural research and development center.

Hashimoto A. G. (1983) Conversion of straw-manure mixtures to methane at

mesophilic and thermophilic temperatures. Biotechnology and Bioengineering,

25(1), 185-200.

Haug R. T. (1980). Compost engineering: principles and practice. Ann Arbor: Ann

Arbor Science.

165

Haug R. T. (1986) Composting process design criteria part I: feed conditioning.

Biocycle, 27, 38-43.

Haug R. T. (1993). The practical handbook of compost engineering. In (pp.

385-436). Boca Raton: Lewis Publishers.

Haug R. T. (1996). Composting plant design and process management. In M.

Debertoldi, P. Sequi, B. Lemmes, & T. Papi (Eds.), The science of composting

(pp. 60-70). London: Blackie Academic and Professional.

Hauhs M. (1996) Summary of a workshop on ecosystem modeling: the end of an era.

The science of the total environment, 183(1-2), 1-5.

Heijnen J. J., and Roele J. A. (1981) A macroscopoc model describing yield and

maintanance relationship in aerobic fermentation processes. Biotechnology

and Bioengineering, 23, 739-763.

Hellebrand H. J. (1998) Emission of nitrous oxide and other trace gases during

composting of grass and green waste. Journal of Agricultural Engineering

Research, 69, 365-375.

Henis Y. (1987). Survival and Dormancy of microorganisms. New York: John Wiley

& Sons Inc.

Herrmann R. F., and Shann J. F. (1997) Microbial community changes during the

composting of municipal solid waste. Microbial Ecology, 33(1), 78-85.

Hoitink H. A. J., and Keener H. M. (1993). Science and engineering of composting:

design, environmental microbiological and utilization aspects. Columbus:

Renaissance publications.

166

Haug R. T. (1986) Composting process design criteria part I: feed conditioning.

Biocycle, 27, 38-43.

Haug R. T. (1993). The practical handbook of compost engineering. In (pp.

385-436). Boca Raton: Lewis Publishers.

Haug R. T. (1996). Composting plant design and process management. In M.

Debertoldi, R Sequi, B. Lemmes, & T. Papi (Eds.), The science of composting

(pp. 60-70). London: Blackie Academic and Professional.

Hauhs M. (1996) Summary of a workshop on ecosystem modeling: the end of an era.

The science of the total environment, 183(1-2), 1-5.

Heijnen J. J., and Roele J. A. (1981) A macroscopoc model describing yield and

maintanance relationship in aerobic fermentation processes. Biotechnology

and Bioengineering, 23, 739-763.

Hellebrand H. J. (1998) Emission of nitrous oxide and other trace gases during

composting of grass and green waste. Journal of Agricultural Engineering

Research, 69, 365-375.

Henis Y. (1987). Survival and Dormancy of microorganisms. New York: John Wiley

& Sons Inc.

Herrmann R. F., and Shann J. F. (1997) Microbial community changes during the

composting of municipal solid waste. Microbial Ecology, 33(1), 78-85.

Hoitink H. A. J., and Keener H. M. (1993). Science and engineering of composting:

design, environmental microbiological and utilization aspects. Columbus:

Renaissance publications.

166

Hu T.-J., Zeng G.-M., Huang D.-L., Yu H.-Y., Jiang X.-Y., Dai F., et al. (2007) Use of

potassium dihydrogen phosphate and sawdust as adsorbents of ammoniacal

nitrogen in aerobic composting process. Journal of Hazardous Materials,

141(3), 736-744.

Huang G. F., Wong J. W. C., Wu Q. T., and Nagar B. B. (2004) Effect of C/N on

composting of pig manure with sawdust. Waste Management, 24, 805-813.

Iannotti D. A., Grebus M. E., Toth B. L., Madden L. V., and Hoitink H. A. J. Oxygen

Respirometry to Assess Stability and Maturity of Composted Municipal Solid

Waste. J. Environ. Qual., 23(6), 1177-1183.

Inbar Y. (1990) New approaches to compost maturity. Biocycle, 31, 64-69.

Iriarte M. L., and Ciria P. (2001) Performance characteristics of three aeration systems

in the composting of sheep manure and straw. Journal of Agricultural

Engineering Research, 79(3), 317-330.

Isik M., and Sponza D. T. (2005) Substrate removal kinetics in an upflow anaerobic

sludge blanket reactor decolorising simulated textile wastewater. Process

Biochemistry, 40(3-4), 1189-1198.

Jacobowitz L. A., and Steenhuis T. S. (1984) Compost impact on soil moisture and

temperature. Biocycle, 25(56-60)

Jeong Y. K., and Hwang S. J. (2005) Optimum doses of Mg and P salts for

precipitating ammonia into struvite crystals in aerobic composting.

Bioresource Technology, 96(1), 1-6.

Jeong Y. K., and Kim J. .S. (2001) A new method for conservation of nitrogen in

167

Hu T.-J., Zeng G.-M., Huang D.-L., Yu H.-Y., Jiang X.-Y., Dai F., et al. (2007) Use of

potassium dihydrogen phosphate and sawdust as adsorbents of ammoniacal

nitrogen in aerobic composting process. Journal of Hazardous Materials,

141(3), 736-744.

Huang G. F., Wong J. W. C., Wu Q. T., and Nagar B. B. (2004) Effect of C/N on

composting of pig manure with sawdust. Waste Management, 24, 805-813.

Iannotti D. A., Grebus M. E., Toth B. L., Madden L. V., and Hoitink H. A. J. Oxygen

Respirometry to Assess Stability and Maturity of Composted Municipal Solid

Waste. J. Environ. Qual., 23(6), 1177-1183.

Inbar Y. (1990) New approaches to compost maturity. Biocycle, 31, 64-69.

Iriarte M. L., and Ciria P. (2001) Performance characteristics of three aeration systems

in the composting of sheep manure and straw. Journal of Agricultural

Engineering Research, 79(3), 317-330.

Isik M., and Sponza D. T. (2005) Substrate removal kinetics in an upflow anaerobic

sludge blanket reactor decolorising simulated textile wastewater. Process

Biochemistry, 40(3-4), 1189-1198.

Jacobowitz L. A., and Steenhuis T. S. (1984) Compost impact on soil moisture and

temperature. Biocycle, 25(56-60)

Jeong Y. K., and Hwang S. J. (2005) Optimum doses of Mg and P salts for

precipitating ammonia into struvite crystals in aerobic composting.

Bioresource Technology, 96(1), 1-6.

Jeong Y. K., and Kim J. .S. (2001) A .new method for conservation of nitrogen in

167

aerobic composting processes. Bioresource Technology, 79(2), 129-133.

Jeris J. S., and Regan R. W. (1973a) Controlling environmental parameters for

optimum composting (I): experimental procedures and temperature. Compost

science, 14(1), 10-15.

