HYDROGEN PRODUCTION THROUGH GASIFICATION AND DARK · PDF fileHydrogen Production through...

Hydrogen Production through Gasification and Dark Fermentation I

HYDROGEN PRODUCTION

THROUGH GASIFICATION AND

DARK FERMENTATION

Carlos Andrés García Velásquez

Universidad Nacional de Colombia Sede Manizales

Facultad de Ingeniería y Arquitectura, Departamento de Ingeniería Química

Manizales, Colombia

2016

II Hydrogen Production through Gasification and Dark Fermentation

Hydrogen production through

gasification and dark fermentation


Thesis submitted in partial fullfilment of the requirements for the degree of:

Master of Science in Engineering - Chemical Engineering

Advisor:

Ph.D., M. Sc, Chemical Engineering Carlos Ariel Cardona Alzate

Research line:

Chemical and Biotechnological Process Engineering

Research group:

Chemical, Catalytic and Biotechnological Processes



Manizales, Colombia

2016

Producción de hidrógeno por medio de

gasificación y fermentación oscura


Tesis presentada como requisito parcial para optar al título de:

Magister en Ingeniería Química

Director:

Ph.D., M. Sc, Ingeniero Químico Carlos Ariel Cardona Alzate

Línea de Investigación:

Ingenieria de Procesos Químicos y Biotecnológicos

Grupo de Investigación:

Procesos Químicos, Catalíticos y Biotecnológicos



Manizales, Colombia

2016

IV Hydrogen Production through Gasification and Dark Fermentation

Este es el comienzo del viaje al cual estoy

destinado…

“… traveling for a living…”

“ … Sí, amigos mios, creo que el agua se usará un día

como combustible, que el hidrógeno y el oxígeno que la

constituyen, utilizados aislada y simultáneamente,

producirán una fuente de calor y de luz inagotable y de

una intensidad mucho mayor que la de la hulla…”

La Isla Misteriosa, Julio Verne, 1874

Acknowledgements

First, I would like to thank to my parents and my brother who have always supported me in my

decisions and also for their patience, especially in this chapter of my life. This is for them. There is

one person that always encourange me to do the things that I have done and despite my bad

temper, she has always been by my side. This is for her too. I want to thank also to the people with

whom I have shared these two years of hard work: Valentina A., Valentina H., Milo, La Coste,

Hector, Migue, Edu, Serna, Mancho, Yessica, Cristian, Manuel, Ashley, Daniela. To the IBA’s

crew, I am very grateful for their patience, especially when I used the gasifier, for their support and

daily help. To my tutor Carlos A. Cardona who has led me down this path, has supported me in the

difficulties and from which I have learned a lot in these years of hard work by his side.

Finally, I want to acknowledge to the National University of Colombia at Manizales, to the

Dirección de Investigación (DIMA), the Sistema de Información de la Investigación de la

Universidad Nacional de Colombia (HERMES) and the Facultad de Ingeniería y Arquitectura

(FIA) for their financial support to develop this thesis. To Professor Ashwany Gupta for giving me

the opportunity to do my internship in his combustion laboratory.

VI Hydrogen Production through Gasification and Dark Fermentation

Abstract

New efforts in the search of alternative clean and renewable energy to replace the current energy

precursors have been assessed in order to reduce emissions to the environment. Lignocellulosic

Biomass (LB) can be used to produce bioenergy due to its high energy potential and availability.

The main objective of this thesis is to evaluate the production of hydrogen through air gasification

and dark fermentation using lignocellulosic biomass as feedstock. For this purpose, the

methodology was divided in two sections: experimental and simulation procedures. The

experimental procedure involves the production of a hydrogen-rich synthesis gas through

gasification and the use of the commercial strain T. Thermosaccharolyticum ATCC 7956 to

produce hydrogen though dark fermentation. Two different gasification configurations were tested:

first, using only lignocellulosic biomass as feedstock and then, mixtures between the raw material

and an adsorbent (in this case, quicklime) were fed in the gasifier. For the dark fermentation

procedure, a previous pretreatment stage of the raw material was included. Subsequently, pure

sugars and hydrolysates (from the pretreatment stage) were tested as carbon source for the

hydrogen production using the commercial strain. In the simulation approach, six (6) scenarios for

hydrogen production were assessed from the techno-economic, energetic and environmental point

of view, considering the biorefinery and stand-alone pathways. As a consequence, the use of low

cost and high available adsorbents in the gasification procedure increased the hydrogen selectivity.

Hydrogen content was not detected in the dark fermentation procedures; however, high

concentration of other metabolites was evidenced, which presents a future scenario for the

implementation of alternative process schemes using this microorganism. The results of the

simulation procedure demonstrated that the production of hydrogen based on the concept of a

biorefinery can improve the profitability, energy efficiency and reduce the emissions of the

processes compared to the stand-alone way.

Keywords: Lignocellulosic residues, hydrogen, bioenergy

Resumen

Nuevos esfuerzos en la búsqueda de alternativas de energía limpia y renovable que reemplacen los

actuales precursores energéticos han sido evaluados con el fin de reducir las emisiones al medio

ambiente. La biomasa lignocelulósica (BL) se puede utilizar directamente para producir energía

debido a su alto potencial energético y disponiblidad. El principal objetivo de esta tesis es evalular

la producción de hidrógeno por medio de gasificación y fermentación oscura utilizando biomasa

lignocelulósica como materia prima. Para este proposito, la metodología fue dividida en dos

secciones: procedimientos experimentales y de simulación. El procedimiento experimental

involucra la producción de un gas de síntesis con alto contenido de hidrógeno por medio de

gasificación y el uso de la cepa comercial T. Thermosaccharolyticum ATCC 7956 para producir

hidrógeno por medio de fermentación oscura. Dos configuraciones de gasificación fueron

evaluadas: primero, utilizando biomasa lignocelulósica como materia prima y luego, se preparon

mezclas entre dichas materias primas y un adsorbente (en este caso, cal viva). Para el

procedimiento de la fermentación oscura, se incluyó una etapa de pretratamiento de la materia

prima. Posteriormente, azucares puros e hidrolizados (obtenidos de la etapa de pretratamiento)

fueron utilizados como fuente de carbono para la producción de hidrógeno utilizando la cepa

comercial. En la etapa de simulación, se evaluaron desde el punto de vista técnico, económico,

energético y ambiental, seis escenarios para la producción de hidrógeno. Como consecuencia, el

uso de un adsorbente de bajo costo y alta disponibilidad, en el procedimiento de gasificación,

incremento la selectividad del hidrógeno. En los procedimientos de fermentación oscura no fue

posible detectar hidrógeno; sin embargo, el microorganismo fue capaz de sintetizar otros

metabolitos en altas concentraciones, lo cual presenta un futuro escenario para la implementación

de nuevos esquemas de proceso utilizando esta cepa comercial. Los resultados del procedimiento

de simulación demostraron que la producción de hidrógeno basado en el concepto de biorefinería

mejoró la rentabilidad económica, eficiencia energética y redujó las emisiones de los procesos en

comparación a las rutas independientes.

Palabras clave: Residuos lignocelulósicos, hidrógeno, bioenergía, sostenibilidad

VIII Hydrogen Production through Gasification and Dark Fermentation

Content

Pág.

1. Energy in Colombia 20

1.1 Energy Matrix .................................................................................................................. 21

1.2 Advantages and deficiencies of the current energy systems ............................................ 24

1.3 Renewable energy sources ............................................................................................... 25

1.4 Non-Interconnected Zones in Colombia .......................................................................... 26

1.5 Biomass suitable for energy production in Colombia ...................................................... 28

1.6 Final Remark .................................................................................................................... 30

2. Hydrogen as energy vector 31

2.1 Methods for hydrogen production .................................................................................... 32

2.1.1 Thermochemical technologies ...................................................................................... 33

2.1.2 Biochemical methods .................................................................................................... 36

2.1.3 Electrochemical Methods .............................................................................................. 37

2.2 Hydrogen economy .......................................................................................................... 38

2.3 Final Remarks .................................................................................................................. 39

3. Raw Material 41

3.1 Pinus Patula (PP) ............................................................................................................ 41

3.1.1 Pinus Patula in the Andean region ............................................................................... 44

3.2 Coffee Cut-Stems (CCS) .................................................................................................. 47

3.2.1 Coffee Cut-Stems in the Andean Region ...................................................................... 48

3.3 Final Remarks .................................................................................................................. 51

4. Methodology 52

4.1 Experimental Procedure ................................................................................................... 52

4.1.1 Raw Material Characterization ..................................................................................... 52

4.1.2 Proximate analysis ........................................................................................................ 55

4.1.3 Elemental analysis......................................................................................................... 56

4.2 Pilot-Scale Gasification .................................................................................................... 57

4.3 Experimental Catalyzed Gasification ............................... ¡Error! Marcador no definido.

4.4 Additional gasification experiences ................................................................................. 61

4.5 Dark Fermentation ........................................................................................................... 61

4.5.1 Dilute-acid hydrolysis ................................................................................................... 62

4.5.2 Detoxification................................................................................................................ 63

4.5.3 Enzymatic Saccharification ........................................................................................... 64

4.5.4 Fermentation ................................................................................................................. 64

4.6 Simulation Procedure ....................................................................................................... 69

4.6.1 Scenarios ....................................................................................................................... 70

4.6.2 Process description ........................................................................................................ 71

4.6.3 Gasification ................................................................................................................... 72

4.6.4 Electricity generation .................................................................................................... 74

4.6.5 Ethanol Fermentation .................................................................................................... 75

4.6.6 Dark Fermentation ........................................................................................................ 76

4.7 Analysis of the proposed scenarios .................................................................................. 78

4.7.1 Economic Assessment ................................................................................................... 78

4.7.2 Energy Analysis ............................................................................................................ 80

4.7.3 Environmental Assessment ........................................................................................... 82

5. Results and Discusion 83

5.1 Overview .......................................................................................................................... 83

5.2 Physicochemical characterization of lignocellulosic biomass ......................................... 83

5.3 Experimental Procedure ................................................................................................... 87

5.3.1 Gasification ................................................................................................................... 87

5.3.1.1 Air Gasification of CCS and PP .................................................................................... 87

5.3.1.2 Effect of CaO in the downdraft gasification ................................................................. 91

5.3.2 Dark Fermentation ........................................................................................................ 93

5.3.2.1 Acid Hydrolysis ............................................................................................................... 93

5.3.2.2 Detoxification ................................................................................................................... 94

5.3.2.3 Enzymatic Saccharification .............................................................................................. 96

5.3.2.4 Dark Fermentation ........................................................................................................... 97

5.4 Stand-alone and biorefinery pathways for hydrogen production: Simulation approach 102

5.4.1 Pinus Patula ................................................................................................................ 102

5.4.1.1 Overall performance ....................................................................................................... 103

5.4.1.2 Techno-economic assessment ........................................................................................ 104

5.4.1.3 Energy Analysis ............................................................................................................. 114

5.4.1.4 Environmental Assessment ............................................................................................ 119

5.4.2 Coffee Cut-Stems (CCS) ............................................................................................. 121

5.4.2.1 Overall Performance ...................................................................................................... 121

5.4.2.2 Techno-economic assessment ........................................................................................ 122

5.4.2.3 Energy Analysis ............................................................................................................. 129

5.4.2.4 Environmental Assessment ............................................................................................ 133

6. General comparison of stand-alone and biorefinery pathways for hydrogen production136

7. Conclusions 138

8. Recommendations 140

A. Approach for the calculation of the elemental analysis and calorific value 141

B. Gasification of Wood chips: Effect of the temperature and gasifying agent Short Internship

in the Maryland University, College Park, USA 144

C. Kinetic Models 153

D. Mathematical modelling of biomass gasification for hydrogen production 157

9. References 174

X Hydrogen Production through Gasification and Dark Fermentation

List of figures

Pág.

Figure 1-1. Production of crude oil (thousands of cubic feet per calendar day) in Colombia. ........ 21

Figure 1-2. Diesel consumption in Colombia. ................................................................................ 22

Figure 1-3. Distribution of the natural gas consumption in different sectors of Colombia ............ 22

Figure 1-4. Distribution of the main energy sources in Colombia ................................................... 24

Figure 1-5. Graphical map of the Non-Interconnected Zones in Colombia (Gray Departments) .... 27

Figure 2-1. Energy sources and methods for hydrogen production ................................................ 34

Figure 3-1. Main applications of roundwood in Colombia .............................................................. 44

Figure 4-1. Process flowsheet of the pilot-scale gasifier in the 10 kW Power Pallet. ..................... 59

Figure 4-2. Flowsheet of the biomass gasification to produce hydrogen from wood residues ....... 68

Figure 4-3. Flowsheet of the biomass gasification to produce hydrogen from wood residues ....... 74

Figure 4-4. Flowsheet of the ethanol production from wood residues using Z. mobilis ................ 76

Figure 4-5. Flowsheet of the hydrogen, ethanol, acetic acid and butyric acid production through

dark fermentation of wood residues. ................................................................................................ 77

Figure 5-1. Concentration profiles of the different gaseous species obtained from the pilot-scale

gasification of CCS. ......................................................................................................................... 88

Figure 5-2. Concentration profiles of different gaseous species obtained from the pilot-scale

gasification of PP. ............................................................................................................................ 90

Figure 5-3. Effect of the biorefinery location and plant capacity in the hydrogen production cost

....................................................................................................................................................... 105

Figure 5-4. Effect of the biorefinery location and plant capacity in the Net Present Value of the

process ........................................................................................................................................... 106

Figure 5-5. Effect of the plant capacity in the hydrogen production for the three scenarios that

involve gasification ........................................................................................................................ 107

Figure 5-6. Effect of the plant capacity in the NPV for the three gasification scenarios ............... 107

Figure 5-7. Sensibility analysis of the economic performance of the low scale biorefinery ........ 108

Figure 5-8. Sensibility analysis of the economic performance of the mid-scale biorefinery ........ 109

Figure 5-9. Contribution of the economic parameters to the total production costs of the

gasification scenarios ..................................................................................................................... 110

Figure 5-10. Effect of the plant capacity in the hydrogen production cost of the dark fermentation

scenarios ......................................................................................................................................... 111

Figure 5-11. Effect of the plant capacity in the economic performance of the dark fermentation

scenarios ......................................................................................................................................... 112

Figure 5-12. Sensibility analysis of the economic performance of the low scale dark fermentation

biorefinery ...................................................................................................................................... 112

Figure 5-13. Sensibility analysis of the economic performance of the mid-scale dark fermentation

biorefinery ...................................................................................................................................... 113

Figure 5-14. Contribution of the economic parameters in the dark fermentation scenarios ......... 113

Figure 5-15. Sankey diagram of the energy balance for the gasification scenarios H2 .................. 115

Figure 5-16. Sankey diagram of the energy balance for the dark fermentation scenarios ............. 116

Figure 5-17. Energy efficiency considering hydrogen as the only product of the evaluated

scenarios ......................................................................................................................................... 117

Figure 5-18. Energy efficiency considering all the possible products from the evaluated scenarios

........................................................................................................................................................ 118

Figure 5-19. Net energy balance of the evaluated scenarios .......................................................... 119

Figure 5-20. GHG balance of the evaluated scenarios .................................................................. 120

Figure 5-21. Environmental assessment of the dark fermentation scenarios ................................ 121

Figure 5-22. Effect of the plant capacity in the hydrogen production cost of the gasification

scenarios. ........................................................................................................................................ 123

Figure 5-23. Effect of the plant capacity in the economic profitability of the gasification scenarios

........................................................................................................................................................ 124

Figure 5-24. Sensibility analysis of the economic performance of the low-scale gasification

biorefinery ...................................................................................................................................... 124

Figure 5-25. Sensibility analysis of the economic performance of the mid-scale gasification

biorefinery ...................................................................................................................................... 125


gasification scenarios ..................................................................................................................... 126

Figure 5-27. Effect of the plant capacity in the hydrogen production cost in the dark fermentation

scenarios ......................................................................................................................................... 127

Figure 5-28. Effect of the plant capacity in the NPV of the dark fermentation scenarios ............ 127

Figure 5-29. Sensibility analysis of the economic profitability of the low scale dark fermentation

biorefinery ...................................................................................................................................... 128

Figure 5-30. Sensibility analysis of the economic performance of the mid-scale dark fermentation

biorefinery ...................................................................................................................................... 128

Figure 5-31. Contribution of the main economic parameters to the total production costs of the

dark fermentation scenarios. .......................................................................................................... 129

Figure 5-32. Sankey diagrams for the gasification scenarios ......................................................... 130

Figure 5-33. Sankey diagrams for the dark fermentation scenarios ............................................... 131

Figure 5-35. Energy efficiency of the evaluated scenarios considering only hydrogen as product

........................................................................................................................................................ 132

Figure 5-36. Energy efficiency of the evaluated scenarios considering all the bioenergy products

........................................................................................................................................................ 132

Figure 5-37. Net energy balance of the evaluated scenarios ......................................................... 133

Figure 5-38. GHG balance of the gasification and dark fermentation scenarios ........................... 134

Figure 5-39. Environmental Potential Impact (PEI) of the dark fermentation scenarios .............. 135

XII Hydrogen Production through Gasification and Dark Fermentation

List of tables

Pág.

