Master’s dissertation submitted in partial fulfilment of the requirements for the joint degree of

International Master of Science

in Environmental Technology and Engineering

an Erasmus+: Erasmus Mundus Master Course jointly organized by

Ghent University, Belgium

University of Chemical Technology, Prague, Czech Republic

UNESCO-IHE Institute for Water Education, Delft, the Netherlands

Academic year 2014 – 2015

Autotrophic denitrification with sulphur compounds

by using fluidized-bed biofilms: kinetics tests and

dynamic mathematical modeling

Host University:

UNESCO-IHE Institute for Water Education, Delft, the Netherlands

Anastasiia Kostrytsia

Promotor: Prof. Piet Lens

Co-promoter: Prof. Giovanni Esposito

This thesis was elaborated at UNESCO-IHE Institute for Water Education and defended at UNESCO-IHE Insti-

tute for Water Education within the framework of the European Erasmus Mundus Programme “Erasmus

Mundus International Master of Science in Environmental Technology and Engineering " (Course N°

2011-0172).

© 2015 Delft, Anastasiia Kostrytsia, Ghent University, all rights reserved

1

2

Abstract

The autotrophic denitrification with reduced sulfur compounds as thiosulfate and elemental

sulfur is considered to be an effective treatment for the nitrate-contaminated drinking water and

wastewater with low carbon content.

In this study, the kinetics of autotrophic denitrification with thiosulfate and elemental sulfur

were investigated by using empirical (experiments) and numerical (modeling) approaches. Auto-

trophic denitrification was performed in the FBRs operated in semi-batch mode and batch assays with

fluidized-bed biofilms: the same biomass has been used with the aim of investigation the effect of dif-

ferent nitrate concentrations on process performance. Parallel to experimental activity, two mathemat-

ical models have been developed in order to simulate dynamically the main processes occurring during

thiosulfate- and sulfur-autotrophic denitrification.

Experimental results show that both thiosulfate- and sulfur-driven autotrophic denitrification,

the nitrate reduction rates increased with the increasing initial nitrate concentration. In autotrophic de-

nitrification with thiosulfate, the highest nitrate removal of 16.0 mg/L· h-1

was achieved in the FBR

that was 1.5 times higher than the one obtained in batch environment. On the contrary, the nitrate re-

moval rate of 10.0 mg/L· h-1

, almost twice higher than the one obtained in the batch assays, was

achieved in the FBR. Thiosulfate, as an intermediate of elemental sulfur oxidation to sulfate, was de-

tected throughout the experimentation and remained stable at approximately 150 mg/L.

The FBR environment was proven to be more effective for the autotrophic denitrification with

thiosulfate than elemental sulfur. The nitrate degradation rate was 1.6 times higher in the FBR with

thiosulfate than with elemental sulfur.

The dynamic mathematical models for thiosulfate- and sulfur-driven autotrophic denitrifica-

tion have been developed to get better understanding of processes and optimize their performance. The

model equations were based on mass conservation principle and expressed as double-Monod kinetics.

The simulation results demonstrate that the proposed models are able to describe dynamically the bio-

logical and physico-chemical processes occurring during autotrophic denitrification with thiosulfate

and elemental sulfur.

The results of this study show the potential of thiosulfate- and sulfur-driven autotrophic deni-

trification with fluidized-bed biofilms for treatment of nitrate contamination in the wastewater.

3

Acknowledgements

I would like to say thank to all the respected researchers and scientists who have inspired,

guided and helped me during master thesis research.

I would like to express my special appreciation and thanks to my promotor Prof. Piet Lens for

encouraging my research and for allowing me to grow as a research scientist.

I would especially like to thank my co-promotor Prof. Giovanni Esposito for his guidance and

for brilliant comments and suggestions.

I am deeply grateful to my co-promoter, Dr. Stefano Papirio, for his patience, constant support

and guidance from the very beginning until the last day of the thesis phase.

I would also like to say special thank you to Dr. Maria Rosaria Mattei and Dr. Luigi Frunzo

for their crucial help with Matlab® and for teaching me so much about mathematical modelling and its

applications.

Thank you to Dr. Eldon Raj for his valuable comments and suggestions that greatly improved

the thesis.

I would like to thank you to Dr. Jack van de Vossenberg for his priceless advice about micro-

biological aspects of my thesis.

I would also like to express my gratitude to the coordinators of IMETE programme and the

European Commission for financial support.

And finally, I would like to thank my family in Ukraine for their endless support and encour-

agement.

.

4

TABLE OF CONTENTS

ABSTRACT 2

ACKNOWLEDGEMENTS 3

List of figures 6

List of tables 7

Abbreviations 8

List of symbols 9

CHAPTER 1. INTRODUCTION 10

1.1. Problem definition 10

1.2. Research question and objectives 11

CHAPTER 2. LITERATURE REVIEW 12

2.1. Methods for nitrate removal from wastewater 12

2.2. Biological aspect of chemolithotrophic denitrification 13

2.2.1. Thiosulfate 14

2.2.2. Elemental sulfur 14

2.3. Engineered aspect of chemolithotrophic denitrification 15

2.3.1. Engineered systems 15

2.3.2. FBRs configuration 17

2.3.3. Advantages and disadvantages of FBRs 18

2.4. Modeling aspect of chemolithotrophic denitrification 18

2.4.1. Type of the models in FBRs 18

2.4.2. Mathematical models aimed at chemolithotrophic denitrification with

sulfur compounds 19

CHAPTER 3. MATERIALS AND METHODS 22

3.1. Media and microbial enrichment in FBRs 22

3.2. Experimental set-up 22

3.2.1. Reactors kinetics experiments 22

3.2.2. Batch kinetics tests 23

3.3. Sampling and analytical methods 24

3.4. Calculations 24

3.4.1. Stoichiometry 24

3.4.2. Evaluation of kinetics parameters 25

3.5. Models development 27

CHAPTER 4. EXPERIMENTAL RESULTS 28

4.1. Kinetics of thiosulfate-driven autotrophic denitrification 28

4.1.1. FBR experiments 28

4.1.2. Batch kinetic tests 28

4.1.3. Comparison between FBR and batch experiments 29

4.2. Kinetics of sulfur-driven autotrophic denitrification 33

4.2.1. FBR experiments 33

4.2.2. Batch kinetic tests 33

4.2.3. Comparison between FBR and batch experiments 36

4.3. Comparison of thiosulfate- and sulfur-driven autotrophic denitrification 38

5

CHAPTER 5. MODELING RESULTS 40

5.1. Mathematical modeling of thiosulfate-driven autotrophic denitrification 40

5.1.1. Model construction 40

5.1.1.1. Biochemical reactions 40

5.1.1.2. Model assumptions 41

5.1.1.3. Model equations 41

5.1.2. Model simulations 44

5.2. Mathematical modeling of sulfur-driven autotrophic denitrification 46

5.2.1. Model construction 46

5.2.1.1. Biochemical reactions 46

5.2.1.2. Model assumptions 47

5.2.1.3. Model equations 48

5.2.2. Model simulation 51

CHAPTER 6. DISCUSSION 53

6.1. Kinetics of thiosulfate-driven autotrophic denitrification 53

6.1.1. FBR experiments 53

6.1.2. Batch kinetic tests 53

6.1.3. Comparison between FBR and batch experiments 54

6.2. Kinetics of sulfur-driven autotrophic denitrification 55

6.2.1. FBR experiments 55

6.2.2. Batch kinetic tests 55

6.2.3. Comparison between FBR and batch experiments 56

6.3. Comparison of thiosulfate- and sulfur-driven autotrophic denitrification 57

6.4. Thiosulfate-driven autotrophic denitrification model 57

6.5. Sulfur-driven autotrophic denitrification model 58

CHAPTER 7. CONCLUSIONS 59

REFERENCES 60

6

List of figures

Fig. 2.1. Schematic illustration of the nitrogen cycle (Richardson et al. 2009) 12

Fig. 2.2. Schematic representation of up-flow FBR (Rabah and Dahab, 2004) 17

Fig. 3.1. Schematic representation of the ‘serum’ bottles used within batch assays 23

Fig. 3.2. The plot of (1/ν) versus (1/S) 25

Fig. 3.2. The plot of the specific substrate utilization rate v with specific growth rate μ 26

Nitrate (a), nitrite (b), thiosulfate (c) and sulfate (d) evolutions in FBR1 kinetic tests at differ-Fig. 4.1.

ent initial nitrate concentrations: 250, 500 and 1000 mg/L 30

Nitrate (a), nitrite (b), thiosulfate (c) and sulfate (d) profiles in batch kinetics with FBR1 bio-Fig. 4.2.

film at initial nitrate concentrations of 400, 600, 900 and 1000 mg/L 31

itrate (I), nitrite (II) and thiosulfate (III) evolutions between FBR and batch Fig. 4.3. Comparison of n

experiments at initial nitrate concentrations of 250 (a) and 1000 (b) mg/L 32

Nitrate (a), nitrite (b), thiosulfate (c) and sulfate (d) evolutions in FBR2 kinetic tests at differ-Fig. 4.4.

ent initial nitrate concentrations: 500 and 1000 mg/L 34

Nitrate (a), nitrite (b), thiosulfate (c) and sulfate (d) evolutions in batch kinetics with FBR2 Fig. 4.5.

biofilm at initial nitrate concentrations of 400, 550 and 1000 mg/L 35

itrate (I), nitrite (II) and sulfate (III) evolutions between FBR and batch ex-Fig. 4.6. Comparison of n

periments at initial nitrate concentrations of 400 (a) and 1000 (b) mg/L 37

itrate (a), nitrite (b) and sulfate (c) evolutions in FBRs kinetic tests with initial nitrate con-Fig. 4.7. N

centrations of 1000 mg/L 39

Fig. 5.1. Proposed model for autotrophic denitrification coupled to thiosulfate oxidation 40

Fig. 5.2. Effect of the different initial nitrate concentration on the NO3-, NO2

-, S2O3

2- and SO4

2- evolu-

tion 45

Fig. 5.3. Proposed model for autotrophic denitrification coupled to elemental sulfur oxidation 46

Fig. 5.4. Evolution of NO3-, NO2

-, S2O3

2- and SO4

2- 52

7

List of tables

Tab.2.1. Engineered applications of chemolithotrophic denitrification with sulfur compounds 15

Tab. 2.2. Mathematical models aimed at chemolithotrophic denitrification with sulfur compounds 20

Tab. 3.1. Mineral growth medium 22

Tab. 3.2. Micronutrient solution 22

Tab. 4.1. Maximum degradation rates and half-saturation constants of thiosulfate-driven denitrifica-

tion kinetics obtained in FBR and batch tests at initial nitrate concentrations of 250 and 1000 mg/L 29

Tab. 4.2. Maximum degradation rate and half-saturation constant of sulfur-driven denitrification ki-

netics obtained in FBR and batch tests at initial nitrate concentrations of 400 and 1000 mg/L 36

Tab. 4.3. Comparison of kinetics constants of thiosulfate- and sulfur-driven denitrifications in FBRs

at nitrate concentration of 1000 mg/L 38

Tab. 5.1. Stoichiometric matrix for model of thiosulfate-driven autotrophic denitrification 43

Tab. 5.2. Overview of the modelling scenarios performed in this study 44

Tab. 5.3. Stoichiometric and kinetics parameters value used for numerical simulations 44

Tab. 5.4. Stoichiometric matrix for model of sulfur-driven autotrophic denitrification 50

Tab. 5.5. Stoichiometric and kinetics parameters value used for numerical simulations 51

Tab. 6.1. Comparison of the thiosulfate-driven denitrification kinetic parameter values obtained in this

study with the existing literature 54

Tab. 6.2. Comparison of sulfur-driven autotrophic denitrification kinetic parameter values obtained in

this study with the existing literature 56

8

Abbreviations

WHO World Health Organization

PBR packed-bed reactor

FBR fluidized-bed reactor

GHGs greenhouse gases

CSTR continuously stirred tank reactor

UASB upflow anaerobic sludge blanket

GAC granular activated carbon

HRT hydraulic retention time

IVS immobilized volatile solids

ITS immobilized total solids

IFS immobilized fixed solids

NR-SO nitrate/nitrite-reducing sulfur-oxidizing bacteria

DO dissolved oxygen

9

List of symbols

𝜇𝑚𝑎𝑥𝑁𝑂3 the maximum specific growth rate of the biomass using nitrate as the electron acceptor (h-1

);

𝜇𝑚𝑎𝑥𝑁𝑂2 the maximum specific growth rate of the biomass using nitrite as the electron acceptor (h-1

);

𝑘𝑑 the bacteria decay coefficient (h-1

);

𝑋 the biomass concentration experimentally quantified as IVS concentration (mg IVS/L);

𝑁𝑂3− the concentration of nitrate (mg N-NO3/L);

𝑁𝑂2− the nitrite concentration (mg N-NO2/L);

𝑁2 the dinitrogen gas concentration (mg N-N2/L);

𝑆2𝑂32− the concentration of thiosulfate (mg S-S2O3

2-/L);

𝑆𝑂42− the concentration of sulfate (mg S-SO4

2-/L);

𝐾𝑆 the half-saturation coefficient for thiosulfate (mg S-S2O32-

/L);

𝐾𝑁𝑂3 the half-saturation coefficient for nitrate (mg N-NO3/L);

𝐾𝑁𝑂2 the half-saturation coefficient for nitrite (mg N-NO2/L);

𝑌𝑁𝑂3 the stoichiometric biomass growth yield related to nitrate (mg IVS/ mg N-NO3);

𝑌𝑁𝑂2 the stoichiometric biomass growth yield related to nitrite (mg IVS/ mg N-NO2);

𝑌𝑆𝑁𝑂3 the thiosulfate to nitrate ratio (mg S-S2O32-

/mg N-NO3);

𝑌𝑆𝑁𝑂2 the thiosulfate to nitrite ratio (mg S-S2O32-

/mg N-NO2).

𝑆𝑠 the concentration of elemental sulfur (mg·d−1);

𝑘𝑠𝑏𝑠 hydrolysis kinetic constant (mg·m−2·d−1);

𝑎∗ hydrolysis surface related parameter (m2 · mg−1);

𝛿 sulfur particle density (mg·m−3);

R elemental sulfur particle radius (m);

ƞ𝑆 the reduction factor for denitrification with elemental sulfur (-);

ƞ𝑆2𝑂3 the reduction factor for denitrification with thiosulfate (-).

10

CHAPTER 1. INTRODUCTION

1.1. Problem definition

Both nitrate and reduced sulfur compounds are considered as environmental pollutants in

groundwater and surface water (Kilic et al., 2014; Ravinchandra et al., 2009). Thus, autotrophic deni-

trification with elemental sulfur or thiosulfate as elector donors is an effective process to remove sim-

ultaneously nitrate and sulfur compounds (Sihinkaya et al., 2014; Sun and Nemati, 2012).

High nitrate concentration is associated with various negative environmental and human

health impacts (Shao et al., 2010). The primary sources of nitrate in groundwater are fertilizers, land-

fills, industrial and domestic wastewaters (Kilica et al., 2014; Kimura et al., 2002; Read-Daily and Ne-

renberg, 2011; Zhang and Shan, 1999). Nitrogen pollution of groundwater could be caused by leach-

ing of nitrate from the use of fertilizers and landfills as well as discharge of the improperly treated

wastewaters (Kilica et al., 2014). The runoff of nitrate into surface waters accelerates the eutrophica-

tion process that negatively influences entire aquatic environments. According to the guidelines of the

European Union, nitrate concentrations in drinking water should not exceed 50 mg-NO3-/L. High con-

centration of nitrate in the drinking water can cause methemoglobinemia in infants and cancer in

adults (Sun and Nemati, 2012).