Jeris J. S., and Regan R. W. (1973b) Controlling environmental parameters for

optimum composting (II): moisture, free air space and recycle. Compost

science, 14(2), 8-15.

Jeris J. S., and Regan R. W. (1973c) Controlling environmental parameters for

optimum composting (III). Compost science, 14(3), 16-22.

Johannessen G. S., James C. E., Allison H. E., Smith D. L., Saunders J. R., and

Mccarthy A. J. (2005) Survival of a shiga toxin-incoding bacteriophage in a

compost model. FEMs micobiology letters, 245(2), 369-377.

Joshua R. S., Macauley B. J., and Mitchell H. J. (1998) Characterization of

temperature and oxygen profiles in widrow processing systems. Compost

Science & Utilization, 6(4), 15-28.

Kayragian M., and Tchobanogious G. (1992) Computation of C/N ratios for various

organic fractions. Biocycle, 33(5), 58-60.

Keeney D. R., and Nelson D. W. (Eds.). (1982). Nitrogen-inorganic forms. Methods

of soil analysis, Part 2, Chemial and microbiological properties. Madison: Soil

Science Society of America.

Kirchmann H., and Widen P. (1994) Separately collected organic household wastes:

chemical composition and composting characteristics. Swedish . journal

168

aerobic composting processes. Bioresource Technology, 79(2), 129-133.

Jeris J. S., and Regan R. W. (1973a) Controlling environmental parameters for

optimum composting (I): experimental procedures and temperature. Compost

science, 14(1), 10-15.

Jeris J. S., and Regan R. W. (1973b) Controlling environmental parameters for

optimum composting (II): moisture, free air space and recycle. Compost

science, 14(2), 8-15.

Jeris J. S., and Regan R. W. (1973c) Controlling environmental parameters for

optimum composting (III). Compost science, 14(3), 16-22.

Johannessen G. S., James C. E., Allison H. E., Smith D. L., Saunders J. R., and

Mccarthy A. J. (2005) Survival of a shiga toxin-incoding bacteriophage in a

compost model. FEMs micobiology letters, 245(2), 369-377.

Joshua R. S., Macauley B. J., and Mitchell H. J. (1998) Characterization of

temperature and oxygen profiles in widrow processing systems. Compost

Science & Utilization, 6(4), 15-28.

Kayragian M., and Tchobanogjous G. (1992) Computation of C/N ratios for various

organic fractions. Biocycle, 33(5), 58-60.

Keeney D. R., and Nelson D. W. (Eds.). (1982). Nitrogen-inorganic forms. Methods

of soil analysis, Part 2, Chemial and microbiological properties. Madison: Soil

Science Society of America.

Kirchmann H., and Widen P. (1994) Separately collected organic household wastes:

chemical composition and composting characteristics. Swedish journal

168

agricultural research, 24, 3-12.

Kirchmann H., and Witter E. (1989) Ammonia volatilization during aerobic and

anaerobic manure decomposition. Plant and Soil, 115, 35-41.

Kishimoto M., Preechaphan C., Yoshida T., and Taguchi H. (1987) Simulation of an

aerobic composting of activated sludge using a statistical procedure.

MICRCEN journal of applied microbiology and biotechnology, 3, 113-124.

Kjeldsen P., and Fischer E. V. (1995) Landfill gas migration-field investigations at

Skellingsted landfill, Denmark. Waste Management and Research, 13(5),

467-484.

Klamer M., and Bath E. (1998) Microbial community dynamics during composting

of straw material studied using phospholipid fatty acid analysis. Ferns

microbiology ecology, 27(1), 9-20.

Koivula N., Raikkonen T., Urpilainen S., Ranta J., and Hanninen K. (2004) Ash in

composting of source-separated catering waste. Bioresource Technology, 93(3),

291-299.

Kubota H., and Kakasaki K. (1991) Accelerated thermophilic composting of garbage.

Biocycle, 6, 66-68.

Kwon S.-H., and Lee D.-H. (2004) Evaluation of Korean food waste composting with

fed-batch operations II: using properties of exhaust gas condensate. Process

Biochemistry, 39(9), 1047-1055.

Laos F., Mazzarino M. J., Walter I., and Roselli L. (1998) Composting of fish waste

with wood by-products and testing compost quality as a soil amendment:

169

agricultural research, 24, 3-12.

Kirchmann H., and Witter E. (1989) Ammonia volatilization during aerobic and

anaerobic manure decomposition. Plant and Soil, 115, 35-41.

Kishimoto M., Preechaphan C., Yoshida T., and Taguchi H. (1987) Simulation of an

aerobic composting of activated sludge using a statistical procedure.

MICRCEN journal of applied microbiology and biotechnology, 3,113-124.

Kjeldsen P., and Fischer E. V. (1995) Landfill gas migration-field investigations at

Skellingsted landfill, Denmark. Waste Management and Research, 13(5),

467-484.

Klamer M., and BMth E. (1998) Microbial community dynamics during composting

of straw material studied using phospholipid fatty acid analysis. Fems

microbiology ecology, 27(1), 9-20.

Koivula N., Raikkonen T., Urpilainen S., Ranta J., and Hanninen K. (2004) Ash in

composting of source-separated catering waste. Bioresource Technology, 93(3),

291-299.

Kubota H., and Kakasaki K. (1991) Accelerated thermophilic composting of garbage.

Biocycle, 6, 66-68.

Kwon S.-H., and Lee D.-H. (2004) Evaluation of Korean food waste composting with

fed-batch operations II: using properties of exhaust gas condensate. Process

Biochemistry, 39(9), 1047-1055.

Laos F., Mazzarino M. J., Walter I., and Roselli L. (1998) Composting of fish waste

with wood by-products and testing compost quality as a soil amendment:

169

experiences in the Patagonia Region of Argentina. Compost Science &

Utilization, 6(1), 59-66.

Lei F., and Vander-Gheynst J. S. (2000) The effect of microbial inoculation and pH on

microbial community structure changes during composting. Process

Biochemistry, 35(9), 923-929.

Lester J. N., and Birkett J. W. (1999). Microbiology and chemistry for environmental

scientist and engineers. In (pp. 184-185). New York: E & FN SPON.

Li Y., Du X. Y., Liu J. L., and Huang G. H. (2010) Formation of Struvite Crystals in a

Simulated Food Waste Aerobic Composting Process. Chemical Research in

Chinese Universities, 26(2), 210-216.

Liang C., Das K. C., and McClendon R. W. (2003) The influence of temperature

andmoisture contents regimes on the aerobic microbial activity of a biosolids

composting blend. Bioresource Technology, 86, 131-137.

Liang Y., Leonard J. J., Feddes J. J. R., and McGill W. B. (2006) Influence of carbon

and buffer amendment on ammonia volatilization in composting. Bioresource

Technology, 97(5), 748-761.