Table 1-1. Potential energy of different crops and residues. Adapted from: [27] ............................ 29

Table 1-2. Potential energy of natural and reforested forests .......................................................... 30

Table 2-1. Energy yield of hydrogen and other common fuels at 25°C and 1 atm [29]. ................. 31

Table 2-2. Types of gasifying agent used in gasification procedures. ............................................. 36

Table 2-3. Description of the technologies used in the hydrogen production. ................................. 40

Table 3-1. Commercial forest species used in Colombia for reforestation programs. ..................... 43

Table 3-2. Production of PP and its residues in three departments in Colombia ............................. 46

Table 3-3. Production cost of PP and its residues in the three collection centers in Colombia ....... 46

Table 3-4. Freight cost between the collection center and the processing plant .............................. 47

Table 3-5. Production of “pergamino” and cherry coffee and its relation with the CCS production.

......................................................................................................................................................... 50

Table 3-6. Raw material purchase cost in the three selected departments ....................................... 50

Table 3-7. Freight cost between the collection center and the processing plant .............................. 51

Table 4-1. Chemical characterization of the quicklime reported by the local distributor. ............... 61

Table 4-2. Growth of T. Thermosaccharolyticum strains in different carbon sources ..................... 65

Table 4-3. Composition of the Clostridium Thermohydrosulfuricum (CT) medium [121] ............. 66

Table 4-4. Composition of the MB medium [47] ............................................................................ 66

Table 4-5. Pure substrate composition for the dark fermentation of T. Thermosaccharolyticum

ATCC 7956 ...................................................................................................................................... 67

Table 4-6. Stand-alone and biorefinery scenarios for the hydrogen production .............................. 71

Table 4-7. Utilities, reagents and products market prices ................................................................ 79

Table 5-1. Physicochemical characterization of Pinus Patula ......................................................... 84

Table 5-2. Physicochemical characterization of different types of wood reported in literature. ..... 85

Table 5-3. Physicochemical characterization of Coffee Cut-Stems ................................................. 86

Table 5-4. Global mass balance of the experimental gasification runs using CCS as raw material 88

Table 5-5. Composition and calorific value of the generated syngas from CCS ............................. 89

Table 5-6. Composition and calorific value of the generated syngas from PP ................................ 90

Table 5-7. Composition and calorific value of the generated syngas from CCS ............................. 91

Table 5-8. Effect of the CaO catalyst in the synthesis gas composition from CCS gasification ..... 92

Table 5-9. Effect of the CaO catalyst in the synthesis gas composition from PP gasification ........ 92

Table 5-10. Sugars yield after the dilute-acid hydrolysis ................................................................. 94

Table 5-11. Monosaccharides conversion in the dilute-acid hydrolysis .......................................... 94

Table 5-12. Sugar and furfural content of the hydrolysate after overliming with Ca(OH)2............. 95

Table 5-13. Detoxification yield from PP and CCS hydrolysates .................................................... 95

Table 5-14. Monosaccharides degradation after the detoxification procedure ................................ 95

Table 5-15. Sugar and furfural content after the enzymatic hydrolysis ........................................... 96

Table 5-16. Sugar consumption of different experimental tests of the dark fermentation ............... 97

Table 5-17. Concentration of the gaseous species in the fermentation medium .............................. 99

Table 5-18. Concentration of secondary metabolites in the fermentation medium ....................... 100

Table 5-19. Sugar consumption of different experimental tests of the dark fermentation ............. 100

Table 5-20. Concentration of the gaseous species in the fermentation medium ............................ 101

Table 5-21. Concentration of secondary metabolites in the fermentation medium ....................... 101

Table 5-22. Overall performance of the stand-alone and biorefinery pathways for hydrogen

production using PP as feedstock. .................................................................................................. 103

Table 5-23. Productivity and yields of the CCS scenarios ............................................................. 122

Hydrogen Production through Gasification and Dark Fermentation XIV

List of Publications

Research Papers

Carlos A. García, Ramiro Betancourt, Carlos A. Cardona. “Stand-alone and biorefinery pathways

to produce hydrogen through gasification and dark fermentation using Pinus Patula”. Journal of

Environmental Management. Elsevier. Status: Accepted. In Press. DOI:

10.1016/j.jenvman.2016.04.001.

Valentina Aristizábal M, Carlos A. García V, Carlos A. Cardona A. “Integrated Production of

Different Types of Bioenergy from Oil Palm through Biorefinery Concept”. Waste and Biomass

Valorization. Springer. Status: Accepted. DOI: 10.1007/s12649-016-9564-7

Carlos. A. García, Carlos. A. Cardona, “Anthocyanin Production Evaluation using Plant Cell

Cultures: Growth and Viability Analysis at Different Process Conditions,” Ing. Univ., vol. 20, no.

1, pp. 7-20, 2016. DOI:10.11144/Javeriana.iyu20-1.apeu

Héctor Forero, Carlos A. García, Carlos Cardona. “Aplicación de la termodinámica en la síntesis

óptima de procesos químicos y biotecnológicos”. Revista de la Facultad de Ciencias Químicas.

ISSN: 1390-1869.

Papers under review

Carlos A. García, Jonathan Moncada, Carlos A. Cardona, “Techno-economic and ex-ante

environmental assessment of bioenergy production systems: comparison of thermochemical and

biochemical pathways in the Colombian context”. Status: Under Review

Carlos A. García, Marjorie Morales, Germán Aroca, Carlos A. Cardona, “Environmental

assessment of hydrogen production based on Pinus patula plantations in Colombia”. Energy.

Status: Under Review

Conferences Papers

Carlos A. García, Alvaro Gomez, Ramiro Betancourt, Carlos A. Cardona, “Environmental

comparison of thermochemical and biochemical ways for producing energy from agricultural solid

residues: Coffee Cut-Stem case”, In: Cyprus, Event: Conference of Sustainable Solid Waste

Management CYPRUS 2016.

Julio Sanchez, Carlos A. García, Carlos A. Cardona, “Biogas Production from Three

Lignocellulosic Residues: Coffe Cut-Stems (CCS), Sugarcane Bagasse and Pinus Patula”, In:

United States, Event: 2015 American Institute of Chemical Engineering (AIChE) Annual Meeting.

Ashley Caballero, Carlos A. García, Laura Daza, Carlos A. Cardona, “Techno-economic

assessment of biodiesel production from palm oil by supercritical transesterification”, In: United

States, Event: 2015 American Institute of Chemical Engineering (AIChE) Annual Meeting.

Julio Sanchez, Sebastian Serna, Carlos A. García, Carlos A. Cardona, “Simulation and

Comparison between the Real and Supplied Oxygen Demand of a Fermentation Process”, In:

United States, Event: 2015 American Institute of Chemical Engineering (AIChE) Annual Meeting.

Sebastian Serna, Sebastian Posada, Carlos A. García, Carlos A. Cardona, “Ethanol Production

from Cocoyam (Xanthosoma sagittifolium). Design and Analysis”, In: United States, Event: 2015

American Institute of Chemical Engineering (AIChE) Annual Meeting.

Carlos A. García, Carlos O. Tascón, Carlos A. Cardona, “Comparison of the Synthesis Gas

Production via Gasification and Combustion from Coffee Cut-Stems”, In: United States, Event:

2015 American Institute of Chemical Engineering (AIChE) Annual Meeting.

Carlos A. García, Alvaro Gomez, Yuri Pisarenko, Carlos A. Cardona, “Mathematical and

Experimental Modelling of Biomass Gasification for Hydrogen Production”, In: United States,

Event: 2015 American Institute of Chemical Engineering (AIChE) Annual Meeting.

Carlos A. García, Natalia Salazar, Carlos E. Orrego, Carlos A. Cardona, “Evaluación del impacto

ambiental de tres procesos para la obtención de energía a partir de Pino Pátula utilizando el

algoritmo de reducción de residuos”, In: Colombia, Event: VI Simposio Internacional:

Biorefinerias y productos sostenibles.

XVI Hydrogen Production through Gasification and Dark Fermentation

Valentina Aristizabal, Carlos A. García, Carlos A. Cardona, “Stand-alone and Biorefinery ways

to produce bioenergy from solid biodiesel wastes in Colombia”, In: Greece, Event: Conference of

Sustainable Solid Waste Management TINOS 2015.

Carlos A. García, Carlos A. Cardona, “Hydrogen production by gasification and dark

fermentation from woody wastes: Energy and Environmental Analysis.”, In: Greece, Event:

Conference of Sustainable Solid Waste Management TINOS 2015.

Carlos A. García, Jonathan Moncada, Carlos A. Cardona, “Economic Comparison of Chemical

and Biochemical ways to produce bioenergy from biomass”, In: Austria, Event: 23th European

Biomass Conference and Exhibition EUBCE 2015.

Hector A. Forero, Valentina Hernandez, Carlos A. García, Carlos A. Cardona, “Use of Graph

Theory for Designing Efficient Biorefineries”, In: United States, Event: 2014 American Institute of

Chemical Engineering (AIChE) Annual Meeting.

Carlos A. García, Juan B. Restrepo, Carlos A. Cardona, “Hydrogen Production and Gasification

from Biomass: Challenges and Strategies”, In: United States, Event: 2014 American Institute of

Chemical Engineering (AIChE) Annual Meeting.

Participation of this Thesis in Research Projects

ERANet-LAC: Latin America, Caribbean and European Union. Project: Development of modular

small-scale integrated biorefineries to produce an optimal range of bioproducts from a variety of

rural, agricultural and agroindustrial residues/wastes with a minimum consumption of fossil energy

(SMIBIO). Position: Researcher in the techno-economic and environmental assessment of small-

scale integrated biorefineries using Coffee Cut-Stems, Coffee Grounds and Milk Whey.

Prizes

This thesis was awarded by the German Ministry of Education and Research with the Green Talent

Award 2016, which was created as an initiative to promote sustainable development research

among different countries around the world.

Introduction

According to the U.S Energy International Administration (EIA), Colombia consumed 38.96

million tons of oil equivalent (Mtoe) of energy in 2012. Oil consumption constituted 41.7%,

followed by natural gas (23.5%), hydropower (11%) and coal (9.8%). The demand of primary and

secondary energy in Colombia has doubled in the period between 1975 and 2009, which required a

rapid growth of the energy conversion capacity [1]. Biomass plays an important role in the energy

matrix of the country, being the second largest renewable energy source after hydropower.

Renewable energy sources contribute with 23.2% to the primary energy matrix; water resources,

wood and bagasse contribute around 11%, 8.7% and 3.5 % of the primary energy matrix in

Colombia, respectively [2]. The demand for wood, cane bagasse and biomass residues have

remained relatively constant since 1975 ranging between 3.72 and 4.47 Mtoe [3].

The high dependence of the main economic sectors in Colombia on the fossil fuels highlights the

necessity of implementing new technologies to produce high-impact products with high energy

potential considering the large amount of wastes generated at different stages of the agroindustrial

and forestry supply chains. There are different methods to transform these residues into bioenergy

products. Thermochemical processes (i.e. gasification, combustion and pyrolysis) have been

gaining importance because they use an extensive range of biomass and have a high productivity.

In contrast, biochemical processes such as dark fermentation to produce hydrogen (as well as

fermentation processes to obtain second generation ethanol) require more research for its

implementation. However, it can be an alternative for bioenergy production with low energy

consumption [4].

Hydrogen is nowadays a promising source of energy that can be used directly and indirectly as

storage fuel with less environmental issues, especially without CO2 emissions [5]. However, only

4% of hydrogen is produced from renewable sources since high percentage of residual biomass is

used directly as feedstock for combustion processes where its energy density is lower [6][7].

Several authors have studied the influence of operating parameters such as temperature, moisture

18 Hydrogen Production through Gasification and Dark Fermentation

content, air/biomass ratio, among others in biomass gasification and the effect of temperature, pH,

substrate concentration and fermentation time in dark fermentation for hydrogen production [8]–

[13].

Hence, the aim of this thesis is to develop a techno-economic, energy and environmental

assessment of six scenarios for the hydrogen production through gasification and dark fermentation

using Pinus Patula and Coffee Cut-Stems as energy sources. Two scenarios were considered as

base cases, which involve the stand-alone thermochemical and biochemical production of

hydrogen and the four remaining scenarios were evaluated based on the biorefinery concept for

different process configurations and products. The techno-economic evaluation was performed

considering the effect of the hierarchy distribution of the products in the hydrogen production cost.

The energy and environmental assessment was carried out in order to compare the energy

efficiency and CO2 emissions of the stand-alone and biorefinery pathways, respectively. Prior to

the simulation approach, experimental data of the air gasification and dark fermentation using the

selected raw material were obtained in order to feedback the simulation procedure.

Hydrogen Production through Gasification and Dark Fermentation 19

Thesis Hypothesis

It is possible to obtain a rich-hydrogen gas that can be used directly as fuel for power generation

from lignocellulosic residues in the non-interconnected zones (NIZ) of Colombia through

gasification and dark fermentation.

Thesis Objectives

General Objective

To evaluate from the techno-economic, energetic and environmental point of view, the hydrogen

production from agro-industrial residues in Colombia

Specific Objectives

To perform the physicochemical characterization of the Coffee Cut-Stems (CCS) and

Pinus Patula (PP) residues.

To assess experimentally the production of synthesis gas through gasification using agro-

industrial residues coupled with the enrichment of the generated syngas using a catalyst.

To evaluate the production of hydrogen through dark fermentation using the selected raw

materials.

To simulate and compare the gasification and dark fermentation processes for hydrogen

production using the selected raw material from the techno-economic, energetic and

environmental point of view.

To simulate biorefineries based on the gasification and dark fermentation concept.


1. Energy in Colombia

Colombia is a country located in the North-western part of South America. With a total land area

of 1,141,748 km2 from which 40.4% of the surface is destined for agriculture activities [14].

However, Colombia has shifted in the last few years from an agricultural economy to one based on

mineral and energy resources. This new economy focus has enhanced the growth of the country

from 4% to almost 5% annually. The principal economic sectors that have influenced this growth

are: the tertiary sector which involves the trade and services activities with a participation of 60%

of the Gross Domestic Product (GDP). The secondary (manufacturing, energy and construction)

and the primary (agriculture and mining) sector have contributed with a 25.3% and 14.8% of the

GDP, respectively. In terms of the Gross Net Income (GNI), Colombia has a GNI per capital of

7,130 USD in 2015, which consolidates Colombia as an upper middle income economy [14]. The

Gini index measures the distribution of the incomes between the population in a given economy; in

2014 Colombia had a Gini index of 53.5 and it was located in the 14th place of countries with high

inequalities according to the United Nations Development Programme (UNDP). The main reason

of the high inequalities is related to the corruption, ineffective policies and weak institutions that

have caused a poor wealth distribution [3].