Hydrogen sulfide, in which sulfur is present with the lowest oxidation number, poses envi-

ronmental and economic problems due to its toxicity, odor, and corrosive characteristics (Sierra-

Alvares et al., 2007). The negative impacts associated with hydrogen sulfide can be seen in sewage

systems, oil fields and petrochemical industry (Vaiopoulou et al., 2005). In autotrophic denitrification,

the product of sulfur complete oxidation is sulfate, which is not as hazardous as hydrogen sulfide and

can be less strictly discharged into surface water bodies (Manconi et al., 2007). However, a concentra-

tion of sulfate lower than 400 mg/l in drinking water is recommended by World Health Organization

(WHO) due to its effect on water taste and the potential laxative properties (Soares, 2002). Neither el-

emental sulfur nor thiosulfate brings negative environmental or human health problems. However,

they could be reduced to hydrogen sulfide under anaerobic conditions.

Packed-bed reactors (PBRs) are commonly applied for autotrophic denitrification with re-

duced sulfur compounds, whilst the existence of some shortcomings such as clogging and limiting

mass transfers (Sánchez et al., 2008). Thus, the study of different bioreactor configurations, such as

the fluidized-bed reactor (FBR), is of particular interest due to its potential advantages.

Autotrophic denitrification kinetics, with elemental sulfur and thiosulfate as electron donors,

have been mostly studied by using pure cultures of Thiobacillus denitrificans and Thiomicrospira de-

nitrificans (Chung et al., 2014; Ravichandra et al., 2009; Sánchez et al., 2008). However, an applica-

tion of autotrophic denitrification with mixed cultures has more scientific interest.

To get better understanding of autotrophic denitrification with thiosulfate and elemental sulfur

and optimize the process performance, the experimental studies should be combined with the process

modeling. However, the limiting studies exist on the dynamical modeling of the autotrophic denitrifi-

cation with thiosulfate (Mora et al., 2015), whilst none has been performed for sulfur-driven auto-

trophic denitrification.

Thus, this research was aimed at getting better understanding of the thiosulfate- and sulfur-

driven autotrophic denitrification by studying process kinetics in the batch assays and FBRs and per-

forming dynamic mathematical modeling.

11

1.2. Research question and objectives

Research question:

To enhance the understanding of thiosulfate- and sulfur-driven autotrophic denitrification by

maintaining two FBRs, performing batch bioassays with fluidized-bed biofilms and developing math-

ematical models able to simulate dynamically the different processes occurring during autotrophic de-

nitrification.

The objectives of the study:

1. To determine kinetics of thiosulfate-driven autotrophic denitrification by performing the ex-

periments with different initial nitrate concentrations in batch microcosms and a FBR operated

in semi-batch mode;

2. To define evolution and performance of autotrophic denitrification with elemental sulfur in

batch and FBR environments at different initial nitrate concentrations;

3. To compare the efficiency of autotrophic denitrification in the FBR with thiosulfate and ele-

mental sulfur;

4. To develop a mathematical model able to simulate dynamically the biological processes oc-

curring during thiosulfate-driven autotrophic denitrification and evaluate the effect of the ini-

tial nitrate concentration on the process performance;

5. To develop a general mathematical model able to describe the kinetics of the autotrophic deni-

trification with elemental sulfur.

12

CHAPTER 2. LITERATURE REVIEW

In particular natural environments (e.g. stratified water bodies, the interface between aerobic

water with an anaerobic sediments and wet soils), the simultaneous presence of electron acceptor (ni-

trate) and electron donor (for example, reduced sulfur compounds), results in chemolithotrophic pro-

cesses (Shao et al., 2010).

Chemolithotrophs are autotrophic denitrifiers, which oxidize sulfur-based compound (e.g.

H2S, S0 and S2O3

2-) or hydrogen, while reducing nitrate to nitrogen gas (Soares, 2002). The application

of hydrogen as an electron donor for autotrophic denitrification is not common because of its high

maintenance and operation costs (Di Capua et al., 2015). Thus, the reduced sulfur compounds are

more widely used.

2.1. Methods for nitrate removal from wastewater

Nitrate removal from wastewater can be performed through physico-chemical or biological

processes (Sun and Nemati, 2012).

Several physico-chemical processes for nitrate removal from wastewater and drinking water

such as reverse osmosis, ion exchange, distillation and electrodialysis have been used (Read-Daily et

al., 2011). The main disadvantages of the physico-chemical methods are low selectivity that results in

the formation of secondary brine waste, high operational cost, and inability of in-situ application (Sa-

hinkaya and Dursun, 2012). Therefore, a biological treatment of nitrate-contaminated wastewater

could be considered an alternative process (Sahinkaya and Dursun, 2012).

Knowles (1982) wrote a detailed review regarding the application of denitrification as a bio-

logical process for nitrate removal from wastewater. Denitrification is an anoxic process that results in

the transformation of nitrate (NO3-) into dinitrogen gas (N2) in four enzymatic steps via the formation

of nitrite (NO2-), nitric oxide (NO), and nitrous oxide (N2O) as intermediates as shown in Fig. 2.1.

Fig. 2.1. Schematic illustration of the nitrogen cycle (Richardson et al. 2009)

13

The relevance of denitrification has been very high for last century and it can be explained by

the following reasons (Knowles, 1982):

denitrification is a main pathway of nitrogen fertilizer loss;

the process has a big potential for nitrogen removal from high-nitrogen waste;

it contributes to N2O production that is one of the most common greenhouse gases (GHGs)

to the atmosphere;

denitrification is part of the global nitrogen cycle.

Denitrification can be carried out under several conditions depending on the choice of micro-

organisms and electron donors (Kimura et al., 2002). Based on the type of electron donors, denitrifica-

tion can be heterotrophic or autotrophic.

Heterotrophic denitrifying bacteria utilize an easily biodegradable organic carbon compound

(acetate, methanol or ethanol etc.) as an energy and carbon source to transform nitrate to nitrogen gas

under anoxic conditions (Sahinkaya and Dursun, 2012). Heterotrophic denitrification is a reliable and

rapid process when sufficient amounts of readily biodegradable substrates are provided with a C/N

ranging between 7 and 9 (Manconi et al., 2007). However, when an adequate carbon source is not

available in some wastewater (e.g. from leather or fertilizer-producing industry, landfill leachate), ad-

ditional supply of an external carbon source could be costly and result in production of excessive

sludge (Chung et al., 2014).

Autotrophic denitrification has been suggested as an alternative to heterotrophic denitrification

of nitrate-contaminated wastewater low in carbon content (Batchelor and Lawrence, 1978; Chung et

al., 2014; Sánchez et al., 2008; Zhang and Lampe, 1999). Energy for autotrophic denitrifying bacteria

is derived from the oxidation of inorganic compounds such as hydrogen or reduced-sulfur compounds

(e.g. H2S, S2O32-

, S0) coupled with the reduction of nitrate (Zhang and Lampe, 1999).

Autotrophic denitrifiers use inorganic carbon compounds (e.g. CO2, HCO3-) as carbon source

(Bachelor and Lawrence, 1978). Therefore, no external organic carbon is needed resulting in a de-

crease of biomass concentration (reduced sludge production and handling), risk of bacterial contami-

nation and operating cost of the process (Shao et al., 2010; Soares, 2002; Zhang and Lampe, 1999).

However, unlike heterotrophic denitrification, autotrophic process has lower reaction rates and con-

sumes alkalinity (Kimura et al., 2002).

Factors as pH, temperature, NO2- and H2S/S

2- concentrations play a major role in autotrophic

denitrification performance. The optimal pH for the autotrophic denitrification is between 6.8 and 8.2

(Chung et al., 2014) and the optimal temperature is 33-35°C (Oh et al., 2000). Nitrite negatively influ-

ences microbial activity in the reactor and at higher concentration than 100 mg/l can inhibit T. denitrif-

icans (Manconi et al., 2007). Sulfide at high concentration is toxic for Thiobacilli (Sublette and Syl-

vester, 1987) and other microorganisms (Manconi et al., 2007).

2.2. Biological aspect of chemolithotrophic denitrification

From the energetic point of view, the oxidation of sulfur compounds like S2-

and S2O32-

to

SO42-

is very appealing to chemolithotrophs as eight electrons are transferred per sulfur atom (Cardoso

et al., 2006).

𝑆2− + 1.6 𝑁𝑂3− + 1.6 𝐻+ → 𝑆𝑂4

2− + 0.8 𝑁2 + 0.8 𝐻2𝑂

∆𝐺°´= -743.9 kL/reaction (2.1)

0.625 𝑆2𝑂32− + 𝑁𝑂3

− + 0.125 𝐻2𝑂 → 1.25 𝑆𝑂42− + 0.5 𝑁2 + 0.25 𝐻+

∆𝐺°´= -765.7 kL/reaction (2.2)

14

The rate of denitrification for a chemolithotrophic enrichment culture depends on the type of

inorganic sulfur compound used as electron donor (Cardoso et al., 2006). S2O32-

is one of the mostly

utilized electron donors due to its bioavailability. Denitrification with hydrogen sulfide or elemental

sulfur also shows relatively high performance (Cardoso et al., 2006) although hydrogen sulfide can al-

so have an inhibitory effect on denitrifying bacteria.

Bacteria that perform denitrification with sulfur compounds as electron donors have a promi-

nent role in sulfur and nitrogen global mineral cycles (Cardoso et al., 2006). Thiobacillus denitrificans

and Thiomicrospira denitrificans are the most representative among these bacteria (Cardoso et al.,

2006). Thiobacillus denitrificans is present in various ecosystems such as hydrothermal vents, deep

sea redox transition zones, sediments, soils, inland soda lakes, etc. (Shao et al., 2010). Isolated in

1904, only in 1991 Thiobacillus denitrificans was shown to outcompete heterotrophic denitrifiers at

the oxic-anoxic interface (Brettar and Rheinheimer, 1991).

2.2.1. Thiosulfate

Denitrification with thiosulfate results in high rate of the process that can be explained by high

bioavailability and non-toxicity of the compound (Oh et al., 2000). Thiosulfate is used by the bacteria

for both energy and synthesis of organic matter (Manconi et al., 2007). Despite high rate of thiosul-

fate-driven autotrophic denitrification, stoichiometry shows that per 1 g of mg N-NO3 remove 11.58 g

of sulfate produced (Trouve et al., 1998).

According to the stoichiometry (Eq. 2.2), for autotrophic denitrification coupled to thiosulfate

oxidation one mole of H+ is produced per four moles of nitrate reduced to nitrogen gas (Sierra-Alvarez

et al., 2007). The pH decreases during denitrification in an inadequately buffered systems and it may

result in inhibitory effects for autotrophic denitrifiers (Oh et al., 2000). Thus, the use of buffering

agents like limestone, sodium bicarbonate, dipotassium phosphate or carbon dioxide etc. is necessary.

In most studies, limestone is used to provide CO2 that serves as carbon and alkalinity source (Kim et

al., 2004; Sahinkaya et al., 2014; Sanchez et al., 2008; Zhang and Shan, 1999).

Thus, sulfate production and alkalinity consumption are the main disadvantages of autotrophic

denitrification with thiosulfate (Chung et al., 2014).

2.2.2. Elemental sulfur

The sulfur properties such as non-toxicity, easy handling and low cost explain its wide appli-

cation for biological denitrification of nitrate contaminated wastewater (Soares, 2002). Additionally,

the efficiency of autotrophic denitrification with sulfur could reach 43.0 mg/L·h-1

that is almost as

high as heterotrophic one (Read-Daily et al., 2011). However, the specific surface area of the sulfur

particles could limit its biological oxidation (Cardoso et al. 2006).

Autotrophic denitrification with elemental sulfur can be described with the following reaction

(Sierra-Alvarez et al., 2007):

0.83 𝑆0 + 𝑁𝑂3− + 0.33 𝐻2𝑂 → 0.83 𝑆𝑂4

2− + 0.5 𝑁2 + 0.66 𝐻+

∆𝐺°´= -547.6 kL/reaction (2.3)

The previous reaction (Eq. 2.3) generates acidity that can be buffered with limestone or bicar-

bonate. Thus, it is necessary to add buffer to the system such as limestone to overcome the issue of al-

kalinity shortage (Koenig and Liu, 2001). The process carried out with elemental sulfur, used as elec-

tron donor, and limestone, used as both inorganic carbon source and buffer, is named sulfur–limestone

autotrophic denitrification (SLAD). The SLAD process has been studied since 1970s. The research has

15

been mainly focused on the operational conditions of the process (optimal sulfur to limestone ratio,

volumetric nitrate loading rate, hydraulic retention time etc.) and its feasibility (Flere and Zhang,

1999; Kilic et al., 2014; Kim et al., 2004; Koenig and Liu, 2001; Sierra-Alvares et al., 2007). The

SLAD systems are known for their reliability, high nitrate removal efficiency. Moreover, they do not

require dosing of expensive electron donors and do not produce waste brines, thus having low costs

(Sierra-Alvares et al., 2007). The SLAD process produces acidity (Sierra-Alvarez et al., 2007). Ac-

cording to the equation 3, per each mg of NO3- N reduced, 4.57 mg CaCO3 is consumed and 7.54 mg

SO42-

is produced (Kim et al., 2004). Thus, the sulfate and acid generation is the main disadvantages of

the SLAD process (Sahinkaya and Dursun, 2012).

2.3. Engineered aspects of chemolithotrophic denitrification

Autotrophic denitrification as a biological process for the wastewater treatment has been stud-

ied over thirty years, but it has not been widely applied because of its low rate compared with hetero-

trophic denitrification (Shao et al., 2010).

2.3.1. Engineered systems

Removal of nitrate from groundwater and drinking water by autotrophic denitrification has

been studied at pilot scale, mostly by using elemental sulfur as electron donor (Tab.2.1). The packed-

bed reactor configuration has been mostly used. There are only a few studies performed in continuous-

ly stirred tank reactor (CSTR), up-flow anaerobic sludge blanket (UASB) and membrane reactors. On-

ly Kim et al. (2004) and Ravichandra et al. (2009) have performed autotrophic denitrification with re-

duced sulfur compounds in FBRs. Thiobacillus denitrificans has been applied in the most studies

(Koenig and Liu, 2002; Ravichandra et al., 2009; Sierra-Alvarez et al., 2007).

Tab.2.1. Engineered applications of chemolithotrophic denitrification with sulfur compounds

Reactor

type

Electron

donor Water type

Identified

microorganism Buffer

Nitrate

removal rate

(mgN-NO3/L·h-

1)

Refer-

ences

PBR

𝑆0

Groundwater

Unidentified Limestone 16

Flere and

Zang,

1999

Unidentified NaHCO3 10 Soares,

2002

Thiobacillus de-

nitrificans Limestone 12.5

Sierra-

Alvarez et

al., 2007

Methylo

virgulaligni,

Sulfurimonas

autotrophica,

Sulfurovum

lithotrophicum,

Limestone 27.5

Kilic et

al., 2014

Drinking

water Unidentified Limestone 17-25

Kuai and

Verstrate,

1999

16

Unidentified Limestone 12.5

Sahinkaya

et al.,

2014

Septic tank

effluent Unidentified Limestone

Non-

estimable

Zhang and

Shan,

1999

Synthetic

wastewater

Thiobacillus

denitrificans Limestone 8-50

Koenig

and Liu,

2002

Unidentified Limestone 8.3

Zeng and

Zhang,

2005

Oil reservoir

brine Unidentified

NaHCO3

Non-

estimable

Sun and

Nemati,

2012

FBR

𝑆0 Synthetic

wastewater Unidentified Limestone

Non-

estimable

Kim et al.,

2004

H2S

Distillery

and dairy

industry

wastewater

Thiobacillus

denitrificans Limestone

Non-

estimable

Ravichan-

dra et al.,

2009

CSTR

𝑆0,S2O32−,

H2S

Synthetic

wastewater

Thiobacillus

denitrificans CO2

Non-

estimable

Sublette et

al., 1987

S2O32−

Synthetic

wastewater Unidentified

NaHCO3,

K2HPO4 6.9

Chung et

al., 2014

𝑆0,

S2O32−,

Groundwater Thiobacillus

denitrificans CO2 6.5

Trouve et

al., 1998

UASB

𝑆0,S2O32−,

H2S

Paper

industry

wastewater

Unidentified

NaHCO3,

K2HPO4

Non-

estimable

Cardoso et

al., 2006

S2O3− Synthetic

wastewater

Thiobacillus

denitrificans,

Thiomicrospira

denitrificans

Limestone Non-

estimable

Sanchez et

al., 2008

Membrane

reactor 𝑆0

Synthetic

wastewater Unidentified

NaHCO3,

K2HPO4 9.3

Kimura et

al., 2002

The first application of SLAD system was performed by Kim et al. (2004) in the PBR and

FBR. PBRs have simple design and are easy to operate but the problem of clogging of the sulfur bed

with an excess biomass occurs (Flere and Zhang, 1999). Moreover, the mass transfer of NO3- from

bulk fluid to the biofilm immobilized on the sulfur and the higher N2O production limit SLAD per-

formance in PBR (Kim et al., 2004).