Lin Y. (2006). Simulation Modeling and Process Control of Composting Systems

under Complexity and Uncertainty. University of Regina,

Lineweaver H., and Burk D. (1934) The determination of enzyme dissociation

constants. Journal of the American Chemical Society, 56(3), 658-666.

Ljung L., and Glad T. (1994). Modeling of dynamic systems. New Jersy: Prentice

Hall.

170

experiences in the Patagonia Region of Argentina. Compost Science &

Utilization, 6(1), 59-66.

Lei F., and Vander-Gheynst J. S. (2000) The effect of microbial inoculation and pH on

microbial community structure changes during composting. Process

Biochemistry, 35(9), 923-929.

Lester J. N., and Birkett J. W. (1999). Microbiology and chemistry for environmental

scientist and engineers. In (pp. 184-185). New York: E & FN SPON.

Li Y., Du X. Y., Liu J. L., and Huang G. H. (2010) Formation of Struvite Crystals in a

Simulated Food Waste Aerobic Composting Process. Chemical Research in

Chinese Universities, 26(2), 210-216.

Liang C., Das K. C., and McClendon R. W. (2003) The influence of temperature

andmoisture contents regimes on the aerobic microbial activity of a biosolids

composting blend. Bioresource Technology, 86, 131-137.

Liang Y., Leonard J. J., Feddes J. J. R., and McGill W. B. (2006) Influence of carbon

and buffer amendment on ammonia volatilization in composting. Bioresource

Technology, 97(5), 748-761.

Lin Y. (2006). Simulation Modeling and Process Control of Composting Systems

under Complexity and Uncertainty. University of Regina,

Lineweaver H., and Burk D. (1934) The determination of enzyme dissociation

constants. Journal of the American Chemical Society, 56(3), 658-666.

Ljung L., and Glad T. (1994). Modeling of dynamic systems. New Jersy: Prentice

Hall.

170

Lyles S. T. (1969). Biology of microorganisms. St. Louis: The C. V. Mosby Co.

MacGregor S. T., Miller F. C., Psarianos K. M., and Finstein M. S. (1981)

Composting process control based on interaction between microbial heat

output and temperature. Applied and Environmental Microbiology, 41(6),

1321-1330.

Madigan M. T., Martinko J. M., Stahl D. A., and Clark D. P. (2006). Brock biology of

microorganisms. In (11 ed., pp. 136): Pearson College Div.

Mahimairaja S., Bolan N. S., Hedley M. J., and Macgregor A. N. (1994) Losses and

transformation of nitrogen during composting of poultry manure with different

amendments-An incubation experiment. Bioresource Technology, 47(3),

265-273.

Marugg C. (1993) A kinetic model of the yard waste composting process. Compost

Science and Utilization, 1(1), 38-51.

Mathur S. P. (1991). Composting process. In Bioconversion of waste materials to

industrial products. New York.

McDonnell E. M. (1993). Evaluation of the Metricor refractive index probe with

fiberoptic cable in simple food systems (Vol. x, 97 leaves).

McDonnell E. M. (1999). Process characterization, and stability and maturity testing

of forced air, static pile processed food scrap and wood chip compost. Cornell

University,

Michel F. C., Pecchia J. A., Rigot J., and Keener H. M. (2004) Mass and nutrient

losses during the composting of dairy manure amended with sawdust or straw.

171

Lyles S. T. (1969). Biology of microorganisms. St. Louis: The C. V. Mosby Co.

MacGregor S. T., Miller F. C., Psarianos K. M., and Finstein M. S. (1981)

Composting process control based on interaction between microbial heat

output and temperature. Applied and Environmental Microbiology, 41(6),

1321-1330.

Madigan M. T., Martinko J. M., Stahl D. A., and Clark D. P. (2006). Brock biology of

microorganisms. In (11 ed., pp. 136): Pearson College Div.

Mahimairaja S., Bolan N. S., Hedley M. J., and Macgregor A. N. (1994) Losses and

transformation of nitrogen during composting of poultry manure with different

amendments-An incubation experiment. Bioresource Technology, 47(3),

265-273.

Marugg C. (1993) A kinetic model of the yard waste composting process. Compost

Science and Utilization, 1(1), 38-51.

Mathur S. P. (1991). Composting process. In Byconversion of waste materials to

industrial products. New York.

McDonnell E. M. (1993). Evaluation of the Metricor refractive index probe with

fiberoptic cable in simple food systems (Vol. x, 97 leaves).

McDonnell E. M. (1999). Process characterization, and stability and maturity testing

of forced air, static pile processed food scrap and wood chip compost. Cornell

University,

Michel F. C., Pecchia J. A., Rigot J., and Keener H. M. (2004) Mass and nutrient

losses during the composting of dairy manure amended with sawdust or straw.

171

Compost Science and Utilization, 12(4), 323-334.

Miller F. C. (1989) Matric water potential as an ecological determinant in compost a

substrate dense system. Microbial Ecology, 18, 59-71.

Miller F. C. (1996). Composting of municipal solid waste and its components. In A. C.

Palmisano, & M. A. Barlaz (Eds.), Microbiology of solid waste (pp. 116-154).

Boca Raton: CRC press.

Molla A., Frakhru'l-Razi A., Hanafi M., and Alam M. (2005) Compost produced by

solid state bioconversion of biosolids: a potential resource for plant growth

and environmental friendly disposal. Communications in soil science and plant

analysis, 36(11&12), 1435-1447.

Monod J. (1949) The growth of bacterial cultures. Annual Review of Microbiology, 3,

371-394.

Montgomery D. C. (2001). experiments with a single factor: the analysis of variance.

In D. C. Montgomery (Ed.), Design and analysis of experiments. 5th ed. New

York: Wiley & Sons, INC.

Mosteller F. (1948) A k-sample slippage test for an extreme population. Annals of

mathematical statistics, 19(1), 58-65.

Mysliwiec M. J., VanderGheynst J. S., and Rashid M. (2001) Dynamic volume

averaged model of heat and mass transport within a compost biofilter (I):

model development. Biotechnology and Bioengineering, 73(4), 282-295.

Nakasaki K. (1987) a new composting model and assessment of optimum operation

for effective drying of composting material. Journal of termentation

172

Compost Science and Utilization, 12(4), 323-334.

Miller F. C. (1989) Matric water potential as an ecological determinant in compost a

substrate dense system. Microbial Ecology, 18, 59-71.

Miller F. C. (1996). Composting of municipal solid waste and its components. In A. C.

Palmisano, & M. A. Barlaz (Eds.), Microbiology of solid waste (pp. 116-154).

Boca Raton: CRC press.

Molla A., Frakhru'l-Razi A., Hanafi M., and Alam M. (2005) Compost produced by

solid state bioconversion of biosolids: a potential resource for plant growth

and environmental friendly disposal. Communications in soil science and plant

analysis, 36(11&12), 1435-1447.