As a consequence of these socioeconomic and political issues, the energy and environmental sector

have suffered serious changes. According to the U.S Energy International Administration (EIA),

Colombia consumed 38.96 million tons of oil equivalent (Mtoe) of energy in 2012. Oil

consumption constituted 41.7%, followed by natural gas (23.5%), hydropower (11%) and coal

(9.8%). The demand of primary and secondary energy in Colombia has doubled in the period

between 1975 and 2009, which required a rapid growth of the energy conversion capacity [1]. In

order to mitigate the increase in the amount of required energy, various efforts have been

implemented. In the oil sector, new explorations and discoveries have been developed; however,

Colombian officials estimate that the current oil reserves will last about seven years [15]. Due to

the different weather events such as “El Niño”, which caused an intensive drought in Colombia,


new coal- and fired- power plants were built to reduce the high dependence of hydropower. The

use of these technologies based on coal and natural gas have increased the environmental impact of

the energy generation in Colombia. According to the Mining and Energy Planning Unit (UPME),

the Greenhouse Gas (GHG) emissions increased 2.5 times between 1975 and 2009 due to the

intensive used of non-renewable and high pollutant energy sources such as oil, coal and natural

gas.

1.1 Energy Matrix

The energy matrix of Colombia has been transformed throughout the last years. Oil is the main

energy source with a contribution to the 41.7% of the primary energy generation. Colombia

produced 1 million barrels per day of petroleum and other liquids in 2015 (see Figure 1-1) [16].

Colombia’s oil production has increased by an annual average of almost 11% since 2008. This

behavior is evidenced due to the intensive use of oil derivatives in the transportation sector,

especially Diesel, despite the implementation of new fuels such as the Natural Gas Vehicle (NGV)

(see Figure 1-2).

Figure 1-1. Production of crude oil (thousands of cubic feet per calendar day) in Colombia.

Adapted from: [17].


Figure 1-2. Diesel consumption in Colombia.

Adapted from: [18]

Regarding other fossil fuels, natural gas contributes to the 23.5% of the total energy sources.

According to the Energy Ministry, Colombia produced 413 billion cubic feet (Bcf) of dry natural

gas in 2015 [19]. The demand of this energy source has increased in an average of 7.4% annual

between 2009 and 2013, due to different factors such as: i) new government policies for the

implementation of natural gas in industrial processes because of its environmental and economic

benefits and ii) the high participation of the thermal energy in the electricity generation due to

different weather conditions such as “El Niño” that has forced the government to implement new

diversification strategies of the electric energy sources (see Figure 1-3).

Figure 1-3. Distribution of the natural gas consumption in different sectors of Colombia

Adapted from: [20]


Colombia is the eleventh coal producer and the fifth coal exporter in the world with a production of

97.6 million short tons (MMst) of coal in 2014 [16]. However, the participation of this energy

source in the Colombian matrix reaches 9.8% of the primary energy sources with an internal

consumption of 7.4 MMst. This behavior can be explained due to the fact that the generation of

electricity in Colombia is mainly from hydropower. According to the Energy International

Admisnitration (EIA), in 2015 Colombia had 15.7 gigawatts (GW) of installed electricity

generation capacity [16]. Of the generated electricity in 2015, hydropower plants provided 70%,

natural gas accounted for 12%, coal accounted for 7%, petroleum accounted for 10% and the

remaining electricity was from other sources.

Renewable energy sources contribute with 23.2% to the primary energy generation, which are

distributed in the following way: water resources contribute about 11%, wood fuel 8.7% and

bagasse with 3.5 % [2]. Biomass plays an important role in the energy matrix of the country as it is

today the second largest renewable energy resource after hydropower. The historical demand of

biomass in the form of wood, cane bagasse and biomass residues has remained relatively constant

since 1975 ranging between 3.72 and 4.47 Mtoe [3]. The use of biomass for energy purposes

depends of the type, composition and its application. Wood is used as traditional fuel for cooking

and heating in farms, where its energy density is very low. However, in 2008 a gasification project

was implemented in Necoclí (Antioquia), located in the northwestern of Colombia, aiming to

produce electricity using wood residues in order to supply energy to the town, which it is not

connected to the Interconnected National System (SIN). Gasification is one of the most promising

technologies for the production of energy using reciprocating engines. Besides, sugarcane bagasse,

which is a by-product of sugarcane processing, is used as fuel in boilers and cogeneration power

plants to provide heat and power. In 2012, sugarcane mills had an installed capacity of 187 MW, of

which 129 MW were needed in order to meet the electricity demand of the mills, whereas 53 MW

were available to generate surplus electricity to be sold to the grid [2].

Finally, the Colombian energy matrix is completed with bioethanol and biodiesel that are used as

fractions in mixtures with gasoline and diesel in the transportation sector. These biofuels contribute

about 1% and 0.7% of the total primary energy, respectively [3]. Bioethanol is produced from

sugarcane molasses through a fermentative, distillation and dehydration process. In 2012,

Colombia produced approximately 292,000 tons of ethanol, which were consumed directly in the

country [21]. According to the Biofuels National Federation (Fedebiocombustibles), the mandatory

blending of ethanol into gasoline was increased to 8% in the Andean, Amazon and Pacific regions

of the country. On the other hand, biodiesel is produced from oil palm through a series of processes


that involve the oil refining, continuous transesterification and biodiesel purification. In 2012,

Colombia produced around 490,000 tons of biodiesel, which were used for internal consumption,

as in the case of ethanol. Figure 1-4 summarizes the energy matrix of Colombia.

Figure 1-4. Distribution of the main energy sources in Colombia

Taken from: [3]

1.2 Advantages and deficiencies of the current energy systems

Nowadays, the production of energy in Colombia is mainly based on fossil fuels and water sources.

Fossil fuels such as oil, gasoline, natural gas, coal, among others have a series of advantages in

comparison to other energy sources. The enactment of a series of regulatory reforms to make the

oil and natural gas sector more attractive to foreign investors led to increase the Colombian

production of thes fuels. Therefore, the principal activity and background of the economy in

Colombia has become the oil industry, which can be evidenced in the current issue with the

petroleum international price. The emission of greenhouse gases (GHG) is related mainly to the

use of fossil fuels in the transportation and industrial sectors. In 2016, Colombia experimented the

hottest semester of the history due to a weather event “El Niño” that reduced the water reservoirs

levels below 50%. This behavior caused the reduction of electricity generation from hydropower

plants, and in order to supply the internal energy demand of Colombia, the government increased

the capacity of some thermal power stations to reach the energy needs of the country. However,

these plants do not have the installed capacity to meet the energy requirements of the country due

to the lack of planning and prevention of these types of disasters.

42%

23%

10%

11%

9%

3% 1% 1%

Petroleum

Natural Gas

Coal

Hydropower

Wood

Bagasse

Bioethanol

Biodiesel


For these and many other reasons, the search for new energy sources has begun to increase in

recent years. The main causes are related to environmental issues, the reduction of oil reserves and

the large amount of generated daily wastes. The high dependence on fossil fuels of the main

economic sectors in Colombia demonstrates the need to implement new technologies to generate

high-impact products with high energy potential, taking advantage of the large amount of

generated wastes at different stages of the agroindustrial supply chain.

1.3 Renewable energy sources

The Mining and Energy Planning Unit (UPME) developed the Program for the Rational and

Efficient Use of Energy and other Forms of Non-Conventional Energy 2010-2015 (PROURE) to

ensure a proper energy supply, competitiveness of the Colombian economy, consumer protection

and promotion of the use of non-conventional energy in a sustainable way with the environment

and natural resources [22]. The main goal of this program was to achieve in 2015 a share of 3.5%

of Non-Conventional Energy Sources (FNCE) in the National Interconnected System (SIN) and

20% of FNCE for Non-Interconnected Zones (NIZ). The non-conventional energy sources include

wind power, biomass, small hydropower plants and solar power, among others. With the law 1715

of 2014, the National Government regulated the integration of the non-conventional renewable

energies to the national energetic system. The main objective of the law 1715 is to promote the

development and use of non-conventional renewable energy sources through the integration in the

electric market, in the Non-Interconnected Zones and in other energy uses [23].

Biofuels have an important role in the energy matrix of Colombia but due to different regulatory

frameworks (Laws 693 of 2001 and 939 of 2004), the share of its use as land fuels is very limited.

Nevertheless, the biofuel sector has great benefits such as tax exemption. The incomes from the

use of crops such as the oil palm have a tax exemption for a period of 10 years. Biofuel plants that

obey with the requirement of a high investment of 75,000 SMMLV (Legal Minimum Wage) or

that have more than 500 employees, they can succeed to a special regime of Free Economic Zone,

which allows the owners of the plants to import machinery without paying duties and a tax rate of

15% [22]. In most of the country, a fraction of 8% ethanol in gasoline by regulation must be

supplied, while a fraction of 10% of biodiesel must be added to diesel.

Colombia does not have a properly structured production chain of bioenergy unlike biofuels.

However, different projects using residues for energy production have been developed. Sugarcane


bagasse is one of the main generated wastes in harvesting and milling of sugarcane mills.

Currently, this residue is being used for power generation through cogeneration systems. There are

several projects related to the use of the generated biogas from landfills, combustion, gasification,

pyrolysis and anaerobic digestion distributed along the country in department such as Caldas,

Cundinamarca, Nariño and Antioquia.

1.4 Non-Interconnected Zones in Colombia

The non-interconnected zones (NIZ) were defined in Colombia in the Article 11 of the Law 143 of

1994, and they were categorized by the resolution 182138 of the Energy and Gas Regulatory

Commission (CREG) in 2007 as the zones that do not have access to the public electricity service

through the National Grid (NG). According to the data of the World Bank [14], the access to

electricity of the population in Colombia reaches 97%, which do not affect the national PIB;

however, in regions such as Amazonia, Orinoquia and Pacific, the electricity access can affect

considerably the local growth. The main characteristics of the NIZ are: they are located in places of

difficult access, far away from the main urban centers, huge ecological importance due to their rich

natural resources and biodiversity, most of the natural national parks and reserves are located there,

lack of public services such as energy, water and sewage. The geographical location, the lack of

physical infrastructure and access routes make very difficult, from the economic and

environmental point of view, the connection of these zones to the National Grid (NG). Therefore,

renewable alternatives could be implemented in these zones considering the high availability and

variety of natural resources [24].

The NIZ comprise the 66% of the total country’s area; which include 5 capital cities and 1,500

towns in the rural area rising to 1.8 million inhabitants that represent the 3% of the total population

of Colombia (see Figure 1-5). From this point of view, the NIZ seem to have no significant effect

on the country growth. Nevertheless, according to the National Institution of Colombia, article

365: “Colombian government must provide the efficient energy supply to the population in

Colombia” gives some statements of the access to electricity in all the communities and

individuals to improve the quality of life. In non-interconnected areas the levels of poverty, access

to education, access and quality of housing, among other indicators, remain neglected with respect

to the national average.


Figure 1-5. Graphical map of the Non-Interconnected Zones in Colombia (Gray Departments)

Taken from: Minning and Energy Ministry, 2013.

Due to the relative high cost and the construction difficulties of hydropower plants for low scale

population, it is very difficult to connect the NIZ to the NG. Considering these drawbacks,

different energy alternatives have been implemented in order to supply the energy demand of the

non-interconnected areas. The highest contribution of electricity in the NIZ came from the use of

diesel generators and from small hydropower plants; however, 96.3% of the electricity generation

capacity comes from the diesel generators [25]. The Institute of Planning and Promotion of

Energy Solutions (IPSE) has promoted different non-conventional energy projects in departments

that are considered as NIZ. Solar and wind energetic solutions have been implemented in different

departments such as Chocó, La Guajira, Amazonas, Vaupés, among others [26]. The number of

hours that a town in the NIZ has electricity is very limited (e.g. 6 hours per day of electricity);

therefore, the main objective of these energetic solutions is to increase the time of the electricity

service supply. Despite the use of the sun and wind to generate electricity and considering the high

availability of natural resources in Colombia, especially in the NIZ, different technologies could be

implemented to supply the energy needs of these zones.

In the departments with lack of energy systems and high generation costs, the sale price of 1 kWh

of electricity varies between 400$ in Guainía to 850$ in Vaupés [24]. These costs are distributed

among the generation, transmission, commercialization and distribution. The generation costs are

related to the production of electricity through water, coal, natural gas and renewable resources.

The second process involve the transmission, which is the transport of the electricity from the


generation plants to the large consumption centers. Subsequently, the supplied energy must be

distributed to the final users in the cities and finally, the commercialization of the energy in

relation to its sale price.

1.5 Biomass suitable for energy production in Colombia

Biomass is the most used renewable resource in the world and it has a great energy potential. This

resource can be obtained from plants, plant-derived materials and animals. Wood has been the first

type of biomass used for cooking and heating water and still remains as the largest biomass energy

source, but recently other biomass can also be used to produce energy. These include food crops,

grassy and woody plants, residues from agriculture or forestry, oil-rich algae, and the organic

component of municipal and industrial wastes. These residues can provide an array of benefits:

- The use of biomass energy has the potential to greatly reduce greenhouse gas emissions.

Burning biomass releases about the same amount of carbon dioxide as burning fossil fuels.

However, biomass releases carbon dioxide that is largely balanced by the carbon dioxide

captured in its own growth (depending how much energy was used to grow, harvest, and in

the fuel production). It is considered by several authors that biomass has zero CO2

emissions into the atmosphere, but there are several factors that can increase the emissions

of these residues such as: the energy used to grow, harvest and the fuel consumption in the

different stages of the crop cultivation.

- The use of biomass can reduce the dependence on fossil fuels; nowadays, biofuels are the

only available renewable liquid transportation fuels.

There are different technologies to transform the biomass in energy: thermal processes such as

combustion and gasification to obtain heat to produce steam and subsequently, electricity or these

technologies can also produce directly bioenergy. On the other hand, biochemical methods use C5-

C6 fractions, obtained in a previously pretreatment stage, to produce different bioenergy products

through microorganisms able to degrade these fermentable sugars. In chapter 2 a more detailed

description of these technologies is presented.

Colombia is characterized by a vast bioenergy potential that remains untapped. Various studies

have recently estimated a theoretical biomass energy potential, ranging between 5 and 18 Mtoe,

depending on the assumptions [3]. The transformation technologies and the biomass availability

are the current constrains for energy exploitation. The energy efficiency of the technologies for

biomass transformation can vary from 20% (e.g. microturbines) to 50% (e.g. gasification).


Logistics and transportation issues are the main bottlenecks of the biomass to be implemented as

bioenergy system. Colombia is a country suitable for agriculture; however, due to its geography

and non-technified cultivation methods, the transportation of the biomass to the processing centers

presents a lot of drawbacks that make the process non profitable. Considering all these

inconveniences, from the theoretical biomass energy potential a fraction ranging between 1 and 10

Mtoe might be technically available. Tables 1-1 and 1-2 summarize some of the most common

energy crops in Colombia considering their production capacity, the main energetic product from a

given crop and the amount of energy that can be obtained from the crop processing.

Sugar cane is one of the most emblematic crops in Colombia due to the variety of products that can

be obtained. Ethanol, sugar, sugarcane bagasse and currently, other residues (e.g. leaves) from the

harvesting of sugarcane are the main bioenergy products of this crop. From the information

provided in Table 1-1, the high energy potential of these sub-products is related to the high

availability of this crop throughout the year in the country. The biomass availability is one the

bottlenecks of most of the biomass systems since the energy crops are harvested seasonally, which

means that the raw material would not be available, carrying new economic challenges for the

implementations of biomass-based processes.