In contrast, in FBRs, the problem of clogging is prevented and the enhanced mass transfer of

NO3- is observed. Therefore, FBRs are better alternative to perform SLAD processes compared to

PBRs as they demonstrate higher nitrogen removal rate (Kim et al., 2004).

17

2.3.2. FBRs configuration

Since 1980s, FBRs have been used extensively in biotechnology as systems for denitrification

(Green et al., 1995), anaerobic digestion, removal of chlorinated compounds and aromatic hydrocar-

bons (Papirio et al., 2013a).

The FBR is an example of biofilm process commonly applied in biological wastewater or con-

taminated water treatment. Fluidized-bed treatment can be performed in up-flow and down-flow mode

depending on the direction of the water flow.

In up-flow FBRs, the recirculated fluid passes upward through the carrier particles bed at suf-

ficient velocity to fluidize the bed (Sutton and Mishra, 1994). The lowest superficial velocity recom-

mended is 45 m/h (Rabah and Dahab, 2004). Thus, the fluidization provides high surface area of carri-

er material for effective biomass immobilization (Shieh and Hsu, 1996). The scheme of the classical

up-flow FBR is shown below (Fig. 2.2):

Fig. 2.2. Schematic representation of up-flow FBR (Rabah and Dahab, 2004)

The carrier materials used in the up-flow FBRs have higher density than water and should pre-

sent good biomass attachment capacities. Typical FBR carrier materials are porous glass beads, granu-

lar activated carbon (GAC), granular sulfur particles, silicate mineral sand, etc. (Papirio et al., 2013a).

The choice of carrier materials under specific fluidization conditions is crucial for the FBRs perfor-

mance (Papirio et al., 2013a).

Granular activated carbon serves as a good carrier material for uniform biofilm formation

around the particle. Additionally, its porosity guarantees the high nutrients concentration for microor-

ganisms, serves as a protection against fluid shear forces and increase the microbial tolerance to in-

hibitory conditions (Papirio et al., 2013a; Sutton and Mishra, 1994).

The operational conditions, such as fluidization degree, pH, temperature and hydraulic reten-

tion time (HRT) control the biofilm formation and growth, and thus the FBRs performance (Papirio et

al., 2013b).

18

2.3.3. Advantages and disadvantages of FBRs

FBRs demonstrate many advantages compared to different suspended and attached growth bi-

ological systems:

1. High biomass concentration up to 40 g/L (compared with 3 g/L obtained in the activated

sludge systems) results in high mass transfer and efficient substrate utilization rate (Papirio et al.,

2013; Rabah and Dahab, 2004). Similar biomass concentration can be seen in the suspended growth

systems like UASB, in which the additional problems of solids carryover and channeling occur (Rabah

and Dahab, 2004).

2. Higher loading rates and lower hydraulic reaction times (HRTs) reduce reactor volume (Pa-

pirio et al., 2013a; Ravinchandra et al., 2009). Commonly, the FBRs hold near 10% of the space taken

by the CSTRs of similar loading capacities (Rabah and Dahab, 2004). With nitrate loading rate of 500

mg NO3 /L·h-1

, almost complete denitrification could be obtained (Rabah and Dahab, 2004).

3. Good mixing conditions and contact between substrate and biomass are demonstrated be-

cause of the fluidization of the bed (Nicolella et al., 1997; Papirio et al., 2013a; Ravinchandra et al.,

2009).

4. The high resistance to inhibitors because of recycle flow dilution occurs (Papirio et al.,

2013).

5. However, the problem of erosion of internals, pipes, and vessels occurs because of abrasion

by particles (Jakobsen, 2008). Additionally, the FBRs application is limited due to its complex hydro-

dynamics and modeling.

2.4. Modeling aspects of autotrophic denitrification

Autotrophic denitrification with reduced sulfur compounds is a complex process that involves

interactions between biological, physical and chemical systems. For comprehensive understanding of

chemolithotrophic denitrification and optimizing its performance in the FBR and batch environment,

empirical or modeling approach could be used. However, if only empirical approach is applied, plenty

of expensive and time-consuming experiments would be required. Therefore, it is recommended to

combine experimental and modeling studies to get better inside of the process while minimizing ex-

perimental costs.

2.4.1. Type of the models in FBRs

Commonly, models for FBRs include the next components (Saravanan and Sreekrishnan,

2006):

1. A model that describes the rate of bacterial processes controlled by substrate concentration

inbulk solution as a combination of microbial rate processes and physical mass transfer.

According to Nicolella et al. (2000), kinetics of bacterial metabolism could be described by a

Monod-type equation. Monod kinetics can be shown either as a first-order reaction or as a zero-order

reaction based on the Ks value, the Monod saturation constant in the Monod expression (Koenig and

Liu, 2001). Therefore, the relationship between microbial growth rate (𝜇) and substrate concentration

(S) is evaluated:

𝜇 =𝜇𝑚𝑎𝑥 ∙𝑆

𝑆+𝐾𝑠 (2.5)

19

where, 𝜇 – specific growth rate of biomass, S - substrate concentration, Ks - the affinity con-

stant of microorganism, 𝜇 max - the maximum specific growth rate for biomass.

Another common way to characterize the bacterial growth rate and kinetics of substrate con-

sumption is by applying Contois equation that is often used for kinetic modeling of insoluble substrate

degradation (Wang and Li, 2014). Thus, biomass concentration and its specific growth rate is related

inversely as shown in Eq. (2.6):

𝜇 =𝜇𝑚𝑎𝑥 ∙𝑆

𝐾𝑐∙𝑋+𝑆 (2.6)

where 𝑋 - biomass concentration, 𝐾𝑐 – a growth coefficient of the Contois function.

2. A bed fluidization model that shows the distribution of solid particles per unit fluidized bed

volume.

Hydrodynamic behaviour of bioparticles has an important effect for the design of the FBRs.

As the biofilm growing, the density of the bioparticles changes and it influences the reactor hydrody-

namic behaviour (Saravanan and Sreekrishnan, 2006). Thus, the settling and fluidization properties of

the bioparticles such as fluidized-bed height is a crucial information for the reactor design as it influ-

ences the solids residence time and specific biofilm surface area (Nicolella et al., 2000; Saravanan and

Sreekrishnan, 2006).

3. A reactor flow model, that connects the bed fluidization and biofilm models developed in

the previous steps to compute the substrate concentration profile along the axial direction in the FBRs,

thus model the reactor as plug-flow one. However, when fluidization rate in the system is higher than

30 %, the FBRs is considered have a good mixing condition and could be modelled as CSTR.

2.4.2. Mathematical models aimed at chemolithotrophic denitrification with sulfur com-

pounds

The important part of modeling of the chemolithotrophic denitrification is to define corre-

sponding model structure with its parameters. Therefore, the literature review of existing mathematical

models for autotrophic denitrification with reduced sulfur compounds was performed (Tab. 2.2).

Most chemolithotrophic denitrification models with elemental sulphur or thiosulfate are sin-

gle-substrate one-step denitrification models with direct nitrate conversion to dinitrogen gas (Tab.

2.2). However, the single-substrate kinetic models cannot describe generation of multiple products in

the biological systems, such as nitrite and dinitrogen gas in denitrification. Nitrite should be accounted

in the model as it could have an inhibition effect on the denitrifying bacteria activity and decrease de-

nitrification rate (Chung et al., 2014; Mora et al., 2015).

In the previous studies, sulfur-driven autotrophic denitrification accounted for diffusion mass

transport through biofilm, therefore models were on biofilm level. When the substrate transport is not

completely effective, the half-order biofilm kinetics models have been used (Tab. 2.2). However,

when additional alkalinity is provided, autotrophic denitrification can be described as first-order kinet-

ics (Koening and Liu, 2001).

Bachelor and Lawrence (1978) developed the first detailed kinetic model of chemolithotrophic

denitrification with elemental sulfur. The model describes three processes that possibly control uptake

of the nitrate and sulfur: transport of sulfur via biofilm, transport of nitrate from bulk solution to the

biofilm and nitrate transport via biofilm to be removed by microorganism. Elemental sulfur is only de-

graded by the microorganisms that colonize its surface (Koening and Liu, 2001). The dissolution of

the elemental sulfur has been assumed to be generated by biofilm enzymes (Koening and Liu, 2001).

Tab. 2.2. Mathematical models aimed at chemolithotrophic denitrification with sulfur compounds

Type

of electron

donor

Type

of model

1-or 2-step

denitrification

Modeled

system

Type of

biomass Variables

Model

calibration and

validation

References

𝑆0 Biofilm

model

1-step

denitrification

CSTR Mixed

culture NO3

-, SO4

2- +/-

Qambrani et al.,

2015

Plug-flow Thiobacillus

denitrificans NO3

- -/- Moon et al., 2004

Plug-flow Thiobacillus

denitrificans NO3

- +/-

Darbi and Virara-

ghavan, 2003

Plug-flow Thiobacillus

denitrificans NO3

- +/-

Koenig and Liu,

2001

CSTR Thiobacillus

denitrificans NO3

- +/+

Batchelor and

Lawrence, 1978

CSTR Mixed

culture NO3

- +/-

Read-Daily et al.,

2011

CSTR Mixed

culture NO3

- +/-

Zeng and Zhang,

2005

S2O32−

Haldane-

and

Monod-

type model

2-step

denitrification CSTR

Mixed

culture

NO2-, NO3

-,

S2O32-

,

SO42-

+/- Mora et al., 2015

Monod-

type model

1-step

denitrification CSTR

Thiobacillus

denitrificans NO3

- +/-

Claus and Kutzner,

1985

21

Mora et al. (2015) improved the thiosulfate–driven autotrophic denitrification model proposed by

Claus and Kutzner (1985) by considering a 2-step denitrification model that accounts the inhibition effect

of the NO2- intermediate on the mixed culture system. The latter model is the only autotrophic denitrifica-

tion model with reduced sulfur compounds (S2O32-

, S0) that include inhibition submodel for nitrite de-

scribed by Haldane kinetics:

𝑟𝑑𝑒𝑛𝑖𝑡 = 𝑟𝑚𝑎𝑥𝑁𝑂2

−

𝐾𝑛𝑜2+𝑁𝑂2−+

𝑁𝑂2−2

𝐾𝑖,𝑁𝑂2

(2.7)

where, 𝑟𝑑𝑒𝑛𝑖𝑡 - specific denitrification rate, 𝑟𝑚𝑎𝑥 – maximum specific denitrification rate, 𝑁𝑂2− -

nitrite concentration, 𝐾𝑛𝑜2 – half-saturation constant for nitrite, 𝐾𝑖,𝑁𝑂2 – inhibition constant for nitrite.

As shown by Eq. 5, specific denitrification rate is controlled by the concentration of the inhibitory

compounds. However, the model of thiosulfate-driven denitrification developed by Mora et al. (2015)

doesn’t account for the dynamics of all compounds in the system.

To conclude, the previously developed model for autotrophic denitrification with reduced sulfur

compounds are very specific and not easy to apply for bioreactors application. Therefore, more generic

models for thiosulfate and sulfur-driven autotrophic denitrification at reactor scale will be developed in

this study.

22

CHAPTER 3. MATERIALS AND METHODS

3.1. Media and microbial enrichment in FBRs

The autotrophic denitrifying cultures used in the present study were enriched for three months in

FBRs by using activated sludge collected from Cassino wastewater treatment plant, Cassino, Italy, as mi-

crobial source.

A mineral growth medium and a micronutrient solution, containing all the essential trace ele-

ments, were prepared as reported by Cardoso et al. (2006):

Tab. 3.1. Mineral growth medium

K2HPO4 KH2PO4 NH4Cl MgCl2 ∙ 6H2O

g/L 0.8 0.3 0.4 0.021

Tab. 3.2. Micronutrient solution

EDTA ZnSO4 ∙ 7H2O CaCl2 ∙ 2H2O MnCl2 (NH4)6Mo7O24 ∙ 4H2O CuSO4 ∙ H2O CoCl2 ∙ 6H2O

g/L 0.5 0.04 0.07 0.03 0.01 0.02 0.02

The amount of the inoculum used in both reactors was 10% of the working reactor volume. The

reactors were purged with helium gas in order to reduce dissolved oxygen (DO) to below 0.50 mg/L. The

enrichment of denitrifying cultures was performed under anoxic conditions with a NO3- concentration of

approximately 500 mg/L in both reactors. Thiosulfate and elemental sulfur were used as electron donors in

FBR1 and FBR2, respectively. In FBR2, limestone was added as buffer and source of inorganic carbon,

whereas external alkalinity was provided in FBR1 through bicarbonate supplementation. The size of sulfur

particle was about 5 mm in diameter. In FBR1, temperature was maintained between 25 and 29ºC at a pH

ranging between 7.0 and 8.0. In FBR2, temperature varied between 20 and 26ºC with pH in the range of

6.2-8.0. The solution in both FBRs was replaced with a fresh medium when NO3- concentration was below

150 mg/L. Samples from FBRs were taken once per week for the analysis of NO3-, NO2

-, S2O3

2-, SO4

2- pH,

DO and temperature were measured directly in the reactors.

3.2. Experimental set-up

In order to evaluate the potential and kinetics of autotrophic denitrification with both thiosulfate

and elemental sulfur with FBR biofilms, both reactors and batch tests were performed.

3.2.1. Reactors kinetics experiments

The experimental set-up consisted of two identical up-flow FBRs, made of Plexiglass with a total

working volume of 2 L each. A schematic diagram of the FBRs installation was as shown in Fig.2.2.

FBR1 was filled with granular activated carbon as biofilm carrier, and thiosulfate was used as

electron donor. In order to study the kinetics of denitrification, FBR1 was operated in batch mode using

three different initial nitrate concentrations (250 (D1), 500 (D2) and 1000 (D3) mg/L) and a nutri-

23

ent/mineral/buffer (NMB) solution prepared as following: 40 times diluted mineral growth medium (Tab.

3.1), 2 ml/l of micronutrients (Tab. 3.2) and 1 g/L NaHCO3 used as source of both inorganic carbon and

alkalinity. Depending on nitrate concentration, thiosulfate was accordingly supplemented to maintain a

S/N ratio ranging between 4.0-5.1 for complete denitrification (Chung et al. 2014).

Sulfur lentils were used in FBR2 as both source of electrons and carrier material. The sulfur-

limestone ratio was 1:1 (v/v) to have a higher denitrification performance as reported by Kilica et al.

(2014). The composition of NMB solution was the same as used in FBR1. Two kinetic tests were per-

formed in FBR2 with initial NO3- concentration of 500 (G1) and 1000 (G2) mg/L.

All the synthetic solutions were prepared using deionized water. With both thiosulfate and ele-

mental sulfur, the electron donors were supplied in higher amount than required by stoichiometry (Eq. 2.2-

2.3) in order to reduce the influence of other electron-consuming processes FBR1 was loaded with 0.5 L

of GAC, and FBR2 was filled with 0.25 L of sulfur granules and 0.25 L of limestone.