Monod J. (1949) The growth of bacterial cultures. Annual Review of Microbiology, 3,

371-394.

Montgomery D. C. (2001). experiments with a single factor: the analysis of variance.

In D. C. Montgomery (Ed.), Design and analysis of experiments. 5th ed. New

York: Wiley & Sons, INC.

Mosteller F. (1948) A k-sample slippage test for an extreme population. Annals of

mathematical statistics, 19(1), 58-65.

Mysliwiec M. J., VanderGheynst J. S., and Rashid M. (2001) Dynamic volume

averaged model of heat and mass transport within a compost biofilter (I):

model development. Biotechnology and Bioengineering, 73(4), 282-295.

Nakasaki K. (1987) a new composting model and assessment of optimum operation

for effective drying of composting material. Journal of termentation

172

technology, 65(4), 441-447.

Nakasaki K., Nag K., and Ariga 0. (2004) Degradation of fats during thermophilic

composting of organic waste. Waste Management and Research, 22(4),

276-282.

Nakasaki K., Nag K., and Karita S. (2005) Microbial succession associated with

organic matter decomposition during thermophilic composting of organic

waste. Waste management research, 23(1), 48-56.

Nakasaki K., and Shoda M. (1987) Oxygen diffusion and microbial activity in the

composting of dehydrated sewage sludge cakes. Journal of fermentation

technology, 56(1), 43-48.

Nakasaki K., Shoda M., and Kubota H. (1985) Comparison of composting of two

sewage sludges. Journal of fermentation and bioengineering, 63(537-543)

Nakasaki K., Yaguchi H., Sasaki Y., and Kubota H. (1992) effect of C/N ratio on

thermophilic composting of garbage. Journal of fermentation and

bioengineering, 73, 43-45.

Nakasaki K., Yaguchi H., Sasaki Y., and Kubota H. (1993) Effects Of pH Control On

Composting Of Garbage. Waste Management & Research, 11(2), 117-125.

Nakhla G., Liu V., and Bassi A. (2006) Kinetic modeling of aerobic biodegradation of

high oil and grease rendering wastewater. Bioresource Technology, 97(1),

131-139.

Namkoong W., Hwang E.-Y., Cheong J.-G., and Choi J.-Y. (1999) A comparative

evaluation of maturity parameters for food waste composting. Compost

173

technology, 65(4), 441-447.

Nakasaki K., Nag K., and Ariga O. (2004) Degradation of fats during thermophilic

composting of organic waste. Waste Management and Research, 22(4),

276-282.

Nakasaki K., Nag K., and Karita S. (2005) Microbial succession associated with

organic matter decomposition during thermophilic composting of organic

waste. Waste management research, 23(1), 48-56.

Nakasaki K., and Shoda M. (1987) Oxygen diffusion and microbial activity in the

composting of dehydrated sewage sludge cakes. Journal of fermentation

technology, 56(1), 43-48.

Nakasaki K., Shoda M., and Kubota H. (1985) Comparison of composting of two

sewage sludges. Journal of fermentation and bioengineering, 63(537-543)

Nakasaki K., Yaguchi H., Sasaki Y., and Kubota H. (1992) effect of C/N ratio on

thermophilic composting of garbage. Journal of fermentation and

bioengineering, 73,43-45.

Nakasaki K., Yaguchi H., Sasaki Y., and Kubota H. (1993) Effects Of pH Control On

Composting Of Garbage. Waste Management & Research, 11(2), 117-125.

Nakhla G., Liu V., and Bassi A. (2006) Kinetic modeling of aerobic biodegradation of

high oil and grease rendering wastewater. Bioresource Technology, 97(1),

131-139.

Namkoong W., Hwang E.-Y., Cheong J.-G., and Choi J.-Y. (1999) A comparative

evaluation of maturity parameters for food waste composting. Compost

173

Science & Utilization, 7(2), 55.

Nelson M. I., Balakrishnan E., and Chen X. D. (2003) A Semenov model of

self-heating in compost piles. Process safety and environmental protection,

81(5), 375-383.

Nergo M. J., Solano M. L., Ciria P., and Carrasco J. (1999) Composting of sweet

sorghum bagasse with other wastes. Bioresource Technology, 67(1), 89-92.

Nobuyuki Y., H. J., and M. T. (2001) Monitoring of the composting process using a

mediator-type biochemical oxygen demand sensor. The analyst, 126(10),

1751-1755.

Pare T., Dinel H., Schnitzer M., and Dumontet S. (1998) Transformations of carbon

and nitrogen during composting of animal manure and shredded paper.

Biology and Fertility of Soils, 26(3), 173-178.

Park M., Singvilay 0., Shin W., Kim E., Chung J., and Sa T. (2004) Effects of

long-term compost and fertilizer application on soil phosphorus status under

paddy cropping system. Communications in soil science and plant analysis,

35(11&12), 1635-1648.

Pascual J. A., Garcia C., Hernandez T., Lerma S., and Lynch J. M. (2002)

Effectiveness of municipal waste compost and its humic fraction in

suppressing Pythium ultimum. Microbial Ecology, 44, 59-68.

Petrie I., and Selimbasic V. (2008) Development and validation of mathematical

model for aerobic composting process. Chemical Engineering Journal, 139(2),

• 304-317.

174

Science & Utilization, 7(2), 55.

Nelson M. I., Balakrishnan E., and Chen X. D. (2003) A Semenov model of

self-heating in compost piles. Process safety and environmental protection,

81(5), 375-383.

Nergo M. J., Solano M. L., Ciria P., and Carrasco J. (1999) Composting of sweet

sorghum bagasse with other wastes. Bioresource Technology, 67(1), 89-92.

Nobuyuki Y., H. J., and M. T. (2001) Monitoring of the composting process using a

mediator-type biochemical oxygen demand sensor. The analyst, 126(10),

1751-1755.

Pare T., Dinel H., Schnitzer M., and Dumontet S. (1998) Transformations of carbon

and nitrogen during composting of animal manure and shredded paper.

Biology and Fertility of Soils, 26(3), 173-178.

Park M., Singvilay O., Shin W., Kim E., Chung J., and Sa T. (2004) Effects of

long-term compost and fertilizer application on soil phosphorus status under

paddy cropping system. Communications in soil science and plant analysis,

35(11&12), 1635-1648.

Pascual J. A., Garcia C., Hernandez T., Lerma S., and Lynch J. M. (2002)

Effectiveness of municipal waste compost and its humic fraction in

suppressing Pythium ultimum. Microbial Ecology, 44, 59-68.

Petric I., and Selimbasic V. (2008) Development and validation of mathematical

model for aerobic composting process. Chemical Engineering Journal, 139(2),

• 304-317.