Table 1-1. Potential energy of different crops and residues. Adapted from: [27]

Crop Productiona

(ton/year)

Energy

Productb

%

Energyc

Energy

Product

Generationd

(ton/year)

Calorific

Valuee

(KJ/kg)

Gross

Energy

Potentialf

(MWh/year)

Oil Palm 2,289,472 Oil 20% 457,894 36,950 536.50

Soybean 55,869 Oil 18% 10,056 34,750 11.08

Cacao 42,855 Oil 50% 21,428 36,000 24.46

Sugarcane 21,897,120

Alcohol 6% 1,210,911 26,700 1,025.22

Bagasse 29% 6,350,165 8,895 1,791.12

Crop Residues 46% 10,072,675 15,000 4,791.04

Corn 700,897 Alcohol 37% 259,332 26,700 219.56

Potato 2,586,451 Alcohol 79% 173,912 26,700 147.24

Rice 1,704,551 Husk 25% 426,138 13,900 187.83

Coffee 487,726 Pulp 50% 243,863 15,880 122,71

a Crop annual production based on the data reported by [27]


b Product that can be obtained from the energy crop.

c Fraction of the energy crop destined to the production of bioenergy.

d Energy product (ton/year) = Crop production(ton/year) ∗ % energy

e Calorific value of the bioenergy product

f The Gross Energy Potential (GEP) is defined as the maximum amount of energy that can be

obtained from a bioenergy product if it is submitted to a thermal process.

On the other hand, biomass from natural and planted forests has been one of the most used

alternatives to produce energy directly or indirectly (see, Table 1-2). However, stronger policies

must be implemented in relation to the deforestation of natural forests for commercial and

expansion purposes. Due to the lack of clear policies, reforestation programs do not have a large

participation in the energy matrix of the country, but its implementation can help to mitigate the

environmental impact on the natural forests.

Table 1-2. Potential energy of natural and reforested forests

Adapted from: [27]

Forest Area

(Ha)

Yield

(m3/ha)

%

Residues

Density

(ton/m3)

Residues

Production

(ton/year)

Calorific

Value

(KJ/kg)

Gross Energy

Potential

(MWh/year)

Natural 2,250,000 53 47% 0,51 1,141,635 18,744 697.98

Reforested 145,000 102 40% 0,53 799,500 17,444 441.82

1.6 Final Remark

Currently, Colombia is a country with high dependence to fossil fuels in most of the economic and

transportation sectors; however, new alternatives have begun to implement using residues from

different agroindustrial, agriculture and forestry supply chains in order to obtain energy products

with low CO2 emissions and high energy yields. Examples such as the use of sugarcane bagasse

and wood wastes for the production of electricity through cogeneration and gasification schemes,

are projects that seek to implement the idea of environment friendly technologies in Colombia.


2. Hydrogen as energy vector

Hydrogen is the most abundant component in the world. It does not exist by itself on earth, but

it can be produced from a variety of sources like coal, natural gas, oil, water and biomass using

different process technologies [5], [6]. The hydrogen energy is based on the fact that hydrogen

reacts with oxygen to produce energy and water as presented in Eq. 2-1.

𝐻2 +1

2 𝑂2 → 𝐻2𝑂 ∆𝐻 = −285,5 𝑘𝐽

𝑚𝑜𝑙⁄ 𝑎𝑡 25°𝐶 (2-1)

Nowadays, hydrogen is a promising source of energy that can be used directly and indirectly as

storage fuel with less environmental issues, especially without CO2 emissions [5]. Hydrogen has

the highest energy density (141.9 kilojoule per gram) in comparison with other hydrocarbon

fuels as summarized in Table 2-1. The future implementation of the hydrogen as energy carrier

depends primarily upon four factors: (i) the future cost of hydrogen, (ii) The advance of the

technologies for hydrogen production, (iii) log-term restrictions on greenhouse gases and (iv)

the cost of competing energy systems, mainly, fossil fuels [28].

Table 2-1. Energy yield of hydrogen and other common fuels at 25°C and 1 atm [29].

Fuel Higher Heating Value (kj/g)

Hydrogen 141.9

Methane 55.5

Gasoline 47.5

Diesel 44.8

Methanol 20

Ethanol 29.7

Coal 36

Propane 49.9

Hydrogen is not a primary energy source, it can serve as an energy carrier such as electricity and

thus, it can replace fossil fuels in a wide range of applications [30]. It could be implemented as a

long-term energy carrier to reduce the high dependence of fossil fuels and the CO2 emissions

into the atmosphere. Different methods can use hydrogen to release energy such as direct

combustion, steam production and fuel cells. Among these methods, the fuel cell is the most

efficient and cleanest technology for releasing energy from hydrogen [31]. The by-product of


the main fuel cell reaction, when hydrogen is the fuel, is pure water which means that a fuel cell

can be essentially ‘zero emission’ [32].

Only 4% of hydrogen is produced from renewable sources, the remaining 96% is produced from

fossil fuels, especially, natural gas [6]. Some renewable sources for hydrogen production are

biomass such as forest residues, wastewater, manure, crop residues, among others. Additionally,

hydrogen can be produced from water using as energy sources: the sun, wind, hydropower, geo-

thermal and nuclear energy. Only biomass can produce hydrogen directly; however, seasonal

availability and transportation issues make the process non-profitable [33]. In order to enhance

the economic profitability of the hydrogen production, energy efficiencies between 60-80% are

required [5]. Despite the high energy potential of the biomass, it is often dumped or used

directly as feedstock for combustion processes where its energy density is very less [7].

The biggest concern in using hydrogen as energy carrier in the transportation sector is related to

safety issues. It is also important to note, that the same concerns were evidenced when gasoline

and diesel were considered as energy carriers of the transportation sector [31].

Hydrogen is a nontoxic gas in concentration below 100 ppm, environmental safe and has low

radiation levels. However, hydrogen is odourless, which can be an issue because it burns with a

colourless flame that may not be visible. The ignition energy (IE) is the minimum amount of

energy required to ignite a combustible vapor or gas. The IE for hydrogen (0.2 mJ) is lower than

for ethanol (0.24 mJ) and methane (0.29 mJ). The self-ignite temperature (IT) is the minimum

temperature at which a substance spontaneously ignite in normal atmosphere. The IT for

hydrogen is 583.1 °C that it is a great advantage over other energy carriers such as gasoline (228

– 501°C) and natural gas (540.1°C). The hydrogen diffusivity in the air is considerably greater

than gasoline. For that reason, it is almost impossible to make hydrogen to explode in an open

area due to the high volatility [31]. Nevertheless, hydrogen is detonable over a very wide range

of concentrations when confined. In summary, hydrogen is safer than other fuels such as

gasoline, diesel and methane. Despite the advantages of hydrogen, low density is one of the

main bottlenecks of this gas so as to be implemented as energy carrier in the transportation

sector. Due to the low density (0.09 kg/Nm3), large storage vessels are required in order to store

enough hydrogen to give the vehicle an adequate driving range [28].

2.1 Methods for hydrogen production

Traditionally, technologies for hydrogen production can be divided into thermochemical

methods (steam methane reforming, gasification, pyrolysis, and combustion), electrochemical

methods (electrolysis and photolysis) and biochemical methods (dark fermentation and photo-


fermentation). Despite the classification based on the used technology, the hydrogen production

can also be classified by the source of energy; splitting in two main groups: fossil fuels energy

sources (coal, natural gas and other hydrocarbons) and renewable energy sources (biomass,

wind and solar). As previously mentioned, approximately 96% of the produced hydrogen comes

from fossil fuels, especially using thermochemical transformation methods such as Steam

Methane Reforming and Partial Oxidation. Figure 2-1 presents the relation between the energy

sources and the technologies for hydrogen production. Depending on the type of energy source,

different transformation methods can be implemented to produce hydrogen. When fossil fuels

are used, the main transformation pathway is the thermochemical conversion. On the other

hand, renewable sources use different technologies to produce hydrogen such as gasification,

fermentation or electrolysis. Biomass can be transformed to hydrogen through thermochemical

and biochemical technologies, depending on the type of biomass. Raw material with high

lignocellulosic content have higher energy yields when they are directly converted to hydrogen

through thermochemical methods, which allow an extensive range of feedstock, being much

faster but less selective, and require high temperature and therefore, larger energy inputs [34]–

[36]. Biomass with high biodegradability and low crystallinity can be used to produce hydrogen

through biochemical methods. These technologies are more hydrogen selective but they still

operate in batch regime, which provides low productivities. On the other hand, electrochemical

methods are widely used to obtain hydrogen through the electrolytic conversion of water but

great volumes of hydrogen cannot be produced by this method because of the production cost.

2.1.1 Thermochemical technologies

The main purpose of thermochemical methods is the separation of hydrogen from the feedstock

through a series of chemical reactions. Depending of the process, the reactions can be

endothermic (require an external energy input) or exothermic (internal energy generation) [37].

Steam Methane Reforming (SMR) is the most used commercial process for hydrogen

production due to its high cost-benefit ratio, using gaseous and liquid hydrocarbons as its main

feedstocks [38]. However, biomass gasification seems to be the most promising solution for the

production of second generation biofuels [5]. The main advantage of the thermochemical

methods is that the overall energy efficiency is higher than 52% and the production cost is lower

than biochemical methods [28]. In this section, a brief description of some thermochemical

processes is presented.


Figure 2-1. Energy sources and methods for hydrogen production

- Steam methane reforming (SMR)

SMR contributes to about 40–50% of total hydrogen production [37]. This process is divided in

four steps: Pretreatment of the raw gas, reforming of the synthesis gas, conversion to hydrogen

rich-gas and purification of hydrogen. Prior to steam reforming, the raw gas is pretreated in

order to convert any sulfur compound to H2S. Then, methane reacts with steam to produce

hydrogen and carbon monoxide (Eq. 2-2). In order to enhance the hydrogen concentration,

carbon monoxide is converted into hydrogen and carbon dioxide using steam as reacting agent

(Eq. 2-3). Finally, a hydrogen purification step is used to clean up the hydrogen rich gas from

carbon dioxide, carbon monoxide, unreacted methane, among others. Pressure Swing

Adsorption (PSA) is the most commonly technology for hydrogen purification. PSA typically

occurs at temperatures in the range of 800 – 1000 °C and pressures between 13 – 20 bar [37].

The heating requirements of the process are supplied via burning of natural gas [39]. The steam

reformation of methane produces a rich-hydrogen gas (70 – 75%), along with small amounts of

methane (2 – 6%), carbon monoxide (7 – 10%) and carbon dioxide (6 – 14%) [40]. The scale of

the process (large and small scale) determines the energy efficiency, which can varies from 85 –

90% in large scale process where some heat is recovered. On the other hand, in small scale

systems, the energy efficiency varies from 47 – 55% [41]. The chemical reactions involved in

the production of hydrogen through SMR are presented below.

𝐶𝐻4 + 𝐻2𝑂 → 𝐶𝑂 + 3𝐻2 ∆𝐻 = +206 𝑘𝑗/𝑚𝑜𝑙 (2-2)


𝐶𝑂 + 𝐻2𝑂 → 𝐶𝑂2 + 𝐻2 ∆𝐻 = −40.9 𝑘𝑗/𝑚𝑜𝑙 (2-3)

- Partial Oxidation (POx)

The second most common method to produce hydrogen from natural gas (mainly methane) or

other hydrocarbons is the Partial Oxidation. Fossil fuels are combusted with limited amount of

oxygen at 1200 – 1400°C and 700 – 1000°C using catalyst [31], [37], [41]. The product from

the partial oxidation consists of a syngas (CO and H2) and a mixture of CO2, N2, H2O and small

amounts of CH4. This process has a faster hydrogen production compared to SMR; however, the

hydrogen productivity per mass of raw material is lesser. According to this, the energy

efficiency varies from 71 – 89% [31]. The chemical reaction involved in the POx for hydrogen

production is presented below.

𝐶𝑛𝐻𝑚 +𝑛

2𝑂2 → 𝑛𝐶𝑂 +

𝑚

2𝐻2

(2-4)

- Gasification

Gasification consists in the conversion of carbonaceous material (mainly coal and biomass) into

a synthesis gas rich on hydrogen, carbon monoxide, carbon dioxide and methane using a

gasifying agent such as air, steam, oxygen or a mixture of these. Depending on the type of

gasifying agent used in the process, different gaseous species, in the synthesis gas can be

obtained as shown in Table 2-2. Using air as a gasifying agent to produce hydrogen has its main

drawback in the high nitrogen content present in the synthesis gas, requiring a downstream

process (i.e. membrane technology) to separate nitrogen from the fuel gas, increasing the

hydrogen production costs. The generation of electricity is the main application of air

gasification because it is the cheapest alternative and it provides a moderate energy content in

the produced synthesis gas. The remaining three types of gasifying agents (steam, oxygen and

oxygen / steam mixtures) have higher hydrogen production yields. Pure oxygen ends up

consuming methane, tar, and any hydrocarbons in the product gas via combustion, which means

that the gas is essentially a pure mixture of H2 and CO [42]. Using steam as gasifying agent

affects the H2/CO ratio, increasing the content of methane and thus, the energy content of the

gas. However, the main limitation of gasification processes, that use other gasifying agent

different to air, is the high acquisition cost of the gasifying agent (in the case of oxygen), and

the cost associated to the production of the gasifying agent; for example, the use of a boiler in

the steam production. Other parameters that affect the gasification performance are:

Temperature, pressure, particle size, moisture content of the feedstock, the equivalence ratio

(ER), among others.

The energy efficiency of the coal gasification (67%) is higher than for biomass gasification (41

– 59%); however, 11 kg of carbon dioxide are released into the atmosphere per kilogram of


produced hydrogen in the coal gasification. The general chemical reaction that describe the

gasification is presented below.

𝐶𝐻𝑥𝑂𝑦 + 𝑤𝐻2𝑂 + 𝑚(𝑂2 + 3.76𝑁2)

= 𝑛𝐻2𝐻2 + 𝑛𝐶𝑂𝐶𝑂 + 𝑛𝐶𝑂2

𝐶𝑂2 + 𝑛𝐻2𝑂𝐻2𝑂 + 𝑛𝐶𝐻4𝐶𝐻4 + 3.76𝑚𝑁2

(2-5)

Table 2-2. Types of gasifying agent used in gasification procedures.

Composition (% Vol) Gasifying Agent

Air [43] Steam [44] Oxygen [45] Oxygen/Steam [10]

Hydrogen 14 – 19 % 20 – 35 % 30 – 50 % 26 – 30 %

Carbon Monoxide 15 – 21 % 40 – 45 % 45 – 50 % 35 – 39 %

Carbon Dioxide 10 – 15 % 10 – 15 % 5 – 35 % 27 – 32 %

Methane 1 – 3 % 5 – 10 % < 2 % 3 – 4 %

Nitrogen 45 – 55 % ------ ------ ------

HHV (Mj/Nm3) 4 – 6 16 – 19 5 - 16 9 – 11

2.1.2 Biochemical methods

High performance of the biological conversion of biomass to hydrogen depends on the ability of

the microorganism to degrade the raw material. Lignocellulosic residues can be used to obtain

different added-value products due to their high cellulose/hemicellulose ratio. These residues

are mainly compose of cellulose and hemicellulose. Lignin is the main constraint for the

integration of these wastes in industrial processes: their high crystallinity and low

biodegradability. Considering this, different pretreatment methods have been studied in order to

enhance the availability of the fibers to be degraded by the microorganisms. Thermal, acid,

alkaline, enzymatic pre-treatment methods are some of the most used processes to enhance the

biomass biodegradability. Biological methods are considered as a promising way of producing

hydrogen as they provide a feasible route for the sustainable supply of H2 with low pollution

and high efficiency [46]. Dark fermentation is one of the methods that use microorganisms to

produce hydrogen from biomass. This process has the advantage over others such as photo-

fermentation since the bioreactor design is smaller and cheaper and it requires the absence of

light so that the organism develops in the best conditions [38]. A brief description of this

process is presented below.