The FBR1 kinetics tests were performed during 26 days while FBR2 kinetics experiments were

carried out for 35 days. The expansion of the bed (30% of the reactor volume) in both FBRs was main-

tained with a recirculation flow by using two magnetic drive pumps (IWAKI MD-10K-22OENL for FBR1

and IWAKI MD-20R-22ONL for FBR2, Iwaki Holland BV, The Netherlands).

3.2.2. Batch kinetics tests

Batch tests were performed by using 100-mL ‘serum’ bottles (Fig. 3.1) and the microbial cultures

enriched in the two FBRs.

Nitrate, thiosulfate and bicarbonate were supplemented from concentrated stock solutions. 5 mL

of biofilm-coated activated carbon and sulfur-limestone biofilm were taken from FBR1 and FBR2, respec-

tively, and added to the bottles. The final working volume was adjusted to 100 mL in each bottle. Anoxic

conditions were maintained by flushing the bottles with helium gas for 2 minutes. After flushing, in the

experiments with thiosulfate, bicarbonate was supplemented to the serum bottles that were then aseptically

sealed with rubber septa and aluminum crimps. Finally, the bottles were placed on a gyratory shaker at

300 rpm at room temperature (26°C).

Fig. 3.1. Schematic representation of the ‘serum’ bottles used within batch assays

24

Batch experiments with FBR1 biofilm:

The kinetics tests with different initial nitrate and thiosulfate concentration were performed with a

S:N ratio between 4.5-5.0. Denitrification was studied by using four different nitrate concentrations: 400

(E1), 600 (E2), 900 (E3) and 1000 (E4) mg/L. Each test was performed in duplicate and conducted till

NO3- concentration reached 160 mg/L.

Batch tests with FBR2 biofilm:

The nitrate removal was investigated by using three different initial nitrate concentrations: 400

(H1), 550 (H2) and 1000 (H3) mg/L. The tests were carried out in triplicates for 15 days.

3.3. Sampling and analytical methods

DO, pH and temperature were measured using a DO sensor (WTW GmbH, Germany) and a pH-

meter (Multi 3410, WTW GmbH, Germany).

During the 3 months culture enrichment phase, the samples from FBRs were taken once per week.

During kinetics tests in FBRs, samples were taken twice per day during the first week and once per day

for the remaining time. Samples were taken with 5-mL disposable syringes and filtered with 0.2 μm fil-

ters. During batch kinetic tests, samples were taken with needles to avoid oxygen transfer into the bottles

with a frequency of two times per day from FBR1- and FBR2-biofilm bottles.

The samples were stored at 4°C up to four days before analysis or at -10°C for longer. Nitrate, ni-

trite, thiosulfate and sulfate in liquid samples were analyzed by ion chromatography (883 Basic IC Plus,

Metrohm, Switzerland). Other parameters, such as immobilized total and volatile solids (ITS and IVS) of

the biofilm-coated activated carbon and sulfur were measured according to APHA (1998). IVS were cal-

culated by using the following formula:

3.4. Calculations

3.4.1. Stoichiometry

Based on the experimental data, the stoichiometry of denitrification with S2O32-

and S0 in both

batch and FBR tests was evaluated as follows:

1. The coupled oxidative and reductive reactions for thiosulfate- (Eq. 3.2-3. 4) and sulfur-driven

denitrification (Eq. 3.1-3.4) were determined (Manconi et al., 2007):

Oxidative reactions

2S0 + 3H2O → S2O3

2- + 6H

+ + 4e

- (3.1)

S2O32-

+ 5H2O → 2SO42-

+ 10H+ + 8e

- (3.2)

Reductive reactions

NO3- + 2H

+ + 2e

- → NO2-- + H2O (3.3)

2NO2- + 8H

+ + 6e

-→ N2 + 4H2O (3.4)

Immobilized Immobilized Immobilized

Volatile Solids = Total Solids - Fixed Solids

(IVS) (ITS) (IFS)

25

2. At first, the difference between the initial and the final soluble concentration was calculated for

NO3. The same principle was used for S2O32-

in thiosulfate-driven denitrification. For sulfate and nitrite,

the difference between the final and initial soluble concentrations was considered.

3. The stoichiometric coefficients were determined by dividing the previously calculated concen-

tration differences for the molar weight of each compound.

4. Based on mass balances, the coefficients of the remaining substances (N2 and S

0) were

evaluated.

3.4.2. Evaluation of kinetics parameters

Based on the experimental data, the kinetics parameters such as half-saturation constants, Ks,

maximum degradation rates (νmax), biomass yields (Y) and maximum biomass growth rates (μmax) were de-

termined.

The half-saturation constants and the maximum reaction rate for both nitrate and thiosulfate were

determined using the Michaelis - Menten equation (Chaplin and Bucke, 1990):

ν =νmax ∙ S

S + Ks (3.5)

where, ν is the rate, S is the generic substrate concentration, Ks is the half-saturation constant

(substrate concentration that results in a degradation rate equal to half of νmax) and νmax is the maximum

degradation rate, occurring when microbial enzymes are completely saturated with substrate.

After raising both side of the Eq. 5 to the power of -1, the next linear transformation was obtained:

1

ν=

Ks

νmax ∙

1

S+

1

νmax (3.6)

Eq. (3.6) was used to calculate Ks and νmax by plotting (1/ν) over (1/S). 1/ νmax was determined as

the intercept and Ks/νmax was equal to the regression slope (Fig. 3.2). Finally, by multiplication of the

slope by νmax, Ks was obtained.

Fig. 3.2. The plot of (1/ν) versus (1/S)

26

The biomass yield coefficient (Y) is an indication of the amount of new biomass produced per unit

of substrate utilized. The biomass growth was evaluated for each set of experiments as the difference be-

tween IVS samples before and after the test.

The relationship between substrate consumed and biomass produced can be expressed as:

dX

dt= Y ∙ (−

dS

dt) (3.7)

where X = immobilized volatile solids concentration (mg/L), t = time (d), S = substrate concentra-

tion (mg/L) and Y = yield coefficient (mg IVS/mg NO3-).

Dividing Eq. (3.7) by X (biomass concentration) and expressing it on a finite time and mass, equa-

tion 8 was obtained:

△X

X∙△t= Y ∙

△S

X∙△t (3.8)

where △X/X∙△t is the specific microbial growth rate, μ (d−1), and △S/X∙△t is the specific

substrate utilization rate, ν (d−1).

Therefore, Eq. (3.8) was rewritten as:

μ =Y∙ v (3.9)

Specific degradation rate and biomass growth rate were calculated for determined intervals of time

as reported in Eq. (3.10-3.11):

vi =(Si−1−Si)/△ti

(Xi−1+Xi)/2 (3.10)

μi =(Xi−Xi−1)/△ti

(Xi+Xi−1)/2 (3.11)

Finally, by plotting the specific growth rate μ versus the specific substrate utilization rate ν to the

yield Y was determined as the slope of the line that fitted the experimental points (as shown in Fig. 3.3):

Fig. 3.3. The plot of the specific substrate utilization rate v with specific growth rate μ

27

To determine the maximum biomass growth rate (μmax), the maximum substrate utilization rate

(νmax) was multiplied by the yield:

μmax =Y∙νmax (3.12)

3.5. Models development

Chemolithotrophic denitrification coupled to thiosulfate and elemental sulfur oxidation were stud-

ied from the kinetics point of view. The kinetic model included the description of the physical and bio-

chemical processes. Model equations were based on mass conservation principle and expressed as double-

Monod kinetics. The developed kinetic model expressed in term of substrate and biomass, and ordinary

differential equations were integrated by using original code on MATLAB platform based on Runge-Kutta

method. The rate equations were presented in the matrix form.

28

CHAPTER 4. EXPERIMENTAL RESULTS

4.1. Kinetics of thiosulfate-driven autotrophic denitrification

Kinetics of thiosulfate-driven autotrophic denitrification was evaluated in the FBR with initial ni-

trate concentration of 250, 500 and 1000 mg/L and batch assays with initial nitrate concentration of 400,

600, 900 and 1000 mg/L. Thiosulfate was supplied in the amount to provide S/N mass ratio of 4.4-5.5.

4.1.1. FBR experiments

NO3-

Three sets of experiments were performed in FBR1 with different initial concentrations of

mg/L. The profiles of NO3-, NO2

-, S2O3

2- and SO4

2- were as reported in 250 (D1), 500 (D2) and 1000 (D3)

Fig. 4.1.

At the beginning of the FBR kinetic tests, pH was equal to 7.5. After 100 h from the beginning of

the experiments, pH was in the range of 6.8-7.0 for all the experiments. Throughout the FBR kinetic tests,

DO was under 0.5 mg/L. During the first 30 h, the highest initial NO3-

higher ni-concentration resulted in

trate removal with nitrate removal efficiency that reached 49, 31 and 21% at initial 1000, 500 (Fig. 4.1a)

and 250 mg/L of nitrate, respectively. The maximum nitrate removal rate of 16.3 mg/L∙h was attained in

experiment D3. After 30 h, nitrate removal was slightly faster in experiment D2 with a constant nitrate

removal rate of 3.5 mg/L∙h compared to 0.5 and 1.5 mg/L∙h obtained in experiments D1 and D3, respec-

tively. At the start-up of the experiment, NO2- concentration was between 103 and 134 mg/L because of an

incomplete replacement of the solution in FBR1 from the previous experimental phase. As shown in Fig.

4.1b, NO2-

remained above 120 mg/L throughout the test. In contrast, concentration in experiment D3 in

NO2-

mg/Lthe other two tests strongly fluctuated and averagely was 116 and 53 in experiments D1 and

D2, respectively, and, thus, lower than in experiment D3.

For the first 30 h of experiments, thiosulfate removal was up to 25 and 40% for experiment D1

and D3, respectively. After 30 h the degradation rate of thiosulfate in the experiment D1 was 1.2 mg/L∙h

and lower than that achieved in experiments D2 and D3. At t=100 h, sulfate concentration was 650, 1900

and 2500 mg/L in experiments D1, D2 and D3, respectively. The mass ratio between sulfate produced and

nitrate removed varied among the experiments: 10, 4.3 and 5.8 in D1, D2 and D3, respectively.

4.1.2. Batch kinetic tests

The profiles of NO3-, NO2

-, S2O3

2- and SO4

2- in the batch bottles with different initial nitrate con-

centrations of 400 (E1), 600 (E2), 900 (E3) and 1000 (E4) mg/L were as shown in Fig. 4.2.

In the kinetics tests with the highest initial nitrate concentration (900 and mg-NO3-/L) a rapid 1000

reduction of nitrate for the first 70 h resulted in the increase of nitrite concentration. The maximum nitrate

removal rate of 11.0 mg/L∙h was obtained in experiment E4. After At 70 h, 70 and 60% of nitrate was re-

moved in experiments E3 and E4, respectively. After 600 h of the experiment, thiosulfate oxidation

SO42-

reached 80, 70, 70 and 55% in E1, E2, E3 and E4 tests, respectively. The highest concentration of

was observed in E1 kinetic test with the highest initial thiosulfate concentration. In E1, the sulfate pro-

duced was 70% of the thiosulfate removed and the ratio of sulfate produced per nitrate removed was in the

), range 3.5-4.2 (gram/gram higher than in the other experiments.

29

4.1.3. Comparison between FBR and batch experiments

The thiosulfate-driven denitrification performance was further evaluated by comparing the effi-

ciencies obtained within both FBRs and batch bioassays as shown in Fig. 4.3.

In the current study, only periodically nitrate removal was faster in the FBR than in the serum bot-

tles. After 30 h, nitrate removal was slightly faster in experiment D2 with a constant nitrate removal rate

of 3.5 mg/L∙h compared to 0.5 and 1.5 mg/L∙h obtained in experiments D1 and D3, respectively. From

100 to 200 h, denitrification was somehow slightly faster in batch bioassays. After 200 h from the begin-

ning of 250 mg-NO3-/l experiments, the nitrate removal rate of 0.26 mg/L∙h was the highest in the FBR

compared to 0.04 mg/L∙h obtained in batch assays. Nitrite concentration in FBR1 kinetic test fluctuated

between 100-200 mg/L and averagely higher than in batch assays. In the batch assay, nitrite reached up to

150 mg-NO2-/L while periodically was below the detection limit. S2O3

2- The highest removal was observed

S2O32-

in FBR1 experiments (Fig. 4.3. III-a, b). At 1000 mg/L of initial nitrate, oxidation rates were simi-

In the current study, the highest molar ratio obtained between sulfate and lar in both FBR and batch tests.

thiosulfate was 1.79. To conclude, thiosulfate and nitrate degradation during some were higher in FBR

NO3-

than in batch environments almost during all the experiments. This was confirmed by the maximum

S2O32-

and degradation rates estimated in FBR experiments as reported in Tab. 4.1.

Tab. 4.1. Kinetics parameters of thiosulfate-driven denitrification kinetics obtained in FBR and

batch tests at initial nitrate concentrations of 250 and 1000 mg/L

Parameter, unit FBR1 batch

𝜈𝑚𝑎𝑥𝑆 mg- S2O32-

/L, ∙d− 1 0.0037 0.0035

𝐾𝑆 mg- S2O32-

/L, 0.015 0.087

𝜈𝑚𝑎𝑥𝑁𝑂3 mg-NO3-/L, ∙d−1 0.005 0.0027

𝐾𝑁𝑂3 mg-NO3-/L, 0.049 0.0062

𝜇𝑚𝑎𝑥𝑁𝑂3, d−1 0.0016 0.0009

mg ISS/mg-NO3Y, 0.33 0.33

of the thiosulfate-driven denitrification The maximum reaction rates and half-saturation constants

were estimated using Michaelis - Menten equation (Eq. 3.5). A linear correlation was used to fit the exper-

imental data and estimate the 𝐾𝑆, 𝐾𝑁𝑂3, 𝜈𝑚𝑎𝑥𝑆 and 𝜈𝑚𝑎𝑥𝑁𝑂3 Michaelis - Menten . The fitting lines of the

equation for both FBR and batch kinetic tests were as shown in Fig.4. The maximum nitrate degradation

rate in FBR1 was almost twice higher than that obtained for batch experiments: 0.005 mg-NO3-

/L∙d−1 0.0027 mg-NO3-/Land ∙d−1 The estimated . 𝜈𝑚𝑎𝑥𝑆 0.0037 and for FBR and batch experiments was

0.0035 mg-S2O32-

/l∙d−1 The highest maximum biomass growth rate (, respectively. 𝜇𝑚𝑎𝑥𝑁𝑂 ) was ob-3

served in FBR environment and was equal to 0.0016 d−1.

Based on the experimental results, the stoichiometry for the autotrophic denitrification with thio-

sulfate was evaluated as reported in Eq. (4.1):

NO3− + 1.77 S2O3

2− → 0,06 NO2− + 0.47 N2 + 3.16 SO4

2− (4.1)

30

Nitrate (a), nitrite (b), thiosulfate (c) and sulfate (d) evolutions in FBR1 kinetic tests at different initial nitrate concentrations: Fig. 4.1.