174

Picard R., and Cook D. (1984) Cross-validation of regression models. Journal of

American Statistical Association, 79(387), 575-583.

Poincelot R. P. (1974) A scientific examination of the principles and practices of

composting. Compost science, 15, 24-31.

Polprasert C. (1989). Organic waste recycling. New York: John wiley & sons.

Rao Bhamidimarri S. M., and Pandey S. P. (1996) Aerobic thennophilic composting

of piggery solid wastes. Water Science and Technology, 33(8), 89-94.

Richard T. L. (1997). The kinetics of soid state aerobic biodegradation. Cornell

University, Ithaca.

Richard T. L., Hamelers H. V. M., Veeken A., and Sliva T. (2002) Moisture

relationships in composting processes. Compost Science and Utilization, 10(4),

286-302.

Richard T. L., and Walker L. P. (2006) Modeling the temperature kinetics of aerobic

solid-state biodegradation. Biotechnology Progress, 22(1), 70-77.

Richard T. L., Walker L. P., and Gosser J. M. the effects of oxygen on solid-state

biodegradation kinetics. In Proceedings of the IBE, 1999 (Vol. 2, pp.

A22-A39)

Robertsson M. (2002). Effects of interrupted air supply on the composting process -

composition of volatile organic acids. In R. N. Insam H., Klammer S. (Ed.),

Microbiology of Composting (pp. 189-201). Berlin-Heidelberg, New York:

Springer-Verlag.

Robinson P., and Stentiford E. I. (1993) Improving the aerated static pile composting

175

Picard R., and Cook D. (1984) Cross-validation of regression models. Journal of

American Statistical Association, 79(387), 575-583.

Poincelot R. P. (1974) A scientific examination of the principles and practices of

composting. Compost science, 15, 24-31.

Polprasert C. (1989). Organic waste recycling. New York: John wiley & sons.

Rao Bhamidimarri S. ML, and Pandey S. P. (1996) Aerobic thermophilic composting

of piggery solid wastes. Water Science and Technology, 33(8), 89-94.

Richard T. L. (1997). The kinetics of soid state aerobic biodegradation. Cornell

University, Ithaca.

Richard T. L., Hamelers H. V. M., Veeken A., and Sliva T. (2002) Moisture

relationships in composting processes. Compost Science and Utilization, 10(4),

286-302.

Richard T. L., and Walker L. P. (2006) Modeling the temperature kinetics of aerobic

solid-state biodegradation. Biotechnology Progress, 22(1), 70-77.

Richard T. L., Walker L. P., and Gosser J. M. the effects of oxygen on solid-state

biodegradation kinetics. In Proceedings of the IBE, 1999 (Vol. 2, pp.

A22-A39)

Robertsson M. (2002). Effects of interrupted air supply on the composting process -

composition of volatile organic acids. In R. N. Insam H., Klammer S. (Ed.),

Microbiology of Composting (pp. 189-201). Berlin-Heidelberg, New York:

Springer-Verlag.

Robinson P., and Stentiford E. I. (1993) Improving the aerated static pile composting

175

method by the incorporation of moisture control. Compost Science and

Utilization, 1, 53-69.

Ryckeboer J., and J. M. (2003) Microbiological aspects of biowaste during

composting in a monitored compost bin. Journal of applied microbiology,

94(1), 127-137.

Ryckeboer J., Mergae J., Vaes K., Klammer S., Clercq D. d., Coosemans J., et al.

(2003a) A survey of bacteria and fungi occurring during composting and

self-heating process. Annals of microbiology, 53(4), 349-410.

Ryckeboer J., Mergaert J., Coosemans J., Deprins K., and Swings J. (2003b)

Microbiological aspects of biowaste during composting in a monitored

compost bin. Journal of applied microbiology, 94, 127-137.

Sasaki N., Suehara K. I., Kohda J., Nakano Y., and T.Yano (2003) Effects of C/N ratio

and pH of raw materials on oil degradation efficiency in a compost

fermentation process. Journal of bioscience and bioengineering, 96(1), 47-52.

Schlegel A. J. (1992) Effect of composted manure on soil chemical-preperties and

nitrogen use by grain-sorghum. Journal of production agriculture, 5(1),

153-157.

Schloss P. D., and Walker L. P. (2000) Measurement of process performance and

variability in inoculated composting reactors using ANOVA and power

analysis. Process Biochemistry, 35(9), 931-942.

Schwab B. S., Ritchie C. J., Kain D. J., Dobrin G. D., Long L. W., and Palmisano A.

C. (1994) Characterization of compost from a pilot plant-scale composting

176

method by the incorporation of moisture control. Compost Science and

Utilization, 1,53-69.

Ryckeboer J., and J. M. (2003) Microbiological aspects of biowaste during

composting in a monitored compost bin. Journal of applied microbiology,

94(1), 127-137.

Ryckeboer J., Mergae J., Vaes K., Klammer S., Clercq D. d., Coosemans J., et al.

(2003a) A survey of bacteria and fungi occurring during composting and

self-heating process. Annals of microbiology, 53(4), 349-410.

Ryckeboer J., Mergaert J., Coosemans J., Deprins K., and Swings J. (2003b)

Microbiological aspects of biowaste during composting in a monitored

compost bin. Journal of applied microbiology, 94, 127-137.

Sasaki N., Suehara K. I., Kohda J., Nakano Y., and T.Yano (2003) Effects of C/N ratio

and pH of raw materials on oil degradation efficiency in a compost

fermentation process. Journal of bioscience and bioengineering, 96(1), 47-52.

Schlegel A. J. (1992) Effect of composted manure on soil chemical-preperties and

nitrogen use by grain-sorghum. Journal of production agriculture, 5(1),

153-157.

Schloss P. D., and Walker L. R (2000) Measurement of process performance and

variability in inoculated composting reactors using ANOVA and power

analysis. Process Biochemistry, 35(9), 931-942.

Schwab B. S., Ritchie C. J., Kain D. J., Dobrin G. D., Long L. W., and Palmisano A.

C. (1994) Characterization of compost from a pilot plant-scale composting

176

utilizing simulated solid waste. Waste Management and Research, 123,

289-303.

Seo J. Y., Heo J. S., Kim T. H., Joo W. II., and Crohn D. M. (2004) Effect of

vermiculite addition on compost produced from Korean food wastes. Waste

Management, 24(10), 981-987.

Sequi P. (1996). The role of composting in sustainable agriculture. In M. DeBertoldi,

P. Sequi, B. Lemmes, & T. Papi (Eds.), The science of composting (pp. 23-29).

London: Blackie academic and professional.

Seymour R. M., Donahue D., Bourdon M., Evans J. R., and Wentworth D. (2001)

Intermittent aeration for in-vessel composting of crab processing waste.

Compost Science & Utilization, 9(2), 98-106.