- Dark fermentation

Dark fermentation is a complex process that involves diverse groups of bacteria where simple

sugars or disaccharides are converted into hydrogen, carbon dioxide and organic acids [13].

Temperature, pH, substrate type and Hydraulic Retention Time (HRT) are the main parameters

that affect the behavior of the microorganism in dark fermentation. Several authors have studied


the production of biohydrogen using different substrates. Ren et al., [47] studied the production

of hydrogen using glucose, xylose and a mixture of these as substrate to evaluate the ability of

the microorganism to degrade both sugars. Ren et al., [48] studied the effect of the enzymatic

hydrolysis of corn stover in the production of hydrogen. Ghimire et al., [4] reviewed a range of

different organic biomass and their biohydrogen potential from laboratory to pilot-scale

systems. Optimal pH range for hydrogen varies from 4.5 to 7, which depends on the type of

microorganism and also whether the raw material is hydrolyzed. Moreover, the temperature of

the fermentation also affects the hydrogen production. The fermentative process can be carried

out at different temperature conditions: mesophilic (35 °C), thermophilic (55 °C) and extreme

thermophilic (>65 °C). Low productivity per unit of mass, due to batch operation regime, is

obtained from this biochemical method. Capital investment is one of the major challenges of the

hydrogen production by dark fermentation; therefore, more research on the scale of the process

at industrial level is required to ensure high productivity and economic profitability.

2.1.3 Electrochemical Methods

Alkaline Water Electrolysis is a well-known process and it has been studied for around 200

years. However, due to the high percentage of hydrogen that is produced from thermochemical

methods, water electrolysis only contributes to the 4% of the worldwide total hydrogen

production. This method has the advantage to produce hydrogen at a high purity (>99.9%),

which is ideal for some high value-added processes such as manufacture of electronic

components [49]. The process scale is one of the most determining parameters in the industrial

application of this technology. Water electrolysis is often limited to small-scale applications and

in particular cases, where the large-scale production plants are not accessible or economical

profitable [50].

A basic water electrolysis unit consists of an anode, a cathode, power supply, and an electrolyte.

The operation of the unit requires a direct current (DC) to maintain the electrical balance and

flow of electrons from the anode to the cathode, where electrons are consumed by protons to

produce hydrogen. The equipment requires external electricity, which is normally provided

from fossil fuel, coal or natural gas; however, the system could be more sustainable if electricity

is derived from renewable sources such as wind, solar, hydro, geo-thermal, among others. The

energy efficiency of this electrolysis systems varies from 70 – 80%, operating at temperature in

the range of 343 – 353 K [51].

An overview of the previously described processes is summarized in Table 2-3. A techno-

economic comparison between different technologies for hydrogen production is carried out


considering the main products, the production cost of hydrogen, energy resource and efficiency.

Additionally, the main advantages and limitations of each technology are shown.

Thermochemical processes such as Steam Methane Reforming, Partial Oxidation and Coal

Gasification use hydrocarbons as raw material; therefore, these methods generate emissions into

the environment. From these three methods, coal gasification has the highest GHG emissions

with 11 kg of CO2 per kg H2, whereas the partial oxidation of natural gas has the lowest

emissions with less than 3 kg CO2 per kg H2. On the other hand, biomass gasification emits

about 5.43 kg CO2 per kg H2; nevertheless, the emissions of the process that use biomass as raw

material can be neglected if the complete biomass cycle is considered. Biomass absorbs carbon

dioxide from the atmosphere during its growth and then CO2 is released again into the

environment as a result of its processing, which can be considered as neutral emissions.

Electrochemical methods only use water as raw material and thus, the environmental impact is

related to the employed energy source. If renewable energy sources are used, there are no GHG

emissions into the atmosphere. Table 2-3 shows also the overall energy efficiency of different

technologies for hydrogen production. Processes that use fossil fuels as raw material have

energy efficiencies that vary from 60 to 89% depending of the energy source (e.g. thermal or

electric power). Biomass efficiency varies between 30% up to 50%, which can be considered a

competitive rate with other production processes.

2.2 Hydrogen economy

The term "hydrogen economy" refers to the vision of using hydrogen as a low-carbon energy

source – replacing, for example, gasoline as a transport fuel or natural gas as a heating fuel.

Hydrogen is attractive because whether it is burned to produce heat or reacted with air in a fuel

cell to produce electricity, the only byproduct is water [52]. Only in the last decade, the idea of a

post-fossil fuel hydrogen-based economy started to gain mainstream interest [28].

The global market for hydrogen is already greater than $40 billion dollars per year [53].

According to the International Energy Agency (IEA), hydrogen consumption in the market can

be distributed as follow: petroleum recovery and refining uses 46.3% of the total hydrogen

production, ammonia production 44.5%, methanol production 3.7% and the remaining is

distributed between the electronic, food and metal production industry.

Hydrogen sales have increased in the last decade, as a result of the stricter standards for fuel

quality which have enhanced the demand of hydrogen in refineries. On the other hand, the

number of hydrogen filling stations around the world have increased because of the high

application of hydrogen through fuel cells in vehicles [31].


World’s hydrogen demand in 2013 was around 255 billion cubic meters and it is expected that

the world consumption of this energy carrier will increase 3.5% annually through 2018. As a

result, a hydrogen demand of 300 billion cubic meters will be required to supply the world

hydrogen requirements. Given this overall outlook of hydrogen, new opportunities will be

created for the production of hydrogen from renewable resources. However, the scale of process

will be a key parameter in the economic profitability. Hydrogen can be produced in two ways:

(i) in large-scale centralized power plants or (ii) in small-scale generation plants [28]. The

selection of the proper production model depends first on the availability of the raw material

and the cost related to its purchase and transportation. In hydrogen production through Steam

Methane Reforming, the production cost of hydrogen is highly dependent on the natural gas

purchase cost, which is higher in small-scale plants than in large-scale plants. Same behavior is

considered in the partial oxidation of natural gas, where the hydrogen production cost is around

1.39 USD/kg. These two methods cannot be considered as a sustainable solutions for hydrogen

production into the long-term, because they are no zero emissions and highly dependent of

natural gas [31]. Coal gasification has a relative low hydrogen production cost in the range of

0.92 USD/kg; although, the energy efficiency is lower than other thermochemical methods and

the CO2 emissions are higher.

Fossil fuels depletion and thus, increase in the natural gas cost can be the decisive factor for

competitive and economic feasible production of hydrogen from biomass. Nevertheless, the

process scale is a constraint because of large plants require high amount of feedstock and the

seasonal availability of biomass cannot meet the feedstock requirements of this large facilities.

2.3 Final Remarks

Currently, the most important energy sources in the world are fossil fuels and thus, the

production of hydrogen through the conversion of natural gas, oil or coal is cost-effective.

Nevertheless, long-term and sustainable production of hydrogen, where the fossil fuels reserves

decrease and the cost of the oil derivatives increase, is no defined. Moreover, the use of biomass

can be considered as a promising source to produce sustainable hydrogen. On the other hand,

biomass, as energy source, has several constraints that must be overcome in order to produce

hydrogen efficiently and highly profitable. Availability, purchase costs, homogeneous

composition, transportation and logistics issues are some of the constraints to be considered.


Table 2-3. Description of the technologies used in the hydrogen production.

Technology Energy

Source

Energy

Source Price

[6] Products

Hydrogen cost

[5], [31], [41]

Energy

Efficiency

[28]

Advantages [37], [38] Disadvantages

Steam Methane

Reforming

(SMR)

Natural gas,

steam Natural gas

3-5 $/GJ

H2, CH4, CO,

CO2

0.75 $/kg H2

(without CO2

sequestration)

60 – 85%

Most developed

industrial process.

Lowest operation

temperature. Best

H2/CO ratio.

Highest GHG

emissions. Fossil fuel

dependence

Partial Oxidation Natural gas,

oil, steam

H2, CO, CO2,

N2, H2O(S), small

amount CH4

1.39 $/kg H2

(residual oil) 71 – 88.5%

Cost-effective, no

catalyst required

Low H2/CO, high

temperatures, require

pure O2

Coal gasification Coal

Coal

1-2 $/GJ

H2, CO, CO2,

H2O(S), CH4,

impurities

0.92 $/kg H2

(without CO2

sequestration)

60 – 67% Cost-effective Low quality H2, CO2

as by-product

Biomass

Gasification Biomass

Biomass

2-5 $/GJ

H2, CO, CO2,

H2O(S), N2 1.21-2.42 $/kg H2 40 – 50%

High gas energy

content. Use of

renewable raw

materials.

Varying H2 content.

Feedstock seasonal

availability.

Transportation and

logistics issues.

Dark

Fermentation Biomass

H2, CO2,

ethanol, organic

acids

R&D R&D

Use of variety of waste

streams. Simple reactor

design. Low Emissions.

Large amounts of

byproducts.

Feedstock seasonal

availability.

Alkaline Water

Electrolysis

Water,

electricity

Water

1.251 $/m3 H2 and O2

2.56-2.97 $/kg H2

(Nuclear source) 25 – 38%

No pollution, cheap

materials

High capital costs,

low system

efficiency, design

issues


3. Raw Material

In this thesis, two raw material were selected for the hydrogen production through gasification

and dark fermentation. Pinus Patula (PP) and Coffee Cut-stems (CCS) are raw materials from

different forest and agroindustrial supply chains in Colombia, respectively. There are different

parameters that encourage the use of these lignocellulosic residues in the production of

bioenergy. High availability, high energy yield and low purchase cost are some of the key

factors that were considered in this work in order to evaluate the hydrogen production from

Pinus Patula (PP) and Coffee Cut-Stems (CCS). A detailed description of the two raw material

is presented below considering six components: a general overview of the raw material in

Colombia, the description of the scenario that is going to be evaluated in the techno-economic

assessment (chapter 5), the location of the collection center, the biomass purchase price, the

analysis of the processing plant location and the freight cost associated to the transportation of

the raw material from the collection center to the processing plant.

3.1 Pinus Patula (PP)

PP is a tree native to the highlands of Mexico. It grows from 24° to 18° North latitude and 1800

to 2700 m above sea level (masl). It has been cultivated in higher altitudes in Colombia (3300

masl). PP is widely distributed in Colombia and has become a useful timber specie for

reforestation programs. Its main application is in the production of sawn wood and the residues

from the sawmilling are used directly as pulp in the production of Kraft paper.

Colombia is considered as a country suitable for forestry; it has approximately 60 million

hectares of natural forest with a deforestation rate of 280,000 Ha per year. Currently, native

forest represents the main source of wood and fibers for the communities and the local industry

accounting to 84.1%. The remaining 15.9% is divided between the forestry plantation (12.4%)

and wood importation (3.5%) [54]. This behavior is presented since recent governments have


not provided clear policies that encourage the sustainable use of natural forests in the country,

and due to the lack of enforcement of laws by the environmental authorities in both informal

and legal forest exploitation and commercialization of wood. Based on the previous statement, a

heterogeneous deterioration in the forest coverage of Colombia is evidenced.

In recent years, reforestation programs have helped to mitigate the demand of wood from native

forests. Colombia has a high potential to develop reforestation programs due to its excellent

weather, geographic and topographic conditions for tree growth, the geostrategic location for

foreign trade and the relative high number of signed free trade agreements [55]. Besides,

Colombia has identified 7.2 million hectares potentially suitable for commercial plantations

[55]. These areas cover different ecological regions from the tropics to the mountainous zones,

which offer a wide range of opportunities for different commercial species: those with high

productivity such as pines, with high-added value such as Teak and also industrial ones such as

the rubber [27]. However, the commercial silviculture in Colombia is under development.

According to the Agriculture and Rural Development Ministry (MADR), Colombia has 477,575

hectares of commercial plantations, which are lesser compared to other countries such as Brazil

(7.4 million hectares), Chile (2.4 million hectares) and Argentina (1.4 million hectares) [56].

Aiming to increase the amount of reforested areas, the Colombian government has created some

incentives for the cultivation of certain forest species (Alnus jorullensis, Bombacopsis quinata,

Cedrela odorata, Schizolobium parahibum, Tabebuia rosea, Eucalyptus grandis, Eucalyptus

globulus, Pinus patula, Gmelina arborea, among others). Resolution 711 of October 31, 1994

determined the forest species involved in the Forestry Incentive Certificate (CIF), which is a

direct contribution in cash, in recognition to positive activities in reforestation. The certificate

consists of a bonus cash of the commercial plantations cultivation costs with productive and

protective purposes. The bonus can mitigate 50% of the cultivation costs if the planted specie

comes from another country and 75% if a native species is planted [57].

The largest areas with forest plantations are located in the Atlantic Coast (Córdoba and

Magdalena Bajo), Andean Region (Antioquia, Cauca) and Orinoquía. The department with the

highest planted area is Antioquia (94,716 Ha), but Vichada has been increasing its share in

recent years (65,079 Ha) [55]. In Antioquia, there are also several wood processing companies

that in principle can be considered as a cluster. Aiming to meet the wood requirements of the

national market, it is highly important to determine the areas that may offer the greatest

physical, socioeconomic and environmental advantages in order to promote the wood

cultivation in a sustainable way. Within this context, the Agricultural Rural Planning Unit

(UPRA) developed a zonification tool to determine the suitable areas for the establishment of

commercial plantations [58]. The suitable area for forest plantations was divided in two


categories: the area with and without some type of determinant. Different zones have a legal or

technical determinant, since they require a complementary social, cultural and/or environmental

analysis to develop commercial forest plantations. Colombia has a total area of 114,174,800

hectares from which only 24,805,854 Ha are suitable for forest plantations. According to the

zonification tool, 56% of the suitable forest plantation areas in Colombia can be planted without

any determinant, and the remaining 44% requires a further complementary analysis [55].

From the 70 species tested in commercial plantations, Colombia uses mainly 12 species in

reforestation programs including: teak (Tectona grandis), melina (Gmelina arborea), acacia

mangium (Acacia mangium), pink eucalyptus (Eucalyptus grandis), eucalyptus (eucalyptus

tereticornis), ceiba (Bombacopsis quinata), oak (Tabebuia rosea), walnut (Cordia alliodora),

cypress (Cupressus lusitanica), Caribbean pine (Pinus caribaea), patula pine (Pinus patula),

pine tecunumanii (Pinus tecunumanii) and oocarpa pine (Pinus oocarpa). According to the

records of commercial plantations, approximately 73% of the planted area corresponds to

introduced species such as pines and eucalyptus; only 9% is planted with native species [59].

Table 3-1. Commercial forest species used in Colombia for reforestation programs.

Forest

Specie

Rotation

(years)

Density

(g cm-3)

Yield

(m3 ha-1 year-1) Location Reference

Eucalyptus

globulus 10 -15 0.7 15 - 20

Antioquia, Boyacá,

Caldas,

Cundinamarca

[60]

Pinus

Patula 30 0.43 27

Antioquia,

Cundinamarca,

Santanderes, Caldas

[61]

[62][63]

Tectona

grandis 25 - 28 0.53 7 - 13 Córdoba [64]

Forest resources can be analyzed considering the plantation yield and the lifespan of the crop.

The volumetric timber yield per unit area and time (m3 ha-1 year-1) is a measure of the

productivity of various tree species and, when used together with the rotation period of the crop,

is a decisive factor when selecting a lignocellulosic source as the most suitable to be used in a

production process [59]. In addition, the geographical distribution of timber resources has a

marked influence on the economy of processing lignocellulosic materials, since the transport of

biomass significantly increases the raw material costs [59]. Table 3-1 presents some

characteristics of commercial forest species commonly used in reforestation programs.