250, 500 and 1000 mg/L

0,00

0,20

0,40

0,60

0,80

1,00

1,20

0 50 100 150 200 250 300 350

NO

3-(

t)/

NO

3-(

0)

Time (h)

a

0

50

100

150

200

250

0 50 100 150 200 250 300 350

Co

nce

ntr

atio

n (

mg-

NO

2- /L)

Time (h)

b

0,00

0,20

0,40

0,60

0,80

1,00

1,20

0 50 100 150 200 250 300 350

S 2O

32-

(t)/

S 2O

32-- (0

)

Time (h)

c

0

1000

2000

3000

4000

5000

0 50 100 150 200 250 300 350N

et

pro

du

ctio

n (

mg-

SO42-

/L)

Time (h)

d

31

Nitrate (a), nitrite (b), thiosulfate (c) and sulfate (d) profiles in batch kinetics with FBR1 biofilm at initial nitrate Fig. 4.2.

concentrations of 400, 600, 900 and 1000 mg/L

0

0,2

0,4

0,6

0,8

1

1,2

0 200 400 600 800

NO

3-(

t)/

NO

3-(

0)

Time (h)

a

0

50

100

150

200

250

0 100 200 300 400 500 600 700

Co

nce

ntr

atio

n (

mg-

NO

2- /

L)

Time (h)

b

0

0,2

0,4

0,6

0,8

1

1,2

0 100 200 300 400 500 600 700

S 2O

32-

(t)/

S 2O

32--(0

)

Time (h)

c

0

500

1000

1500

2000

2500

3000

3500

0 100 200 300 400 500 600 700N

et

pro

du

ctio

n, m

g-SO

42-

/L

Time (h)

d

32

itrate (I), nitrite (II) and thiosulfate (III) evolutions between FBR and Fig. 4.3. Comparison of n

batch experiments at initial nitrate concentrations of 250 (a) and 1000 (b) mg/L

0,00

0,20

0,40

0,60

0,80

1,00

1,20

0 200 400 600 800

NO

3-(

t)/

NO

3-(

0)

Time (h)

I-a

0,00

0,20

0,40

0,60

0,80

1,00

1,20

0 200 400 600 800

NO

3-(

t)/

NO

3-(

0)

Time (h)

I-b

0,00

50,00

100,00

150,00

200,00

250,00

0 200 400 600 800

Co

nce

ntr

atio

n (

mg-

NO

2- /

L)

Time (h)

II-a

0,00

50,00

100,00

150,00

200,00

250,00

0 100 200 300 400 500 600

Co

nce

ntr

atio

n (

mg-

NO

2- /L)

Time (h)

II-b

0,00

0,20

0,40

0,60

0,80

1,00

1,20

0 200 400 600 800

S 2O

32-(t

)/S 2

O32-

- (0)

Time (h)

III-a

0,00

0,20

0,40

0,60

0,80

1,00

1,20

0 100 200 300 400 500 600

S 2O

32-

(t)/

S 2O

32-- (0

)

Time (h)

III-b

33

4.2. Kinetics of sulfur-driven autotrophic denitrification

4.2.1. FBR experiments

Performance of sulfur-driven autotrophic denitrification was evaluated by studying NO3-, NO2

-,

S2O32-

and SO42-

evolution in the FBR with initial nitrate concentration of 500 (G1) and 1000 (G2) mg/l as

shown in Fig. 4.4. At the start of the FBR kinetic tests, pH was adjusted to 7.5. At t=400 h, the pH de-

creased up to 6.5-6.8 in the experiments. During all the experiments in the FBR, DO was maintained be-

low 0.5 mg/L.

The highest initial nitrate concentration resulted in the highest nitrate removal rate. For the first

70h, nitrate reduction rate was 5.7 and 0.8 mg/L∙h−1 in G2 and G1 kinetic tests, respectively. After t=70 h,

when nitrate concentration reached 600 mg/l in experiment G2, the denitrification rate was similar to that

one of experiment G1 and was equal to 1.2 mg/L∙h−1. At 350 h, nitrate was reduced up to 200 mg/L in the

FBR kinetic tests and depletion of nitrite took place. At the beginning of experiment G2, NO2- concentra-

tion was 140 mg/L because of a partial replacement of solution from the earlier experimental stage. Nitrite

concentrations fluctuated and averagely were 210 and 170 mg/L in experiment G1 and G2, respectively.

Thiosulfate was observed throughout the kinetic tests and it strongly fluctuated between 120 and

320 mg/L in both experiments. At t=400h, sulfate production was 1300 and 1600 mg/L in experiments G1

and G2, respectively. The higher mass ratio of sulfate produced per nitrate reduced (gram/gram) was ob-

served in experiment G1 and was equal to 3.8.

4.2.2. Batch kinetic tests

Kinetic tests were performed in the batch bottles with different initial nitrate concentration of 400

(H1), 550 (H2) and 1000 (H3) mg/L. NO3-, NO2

-, S2O3

2- and SO4

2- evolution was reported as shown in Fig.

4.5.

In the experiment with the highest initial nitrate concentration, the slightly higher denitrification

rate was observed. At t=80 h, nitrate reduction reached up to 25, 25 and 40% in experiments H1, H2 and

H3, respectively. The maximum nitrate removal rate of 10.0 mg/L∙ h−1 was attained in experiment H3.

After 150 h from the beginning of the experiment, the highest nitrate reduction of 1.5 mg/L∙ h−1 remained

in experiment H3. The NO2- concentration in kinetic tests H1 and H2 were strongly fluctuated and reached

up to 175 mg/L while in experiment H3 nitrite was produced gradually up to 300 mg/L. After 300 h, when

nitrate concentration was reduced up to 160 mg/L, nitrite depletion occurred.

Throughout the kinetic tests, the thiosulfate concentration between 50 and 150 mg/L was detected.

The amount of elemental sulfur supplied in each bottle exceeded stoichiometric amount in 100 times: for

example, in batch test H3, 1.45 mole/L of elemental sulfur was supplied.

During the first 100 h of the experiments, the sulfate production rate was similar for all kinetic

tests and was equal to 3.5 mg/L∙h−1. On the contrary, at t=350 h, the experiment with the highest initial

nitrate concentration (H3) demonstrated the highest sulfate production up to 1200 mg/L, while in the ex-

periment H1 and H2 the sulfate concentrations were 750 and 1050 mg/L, respectively.

34

Nitrate (a), nitrite (b), thiosulfate (c) and sulfate (d) evolutions in FBR2 kinetic tests at different initial nitrate concentrations: Fig. 4.4.

500 and 1000 mg/L

0,00

0,20

0,40

0,60

0,80

1,00

1,20

0 100 200 300 400 500

NO

3-(

t)/

NO

3-(

0)

Time (h)

a

0

50

100

150

200

250

300

0 100 200 300 400 500

Co

nce

ntr

atio

n (

mg-

NO

2- /

lL)

Time (h)

b

0

50

100

150

200

250

300

350

400

0 100 200 300 400 500

Co

nce

ntr

atio

n (

mg-

S 2O

32-

/L)

Time (h)

c

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 100 200 300 400 500

Ne

t p

rod

uct

ion

(m

g-SO

42-

/L)

Time (h)

d

35

Nitrate (a), nitrite (b), thiosulfate (c) and sulfate (d) evolutions in batch kinetics with FBR2 biofilm at initial nitrate Fig. 4.5.

concentrations of 400, 550 and 1000 mg/L

0,00

0,20

0,40

0,60

0,80

1,00

1,20

0 100 200 300 400

NO

3-(

t)/

NO

3-(

0)

Time (h)

a

0

50

100

150

200

250

300

350

0 100 200 300 400

Co

nce

ntr

atio

n (

mg-

NO

2- /L)

Time (h)

b

0

20

40

60

80

100

120

140

160

180

0 100 200 300 400

Co

nce

ntr

atio

n (

mg-

S 2O

32- /

L)

Time (h)

c

0,00

200,00

400,00

600,00

800,00

1000,00

1200,00

1400,00

0 100 200 300 400

Ne

t p

rod

uct

ion

(m

g-SO

42- /

L)

Time (h)

d

36

4.2.3. Comparison between FBR and batch experiments

Sulfur-based autotrophic denitrification was further investigated by comparing FBR and batch ex-

periments with different initial nitrate concentrations of 400 and 1000 mg/L as shown in Fig. 4.6.

For 400 mg/L initial nitrate kinetic tests, the slightly higher nitrate utilization rate of 0.7 mg/L∙h−1

was observed in FBR than in the batch experiment. This was confirmed by the highest nitrate degradation

rate of for the FBR experiments as reported in Tab. 4.2. However, the maximum nitrate removal rate was

of 10.0 mg/L∙ h−1 was observed in the batch environment. The higher initial nitrate concentration of 1000

mg/L resulted in the higher denitrification rates of 2.0 and 2.3 in the batch and FBR experiments, respec-

tively. However, after 150 h from the beginning of experiment K1, the lowest denitrification rate of 0.16

mg/L∙h−1 was in the FBR and its nitrite concentration averagely was 210 mg/L and was higher than 120

mg/L observed in batch assay. After 350 h from the start of the experiments, the most of nitrate was re-

duced and its removal reached 60% in every kinetic test and the nitrite degradation took place. As shown

in Fig. 4.6a, in the experiments with the higher initial nitrate concentration, the higher sulfate production

of 4.3 and 3.1 mg/L∙h−1 was obtained for FBR and batch kinetic tests, respectively.

Michaelis - Menten equation (Eq. 3.5) was applied to determine the maximum reaction rates and

of nitrate. The experimental points was fitted by the line to obtain the half-saturation constants 𝜈𝑚𝑎𝑥𝑁𝑂 3

and 𝐾𝑁𝑂 ,. The slightly higher 3 0.0032 mg/Lmaximum nitrate degradation rate of ∙d−1 was indicated in the

0.0031 mg/LFBR environment compared with the ∙d−1 in the batch assays.

Tab. 4.2. Maximum degradation rate and half-saturation constant of sulfur-driven denitrification

kinetics obtained in FBR and batch tests at initial nitrate concentrations of 400 and 1000 mg/L

Parameter, unit FBR Batch

𝜈𝑚𝑎𝑥𝑁𝑂3 mg-NO3-/L, ∙d−1 0.0032 0.0031

𝐾𝑁𝑂3, mg/L 0.025 0.026

g cells/ mg NO3-

Y, m 0.55 0.55

𝜇𝑚𝑎𝑥𝑁𝑂3 ℎ−1, 0.0018 0.0017

Additionally, based the stoichiometric reactions were calculated on the experimental data for sul-

fur-driven autotrophic denitrification:

𝑁𝑂3− + 1.16 𝑆0 → 0.42 𝑁2 + 0.16 𝑁𝑂2

− + 1.04 𝑆𝑂42− + 0.06 𝑆2𝑂3

2− (4.2)

As shown in the Fig. 4.6 III-a, b, the higher sulfate production was observed in the FBR2 envi-

ronment compared with the batch one. It was confirmed by the calculated stoichiometry: 1.04 molar ratio

of the generated sulfate per nitrate consumed.

37

itrate (I), nitrite (II) and sulfate (III) evolutions between FBR and batch experiments at initial nitrate Fig. 4.6. Comparison of n

concentrations of 400 (a) and 1000 (b) mg/L

0,00

0,50

1,00

1,50

0 100 200 300 400 500

NO

3-(

t)/

NO

3-(

0)

Time (h)

I-a

0,00

0,50

1,00

1,50

0 100 200 300 400 500

NO

3-(

t)/

NO

3-(

0)

Time (h)

I-b

0,00

100,00

200,00

300,00

0 100 200 300 400 500

Co

nce

ntr

atio

n (

mg-

NO

2- /L)

Time (h)

II-a

0

100

200

300

400

0 100 200 300 400 500

Co

nce

ntr

atio

n (

mg-

NO

2- /L)

Time (h)

II-b

0

500

1000

1500

2000

0 100 200 300 400 500Ne

t p

rod

uct

ion

(m

g-SO

42-/L

)

Time (h)

III-a

0

500

1000

1500

2000

0 100 200 300 400 500

Ne

t p

rod

uct

ion

(m

g-SO

42-/L

)

Time (h)

III-b

38

4.3. Comparison of thiosulfate- and sulfur- driven autotrophic denitrification

Autotrophic denitrification with initial nitrate concentration of 1000 mg/l is further evaluated in

the FBR fed with thiosulfate (FBR1) and elemental sulfur (FBR2) as illustrated in Fig. 4.7. The NO3-,

NO2- and SO4

2- evolution was compared in both reactors for the first 200 h of the experiment because the

FBR1 kinetic test lasted 200 h.

The highest overall denitrification rate of 4.0 mg-NO3-/L∙h−1 was observed in the FBR1 compared

to 2.4 mg-NO3-/L∙h−1 in the FBR2. After 200 h from the beginning of the experiments, 80 and 55% of ni-

trate was removed in the FBR1 and FBR1, respectively. Moreover, denitrification with thiosulfate was

shown to be a more complete process, resulting in a lower nitrite accumulation as reported in the stoichi-

ometric reactions (Eq. 4.1-4.2). In the FBR2, average nitrite concentration in the FBR2 of 170 mg/l was

slightly higher than 153 mg/l detected in the FBR1.

At t=200 h, 3500 mg/l of sulfate was produced in the FBR1 kinetic test compared with 1000 mg/l

in the FBR2. The sulfate generation rate of 17.5 mg/L∙h−1 was 3.5 times higher in the FBR1 experiment

than in the FBR2.

Tab. 4.3. Comparison of kinetics constants of thiosulfate- and sulfur-driven denitrifications in

FBRs at nitrate concentration of 1000 mg/L

Parameter, unit FBR1 FBR2

g ISS/ g NO3-

Y, 0.33 0.55

νmaxNO3 mg-NO3-/L, ∙d−1 0.0050 0.0032

𝐾𝑁𝑂3, mg/L 0.049 0.025

μmaxNO3, d−1 0.0017 0.0018

The results of kinetics study illustrates that the maximum nitrate degradation rate calculated from

the Michaelis - Menten equation was higher for thiosulfate-driven denitrification performed in FBR1 and

was equal to 0.005 mg/L∙d−1 The estimated half-saturation constant for nitrate was higher in (Tab. 4.3).

FBR with thiosulfate. The biomass yield of nitrate reduction with thiosulfate and elemental sulfur were

equal to 0.33 and 0.55 mg-IVS/mg-NO3-, respectively.

39

itrate (a), nitrite (b) and sulfate (c) evolutions in FBRs kinetic tests with initial Fig. 4.7. N

nitrate concentrations of 1000 mg/L

0,00

0,20

0,40

0,60

0,80

1,00

1,20

0 100 200 300 400 500

NO

3-(

t)/

NO

3-(

0)

Time (h)

a

0,00

50,00

100,00

150,00

200,00

250,00

300,00

0 100 200 300 400 500

Co

nce

ntr

atio

n (

mg-

NO

2- /L)

Time (h)

b

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0 100 200 300 400 500

Co

nce

ntr

atio

n (

mg-

S 2O

32

- /L)

Time (h)

c

40

CHAPTER 5. MODELING RESULTS

5.1. Mathematical modeling of thiosulfate-driven autotrophic denitrification

The main objective of this study was to develop a mathematical model able to simulate dynami-

cally the biological processes occurring during thiosulfate- and sulfur-driven autotrophic denitrification.

5.1.1. Model construction

5.1.1.1. Biochemical reactions

The developed model considers the biological pathways reported in Fig. 5.1. Thiosulfate-driven

autotrophic denitrification is carried out by denitrifying microorganisms, named in the model ni-

trate/nitrite-reducing sulfur-oxidizing bacteria (NR-SO), which are able to reduce oxidized nitrogen com-

pounds to dinitrogen gas with simultaneous thiosulfate oxidation.

Fig. 5.1. Proposed model for autotrophic denitrification coupled to thiosulfate oxidation

According to Mora et al. (2015), a two-step denitrification process has been considered: NR-SO

consume nitrate and produce nitrite which are further reduced to dinitrogen gas. Contextually, thiosulfate

is oxidized to sulfate which constitutes the final product of the process.

The stoichiometry of the process has been assumed as reported in Mora et al. (2015):

𝑆2𝑂32− + 2.626 𝑁𝑂3

− + 0.043𝐶𝑂2 + 0.644 𝐻𝐶𝑂3− + 0.137𝑁𝐻4

+ + 0.631𝐻2𝑂 →

0.137𝐶5𝐻7𝑂2𝑁 + 2.62𝑁𝑂2− + 1.494𝐻+ + 2𝑆𝑂4

2− (5.1),

𝑆2𝑂32− + 2.070 𝑁𝑂2

− + 0.028𝐶𝑂2 + 0.419 𝐻𝐶𝑂3− + 0.089𝑁𝐻4

+ + 0.400𝐻+ →

0.089𝐶5𝐻7𝑂2𝑁 + 1.035𝑁2 + 0.275𝐻2𝑂 + 2𝑆𝑂42− (5.2),

41

where: Eq. (5.1) describes the growth of NR-SO on nitrate and thiosulfate with production of ni-

trite and sulfate; Eq. (5.2) represents the growth of the same microorganisms on nitrite with final produc-

tion of dinitrogen gas.