Sikora L. J. (1999) MSW compost reduces nitrogen volatilization during dairy manure

composting. Compost Science and Utilization, 7(4), 34-41.

SmArs S., Gustafsson L., Beck-Friis B., and Jonsson H. (2002) Improvement of the

composting time for household waste during an initial low pH phase by

mesophilic temperature control. Bioresource Technology, 84(3), 237-241.

Smith L. H., McCarty P. L., and Kitanidis P. K. (1998) Spreadsheet method for

evaluation of biochemical reaction rate coefficients and their uncertainties by

weighted nonlinear least-squares analysis of the integrated Monod Equation.

Appllied and Environmental Microbiology, 64(6), 2044-2050.

Smith R., and Eilers R. G. (1980). Numerical simulation of aerated sludge composting

reported to USEPA.

177

utilizing simulated solid waste. Waste Management and Research, 123,

289-303.

Seo J. Y., Heo J. S., Kim T. H., Joo W. H., and Crohn D. M. (2004) Effect of

vermiculite addition on compost produced from Korean food wastes. Waste

Management, 24(10), 981-987.

Sequi P. (1996). The role of composting in sustainable agriculture. In M. DeBertoldi,

P. Sequi, B. Lemmes, & T. Papi (Eds.), The science of composting (pp. 23-29).

London: Blackie academic and professional.

Seymour R. M., Donahue D., Bourdon M., Evans J. R., and Wentworth D. (2001)

Intermittent aeration for in-vessel composting of crab processing waste.

Compost Science & Utilization, 9(2), 98-106.

Sikora L. J. (1999) MSW compost reduces nitrogen volatilization during dairy manure

composting. Compost Science and Utilization, 7(4), 34-41.

Sm&rs S., Gustafsson L., Beck-Friis B., and Jonsson H. (2002) Improvement of the

composting time for household waste during an initial low pH phase by

mesophilic temperature control. Bioresource Technology, 84(3), 237-241.

Smith L. H., McCarty P. L., and Kitanidis P. K. (1998) Spreadsheet method for

evaluation of biochemical reaction rate coefficients and their uncertainties by

weighted nonlinear least-squares analysis of the integrated Monod Equation.

Appllied and Environmental Microbiology, 64(6), 2044-2050.

Smith R., and Eilers R. G. (1980). Numerical simulation of aerated sludge composting

reported to USEPA.

Soares H. M., Cardenas B., Weir D., and Switzenbaum M. S. (1995) Evaluating

pathogen regrowth in biosolids compost. Biocycle, 36, 70-75.

Solano M. L., Iriarte F., Ciria P., and Negro M. J. (2001a) Performance characteristics

of three aeration systems in the composting of sheep manure and straw.

Journal of Agricultural Engineering Research, 79(3), 317-329.

Solano M. L., Iriarte F., Ciria P., and Nergo M. J. (2001b) Performance characteristics

of three aeration systems in the composting of sheep manure and straw.

Journal of Agricultural Engineering Research, 79(3), 317-329.

Stabnikova 0., Ding H. B., Tay J. H., and Wang J. Y. (2005) Biotechnology for

aerobic conversion of food waste into organic fertilizer. Waste Management

and Research, 23(1), 39-47.

Stentiford E. I. (1993). Diversity of composting systems. In A. J. Hoitink, & H. M.

Keener (Eds.), Science and engineering of composting: design environmental,

microbiological and utilization aspects (pp. 100). Worthington: Renaissance

Publications.

Stentiford E. I. (1996). Composting control: principles and practice. In M. Debertoldi,

P. Sequi, B. Lemes, & T. Papi (Eds.), In: The science of composting. (pp.

49-59). London: Blackie academic and professional.

Stombaugh D. P., and Nokes S. E. (1996) Development of a biologically based

aerobic composting simulation model. Transactions of the ASAE, 39(1),

239-250.

Storm P. E. (1985) Effects of temperature on bacterial species-diversity in

178

Soares H. M., Cardenas B., Weir D., and Switzenbaum M. S. (1995) Evaluating

pathogen regrowth in biosolids compost. Biocycle, 36, 70-75.

Solano M. L., Iriarte F., Ciria P., and Negro M. J. (2001a) Performance characteristics

of three aeration systems in the composting of sheep manure and straw.

Journal of Agricultural Engineering Research, 79(3), 317-329.

Solano M. L., Iriarte F., Ciria P., and Nergo M. J. (2001b) Performance characteristics

of three aeration systems in the composting of sheep manure and straw.

Journal of Agricultural Engineering Research, 79(3), 317-329.

Stabnikova O., Ding H. B., Tay J. H., and Wang J. Y. (2005) Biotechnology for

aerobic conversion of food waste into organic fertilizer. Waste Management

and Research, 23(1), 39-47.

Stentiford E. I. (1993). Diversity of composting systems. In A. J. Hoitink, & H. M.

Keener (Eds.), Science and engineering of composting: design environmental,

microbiological and utilization aspects (pp. 100). Worthington: Renaissance

Publications.

Stentiford E. I. (1996). Composting control: principles and practice. In M. Debertoldi,

P. Sequi, B. Lemes, & T. Papi (Eds.), In: The science of composting, (pp.

49-59). London: Blackie academic and professional.

Stombaugh D. P., and Nokes S. E. (1996) Development of a biologically based

aerobic composting simulation model. Transactions of the ASAE, 39(1),

239-250.

Storm P. E. (1985). Effects of temperature on bacterial species-diversity in

178

thermophilic solid waste composting. Applied and Environmental

Microbiology, 50(4), 899-905.

Strom P. F. (1985) Effect of temperature on bacterial species-diversity in thermphilic

solid-waste composting. Applied and Environmental Microbiology, 50(4),

899-905.

Suler D. J., and Finstein M. S. (1977) Effects of temperature, aeration, and moisture

on CO2 formation in bench-scale, continuously thermophilic composting of

solid waste. Applied and Environmental Microbiology, 33(2), 345-350.

Sun X. (2006). Nitrogen Transformation in Food-Waste Composting. University of

Regina,

Sundberg C., and Jonsson H. (2005) Process inhibition due to organic acids in

fed-batch composting of food waste — influence of starting culture.

Biodegradation, 16(3), 205-213.

Sundberg C., Smars S., and Jonsson H. (2004) Low pH as an inhibiting factor in the

transition from mesophilic to thermophilic phase in composting. Bioresource

Technology, 95(2), 145-150.

Suzuki T., Ikumi Y, Okamoto S., Watanabe I., Fujitake N., and Otsuka H. (2004)

Aerobic composting of chips from clear-cut trees with various co-materials.

Bioresource Technology, 95(2), 121-128.

Svensson K., Odlare M., and Pell M. (2004) The fertilizing effect of compost and

biogas residues from source separated household waste. The Journal of

Agricultural Science, 142(04), 461-467..