Colombia extracts annually 11 million cubic meters of wood from the natural forest, from which

60% are used as fuel and in the production of coal. The remaining 40% is destined to the


production of roundwood, which is used in the sawn wood, pulp and paper, panels and boards

industries. Figure 3-1 summarizes the main applications of the Pinus Patula in Colombia.

Figure 3-1. Main applications of roundwood in Colombia

3.1.1 Pinus Patula in the Andean region

- Scenario description

In order to evaluate the economic performance of the hydrogen production, three parameters

were assessed. Biomass availability, location of the collection center and the processing plant

were used as key parameters in the economic assessment of the gasification and dark

fermentation processes. The optimum size of a processing plant involves tradeoffs between

economies of scale with larger plants and increased costs of feedstock transportation [33]. Low

scale processes have high initial investment and operation costs, in contrast to high scale

processes; however, large plant sizes mean larger transportation distances for collecting

biomass. Carolan et al., [33] evaluated the effect of the process scale in the economic behavior

of the cellulosic bioethanol production biorefinery, and it was concluded that future

biorefineries are likely to be large facilities with capacities in the range of 5,000-10,000 ton per

day of biomass, if not larger. The costs of biomass as fuel was divided in two components: the

costs associated with the purchase of biomass in the collection centers and the transportation

logistics costs (freight costs). Transportation costs in Colombia are very high due to the

heterogeneous geography and the high diesel price. The transportation logistics can be a

decisive criteria when assessing the economic viability of the project. Aiming to evaluate the

biomass cost on the profitability of the proposed scenarios in the simulation procedure (see,

Section 4.6.3), a location-based problem was proposed, from which three locations or

departments were selected as collection centers, considering as main criteria the high

availability. Based on the data reported in Table 3-1, Antioquia, Caldas and Cundinamarca

were selected as the most important biomass collection centers in the Andean region. The

Roundwood

Industrial Roundwood

SawnwoodPulp and

PaperPlywood

Veneers

Panels and Boards

Others

Wood for fuel and carbon


processing plant was located considering, between these three locations, the department that has

the lowest biomass purchase and transportation costs, and also the highest biomass availability.

The collection logistics is one of the most important issues that affects the implementation of

these biomass-based processes. Due to the heterogeneous distribution of the PP in the

geography of each department, the collection of the raw material may be regarded as a

bottleneck of the production process. According to this, the amount of raw material that can be

collected and used in the processing plant is limited. In this study, it is considered that fractions

above 50% of the total PP production in each department cannot be used as raw material in the

processing plant, because of the collection logistic issues.

- Collection center location

PP is widely distributed in Colombia; however, there are some departments that have a high

forestry potential because of the climate conditions or geography, which favors the pine

cultivation. According to the National Research Corporation and Forestry Development

(CONIF), Antioquia, Caldas and Cundinamarca have the highest PP cultivated area in the

Andean region, since these areas are destined to reforestation programs in Colombia [63], [65].

Antioquia (6°13′00″N 75°34′00″O) is a department located in the northwest of Colombia and

has a reforestation area of 94,716 Ha. Caldas (5°06′N 75°33′O) is located in the center region of

Colombia and possess a reforestation area of 21,079 Ha. Finally, Cundinamarca (4°36′00″N

74°05′00″O) is located also in the center region and has a reforestation area of 33,490 Ha. The

geography and cultivation techniques are different from one department to another, for this

reason, the yield of PP changes and thus, affecting the productivity of the crop. The main

application of the PP is the production of sawn wood, which is used directly in the furniture

industry. After the activities that involve the transformation of the roundwood in sawn wood,

plywood, among others, approximately 30% of this wood remains as residue [27]. These

residues still have a great potential to be used for bioenergy production. Table 3-2 presents a

general overlook of the availability of PP in the three departments.

- Raw Material Cost

Biomass cost is related to the production costs of PP, which involves different stages: seedling

plantation, agrochemical application, ground preparation, pruning, cutting, debarking and the

transportation to the collection center. The PP cost varies from one department to another,

depending on the geography of the zone, the silviculture practices (applied agrochemicals,

diesel consumption in the machinery, among others) and the soil fertility. The wood price in

Colombia is calculated based on the growing zone, the specie and the wood denomination (pulp

wood, splintered wood, roundwood) [27], [66]. In this thesis, the PP price involves the

transformation cost of the tree in roundwood for the three evaluated departments. It was also


considered that the purchase price of the residues, from different PP applications, corresponds to

3% of the roundwood production cost. Table 3-3 presents the production cost of roundwood

and its residues from PP plantations.

Table 3-2. Production of PP and its residues in three departments in Colombia

Department PP Cultivated

Area (Ha)a

PP Yield

(𝐦𝟑𝐇𝐚−𝟏𝐲𝐞𝐚𝐫−𝟏)b

PP Productivity

(ton/year)

Residues

(ton/year)

Antioquia 94,716 20 852,444 102,293

Caldas 21,079 17 161,254 19,350

Cundinamarca 33,490 19.3 290,860 34,903

a Data taken from the Ministerio de Agricultura y Desarrollo Rural [66].

b Data taken from the National Research Corporation and Forestry Development (CONIF) [63],

[65].

Table 3-3. Production cost of PP and its residues in the three collection centers in Colombia

Location of the collection

center

PP cost

(USD/ton)a

Residues

(USD/ton)

Antioquia 348.2 10.4

Caldas 237.0 7.1

Cundinamarca 407.4 12.2

a Data taken from the Ministerio de Agricultura y Desarrollo Rural [66]

- Processing Plant Location

The processing plant location was selected from the three evaluated departments, considering

that the department with the lowest biomass purchase and transportation cost will be the suitable

location of the hydrogen production plant. The capital cities of each department were selected as

the collection center and processing plant locations, assuming that all raw material from the

department is collected in the capital city. In this study, the supply chain of the raw material

across the department was not considered since this analysis involves an optimization problem

that must be considered in further works. The transportation cost of the raw material from the

collection center to the processing plant was calculated based on the freight cost between the

main cities of each department. The freight cost was determined based on the distance between

the main cities and the consumption of diesel of a CS-truck, which is normally used to transport

wood in Colombia. Table 3-4 presents the freight cost between the cities per ton of raw

material.


Table 3-4. Freight cost between the collection center and the processing plant

Collection Center Processing Plant

Location

Freight Cost

(USD/ton)a

Medellin (Antioquia) Manizales 7.29

Bogotá D.C (Cundinamarca) Medellin 13.74

Manizales (Caldas) Bogotá D.C 10.61

a Data taken from the Information System for the Regulation of Road Freight Transport [67].

3.2 Coffee Cut-Stems (CCS)

Coffee is the second most traded commodity in the world after petroleum, which has a

significant importance to the global economy [68]. Most consumption takes place in

industrialized countries, while over 90% of coffee is produced in developing countries [69].

Colombia is the fourth largest coffee producer in the world after Brazil, Vietnam and Indonesia

[70]. The coffee cultivation and exportation are managed by the Federación Nacional de

Cafeteros (FNC; National Coffee Growers Federation). The federation sponsors farmers in the

coffee-growing zones through extensive social and economic aid programs [69]. The produced

coffee in Colombia is the Arabica, which is originated from the particular combination of

diverse factors: the latitude and altitude of Colombia’s coffee growing zones, its soils, the

botanical origin of the species, and the varieties of produced coffees, the climate and rain

pattern [71].

The Arabica coffee produced in Colombia needs specific climatic conditions for its production.

The ideal conditions for the cultivation of this specie are found between 1,200 m (4,000 ft) and

1,800 m (6,000 ft) above sea level, with temperatures between 17 and 23 °C (62 and 75 °F), and

with precipitation close to 2,000 mm (78 in.) per year, evenly distributed throughout the year

[69]. The specific geographic location of each Colombian coffee growing region determines its

particular conditions of water availability, temperature, solar radiation, and wind regime for

coffee cultivation [72]. Most Colombian coffee growing areas are located in the departments of

Antioquia, Boyacá, Caldas, Cauca, Cesar, Caquetá, Casanare, Cundinamarca, Guajira, Huila,

Magdalena, Meta, Nariño, Norte de Santander, Quindío, Risaralda, Santander, Tolima, and

Valle.

According to the FNC [69], the green coffee production in Colombia was 9.9 million of bags

(60 kg-bag) in 2013. In the coffee process is estimated that less than 5% of the generated

biomass is used in the production of the beverage and the remaining fraction represents


lignocellulosic material such as leaves, branches and stems, which are generated from the

renovation of coffee plantations.

In the coffee cultivation, the “zoqueo” process is highly recommended for a good and constant

production of coffee [73]. The “zoqueo” process involves cutting the main coffee stem at a

height between 20 and 30 cm above the ground, inducing the growth of new stems that replace

the cut stem [74]. It is estimated that in Colombia, the renovation of coffee plantations should

be done in the fifth or sixth year of the plantation. This activity produces a considerable

increment in low-productive coffee crops, besides it is more profitable than a new plantation

[75], [76]. The residues obtained from the “zoqueo” process are called Coffee Cut-Stems (CCS)

and can be considered as a potential energy source, but also as a substitute for the wood used in

the manufacture of chipboard, veneers, among others [77].

In coffee-producing countries such as Colombia, coffee tree wood as waste is abundant, either

from cuts or renovations, because between 80,000 and 90,000 coffee hectares are renovated per

year, from which on average 17 tonnes of dry wood per hectare can be obtained. These residues

would serve to produce, approximately, 690 GWe every year [78]. Currently, most of the forest

residues are used directly in combustion processes for cooking and heating in rural areas.

According to Roa et al., [79], CCS have a higher calorific value (19.75 MJ/kg) than other by-

products of the coffee process such as the coffee pulp (15.88 MJ/kg) and coffee dust (17.90

MJ/kg). This residue is normally used in the mechanical drying of the coffee grain with a

consumption of 4 kg of CCS to dry 12.5 kg of dry coffee grain [80]. However, the energy

content of these residues is no properly used and the direct emissions related to the combustion

processes are relative high. For this reason, different thermochemical and biochemical methods

have been tested for the transformation of these residues in bioenergy and/or biochemical

products. Bioethanol, furfural, octane, nonane, HMF and synthesis gas are some of the products

that can be obtained from CCS as raw material [81], [82].

3.2.1 Coffee Cut-Stems in the Andean Region

- Scenario description

The same methodology proposed in section 3.1.1 was used for the location-based problem of

the CCS in the Andean region. For this purpose, three departments were selected, based on the

coffee cultivated area, as the CCS collection centers: Caldas, Antioquia and Huila. The

processing plant location was selected considering as main parameters the biomass purchase and

transportation costs, and also the CCS availability. The transportation logistics of the CCS may

be coupled to the coffee supply chain; therefore, the recollection and transportation of CCS can

be done immediately after the coffee recollection in the farms and subsequently, they can be


transported to the collection centers. The CCS recollection process can be done by the farmers

and in order to encourage its collection, an economic incentive to these residues can be added.

The biomass availability is one of the drawbacks of the use of CCS as raw material, since the

residue is harvested every 5 or 6 years and thus, the raw material supply is limited This behavior

compromises a stable productivity and consequently, the economic profitability of the process.

The heterogeneous distribution of coffee farms throughout the geography of the selected

departments implies that the total CCS production would not be available for the processing

plant. Based on this, it was assumed that fractions above 50% of the total CCS production in

each department cannot be considered as feedstock for the processing plant because of the

logistics issues.

- Collection center location

According to the FNC, the departments with the highest cultivated coffee area are: Antioquia,

Huila and Caldas due to its climate, soil and geographical conditions. Huila (2°59′55″N

75°18′16″O) is located in the southwest of Colombia and has a coffee cultivated area of 154,980

Ha. Antioquia (6°13′00″N 75°34′00″O) is located in the northwest of Colombia and has a coffee

cultivated area of 130,990 Ha. Finally, Caldas (5°06′N 75°33′O) is located in the center region

of Colombia and possess a coffee cultivated area of 74,530 Ha. The amount of CCS that is

produced in these departments was calculated based on the following assumptions:

The cherry coffee is the grain that is harvested from the coffee tree and it is used as

raw material in the coffee processing process. The “pergamino” coffee is the

product obtained at the end of the coffee production process. In order to evaluate

the amount of cherry coffee that is required to produced 1 kilogram of “pergamino”

coffee, the Centro Nacional de Investigación de Café (CENICAFÉ) determined the

conversion factors between different coffee grain status [83]. Based on the reported

data, 4.92 kilogram of cherry coffee are required to produce 1 kilogram of

“pergamino” coffee.

Based on the Departamento Administrativo Nacional de Estadísticas (DANE), the

productivity of “pergamino” coffee in each department varies depending on the

geography and cultivation techniques. Table 3-5 summarizes some information

about the production of “pergamino” and cherry coffee, and CCS in the selected

departments.

According to the Federación Nacional de Cafeteros (FNC), an average of 0.6

kilogram of CCS are obtained per kilogram of processed cherry coffee [84].


Since the CCS is harvested every 5 to 6 years, the amount of CCS that is produced

from the coffee plantations was divided by 5.

Table 3-5. Production of “pergamino” and cherry coffee and its relation with the CCS

production.

Department

Coffee

Plantations

(Ha)a

“Pergamino”

Coffee Yield

(Ton/Ha)b

Cherry Coffee

Production

(Ton)

CCS

Productivity

(Ton/year)

Huila 154,980 1.2 914,072 109,688

Antioquia 130,990 1.1 708,197 84,983

Caldas 74,530 1.2 439,577 52,749

a Data taken from the economic profile of each department according to the Ministerio de

Comercio, Industria y Turismo [85]–[87]

b Data taken from the National Agricultural Census [88]

- Raw material cost

The calculation of the CCS purchase cost was performed based on three parameters: the

international “pergamino” coffee price, the mass ratio between the “pergamino” and cherry

coffee production, and the CCS productivity from the cherry coffee (0.6 kg CCS per kg cherry

coffee). According to the FNC, the international price of the “pergamino” coffee, in the first

semester of 2016, in the three selected departments had a similar trend with small variations as

observed in Table 3-6. It was assumed that the contribution of the “pergamino” coffee price to

the CCS price was approximately 5%. Due to this small variations in the “pergamino” coffee

price, it can be evidenced that the CCS purchase price may not have a significant effect on the

selection of the suitable collection center location and therefore, the decision criteria in the

selection of the processing plant location will be the transportation cost.

Table 3-6. Raw material purchase cost in the three selected departments

Department “Pergamino” Coffee Price

(USD/ton)a

CCS price

(USD/ton)

Huila 2,130 23.86

Antioquia 2,132.3 23.88

Caldas 2,134.3 23.84

a Data taken from the Federación Nacional de Cafeteros (FNC) [89]


- Processing Plant location

As mentioned before in section 3.1.1 for the PP case, the processing plant location was

evaluated based on the department with the lowest biomass purchase and transportation cost.

Nevertheless, the biomass purchase cost has no significant variation between the selected

departments, as mentioned before. The transportation cost was calculated based on the freight

cost between the main cities of the departments. Table 3-7 presents the freight cost between the

main cities per ton of raw material.

Table 3-7. Freight cost between the collection center and the processing plant

Collection Center Processing Plant

Location

Freight Cost

(USD/ton)a

Neiva (Huila) Medellín 25.81

Medellín (Antioquia) Manizales 11.30

Manizales (Caldas) Neiva 21.28

a Data taken from the Information System for the Regulation of Road Freight Transport [67]

3.3 Final Remarks

The process scale assessment based on the raw material availability and therefore, its purchase

cost and transportation logistic issues, can provide a general overlook of the advantages and

limitations for the implementation of different process schemes for the hydrogen production.