5.1.1.2. Model assumptions

The following assumptions were applied in the model:

1. The following components have been taken into account in model formulation:

- Substrates: nitrate (NO3-, mg N/L), thiosulfate (S2O3

2-, mg S/L);

- Intermediate: nitrite (NO2-, mg N/L);

- Products: sulfate (SO42-

, mg S/L), dinitrogen gas (N2, mg N/L);

- Biomass: NR-SO bacteria (X, mg IVS (immobilized volatile solids/L).

2. The biological system in the batch assay has been modelled as a CSTR.

3. Denitrification has been described as two-step process: sequential oxidation of nitrate to nitrite-

and dinitrogen gas (Kaelin et al., 2009):

NO3- →

NO2- →

N2

(5.3)

4. Autotrophic organisms have been divided based on the type of electron acceptor used (Mo-

zumder et al., 2014): nitrite (Xno2) and nitrate (Xno3). This distinction was artificially made, therefore re-

action rates were described in terms of total autotrophic population.

X= Xno2+Xno3 (5.4)

5. Double-Monod equation has been used models to consider the simultaneous presence of elec-

tron donor (𝑆1) and electron acceptor (𝑆1) in the process (Mora et al. 2015):

𝜇 = 𝜇𝑚𝑎𝑥 ·𝑆1

𝑆1+𝐾𝑆1·

𝑆2

𝑆2+𝐾𝑆2 (5.5)

6. Maximum growth rate of the NR-SO biomass on nitrite (𝜇𝑚𝑎𝑥𝑁𝑂3) has been assumed equal to

the one on nitrate (𝜇𝑚𝑎𝑥𝑁𝑂3).

7. As usually assumed in mathematical modelling of these processes: 𝑘𝑑 << 𝜇𝑚𝑎𝑥𝑁𝑂3.

8. No nitrite, dinitrogen gas, and sulfate were present at the beginning of the experiments.

5.1.1.3. Model equations

The kinetic expressions for thiosulfate driven autotrophic denitrification are summarized below:

1. Biomass growth

𝑑[𝑋]

𝑑𝑡= 𝑋 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32− ∙ (𝜇𝑚𝑎𝑥𝑁𝑂3 ∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− + 𝜇𝑚𝑎𝑥𝑁𝑂2 ∙𝑁𝑂2

−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂2−+𝑁𝑂3

−) − 𝑘𝑑 ∙ 𝑋 (5.6)

2. Reduction of NO3- to NO2

-

42

𝑑[𝑁𝑂3−]

𝑑𝑡= −

1

𝑌𝑁𝑂3∙ 𝜇𝑚𝑎𝑥𝑁𝑂3 ∙ 𝑋 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32− ∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− (5.7)

3. NO2- generation and its reduction to N2

𝑑[𝑁𝑂2−]

𝑑𝑡= 𝑋 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32− ∙ (𝜇𝑚𝑎𝑥𝑁𝑂3 ∙

1

𝑌𝑁𝑂3∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− − 𝜇𝑚𝑎𝑥𝑁𝑂2 ∙1

𝑌𝑁𝑂2∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂2−+𝑁𝑂3

−)

(5.8)

4. N2 production

𝑑[𝑁2]

𝑑𝑡=

1

𝑌𝑁𝑂2∙ 𝜇𝑚𝑎𝑥𝑁𝑂2 ∙ 𝑋 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32− ∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂2−+𝑁𝑂3

− (5.9)

5. S2O32-

utilization

𝑑[𝑆2𝑂32−]

𝑑𝑡= −𝑋 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32− ∙ (𝜇𝑚𝑎𝑥𝑁𝑂3 ∙

𝑌𝑆𝑁𝑂3

𝑌𝑁𝑂3∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− + 𝜇𝑚𝑎𝑥𝑁𝑂2 ∙𝑌𝑆𝑁𝑂2

𝑌𝑁𝑂2∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂2−+𝑁𝑂3

−) (5.10)

6. SO42-

production

𝑑[𝑆𝑂42−]

𝑑𝑡= −

𝑑[𝑆2𝑂32−]

𝑑𝑡 (5.11)

where:

𝜇𝑚𝑎𝑥𝑁𝑂3 is the maximum specific growth rate of the biomass using nitrate as the electron acceptor (h-1

);

𝜇𝑚𝑎𝑥𝑁𝑂2 is the maximum specific growth rate of the biomass using nitrite as the electron acceptor (h-1

);

𝑘𝑑 denotes the bacteria decay coefficient (h-1

);

𝑋 represents the biomass concentration experimentally quantified as IVS concentration (mg IVS/L);

𝑁𝑂3− denotes the concentration of nitrate (mg N-NO3/L);

𝑁𝑂2− is the nitrite concentration (mg N-NO2/L);

𝑁2 represents the dinitrogen gas concentration (mg N-N2/L);

𝑆2𝑂32− is the concentration of thiosulfate (mg S-S2O3

2-/L);

𝑆𝑂42− is the concentration of sulfate (mg S-SO4

2-/L);

𝐾𝑆 is the half-saturation coefficient for thiosulfate (mg S-S2O32-

/L);

𝐾𝑁𝑂3 is the half-saturation coefficient for nitrate (mg N-NO3/L);

𝐾𝑁𝑂2 is the half-saturation coefficient for nitrite (mg N-NO2/L);

𝑌𝑁𝑂3 denotes the stoichiometric biomass growth yield related to nitrate (mg IVS/ mg N-NO3);

𝑌𝑁𝑂2 denotes the stoichiometric biomass growth yield related to nitrite (mg IVS/ mg N-NO2);

𝑌𝑆𝑁𝑂3 represents the thiosulfate to nitrate ratio (mg S-S2O32-

/mg N-NO3);

𝑌𝑆𝑁𝑂2 represents the thiosulfate to nitrite ratio (mg S-S2O32-

/mg N-NO2).

A schematic representation of the Eqs. (5.6)-(5.11) is reported in Tab. 5.1.

43

Tab. 5.1. Stoichiometric matrix for model of thiosulfate-driven autotrophic denitrification

Aij i component S2O3

2−

[gS∙m−3]

NO2−

[gN∙m−3]

NO3−

[gN∙m−3]

N2

[gN∙m−3]

SO42−

[gS∙m−3]

X

[gIVS∙m−3] Rate (𝜌𝑖 , 𝑔 𝐼𝑉𝑆∙m−3∙d−1)

j process

XNO2

XNO3

1. Autotrophic

growth on NO3−

− YSNO3

YNO3

1

YNO3

−1

YNO3

YSNO3

YNO3

1 𝜇𝑚𝑎𝑥1 ∙ 𝑋 ∙𝑆2𝑂3

2−

𝐾𝑆+𝑆2𝑂32− ∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

−

2. Autotrophic

growth on NO2−

−YSNO2

YNO2

−1

YNO2

1

YNO2

YSNO2

YNO2

1 𝜇𝑚𝑎𝑥2 ∙ 𝑋 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32− ∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂2−+𝑁𝑂3

−

3. Decay of X -1 𝑘𝑑 ∙ 𝑋

44

5.1.2. Model simulations

The model introduced in the previous sections has been used to simulate thiosulfate-driven denitri-

fication under different initial conditions. In particular, four simulation scenarios have been considered as

reported in Tab. 5.2. They differ on the initial nitrate and thiosulfate concentrations, that have been

changed in order to maintain the S:N ratio in the range 5.2-5.3. The initial biomass concentration has been

assumed equal to 5924 mg/L for all the simulations. The four series of simulations have been carried out

in order to evaluate the effect of initial nitrate concentration on process performance.

Tab. 5.2. Overview of the modelling scenarios performed in this study

Scenarios NO3

-

(mgN∙m−3)

S2O32-

(mgS∙m−3)

S:N ratio

(mg/mg) Initial NO3

- concentra-

tion (mg∙m−3) Scenario name

400 Scenario E1 85 450 5.3

600 Scenario E2 147 783 5.3

900 Scenario E3 209 1071 5.2

1000 Scenario E4 239 1260 5.3

The stoichiometric and kinetic parameters used in the model simulations are reported in Tab. 5.3. The val-

ue of half-saturation constant for thiosulfate (𝐾𝑆) and for nitrate (𝐾𝑁𝑂3) have been evaluated by graphical

calibration that is defined in Tab. 5.3.

Tab. 5.3. Stoichiometric and kinetics parameters value used for numerical simulations

Parameter Value Unit References

Stoichiometric parameters

𝑌𝑁𝑂3 0.39 mg IVS/ mg N-NO3 Chung et al., 2014

𝑌𝑁𝑂2 0.14 mg IVS/ mg N-NO2 Chung et al., 2014

𝑌𝑆𝑁𝑂3 1.74 mg S-S2O32-

/mg N-NO3 Mora et al., 2015

𝑌𝑆𝑁𝑂2 2.2 mg S-S2O32-

/mg N-NO2 Mora et al., 2015

Kinetic parameters

𝜇𝑚𝑎𝑥𝑁𝑂3 0.0015 h-1

Adapted from Mora et al., 2015

𝜇𝑚𝑎𝑥𝑁𝑂2 0.0015 h-1

This study

𝐾𝑆 200.0 mg S-S2O32-

/L This study

𝐾𝑁𝑂3 56.4 mg N-NO3/L This study

𝐾𝑁𝑂2 35.0 mg N-NO2/L Chung et al., 2014

𝑘𝑑 0.0001 h-1

This study

Modelling results are reported in Fig. 5.2 in terms of NO3 (a) , NO2 (b), S2O32-

(c) and SO42-

(d) profiles

for all the simulation scenarios.

45

Fig. 5.2. Effect of the different initial nitrate concentration on the NO3-, NO2

-, S2O3

2- and SO4

2- evolution

46

As shown in Fig. 5.2a, the increased initial nitrate concentration results in a higher nitrate removal

rate which reaches a maximum of 5 mg/ L·h−1 for scenario E4. For all the simulation scenarios, nitrate is

completely consumed after 200h. Fig. 5.2b shows NO2- trends over time. The profile reproduces the be-

havior of a reaction intermediate: at the beginning of the simulation, the concentration increases up to a

maximum (50h) which varies according to the initial concentration of nitrate; after 50h nitrite concentra-

tion starts to decrease due to its reduction to dinitrogen gas, reaching complete depletion after 300 h.

Fig. 5.2c displays dynamic behavior of thiosulfate over time. As expected, the highest thiosulfate

removal rate was observed in the scenario with the highest initial nitrate concentration: 5.2, 9.1, 12.5 and

14.7 mg- S2O32-

/L·h−1, respectively. The sulfate profiles are reported in Fig. 3d. The highest sulfate pro-

duction rate of 16 mg/ L·h−1 is observed in the scenario E4. Due to the complete depletion of thiosulfate

after 300h, the production of sulfate stops at the same time.

5.2. Mathematical modeling of sulfur-driven autotrophic denitrification

5.2.1. Model construction

5.2.1.1. Biochemical reactions

The proposed model accounts for the biological and physico-chemical pathways summarized in

Fig. 1. Autotrophic denitrification with elemental sulfur is performed by microorganisms named in the

model nitrate/nitrite-reducing sulfur oxidizing bacteria (NR-SO) that convert nitrate and nitrite to dinitro-

gen gas by oxidizing elemental sulfur and thiosulfate.

Fig. 5.3. Proposed model for autotrophic denitrification coupled to elemental sulfur oxidation

Similarly to Mora et al. (2015), the autotrophic denitrification has been modeled as a two-step

process: sequential conversion of nitrate to nitrite with its further oxidation to dinitrogen gas is performed

by NR-SO bacteria. Prior to the microbial uptake of the elemental solid sulfur (SS), its hydrolysis occurs,

47

resulting in the formation of bioavailable sulfur (Sb). The latter step is assumed to be abiotic and inde-

pendent from the denitrification process. Further, the microbial utilization of bioavailable sulfur and its

oxidation to thiosulfate takes place. Finally, thiosulfate is oxidized to sulfate which constitutes the final

product of the process.

The stoichiometry of the process has been assumed based on the oxidative and reductive reactions

(Eq. 3.1-3.4):

2S0 +NO3

- → S2O32-

+NO2—

(5.12)

2S0 + 2NO2

- → S2O32-

+ N2 (5.13)

S2O32-

+NO3- → 2SO4

2- + NO2

-- + H2O (5.14)

S2O32-

+ 2NO2- → 2SO4

2- +N2 (5.15)

where the growth of the biomass NR-SO on elemental sulfur with nitrate (Eq. 5.12) and nitrite

(Eq. 5.13) as electron acceptors results in thiosulfate production, that is further converted by the same mi-

croorganisms to sulfate by reducing oxidized nitrogen compounds (Eq. 5.14-5.15).

5.2.1.2. Model assumptions

The following assumptions have been taken into account in model definition:

1. The following components are included in the model:

Substrates: nitrate (NO3-, mg N/L), bioavailable sulfur (S

b, mg S/L);

- Intermediates: nitrite (NO2-, mg N/L), thiosulfate (S2O3

2-, mg S/L);

- Products: sulfate (SO42-

, mg S/L), dinitrogen gas (N2, mg N/L);

- Biomass: NR.-SO bacteria (X, mg IVS (immobilized volatile solids/L)).

2. The biosystem in the batch assays has been modelled as a CSTR.

3. Denitrification has been modeled as a two-step process with nitrate oxidation to nitrite followed

by nitrite oxidation to dinitrogen gas (Kaelin et al., 2009):

NO3- →

NO2- →

N2

(5.16)

4. Hydrolysis of solid elemental sulfur (𝑆𝑠) has been assumed to be independent of denitrification

process and thus occurrs preliminary to the sulfur oxidation. The hydrolysis process has been modeled by

using a surface-based kinetics approach (Esposito et al., 2011):

𝑑[𝑆𝑠]

𝑑𝑡 = − 𝑘𝑠𝑏𝑠 ∙ 𝑎∗ ∙ 𝑆𝑠 (5.17)

𝑎∗ =3

𝛿·𝑅 (5.18)

where 𝑆𝑠 – concentration of elemental sulfur (mg·d−1), 𝑘𝑠𝑏𝑠 – hydrolysis kinetic constant

(mg·m−2·d−1), 𝑎∗ - hydrolysis surface related parameter (m2 · mg−1), 𝛿 – sulfur particle density

(mg·m−3), R – elemental sulfur particle radius (m).

The "fictitious" bioavailable sulfur (Sb), formed as a result of the solid sulfur hydrolysis, is further

oxidized to thiosulfate and then sulfate:

48

SS →

Sb →

S2O32-

→

SO42-

(5.19)

5. Autotrophs has been divided in the four groups based on the type of substrate (Sb

and S2O32-

)

and electron acceptor (NO3- and NO2

-) used:

X = XNO2S + XNO3S + XNO2S2O3 + XNO3S2O3 (5.20)

6. This distinction has been artificially made, therefore reaction rates were described in terms of

total autotrophic population (X).

7. Double-Monod equation was used to consider the simultaneous presence of electron donor

(𝑆1) and electron acceptor (𝑆1) in the process (Mora et al., 2015):

𝜇 = 𝜇𝑚𝑎𝑥 ·𝑆1

𝑆1+𝐾𝑆1·

𝑆2

𝑆2+𝐾𝑆2 (5.21)

8. The maximum growth rate of the NR-SO biomass on nitrite (𝜇𝑚𝑎𝑥𝑁𝑂3) was assumed equal to

the one on nitrate (𝜇𝑚𝑎𝑥𝑁𝑂3).