179

thermophilic solid waste composting. Applied and Environmental

Microbiology, 50(4), 899-905.

Strom P. F. (1985) Effect of temperature on bacterial species-diversity in thennphilic

solid-waste composting. Applied and Environmental Microbiology, 50(4),

899-905.

Suler D. J., and Finstein M. S. (1977) Efffects of temperature, aeration, and moisture

on C02 formation in bench-scale, continuously thermophilic composting of

solid waste. Applied and Environmental Microbiology, 33(2), 345-350.

Sun X. (2006). Nitrogen Transformation in Food-Waste Composting. University of

Regina,

Sundberg C., and Jonsson H. (2005) Process inhibition due to organic acids in

fed-batch composting of food waste — influence of starting culture.

Biodegradation, 16(3), 205-213.

Sundberg C., SmSrs S., and Jonsson H. (2004) Low pH as an inhibiting factor in the

transition from mesophilic to thermophilic phase in composting. Bioresource

Technology, 95(2), 145-150.

Suzuki T., Ikumi Y., Okamoto S., Watanabe I., Fujitake N., and Otsuka H. (2004)

Aerobic composting of chips from clear-cut trees with various co-materials.

Bioresource Technology, 95(2), 121-128.

Svensson K., Odlare M., and Pell M. (2004) The fertilizing effect of compost and

biogas residues from source separated household waste. The Journal of

Agricultural Science, 142(04), 461-467..

Swatek F. E. (1967). Textbook of microbiology. St. Louis: The C. V. Mosby Co.

Szmidt R., and Fox C. (2001) Compost processes - interdependencies for process

control and compost quality. Acta horticulture, 549, 55-61.

Tam N. F. Y., and Tiquia S. M. (1999) Nitrogen transformation during co-composting

of spent pig manure, saw dust litter and sludge under forced -aerated system.

Environmental Technology, 20(3), 259-267.

Thambirajah J. J., Zullcali M. D., and M. A. Hashi (1995) Microbiological and

biochemical changes during the composting of oil palm

empty-fruit-bunches-effect of nitrogen supplementation on the substrate.

Bioresource Technology, 52(2), 133-144.

Tiquia S. M., and Tam N. F. Y. (1998) Elimination of phytotoxicity during

co-composting of spent pig-manure sawdust litter and pig sludge. Bioresource

Technology, 65(1&2), 43-49.

Tiquia S. M., and Tam N. F. Y. (2000) Fate of nitrogen during composting of chicken

litter. Environmental Pollution, 110, 535-541.

Tomati U., Madejon E., Galli E., Capitani D., and Segre A. L. (2001) structural

changes of humic acids during olive mill pomace composting. Compost

Science & Utilization, 9(2), 134-142.

Trefry M. G., and Franzmann P. D. (2003) An extended kinetic model accounting for

nonideal microbial substrate mineralization in environmental samples.

Geomicrobiology Journal, 20(2), 113-129.

Uif S. (1998). Systems analysis of waste management-the ORWARE model, transport

180

Swatek F. E. (1967). Textbook of microbiology. St. Louis: The C. V. Mosby Co.

Szmidt R., and Fox C. (2001) Compost processes - interdependences for process

control and compost quality. Acta horticulture, 549, 55-61.

Tam N. F. Y., and Tiquia S. M. (1999) Nitrogen transformation during co-composting

of spent pig manure, saw dust litter and sludge under forced -aerated system.

Environmental Technology, 20(3), 259-267.

Thambirajah J. J., Zulkali M. D., and M. A. Hashi (1995) Microbiological and

biochemical changes during the composting of oil palm

empty-fruit-bunches-effect of nitrogen supplementation on the substrate.

Bioresource Technology, 52(2), 133-144.

Tiquia S. M., and Tam N. F. Y. (1998) Elimination of phytotoxicity during

co-composting of spent pig-manure sawdust litter and pig sludge. Bioresource

Technology, 65(1 &2), 43-49.

Tiquia S. M., and Tam N. F. Y. (2000) Fate of nitrogen during composting of chicken

litter. Environmental Pollution, 110,535-541.

Tomati U., Madejon E., Galli E., Capitani D., and Segre A. L. (2001) structural

changes of humic acids during olive mill pomace composting. Compost

Science & Utilization, 9(2), 134-142.

Trefry M. G., and Franzmann R D. (2003) An extended kinetic model accounting for

nonideal microbial substrate mineralization in environmental samples.

Geomicrobiology Journal, 20(2), 113-129.

Uif S. (1998). Systems analysis of waste management-the ORWARE model, transport

180

and compost sub-models. Swedish university of agricultural science, Uppsala.

Van Lier J. J. C., Ginkel J. T. V., Straatsma G., Gerrits J. P. G., and Griensven L. J. L.

D. V. (1994) Composting of mushroom substate in a fermentation tunnel:

compost parameters and a mathmatical model. Netherlands journal of

agricultural science, 42(4), 271-292.

VanderGheynst J. S. (1997). Experimentation, modeling and analysis of a high-solids

aerobic decomposition process. Cornell University, Ithaca.

VanderGheynst J. S., Gossett J. M., and Walker L. P. (1996) HIgh-solids aerobic

decomposition: pilot-scale reactor development and experimentation. Process

Biochemistry, 32(5), 361-375.

VanderGheynst J. S., and Lei F. (2000) The effect of microbial inoculation and pH on

microbial community structure changes during composting. Process

Biochemistry, 35, 923-940.

Venglovsky J., Sasakova N., Vargova M., Pacajova Z., Placha I., Petrovsky M., et al.

(2005) Evolution of temperature and chemical parameters during composting

of the pig slurry solid fraction amended with natural zeolite. Bioresource

Technology, 96(2), 181-189.

Vinci B. J., Summerfelt S. T., Timmons M. B., and Watten B. J. Carbon dioxide

control in intensive aquaculture: design tool development. In Proceedings of

the Aquacultural Engineering Society, 1996 (Vol. 2, pp. 399-419)

Vogelaar J. C. T., Klapwijk B., Temmink H., and van Lier J. B. (2003) Kinetic

comparisons of mesophilic and thermophilic aerobic biomass. Journal of

181

and compost sub-models. Swedish university of agricultural science, Uppsala.

Van Lier J. J. C., Ginkel J. T. V., Straatsma G., Gerrits J. P. G., and Griensven L. J. L.

D. V. (1994) Composting of mushroom substate in a fermentation tunnel:

compost parameters and a mathmatical model. Netherlands journal of

agricultural science, 42(4), 271-292.

VanderGheynst J. S. (1997). Experimentation, modeling and analysis of a high-solids

aerobic decomposition process. Cornell University, Ithaca.

VanderGheynst J. S., Gossett J. M., and Walker L. P. (1996) High-solids aerobic

decomposition: pilot-scale reactor development and experimentation. Process

Biochemistry, 32(5), 361-375.