The relation between the raw material supply chain and the production process can be used to

evaluate the economic performance of the hydrogen production. It is noteworthy that some

parameters such as the seasonal availability, recollection and physicochemical characteristics of

the raw material have great influence in the future implementation of bioenergy productive

schemes. The aim of this preliminary analysis of the supply chain of the selected raw material

was intended to provide some guidelines for the evaluation of the hydrogen production through

thermochemical and biochemical technologies, considering the raw material supply as a key

criteria for the techno-economic assessment of different process schemes.


4. Methodology

In this chapter, a brief description of the experimental and modeling procedure used in this

thesis is presented. The experimental procedure comprises the characterization of the raw

material, pilot-scale gasification and laboratory-scale dark fermentation. Subsequently, in the

simulation procedure, different scenarios for hydrogen production under stand-alone and

biorefineries pathways are described. Finally, the guidelines in order to analyze the processes

from the economic, energy and environmental point of view are presented.

4.1 Experimental Procedure

4.1.1 Raw Material Characterization

The physicochemical characterization of the raw material involves the determination of the

chemical composition, proximate, and elemental analysis and the calorific value. The chemical

composition of the biomass comprises the determination of the cellulose, hemicellulose, lignin,

extractives and ash content. On the other hand, proximate analysis breaks down the biomass in

volatile matter, fixed carbon or char and ash. Subsequently, elemental analysis presents directly

the constitutive elements in the organic part of biomass. Finally, the calorific value of the

biomass gives an insight of the amount of energy that can release this material if it is submitted

directly to a combustion process. A detailed description of the experimental procedure for the

biomass characterization is summarized below.

- Chemical composition

Plants are composed mainly by water; however on dry basis, the plant cells consist of sugar-

based polymers (carbohydrates), lignin and lesser amounts of extractives, starch and inorganics.

These chemical components are distributed throughout the cell wall. There are different

parameters that affect the chemical composition of the plant such as the type of plant, the part of

the plant that is analyzed, the geographical location, age, climate and soil conditions, among


others [90]. The following procedures describe the methods used in the determination of

extractives, holocellulose (cellulose + hemicellulose), lignin and ash content.

- Sample Preparation

Samples should be dried and milled before they can be used in further determinations. Prior to

the milling process, raw materials were placed on a horizontal table and were exposed directly

to the sun for two to three days until a moisture content < 10% was reached. The moisture

content of the raw material was measured in a Shimadzu moisture balance MOC - 120H.

Subsequently, samples were ground to pass 40 mesh (0.04 mm) using a gyratory mill (SR200

Gusseisen, Retsch GmbH, Germany) based on the procedure reported by Hames et al., [91].

- Extractives Determination

Extractives are a group of chemicals mainly composed of fats, fatty acids, fatty alcohols,

phenols, terpenes, steroids, resin acids, rosin, waxes, etc. These chemicals exist as monomers,

dimers, and polymers. They derive their name as chemicals that are removed by one of several

extraction procedures

The extractives content was measured based on the experimental procedure proposed by

National Renewable Energy Laboratories (NREL/TP-510-42619) [92]. Ethanol and water were

selected as solvents for this procedure. First, 10 grams of raw material were weighed and added

into a tared extraction thimble. Then, the thimble was placed on the Soxhlet syphon tube

considering that the thimble must not exceed the Soxhlet syphon height. Subsequently, 250mL

of distilled water were added to the tared receiving flask with several boiling chips to prevent

bumping. The heating mantles were adjusted to provide a minimum of 4-5 siphon cycles per

hour. After 24 hours of extraction, the thimble with the raw material was placed in an oven

overnight at temperature not exceeding 45°C. The dried thimble were removed from the oven

and transfer to a desiccator for one hour and the weight was recorded. The same procedure in

the case of ethanol as solvent was performed.

- Holocellulose Determination

Most of the plants are composed mainly of cellulose and hemicellulose polymers with minor

amounts of other sugar polymers such as starch. The combination of cellulose and the

hemicelluloses are called holocellulose and usually accounts for 65–70 percent of the plant dry

weight [90].

Holocellulose content determination was based on the chlorination method reported by the

ASTM Standards (D-1104). Free-extractives sample (2.5 g) was added to a 250ml Erlenmeyer

flask with water (80 ml), acetic acid (0.5 ml) and sodium chlorite (1 g). It is highly

recommended to put an erlenmeyer flask (100 ml) inverted in the neck of the reaction flask to


avoid the release of gases from the chlorination reaction. The mixture was stirred and placed in

a water bath at 70°C for 6 hours. After the first hour, acetic acid (0.5 ml) and sodium chloride (1

g) were added. Subsequently, the same amounts of acetic acid and sodium chloride were added

until the reaction time (6 hours) was reached. The sample was left without further addition of

acetic acid and sodium chloride in the water bath overnight. At the end of 24 hours of reaction,

the holocellulose was filtered on a filter paper using a Buchner funnel until the yellow color and

the odor of chlorine dioxide was removed. Afterwards, the filter was placed in a porcelain tray

in the oven at 105° for 24 hours. The dried sample was transferred to a desiccator for 1 hour and

weighed.

- Cellulose Determination

Cellulose is the most abundant organic chemical on the face of the earth. On a dry weight basis,

most plants consist of approximately 45-50% cellulose. The preparation of cellulose is a

continuous procedure of holocelluose determination in pursuit of the ultimately pure form of

fiber based on the procedure reported by Hames et al., [91].

Free-extractives holocellulose (2 g) was added to a 250 ml beaker with a glass cover. Then, 25

ml of a NaOH solution (17.5%v/v) was prepared and maintained at 20°C. Afterwards, 10 ml of

17.5% NaOH solution was added to the holocellulose in the 250 ml beaker and with a glass rod,

the sample and NaOH solution, were completely mixed. At 5 minute intervals, 5 ml of NaOH

solutions was added and then, the mixture was placed in a water bath at 20°C for 30 min.

Subsequently, 33 ml of water were added, the mixture was thoroughly stirred and maintained at

20°C for 1 h before filtering. The holocellulose was transferred to the filter paper and then

washed with 100 ml of 8.3% NaOH solution. Additionally, the cellulose was subjected to an

acid treatment using 10% acetic acid solution. The acid solution was poured in the filter and was

left for 3 min without suction. In order to reduce the amount of acid in the cellulose, the sample

was washed with water at 20°C until the acid concentration was completely drained. Finally, the

filter paper and the cellulose was removed from the filter and were placed in an oven at 105°C

overnight. At the end of the 24 h, the cellulose was transferred to the desiccator for 1 h and then,

weighed.

- Hemicellulose Determination

In general, the hemicellulose fraction of plants consists of a collection of polysaccharide

polymers. On biomass, the hemicellulose corresponds to approximately 25-30%, on dry basis.

This percentage can change depending of the biomass source, climate, and soil conditions,

among others. The hemicellulose content was calculated from the subtraction between the

holocellulose and cellulose content.


- Lignin Determination

Lignins are amorphous, highly complex, mainly aromatic polymers of phenyl-propane units.

The function of lignin in plants is as an encrusting agent in the cellulose/hemicellulose matrix.

The procedure for the lignin content determination was based on a modified version of the

TAPPI T222 acid-insoluble lignin in wood and pulp [91], [93]. Free-extractives and –moisture

sample was required. Approximately 200 mg of dried sample was added into a 100 mL

centrifuge tube. Then, 1 ml of 72% (w/w) H2SO4 per each 100 mg of sample was poured. The

sample and acid were completely mixed and were placed in a water bath at 30°C for 1 h.

Subsequently, the acid concentration was reduced adding 56 ml of water. The mixture was

autoclaved at 121°C, 15 psia for 1 h before filtering. The residue in the filter paper was washed

with hot water until the sulfuric acid concentration was reduced. Then, the filter paper and the

lignin were placed in an oven at 105°C overnight. Finally, the lignin was transferred into a

desiccator and weighed.

- Ash Determination

Ash content is an approximate measure of the mineral salts and other inorganic matter in the

fiber after combustion at a temperature of 575 ± 25°C. The inorganic content can be quite high

in plants containing large amounts of silica.

The ash content was determined according to the experimental procedure proposed by National

Renewable Energy Laboratories (NREL/TP-510-42622). A sample of 0.5g was added into a

crucible and the weight was recorded. The sample was placed in a furnace with a temperature

ramp program as indicate below: Ramp from room temperature to 105°C. Hold at 105°C for 12

minutes. Ramp to 250°C at 10°C per minute. Hold at 250°C for 30 minutes. Ramp to 575°C at

20°C per minute. Hold at 575°C for 180 minutes. Allow temperature to drop to 105°C. Hold at

105°C until samples are removed. After ignition, the crucible was carefully removed from the

furnace and was placed directly into a desiccator. When cooled to room temperature, the weight

was recorded on the analytical balance.

4.1.2 Proximate analysis

When considering biomass thermal conversion, proximate analysis is one of the most important

characterization methods. This method involves the determination of moisture, ash, volatile

matter and fixed carbon content of the raw material. These components affect both the

combustion behavior and the plant design. In that way, high moisture values decrease the

combustion yield, while high volatile matter/fixed carbon ratios increase the fuel reactivity [94].


A detailed description of the procedure involve in the determination of the volatile matter, fixed

carbon and ash content is presented below.

- Volatile Matter Determination

Volatile matter refers to the components of biomass, except for moisture, which are liberated at

high temperature in the absence of air. The volatile matter determination was based on the

standard method proposed by the American Society of Testing and Materials (ASTM D3175-

11) [95].

A sample (1 g) was added into a previously weighed platinum crucible and then, it was placed

in a furnace at 950°C for 7 min. It is highly important to cover the crucible with the platinum

cap in order to avoid that the carbon deposit burn away. After heating for exactly 7 min, the

crucible was removed from the furnace and without disturbing the cover, it was allowed to cool.

Weighed as soon as cold. The percentage loss of weight minus the percentage moisture equals

the volatile matter.

- Fixed Carbon Determination

The fixed carbon content is the carbon found in the material which is left after volatile materials

are driven off. Fixed carbon is used as an estimate of the amount of char that will be yielded

from a biomass sample. The fixed carbon can be calculated from the subtraction between the

volatile matter, the moisture and the ash content of the biomass [96]. Ash content was

determined considering the same procedure in section 4.1.1.

4.1.3 Elemental analysis

Elemental analysis or ultimate analysis is the second typical way (after proximate analysis) to

represent the components in the organic part of fuels. Elemental analysis includes the

identification and quantification of elements in a sample, determination of the elemental

composition and trace level elements. The main identified elements in this analysis are the

carbon, hydrogen, oxygen and nitrogen or almost always refer to CHON. From this analysis,

secondary elements can be quantified such as sulphur, chlorine, among others. A detailed

description of the procedure used in the determination of the elemental analysis is presented

below.

- Carbon, hydrogen and nitrogen determination

The quantification of the elemental composition of the raw material was performed in the

University of Alicante in a CHNS elemental microanalyzer with Micro detection system

TruSpec (LECO, USA). The equipment uses a combination of continuous carrier gas flow,

infrared and thermal conductivity detectors giving simultaneous detection of CHNS in less than

https://en.wikipedia.org/wiki/Volatility_(chemistry)


four minutes. Before the determination, the samples should be ground to a uniform consistency.

Subsequently, 0.15 g of biomass sample was weighed into a 502-186 Tin Foil Cup and then, the

sample was placed in the equipment and proceed with the analysis.

- Heat of Combustion

The heat of combustion is the total energy release when a substance, biomass, coal, among

others, undergoes complete combustion with oxygen under standard conditions. This energy

value is characteristic for each substance. The units of the heat of combustion are energy per

unit of mass or mole such as kJ/kg or kJ/mol. The heating value is commonly determined by the

use of a bomb calorimeter. A detailed description of the equipment and the procedure used in

the determination of the heat of combustion is summarized below.

The determination of the calorific value was based on the American Society for Testing and

Materials (ASTM D-5468). The procedure was developed in the University of Alicante using a

bomb calorimeter (IKA Werke model C5003, Brazil) with a measuring cell controller and a

refrigeration system. About 1 g of the solid sample was weighed in a crucible, which was

introduced into a stainless steel container. The decomposition vessel or bomb was filled with

oxygen (30 bar) (Quality 3.5: Technical oxygen 99.95%). The sample was ignited (e.g. a cotton

thread attached to a solid ignition wire) within the decomposition vessel and it was burned.

During the combustion process, the central temperature in the furnace may rise to 1000 ° C, and

the pressure also increases. Under these conditions, all the organic materials were burned and

oxidized. The generated heat during the combustion process was measured using the adiabatic

measuring method.

An approach to the calculation of elemental analysis and calorific value of the raw material is

presented in Annex A. The calculation is based on data reported in literature for a variety of

feedstock and the experimental determination of proximate analysis. This approach is proposed

as an alternative to the experimental methods to estimate the heating value and the elemental

analysis, which are time consuming as well as expensive and have higher possibilities of

experimental errors [97]–[99].

4.2 Pilot-Scale Gasification

Gasification consists in the transformation of carbonaceous material (i,e. lignocellulosic

biomass) into synthesis gas with high content of hydrogen, carbon monoxide, carbon dioxide

and methane using air as gasifying agent. The experimental gasification procedure of PP and

CCS involved four stages: (i) feedstock pretreatment, (ii) biomass gasification, (iii) syngas

purification and (iv) electricity generation. Particle size and moisture content are the key


parameters in the pretreatment stage due to their effect on the hydrogen content and,

consequently in the gasification performance. Zainal et al., [100] evaluated the effect of the

biomass moisture content in the hydrogen composition and calorific value of the generated

syngas in a downdraft gasifier. High moisture content increases hydrogen production, but the

calorific value of the syngas decreases because of the high energy requirements to evaporate the

water and thus, promoting exothermic reactions, which produce CO2 and H2O. Small particles

have larger surface area and therefore, faster heating rate affecting the product gas composition

[43]. Particle size between 0.5-1 cm and a moisture content < 20% were selected based on

literature reports and the requirements of the pilot-scale gasifier. Woody residues were acquired

chipped from the distributor and they were exposed directly to the sun on a horizontal table for

3 days until a moisture content between 10 – 20% was reached. For each gasification procedure,

12 kg of raw material were fed in the gasifier.

The gasification equipment used in the pilot-scale gasification was a 10 kW Power Pallet (All

Power Labs, California, USA) integrated with a combustion engine and a generator. Figure 4-1

presents a schematic process flowsheet of the pilot-scale gasifier used in the experimental

procedure. The process starts with the supply of biomass into the hopper. Then, the feedstock

passes through an auger and enters into the gasifier reactor. The auger is controlled by a level

switch incorporated into the reactor. Biomass fills the reactor and undergoes in the gasification

stages: pyrolysis, combustion and reduction. The gasifier uses air as gasifying agent; therefore,

the high temperature syngas (750 – 850°C) that is generated in the reactor, heats up the air flow

before it enters into the combustion stage. Ash is produced as main by-products from the

gasification stages. Ashes are collected through a vibratory grater which separates the ashes

from the carbonized biomass (char). Subsequently, the hot syngas passes through a cyclone to

separate the remaining ashes and char particles. After the cyclone, the syngas still has enough

energy as heat to be used. For this reason, the syngas uses its high heating energy to remove part

of the moisture in the biomass that is fed through the auger. The generated gas has some

impurities such as tars, water and char particles that must be removed to avoid damage of the

gas engine. In this sense, the gas passes through a four-level filter in order to remove these

impurities. The first level of the filter contains big pieces of wood (average particle size of 5-10

cm), the second level has smaller pieces of wood (3-5 cm), the third level is filled with shavings

from the furniture industry, and the last level consists of a foam filter. During the gasification

procedures, the composition of the synthesis gas was monitored using a portable gas analyzer

(GASBOARD 3100p, Wuhan, China). The gas analyzer measures the outlet gas composition in

terms of hydrogen, carbon dioxide, carbon monoxide, methane, oxygen content and

additionally, it calculates the calorific value of the syngas. Finally, the purified syngas is

submitted to a gas engine which uses it as fuel to generate electricity. The generator has an


electrical capacity between 2 – 10 kW. An overall balance of the process indicates that 1 kg of

biomass can produce 0.75 kWh of electricity, which can be used to turn on approximately 50

energy saving bulbs of 15 watts for 1 hour [101].