9. As usually assumed in mathematical modeling of these processes: 𝑘𝑑 << 𝜇𝑚𝑎𝑥𝑁𝑂3.

10. Nitrite, dinitrogen gas, thiosulfate and sulfate were initially absent in the system.

11. Sulfur particles were assumed to have an identical spherical form.

12. Considering that the stoichiometry of the sulfur-driven autotrophic denitrification still has to

be evaluated, the stoichiometry of thiosulfate-driven autotrophic denitrification, proposed by Mora et al.

(2015) was used in the current study (Eq. 5.1-5.2). Moreover, stoichiometric sulfur to nitrate ratio is as-

sumed to be the same as thiosulfate to nitrate ratio 𝑌𝑆𝑁𝑂3 (mg S∙mg−1N), and sulfur to nitrite ratio is equal

to thiosulfate to nitrite ratio 𝑌𝑆𝑁𝑂2 (mg S∙mg−1N).

13. The value of the half-saturation constant for elemental sulfur (𝐾𝑆𝑏) is assumed to be the same

as for the thiosulfate (𝐾𝑆).

14. Following the consideration that thiosulfate is more bioavailable than elemental sulfur for de-

nitrifiers (Qambrani et al., 2015) we assume that: ƞ𝑆2𝑂3=1 and ƞ𝑆=0.5.

5.2.1.3. Model equations

The kinetic equations for sulfur-driven autotrophic denitrification are summarized below:

1. Biomass growth 𝑑[𝑋]

𝑑𝑡= ƞ𝑆 · 𝑋 ∙

𝑆𝑏

𝐾𝑆𝑏+𝑆𝑏∙ (𝜇𝑚𝑎𝑥1 ∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− + 𝜇𝑚𝑎𝑥2 ∙𝑁𝑂2

−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂3−+𝑁𝑂2

−) + ƞ𝑆2𝑂3 ∙ 𝑋 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32− ∙ (𝜇𝑚𝑎𝑥1 ∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− + 𝜇𝑚𝑎𝑥2 ∙𝑁𝑂2

−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂3−+𝑁𝑂2

−) − 𝑘𝑑 ∙ 𝑋 (5.22)

2. Reduction of NO3- to NO2

-

𝑑[𝑁𝑂3−]

𝑑𝑡= −

1

𝑌𝑁𝑂3∙ 𝜇𝑚𝑎𝑥1 · 𝑋 ∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− · (ƞ𝑆 ∙𝑆𝑏

𝐾𝑆𝑏+𝑆𝑏+∙ ƞ𝑆2𝑂3 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32−) (5.23)

3. NO2-- N production and its reduction to N2

49

𝑑[𝑁𝑂2−]

𝑑𝑡=

1

𝑌𝑁𝑂3∙ 𝜇𝑚𝑎𝑥1 · 𝑋 ∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− · (ƞ𝑆 ∙𝑆𝑏

𝐾𝑆𝑏+𝑆𝑏+∙ ƞ𝑆2𝑂3 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32−) −

1

𝑌𝑁𝑂2∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝜇𝑚𝑎𝑥2 ∙ 𝑋 ∙𝑁𝑂2

−

𝑁𝑂3−+𝑁𝑂2

− (ƞ𝑆 ∙𝑆𝑏

𝐾𝑆𝑏+𝑆𝑏+ ƞ𝑆2𝑂3 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32−) (5.24)

4. N2 generation

𝑑[𝑁2]

𝑑𝑡=

1

𝑌𝑁𝑂2∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙ 𝜇𝑚𝑎𝑥2 ∙ 𝑋 ∙

𝑁𝑂2−

𝑁𝑂3−+𝑁𝑂2

− (ƞ𝑆 ∙𝑆𝑏

𝐾𝑆𝑏+𝑆𝑏+ ƞ𝑆2𝑂3 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32− ) (5.25)

5. Hydrolysis of solid sulfur to bioavailable sulfur 𝑑[𝑆𝑠]

𝑑𝑡= − 𝑘𝑠𝑏𝑘 ∙ 𝑎∗ ∙ 𝑆𝑠 (5.26)

6. Production of bioavailable sulfur and its oxidation to S2O32

𝑑[𝑆𝑏]

𝑑𝑡=−

𝑑[𝑆𝑠]

𝑑𝑡− ƞ𝑆 ∙ 𝑋 ∙

𝑆𝑏

𝐾𝑆𝑏+𝑆𝑏∙ (𝜇𝑚𝑎𝑥1 ∙

𝑌𝑆𝑁𝑂3

𝑌𝑁𝑂3∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− + 𝜇𝑚𝑎𝑥2 ∙𝑌𝑆𝑁𝑂2

𝑌𝑁𝑂2∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂3−+𝑁𝑂2

−) (5.27)

7. S2O32-

production and its oxidation to SO42-

𝑑[𝑆2𝑂32−]

𝑑𝑡= ƞ𝑆 ∙ 𝑋 ∙

𝑆𝑏

𝐾𝑆𝑏+𝑆𝑏∙ (𝜇𝑚𝑎𝑥1 ∙

𝑌𝑆𝑁𝑂3

𝑌𝑁𝑂3∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− + 𝜇𝑚𝑎𝑥2 ∙𝑌𝑆𝑁𝑂2

𝑌𝑁𝑂2∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂3−+𝑁𝑂2

−) −

ƞ𝑆2𝑂3 ∙ 𝑋 ∙𝑆2𝑂3

2−

𝐾𝑆+𝑆2𝑂32− ∙ (𝜇𝑚𝑎𝑥1 ∙

𝑌𝑆𝑁𝑂3

𝑌𝑁𝑂3∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− + 𝜇𝑚𝑎𝑥2 ∙𝑌𝑆𝑁𝑂2

𝑌𝑁𝑂2∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂3−+𝑁𝑂2

−)

(5.28)

8. SO42-

production

𝑑[𝑆𝑂42−]

𝑑𝑡= ƞ𝑆2𝑂3 ∙ 𝑋 ∙

𝑆2𝑂32−

𝐾𝑆+𝑆2𝑂32− ∙ (𝜇𝑚𝑎𝑥1 ∙

𝑌𝑆𝑁𝑂3

𝑌𝑁𝑂3∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

− + 𝜇𝑚𝑎𝑥2 ∙𝑌𝑆𝑁𝑂2

𝑌𝑁𝑂2∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂3−+𝑁𝑂2

−) (5.29)

where:

ƞ𝑆 represents the reduction factor for denitrification with elemental sulfur (-);

ƞ𝑆2𝑂3 represents the reduction factor for denitrification with thiosulfate (-),

and for others, take a look in section 5.1.1.3.

The matrix form of sulfur-driven autotrophic denitrification model is reported in Tab. 5.4.

50

Tab. 5.4. Stoichiometric matrix for model of sulfur-driven autotrophic denitrification

Bij

i

component

Ss

[gS∙

m−3]

Sb

[gS∙m−3]

S2O32−

[gS∙m−3]

NO2−

[gN∙m−3]

NO3−

[gN∙m−3]

N2

[gN∙m−3]

SO42−

[gS∙m−3] X [gIVS∙m−3] Rate (𝜌𝑖 , 𝑔 𝐼𝑉𝑆∙m−3∙d−1)

j process X1,NO3 X1,NO2 X1,NO3 X1,NO3

1. Hydrolysis -1 +1 𝑘𝑠𝑏𝑘 ∙ 𝑎∗ ∙ 𝑆𝑠

2. Autotrophic

growth on

NO3− & Sb

− YSNO3

YNO3

YSNO3

YNO3

1

YNO3

−1

YNO3

1 𝜇𝑚𝑎𝑥1 ∙ ƞ𝑆 ∙ 𝑋 ∙𝑆𝑏

𝐾𝑆𝑏+𝑆𝑏∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

−

3. Autotrophic

growth on

NO2− & Sb

− YSNO2

YNO2

YSNO2

YNO2

−1

YNO2

1

YNO2

1 𝜇𝑚𝑎𝑥2 ∙ ƞ𝑆 ∙ 𝑋 ∙𝑆𝑏

𝐾𝑆𝑏+𝑆𝑏∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂3−+𝑁𝑂2

−

4. Autotrophic

growth on

NO3− & S2O3

2−

− YSNO3

YNO3

1

YNO3

−1

YNO3

YSNO3

YNO3

1 𝜇𝑚𝑎𝑥1 ∙ ƞ𝑆2𝑂3 ∙ 𝑋 ∙𝑆2𝑂3

2−

𝐾𝑆+𝑆2𝑂32− ∙

𝑁𝑂3−

𝐾𝑁𝑂3+𝑁𝑂3− ∙

𝑁𝑂3−

𝑁𝑂3−+𝑁𝑂2

−

5. Autotrophic

growth on

NO2− & S2O3

2−

− YSNO2

YNO2

−1

YNO2

1

YNO2

YSNO2

YNO2

1 𝜇𝑚𝑎𝑥2 ∙ ƞ𝑆2𝑂3 ∙ 𝑋 ∙𝑆2𝑂3

2−

𝐾𝑆+𝑆2𝑂32− ∙

𝑁𝑂2−

𝐾𝑁𝑂2+𝑁𝑂2− ∙

𝑁𝑂2−

𝑁𝑂3−+𝑁𝑂2

−

6. Decay of X -1 𝑘𝑑 ∙ 𝑋

51

5.2.2. Model simulations

The model presented in the previous section has been applied to simulate denitrification with ele-

mental sulfur. A numerical simulation with an initial nitrate concentration of 222 mgN/L has been execut-

ed for 16 days. The initial biomass concentration has been assumed equal to 7839 mg/L. The elemental

sulfur has been supplied in excess to the system in a concentration of 46495 mg/L.

The stoichiometric and kinetic parameters used for the numerical simulation are shown in Tab. 1.

Tab. 5.5. Stoichiometric and kinetics parameters value used for numerical simulations

Parameter Value Unit References

Stoichiometric parameters

𝑌𝑁𝑂3 0.39 mg IVS/ mg N-NO3 Chung et al., 2014

𝑌𝑁𝑂2 0.14 mg IVS/ mg N-NO2 Chung et al., 2014

𝑌𝑆𝑁𝑂3 1.74 mg S/mg N-NO3 Mora et al., 2015

𝑌𝑆𝑁𝑂2 2.2 mg S/mg N-NO2 Mora et al., 2015

Kinetic parameters

𝜇𝑚𝑎𝑥1 0.0015 h-1

Adapted from Mora et al., 2015

𝜇𝑚𝑎𝑥2 0.0015 h-1

This study

𝐾𝑆 200.0 mg S-S2O32-

/L This study

𝐾𝑆𝑏 200.0 mg S/L This study

𝐾𝑁𝑂3 56.4 mg N-NO3/L This study

𝐾𝑁𝑂2 35.0 mg N-NO2/L Chung et al., 2014

𝑘𝑑 0.0001 h-1

This study

ƞ𝑆 0.5 - This study

ƞ𝑆2𝑂3 1 - This study

𝑘𝑠𝑏𝑘 1000 mg·m

2·h

-1

This study

𝑎∗ 0.0000003 mg·m

2

This study

Simulation results are presented in Fig. 5.4 in terms of NO3-, NO2

-, S2O3

2- and SO4

2- profiles.

52

Fig. 5.4. Evolution of NO3

-, NO2

-, S2O3

2- and SO4

2-

As shown in Fig. 5.4, nitrate concentration decreases over time with a rate of 3.3 mg/L·h-1

, until

reaching complete depletion after 300h. Nitrate reduction results in the production of nitrite, which attains

a maximum concentration of 180 mg/L when nitrate removal reaches 50%. Similarly to the previous mod-

el, nitrite profile reproduces the behavior of a reaction intermediate: at the beginning of the simulation, the

concentration increases up to a maximum (80h); after 80h nitrite concentration starts to decrease due to its

reduction to dinitrogen gas, reaching complete depletion after 300 h.

Sulfate is produced with a rate of 3.5 mg/L·h-1

and its concentration remains constant after 300 h

when nitrate is depleted. Thiosulfate concentration is increasing with the rate of 2.4 mg/L·h-1

for first 300

h while the reduction of nitrate occurs. After 300 h, thiosulfate concentration remains constant. The dy-

namics of elemental and bioavailable sulfur are not shown as their concentrations keep much higher than

the ones reported in Fig. 5.4.

CHAPTER 6. DISCUSSION

6.1. Kinetics of thiosulfate-driven autotrophic denitrification

This work demonstrates that nitrate can be reduced in both FBRs and batch assays with thio-

sulfate as an electron donor.

6.1.1. FBR experiments

At the beginning of the FBR experimentation (up to 30 h), the highest denitrification rate was

observed in experiment D1 with the highest initial NO3-

However, after 30 h concentration (Fig. 4.1).

nitrite accumulation in experiment D1 resulted in the lowest nitrate removal rate likely due to nitrite

inhibition on autotrophic denitrification as previously reported (Chung et al., 2014). The nitrite con-

centration in the experiments D1 and D2 reached 200 mg/L that is reported to be inhibitory for the de-

nitrifiers activity (Campos et al., 2008). Additionally, the lower denitrification performance in FBR1

mg-NO3-/L may have been due to the lower operating temperature (20 to 25ºC) than with initial 1000

in the other two experiments (24-27 ºC) that slowed down the activity of denitrifiers (Oh et al., 2002).

The highest initial nitrate concentration resulted in the highest thiosulfate degradation rate

(Campos et al., 2008). At t=30 h in experiment D2, only a 20% thiosulfate removal was observed may

have been because of the limited available data during that period. At the first 30 h of the experiment,

lower degradation rate in all the kinetic tests is likely to the slower denitrification because of nitrite in-

hibition. The highest initial thiosulfate concentration resulted in the higher net sulfate production (Fig.

4.1d).

Referring to the stoichiometry of the nitrate reduction with sulfate production by Matsui and

Yamamoto (1986), per 1 mg nitrate removed 12.15 mg sulfate is generated. As reported in Fig. 1 a, d,

only experiment D1 had similar stoichiometry of 150 mg-NO3-/L removed per 1500 mg-SO4

2-/L pro-

duced. Sulfate is known to have an inhibitory effect on denitrification starting from 1500 mg-SO42-

/L

(Campos et al., 2008).

Denitrification rate decreased in experiments D1 and D2 after 50 h when sulfate production

was lower than 1000 mg-SO42-

/L. Therefore, at t=50 h denitrifiers activity slowed down likely due to

nitrite inhibition alone.

During the experiments, pH decreased from 7.5 to 6.8 because thiosulfate-driven autotrophic

denitrification produces acidity (Sierra-Alvarez et al., 2007). However, pH was still in the optimal

range of 6.8-8.2 for autotrophic denitrifiers activity (Chung et al., 2014).

6.1.2. Batch kinetic tests

As shown in Fig. 4.2., in the first 50 h of experiments E1, E2, E3 and E4, the nitrate removal

rates were very similar in all batch tests likely due to the absence of denitrifiers inhibition by interme-

diate such as NO2-. A high concentration of NO2

- above 150 mg/L in all kinetic tests could have result-

ed in its inhibition effect on the denitrification process as was reported previously (Chung at al., 2014;

Claus and Kutzner 1985). At t=166, nitrate removal rate decreased significantly in each batch bottle

likely due to insufficient amount of substrate that was equal to 200 mg/L.

At first 50 h of experiment E4, the higher NO2- production was due to higher supply of the

NO3-. Similar results were obtained by Chung et al. (2014) due to the fact that denitrification is a se-

quential reduction of nitrate to nitrite and then to dinitrogen gas.

54

The highest sulfate production was observed in experiment E4 with highest initial thiosulfate

At t=150 h, in E3 and E4 tests sulfate production only reached 1500 mg/L, above which concentration.

denitrifying activity might be inhibited by sulfate (Campos et al., 2008). Therefore, in these experi-

ments, the lower denitrification performance was probably not only due to the lowest initial nitrate

concentration but also due to a combined inhibitory effect of nitrite and sulfate.