VanderGheynst J. S., and Lei F. (2000) The effect of microbial inoculation and pH on

microbial community structure changes during composting. Process

Biochemistry, 35,923-940.

Venglovsky J., Sasakova N., Vargova M., Pacajova Z., Placha I., Petrovsky M., et al.

(2005) Evolution of temperature and chemical parameters during composting

of the pig slurry solid fraction amended with natural zeolite. Bioresource

Technology, 96(2), 181-189.

Vinci B. J., Summerfelt S. T., Timmons M. B., and Watten B. J. Carbon dioxide

control in intensive aquaculture: design tool development. In Proceedings of

the Aquacultural Engineering Society, 1996 (Vol. 2, pp. 399-419)

Vogelaar J. C. T., Klapwijk B., Temmink H., and van Lier J. B. (2003) Kinetic

comparisons of mesophilic and thermophilic aerobic biomass. Journal of

181

Industrial Microbiology &amp; Biotechnology, 30(2), 81-88.

Waksman S. A., Cordon T. C., and Hulpoi N. (1939) Influence of temperature upon

the microbiological population and decompostition Soil Science, 47, 83-98.

Walker P. (2008). Food Residuals: Waste Product, By-Product, or Coproduct. In

Food Waste to Animal Feed (pp. 17-30): Iowa State University Press.

Weppen P. (2001) Process calorimetry on composting of municipal organic wastes.

Biomass and bioenergy, 21(4), 289-299.

Whang D. S., and Meenaghan G. F. (1980) Kinetic model of composting process.

Compost science, 21, 44-46.

Wilson G. B. (1989) Combining raw materials for composting. Biocycle, 30(8),

82-85.

Wilson G. B., and Dalmat (1986) Process Control: Measuring Composting Stability.

Biocycle, 27(7), 34-37.

Witter E., and Lopez-Real J. (1987) The potential of sewage sludge and composting in

a nitrogen recyling strategy for agriculture. Biological agriculture and

horticulture, 5, 1-23.

Witter E., and Lopez-Real J. (1988) Nitrogen losses during the composting of sewage

sludge, and the effectiveness of clay soil, zeolite, and compost in adsorbing the

volatilized ammonia. Biological Wastes, 23(4), 279-294.

Wong J. W. C., and Fang M. (2000) Effects of lime addition on sewage sludge

composting process. Water Research, 34(15), 3691-3698.

Wong J. W. C., Li S. W. Y., and Wong M. H. (1995) Coal Fly Ash as a Composting

182

Industrial Microbiology & amp; Biotechnology, 30(2), 81-88.

Waksman S. A., Cordon T. C., and Hulpoi N. (1939) Influence of temperature upon

the microbiological population and decompostition Soil Science, 47, 83-98.

Walker P. (2008). Food Residuals: Waste Product, By-Product, or Coproduct. In

Food Waste to Animal Feed (pp. 17-30): Iowa State University Press.

Weppen P. (2001) Process calorimetry on composting of municipal organic wastes.

Biomass and bioenergy, 21(4), 289-299.

Whang D. S., and Meenaghan G. F. (1980) Kinetic model of composting process.

Compost science, 21,44-46.

Wilson G. B. (1989) Combining raw materials for composting. Biocycle, 30(8),

82-85.

Wilson G. B., and Dalmat (1986) Process Control: Measuring Composting Stability.

Biocycle, 27(7), 34-37.

Witter E., and Lopez-Real J. (1987) The potential of sewage sludge and composting in

a nitrogen recyling strategy for agriculture. Biological agriculture and

horticulture, 5, 1-23.

Witter E., and Lopez-Real J. (1988) Nitrogen losses during the composting of sewage

sludge, and the effectiveness of clay soil, zeolite, and compost in adsorbing the

volatilized ammonia. Biological Wastes, 23(4), 279-294.

Wong J. W. C., and Fang M. (2000) Effects of lime addition on sewage sludge

composting process. Water Research, 34(15), 3691-3698.

Wong J. W. C„ Li S. W. Y., and Wong M. H. (1995) Coal Fly Ash as a Composting

Material for Sewage Sludge: Effects on Microbial Activities. Environmental

Technology, 16(6), 527-537.

Wong J. W. C., Mak K. F., Chan N. W., Lam A., Fang M., Zhou L. X., et al. (2001)

Co-composting of soybean residues and leaves in Hong Kong. Bioresource

Technology, 76(2), 99-106.

Xi B., Zhang G., and Liu H. (2005) Process kinetics of inoculation composting of

municipal solid waste. Journal of Hazardous Materials, 124(1-3), 165-172.

Yan F., Schubert S., and Mengel K. (1996) Soil pH increase due to biological

decarboxylation of organic anions. Soil biology and Biochemistry, 28(4-5),

617-624.

Yoshida N., Hoashi J., Morita T., McNiven S. J., Yano K., Yoshida A., et al. (2001)

Monitoring of the composting process using a mediator-type biochemical

oxygen demand sensor. Analyst, 126(10), 1751-1755.

Yu H., and Huang G. H. (2009) Effects of sodium acetate as a pH control amendment

on the composting of food waste. Bioresource Technology, 100(6), 2005-2011.

Zhang Z. (2000). The effects of moisture and free air space on composting rates. Iowa

state university, Ames.

Zucconi F., Pera A., Forte M., and Debertoldi M. (1981) Evaluating toxicity of

immature compost. Biocycle, 22(2), 54-57.

183

Material for Sewage Sludge: Effects on Microbial Activities. Environmental

Technology, 16(6), 527-537.

Wong J. W. C., Mak K. F., Chan N. W., Lam A., Fang M., Zhou L. X., et al. (2001)

Co-composting of soybean residues and leaves in Hong Kong. Bioresource

Technology, 76(2), 99-106.

Xi B., Zhang G., and Liu H. (2005) Process kinetics of inoculation composting of

municipal solid waste. Journal of Hazardous Materials, 124(1-3), 165-172.

Yan F., Schubert S., and Mengel K. (1996) Soil pH increase due to biological

decarboxylation of organic anions. Soil biology and Biochemistry, 28(4-5),

617-624.

Yoshida N., Hoashi J., Morita T., McNiven S. J., Yano K., Yoshida A., et al. (2001)

Monitoring of the composting process using a mediator-type biochemical

oxygen demand sensor. Analyst, 126(10), 1751-1755.

Yu H., and Huang G. H. (2009) Effects of sodium acetate as a pH control amendment

on the composting of food waste. Bioresource Technology, 100(6), 2005-2011.

Zhang Z. (2000). The effects of moisture and free air space on composting rates. Iowa

state university, Ames.

Zucconi F., Pera A., Forte M., and Debertoldi M. (1981) Evaluating toxicity of

immature compost. Biocycle, 22(2), 54-57.

183