6.1.3. Comparison between FBR and batch experiments

The FBR environment provided a better contact between the substrates and microbial cultures

compared to the batch systems (Papirio et al., 2013), therefore the highest nitrate removal is expected

to be in the FBR. On the contrary, the denitrification rate was faster in FBR environment only after

200 h and before 150 h. During the other time of the experiments, a faster denitrification in batch as-

says might have been due to the high nitrite concentration and the lower operating temperature in FBR

that lowered its denitrification performance. S2O32-

As shown in Fig. 4.3 III-a, b, the highest oxidation

The estimated was observed in the FBR likely due to better mixing conditions than in batch bottles.

kinetic parameters of thiosulfate-driven denitrification were compared with those reported in previous

studies as reported in Tab. 6.1.

Tab. 6.1. Comparison of the thiosulfate-driven denitrification kinetic parameter values ob-

tained in this study with the existing literature

Parameters

Sources

Claus and

Kutzner

(1985)

Chung et. al.,

(2014)

Mora et al.,

(2015) This study

Reactor/ system CSTR CSTR CSTR FBR1 Batch

tests

𝐾𝑁𝑂3 mg-NO3-/l , 0.19 23.9 3.74 0.049 0.0062

𝜇𝑚𝑎𝑥𝑁𝑂3, d− 1 2.64 - 0.72 0.0016 0.0009

mg VSS/mg-NO3Y, 0.57 0.53 0.52 0.33 0.33

The maximum biomass growth rate (μmaxNO3) estimated in this work was two magnitudes lower

than those reported elsewhere. Claus and Kutzner (1985) obtained a highest value for μmaxNO3 as a pure

culture of T. denitrificans was used in contrast with the mixed enrichment cultures in this research.

Therefore, more complex ecological interactions among microorganisms in mixed culture could result

in lower growth rate of autotrophic denitrifiers (Zeng and Zhang, 2005). The estimated half-saturation

constant for nitrate (KNO3) was lower than reported in other studies. The lower obtained biomass yield

mg-NO3 (Y) of 0.33 mg IVS/ coefficient in the current study will result in lower sludge production,

thus less operating costs.

Based on the calculated in stoichiometry of sulfur-driven autotrophic denitrification (Eq. 4.2),

the molar ratio of sulfate produced per thiosulfate oxidized was 1.79, lower than that of 2 reported by

Oh et al. (2000). The lower production of sulfate observed in the present study was probably due to the

occurrence of other sulfur-reducing or oxidizing concomitant processes. The SO42-

reduction to H2S or

elemental sulfur production may have most likely taken place as was previously observed by Campos

et al. (2008).

55

6.2. Kinetics of sulfur-driven autotrophic denitrification

6.2.1. FBR experiments

Similar to the previous study (Kilic et al., 2014), the highest nitrate reduction rate was ob-

served in the experiment with the highest initial nitrate concentration as shown in Fig. 4.4. For FBR

experiment G2, the estimated nitrate removal rate reached up to 5.7 mg/L∙h that is higher than the lit-

erature values of 0.5-4.5 mg/L∙h for PBRs (Moon et al., 2004; Sahinkaya and Dursun , 2012). The

higher denitrification rate was obtained in the current study likely due to better contact between sulfur

particles and biomass provided by the FBR environment compared with the PBR (Kim et al., 2004).

At t=300 and 200 h for experiments 1 and 2, respectively, nitrate removal reached 60% and its

concentration was equal to 200 mg/L. At the same time, the nitrite degradation started. Kim et al.

(2004) confirmed that nitrite degradation occurred when most of the nitrate was depleted.

Thiosulfate in concentration of 110-340 mg/L was detected throughout FBR kinetic tests. On

the contrary, in most studied sulfate is exclusively indicated product in most sulfur-based bioreactor

Moon et al., 2004; Sun and Nemati, 2012) and it was only detected transiently by Sierra-Alvarez et al. (

(2007).

The highest initial nitrate concentration resulted in the highest sulfate production as was ob-

served previously by Batchelor and Lawrence (1978). The mass ratio of produced sulfate per reduced

nitrate in the FBR was between 1.8 and 3.8 that is lower than described by the previous studies (Moon

et al., 2004). It is likely due to activity of the sulfate-reducing bacteria that converted sulfate to hydro-

gen sulfide as was described by Moon et al. (2004). The organic carbon needed for the latter process

may be generated from organic acids produced by sulfur-oxidizing bacteria in the FBRs (Zhang and

Shan, 1999) or from natural bacterial lyses (Moon et al., 2004).

sulfur-limestone ratio of 1:1 (v/v) as recommended Limestone was supplied to the FBRs with

in the literature (Sahinkaya et al., 2014). to provide sufficient alkalinity In the FBR kinetic tests with

elemental sulfur, pH decreased from 7.5 to 6.5 due to acidity production in sulfur-driven autotrophic

(Sahinkaya and Dursun, 2012)denitrification . The measured pH was slightly lower than the optimal

values of 6.8-8.2 for autotrophic denitrifiers (Chung et al., 2014), but it was still higher than 6.0 and

Sahinkaya et al., 2011).thus was not inhibitory for the denitrifying microorganism (

6.2.2. Batch kinetic tests

As shown in Fig. 4.5, the fastest nitrate reduction rate was obtained in the batch bottle with the

highest initial nitrate concentration as was previously observed in literature (Sahinkaya et al., 2014). In

the current study, thiosulfate was detected throughout the FBR experiments. On the contrast, Sun and

Nemati (2012) as well as Sierra-Alvarez et al. (2007) didn’t observe any thiosulfate (detected level

was below 100 mg/L) in the batch experiments with different initial nitrate concentrations.

Mass transfer from elemental sulfur is considered to be a limiting factor in autotrophic denitri-

fication (Sierra-Alvarez et al., 2007). Therefore, in the experiments sulfur was supplied in 100 times

higher amount than required by stoichiometry in order to increase sulfur surface area and improve its

mass transfer.

In the experiment with the highest initial nitrate concentration, the sulfate production was the

highest. The possible explanation may be the higher oxidation rate of the elemental sulfur was in the

batch bottles with the higher initial nitrate concentration due to the coupled oxidation-reduction reac-

tions as was described previously (Batchelor and Lawrence, 1978; Sierra-Alvarez et al., 2007).

56

6.2.3. Comparison between FBR and batch experiments

As shown in Fig. 4.6, the slightly higher overall denitrification efficiency of 0.7 and 2.3

mg/L·h-1

in FBR environment than 0.6 and 2.0 mg/L·h-1

of batch experiments may be explained due to

the enhanced mass transfer in the reactor environment (Papirio et al., 2013a). Therefore, faster denitri-

fication was observed in the FBR environment, except the time when the nitrite concentration was

above 200 mg/L. It is likely due to denitrifiers inhibition by nitrite at concentration higher 200 mg/L as

was reported by Chung et al. (2014).

Autotrophic denitrification is the process of nitrate conversion to nitrite with further reduction

to dinitrogen gas (Chung et al., 2014). Therefore, in both FBR and batch experiments, when most of

nitrate was consumed and therefore converted to nitrite, the nitrite degradation started as was observed

by Kim et. al. (2004). The nitrite accumulation indicates that in both FBR and batch environment ni-

trate to nitrite conversion is faster than that one of nitrite to dinitrogen gas. Our results were similar to

those of Sierra-Alvarez et al. (2007). The sulfate production was higher in the FBR than in the batch

experiments because of better contact between biomass and substrate (Papirio et al. 2013b).

Kinetic parameters of sulfur-driven denitrification obtained in current study were compared

with the literature values as shown in Tab. 6.2.

Tab. 6.2. Comparison of sulfur-driven autotrophic denitrification kinetic parameter values ob-

tained in this study with the existing literature

Parameters

Sources

Batchelor and

Lawrence (1978)

Zeng and

Zhang (2005) This study

Reactor/ system CSRT CSTR FBR Batch tests

S0 particles

diameter, mm 0.084 2.38-4.76 5.0 5.0

𝐾𝑁𝑂3 mg-NO3-/l , 0.01 0.089 0.025 0.026

𝜇𝑚𝑎𝑥𝑁𝑂3 ℎ−1 , 0.11 0.006 0.0018 0.0017

g cells/ mg NO3-

Y, m 0.56 0.85-1.11 0.55 0.55

In the current study, the maximum biomass growth rate (𝜇𝑚𝑎𝑥𝑁𝑂 ) was similar to the one ob-3

tained by Zeng and Zhang (2005) and one magnitude lower than in Batchelor and Larence (1978) ex-

periments. The difference could be attributed to the usage of pure culture of T. denitrificans by Batch-

elor and Lawrence (1978) and mixed enrichment cultures in Zeng and Zhang (2005) study and in the

current research. Therefore, co-existence of different microorganisms could result in lower growth rate

for autotrophic denitrifiers (Zeng and Zhang, 2005). Moreover, the sulfur particles used in this study

had bigger diameter (Tab. 6.2). Therefore, in the current study the biggest size of sulfur particles could

have resulted in its smaller specific surface area and therefore worse contact between biomass and

substrate as was previously observed by (Christianson and Summerfelt, 2014).

The estimated half-saturation constant for nitrate (𝐾𝑁𝑂 ) in this study are within the range de-3

scribed in the literature. The obtained biomass yield coefficient (Y) value is slightly lower than in the

literature that will result in less sludge produced. Additionally, the different hydraulic and operating

57

conditions applied in the current study compared to the literature may affect evaluation of kinetic pa-

rameters (Zeng and Zhang, 2005).

In the previous studies (Sierra-Alvarez et al., 2007; Kilic et al., 2014), the sulfate produced per

nitrate consumed (mg/mg) ratio was 0.83 and lower than in the current study (Eq. 4.2).

6.3. Comparison of thiosulfate- and sulfur-driven autotrophic denitrification

In this study, the nitrate removal rate in the FBR with thiosulfate was 1.6 times higher than in

the FBR with elemental sulfur that was confirmed by calculated maximum nitrate degradation rates

(Tab. 4.3). Higher autotrophic denitrification rate with thiosulfate than with elemental sulfur was pre-

viously reported in the literature (Cardoso et al. 2006; Trouve et al. 1998). It could be explained due to

high bioavailability and non-toxicity of thiosulfate for autotrophic denitrifiers (Cardoso et al., 2006).

In autotrophic denitrification with elemental sulfur, the prior step is a conversion of solid sulfur to bio-

available form that further could be uptaken by the microorganisms (Qambrani et al., 2015). There-

fore, due to limiting mass transfer, sulfur-driven autotrophic denitrification demonstrated lower pro-

cess rate.

However, the higher mass ratio of sulfate generated per nitrate utilized (mg/mg) of 4.3 was

observed in the FBR with thiosulfate. Kilic et al. (2014) obtained similar results of the higher sulfate

production from thiosulfate-driven denitrification. The sulfate production should be minimized and

controlled in the drinking water because its concentration higher than 400 mg/L could negatively af-

fect its taste (WHO).

In the FBR, thiosulfate-driven autotrophic denitrification demonstrated lower value of biomass

yield coefficient (Y) and less produced biomass compared with elemental sulfur (Tab. 4.3). Finally,

less sludge handling would be required for thiosulfate-driven autotrophic denitrification in the FBR.

To conclude, higher denitrification rate as well as less sludge production was observed in the

FBR with thiosulfate compared with sulfur, but with more sulfate generated.

6.4. Thiosulfate-driven autotrophic denitrification model

Numerical simulations have been carried out to demonstrate the model capability of simulat-

ing thiosulfate-driven autotrophic denitrification and predicting the effect of the initial nitrate concen-

tration on process performance. The presented model simulates a two-step denitrification process and

takes into account the growth of autotrophic biomass on two different electron acceptors (nitrate and

nitrite). To this purpose, a competitive kinetics has been used: depending on the concentration of the

two compounds, microorganisms will use one or another.

The simulation results show complete nitrate and thiosulfate removal after 300 h. As shown in

Fig 5.2, this trend does not reproduce the experimental results as nitrate and thiosulfate concentrations

remain non-zero for all the observation time. However, the higher nitrate and thiosulfate removal

which characterizes the numerical simulations, results in a higher sulfate production (Fig. 5.2d) con-

firming the consistency of the model.

In addition, as shown in Fig.5.2, the nitrate and thiosulfate removal stops at 200 and 300 h, re-

spectively, due probably to the low substrate concentration. Conversely, in the experimental data (Fig.

4.2), the considerable decrease in the nitrate reduction has been observed when nitrated concentration

reaches 200 mg/L in each batch assay. This means that from the biological point of view 200 mg/L

can be considered as the threshold concentration for denitrifying bacteria in this study.

The NO2- simulation trends are similar to the experimental results (Fig, 4.2): increasing nitrite

concentration up to t=50 h with further depletion. After 100 h, the simulation results for NO2- are

slightly different from the experimental ones (Fig. 4.2) as strong nitrite fluctuation was observed dur-

58

ing the experiments with often nitrite be under detection limits. The latter could be explained by the

fast conversion of nitrite to dinitrogen gas (Chung et al., 2014). Moreover, the presence of nitrite in

concentration higher than 100 mg/L may have inhibited the denitrifiers activity in the batch kinetic

tests (Chung et al., 2014). However, nitrite inhibition wasn’t considered in the proposed model. There-

fore, this could explain the highest nitrate and sulfate removal as well as sulfate production.

6.5. Sulfur-driven autotrophic denitrification model

Model simulation has been executed to evaluate NO3-, NO2

-, S2O3

2- and SO4

2- dynamics during

sulfur-driven autotrophic denitrification. The present model accounts for the growth of the same bio-

mass (NR-SO) on two different substrates (nitrate and nitrite). Therefore, a competitive kinetics has

been used to take into account the effect of nitrate and nitrite concentrations on bacterial growth. Sul-

fur uptake has been modeled by introducing a new variable which represents the bioavailable sulfur

(Sb). The formation of this bioavailable form of elemental sulfur is modeled by a surface based kinetic

equation which relates the hydrolysis rate to the specific surface of sulfur particles.

The developed model for sulfur-driven autotrophic denitrification was presented, but not yet

calibrated because many parameters were assumed or taken from the literature. Therefore, additional

experiments are needed to get further inside into the process.

59

CHAPTER 7. CONCLUSIONS

To achieve a high performance of autotrophic denitrification with thiosulfate and elemental

sulfur used as electron donors, the further inside into the process kinetics is needed. Therefore, the ex-

periments in the FBRs and batch assays as well as dynamic mathematical modeling of the processes

were performed.

1. Thiosulfate-driven autotrophic denitrification was successfully carried out in FBR environ-

ment. The highest initial nitrate concentration resulted in the highest nitrate removal rate in both FBR

and batch assays. The maximum nitrate removal rate of 16.0 mg/L·h-1

was achieved in the FBR that

was 1.5 times higher than that obtained in the batch tests.

2. In autotrophic denitrification with elemental sulfur as electron donor, the highest nitrate re-

moval was also achieved with the highest fed nitrate concentration. The maximum nitrate removal

rates were 10.0 and 5.7 mg/L·h-1

in batch and FBR experiments, respectively. Thiosulfate, as an inter-

mediate of sulfur oxidation to sulfate, was detected throughout the experimentation and remained sta-

ble at approximately 150 mg/L.

3. The FBR environment was proven to be more effective for the autotrophic denitrification

with thiosulfate than elemental sulfur. The nitrate degradation rate was 1.6 times higher in the FBR

with thiosulfate than with elemental sulfur.

4. The developed model for autotrophic denitrification with thiosulfate is able to describe rea-

sonably the dynamics of NO3-, NO2

-, S2O3

2- and SO4

2- and evaluate the effect of the initial nitrate con-

centration on the denitrification performance. Model calibration is needed in order to apply the devel-

oped model for the prediction of thiosulfate-driven autotrophic denitrification performance under dif-

ferent conditions.

5. A mathematical model able to simulate the biological and physico-chemical processes pre-

vailing during sulfur-driven autotrophic denitrification has been developed. Further experimental ac-

tivity is needed to verify modeling assumptions and quantify process stoichiometry. A sensitivity

analysis should be performed to determine which parameters affected the results the most. The high

sensitivity parameters should be later refined by model calibration.

60

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