Figure 4-1. Process flowsheet of the pilot-scale gasifier in the 10 kW Power Pallet.

In order to determine the overall mass balance of the gasifier, the initial amount of biomass and

the unreacted feedstock from the gasification procedure were measured. Ash and the remaining

char were also measured. The amount of wood that was converted into synthesis gas was

determined by the subtraction of the initial biomass with the final char and ash content. The

amount of produced syngas was determined based on the stoichiometry of the gasification

reaction as follows.

Stoichiometrical ratio (SR) is a characteristic parameter of the fuels that is determined by the

complete combustion of the raw material to fuel. Zhu et al., [102] presented different

correlations to calculate the SR based on the high heating value (HHV) of the raw material. It is

also presented the SR values for different raw materials such as liquid fuels, coal and biomass.

Based on the reported data, hardwood and softwood have an average SR of 6.1 kg air per kg

biomass. This means that 1 kg of hardwood and/or softwood needs 6.1 kg of air for complete

combustion. Based on this statement, the combustion stoichiometry of wood materials is

described by the following equation (Eq. 4-1).

1.0 𝑘𝑔 𝑤𝑜𝑜𝑑 + 6.1 𝑘𝑔 𝑎𝑖𝑟 → 7.1 𝑘𝑔 𝑓𝑙𝑢𝑒 𝑔𝑎𝑠 (4-1)

Where “flue gas” consists mainly of H2O, CO2 and N2. Normally, the gasification procedure is

carried out at a nominal 0.25 ER (equivalence ratio) and thus, the amount of air that is used in

the gasification reaction is calculated from the relation between the equivalence ratio and the

amount of air from the complete combustion (e.g. 0.25 × 6.1 = 1.5 𝑘𝑔 𝑜𝑓 𝑎𝑖𝑟). In gasification,

the combustion reaction is basically divided in two steps as shown in the following equations


(Eq. 4-2 and 4-3). The first reaction (Eq. 4-2) describes the gasification, where a

stoichiometrical amount of air is supplied to the process to obtained syngas.

1.0 𝑘𝑔 𝑤𝑜𝑜𝑑 + 1.5 𝑘𝑔 𝑎𝑖𝑟 → 2.5 𝑘𝑔 𝑠𝑦𝑛𝑔𝑎𝑠 (4-2)

To burn the synthesis gas, an additional 4.9 kg of air must be added.

2.5 𝑘𝑔 𝑠𝑦𝑛𝑔𝑎𝑠 + 4.6 𝑘𝑔 𝑎𝑖𝑟 → 7.1 𝑘𝑔 𝑓𝑙𝑢𝑒 𝑔𝑎𝑠 (4-3)

Note that overall mass balance is exactly the same as in the case of the combustion reaction.

Based on the gasification stoichiometry, the synthesis gas flux and yield of the gasification

procedure was calculated.

4.3 Effect of CaO in the biomass gasification

Among the gasifying agents used in the production of hydrogen through biomass gasification,

air produces the lowest hydrogen content. Nevertheless, air gasification is the most suitable

technology for the production of bioenergy in small scale processes and Non-Interconnected

Zones (NIZ). This technology has a relative medium maturity, the gasifying agent must not be

produced in situ (i.e. steam) and it does not involve a purchase cost in comparison to oxygen in

some gasification procedures in which it is used pure or as a mixture with steam. Low hydrogen

content in the air gasification makes the process economically infeasible, when considering for

hydrogen production and purification. In order to increase the hydrogen content in the syngas,

the use of an adsorbent is proposed as an alternative to enhance the hydrogen selectivity and

thus, the feasibility of the process. Several authors have studied the effect of catalyst/adsorbents

on the behavior of the syngas composition in the biomass gasification using different gasifying

agents. CaO, nickel and iron are some of the catalyst/adsorbents more used in the gasification

procedures due to their strong influence in the tar decomposition and synthesis gas composition

[103]–[105]. The use of calcium oxide (CaO) dually acknowledged as a tar reforming catalyst

and a CO2 sorbent has currently gained lots of attention due to its cheapness and abundance

[106]. Jordan et al., [103] evaluated the behavior of the syngas composition and tar destruction

using different concentration of CaO in an air-blown downdraft gasifier. The synthesis gas

composition and tar degradation were evaluated using different concentrations of CaO (2, 4 and

6%wt.). The hydrogen content increased 35% when the gasification with 6%wt. CaO was

carried out in comparison to the gasification without adsorbent. Hydrogen content in the syngas

was increased due to the tar cracking catalyzed by the CaO. In this thesis, CaO was proposed as

adsorbent aiming to evaluate the behavior of the syngas composition in the pilot-scale 10kW

Power Pallet.


Quicklime (Calcium Oxide) was acquired from a local distributor (Pintucales, Manizales,

Caldas) with the chemical characterization presented in Table 4-1. Prior to the gasification

procedure, the quicklime was crushed with a hammer until a particle size between 0.5 – 1 cm

was obtained. Subsequently, the quicklime was calcined in a muffle oven at 900°C for 3 hours

and then, gradually cooled down with nitrogen gas to avoid a carbonation reaction of the

calcined quicklime [106]. Calcined CaO and woody residues were mixed manually at 10wt%

and 20wt% CaO, respectively. No additional runs were carried out with higher percentage of

CaO due to the unavailability of wood chips. The same gasification procedure from section 4.2

was used and the behavior of the syngas composition was monitored by the portable gas

analyzer.

Table 4-1. Chemical characterization of the quicklime reported by the local distributor.

Characteristics Component Composition

Available Calcium Oxide CaO ≥ 90%

Total Calcium Oxide CaO ≥ 92%

Magnesium Oxide MgO ≤ 1%

Sulfur Content S ≤ 0.1%

Silica Content SiO2 ≤ 0.5%

Iron Content Fe2O3 ≤ 0.1%

Aluminum Content Al2O3 ≤ 0.1%

Phosphorus Content P ≤ 0.1%

4.4 Additional gasification experiences

As part of this thesis, a short internship in the combustion laboratory at the University of

Maryland with the professor Ashwani K. Gupta was carried out. The main objective of this

internship was to evaluate the effect of various gasifying agents (steam, carbon dioxide and a

mixture of these) in the hydrogen composition of the generated gas. A detailed description of

the experimental procedure developed in the University of Maryland is summarized in Annex

B.

4.5 Dark Fermentation

Among the methods to transform lignocellulosic biomass into bioenergy, dark fermentation has

gained great interest. There is a wide range of microorganisms that are capable of producing

hydrogen via dark fermentation. This includes strict anaerobes (Clostridia, methylotrophs,

rumen bacteria, methanogenic bacteria, archaea), facultative anaerobes (E. coli, Enterobacter,


Citrobacter), and even aerobes (Alcaligenes, Bacillus) [12]. In the recent years, extensive

research has also been carried out in hydrogen production at high temperature, using

thermophilic or hyperthermophilic bacteria such as Thermoanaerobacterium

Thermosaccharolyticum, since the increase of temperature in principle improves the reaction

kinetics [12]. In this section, the production of hydrogen from wood residues using as

microorganism Thermoanaerobacterium Thermosaccharolyticum ATCC 7956 is described.

Main emphasis is made on the pretreatment step of the raw material and the transformation of

fermentable sugars into hydrogen. Fermentation procedure can be divided in four steps: acid

hydrolysis, enzymatic saccharification, dark fermentation, hydrogen and metabolites

quantification.

Dark fermentation is a complex process manifested by diverse groups of bacteria where simple

sugars or disaccharides are converted into hydrogen, carbon dioxide and organic acids [13]. Due

to high cellulose crystallinity and low biodegradability, lignocellulosic biomass may require a

pretreatment prior to biohydrogen fermentation [11]. A detailed description of the steps involve

in the dark fermentation is presented below.

4.5.1 Dilute-acid hydrolysis

The first step involves the pretreatment of the lignocellulosic biomass in order to obtain mainly

C5 fractions used in the fermentation step. Dilute acid hydrolysis under mild conditions is the

main process used for the saccharification of lignocellulosic biomass. Rafiqul et al., [107]

evaluated the performance of the dilute acid hydrolysis of Meranti wood at different sulfuric

acid concentrations (2-6 %wt.) and residence time (0-120 min). Highest acid concentration

(6%wt.) enhanced the content of xylose and glucose, and reduced the residence time to 20 min.

On the other hand, low xylose and glucose concentration, and higher residence time (>100 min)

are obtained when the process was carried out with a low acid concentration (2%wt.). However,

high acid concentration also enhanced the formation of toxic compounds that may inhibit the

performance of the microorganism. For this reason, mild conditions (sulfuric acid 2%wt, 115°C

and 3 hours) were selected aiming to keep a low sugars degradation and also increase the

concentration of glucose and xylose in the hydrolyzate. The solid to dilute-acid ratio was fixed

at 1:10 (w/w). These conditions were selected based on different studies related to the acid

hydrolysis of wood materials [107]–[109]. After the hydrolysis procedure, the solid and liquid

fractions were separated by filtration. Liquid fraction was collected and placed in the

refrigerator for further analysis. Solid fraction was washed with water until the pH of the

solution rises up to 4.5 – 5.


Due to the difficulty in finding a strict mechanism for hydrolysis reactions, it is usual to use

simplified models to determine the kinetics of the hydrolysis of lignocellulosic materials [110].

Some of the proposed models in literature are pseudo-homogeneous irreversible first-order

reactions. The general reaction can be described in Eq. (4-4).

𝑃𝑜𝑙𝑦𝑚𝑒𝑟𝑠 (𝑃) → 𝑀𝑜𝑛𝑜𝑚𝑒𝑟𝑠(𝑀) → 𝐷𝑒𝑐𝑜𝑚𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑠 (4-4)

In order to determine the conversion of the quantified monosaccharides by HPLC and based on

the previous reaction (Eq. (4-4)), the potential concentration of the monosaccharides (𝑀𝑛) was

calculated considering that the polymer is completely converted into its constitutive components

through the following equation [110]:

𝑀𝑛 = (𝑃𝑀𝑀

𝑃𝑀𝑃) (

𝐶𝑋𝑛

𝐿𝑆𝑅) ∗ 5

(4-5)

Where 𝑃𝑀𝑀 and 𝑃𝑀𝑃 are the molecular weight of the monosaccharide and polymer,

respectively. 𝐶𝑋𝑛 is the initial content of the polymer in the raw material from the chemical

characterization (see, Tables 5-1 and 5-3) and 𝐿𝑆𝑅 is the liquid/solid ratio (10 g liquid/ g of

wood).

4.5.2 Detoxification

One problem associated with the dilute-acid hydrolysis is the formation of toxic compounds

such as phenolic. Furfural and hydroxymethyl furfural are the two main decomposition products

formed by acid hydrolysis [111]. These compounds may inhibit the cell growth. Several

methods have been reported to remove these toxic compounds from lignocellulosic hydrolysates

such as: neutralization, overliming, steam stripping, ion exchange, solvent extraction, among

others. Treatment with Ca(OH)2 (overliming) seems to be a good alkali treatment for

hydrolysate’s detoxification [112]. The main drawback of this treatment is that sugars are also

degraded. Purwadi et al., [113] evaluated the performance of the alkaline detoxification of acid

hydrolysates at different temperatures and pH in terms of the sugar, furfural and HMF

degradation using Ca(OH)2. As a result, higher temperatures (> 45°C) are recommended in

order to avoid high pH, where high amount of sugars are degraded and also less lime and acid

are used.

For this procedure, Ca(OH)2 was added to increase the pH (up to 10) and keep this condition for

20 min at 60°C. Subsequently, the resulting precipitate was removed by centrifugation. Then,

the pH of the supernatant was adjusted back to 6.5 using H2SO4. Afterwards, the supernatant

was again filtered aiming to remove the remaining precipitate. In order to evaluate the


performance of the detoxification process, sugar degradation and detoxification yield were

calculated. Samples were taken at the beginning and the end of the detoxification procedure

aiming to determine the monosaccharides and furfural concentration and thus, the sugar

degradation and detoxification yield.

4.5.3 Enzymatic Saccharification

The unconverted solid fraction from the acid hydrolysis pretreatment can be degraded by

enzymatic saccharification. The cellulose degradation process was carried out with a total solid

content of 2.5%w/v at 50°C using cellulases (celluclast 1.5 L, Novozymes, Denmark) and a

multienzyme complex Viscozyme L (Novozymes, Denmark). An enzyme dosage of 60 filter

paper units (FPU) per gram of cellulose or substrate for celluclast 1.5L and an enzyme load of

64 pNPGU per gram of cellulose or substrate for Viscozyme L were added. The reaction

volume and pH were adjusted using a Citrate Buffer 0.1 M (pH 4.8 – 5.0). The operational

conditions of the saccharification (50°C, 72 hours and 100 rpm) were adapted from Agrawal et

al., [114] and Normark et al., [109]. Subsequently, the liquid fraction (rich in hexoses) was

separated from the solid by filtration and placed in the refrigerator for further analysis.

4.5.4 Fermentation

In this section, a detailed description of the components involved in the dark fermentation

procedure is presented. First, a brief description of the microorganism Thermoanaerobacterium

thermosaccharolyticum ATCC 7956, especially regarding to its phenotypic features, is

developed. Then, the growth medium components and compositions are presented. Based on the

phenotypic features and reports in literature, different substrates to be used as carbon source by

the microorganism are analyzed. Subsequently, a brief description of the experimental setup

used in this thesis to determine the amount of gaseous products (H2 and CO2) is carried out.

Finally, the quantitative and qualitative methods used to determine hydrogen, sugars and

secondary metabolites concentration are described.

- Microorganism

The strain T. Thermosaccharolyticum is a thermophile saccharolytic microorganism that

effectively produces hydrogen and other secondary metabolites (ethanol, acetic acid, lactic acid,

among others) from carbohydrates [47]. The strain ATCC 7956 and other T.

Thermosaccharolyticum strains can grow in a variety of complex and simple carbohydrates as

can be observed in Table 4-2. The optimal growth conditions of the microorganism are 55°C

and an anaerobic atmosphere (N2 atmosphere). There are different reports of the production of

hydrogen using different T. Thermosaccharolyticum strains (W16, FH1, PDU-2, KKU19, TERI


S7, among others) [48], [115], [116]. However, there are no reports of the use of the commercial

T. Thermosaccharolyticum ATCC 7956 in the hydrogen production.

- Growth medium

According to the Leibniz Institute (DSMZ), the suitable growth medium for the T.

Thermosaccharolyticum ATCC 7956 is a Clostridium Thermohydrosulfuricum (CT) medium,

which its main components are presented in Table 4-3. In order to ensure the suitable growth

conditions for the microorganism, the pH of the medium must be adjusted to 6.8 – 7.5 and

sparge the medium with 100% N2 gas to make it anoxic (30-45 min). Then, the medium is

autoclaved at 115°C and 15 psia.

Table 4-2. Growth of T. Thermosaccharolyticum strains in different carbon sources

Carbon source W16

[47]

PSU-2

[117]

KKU-19

[115]

ATCC-7956

[118]

Sucrose + + + +

Arabinose + + + +

D-xylose + + + +

Lactose + + + +

Glucose + + + +

Mannose + + + +

Galactose + + + +

Xylan + + + +

Cellulose + + + ND

Yeast extract + + + ND

Starch + + + +

Glycerol - - - -

ND: not determined.

Individual isolates were cultivated anaerobically in a MB medium aiming to determine the

effect of different substrates in the hydrogen and secondary metabolites production. The

components and composition of the MB medium are presented in Table 4-4. The modified MB

medium is widely used in different literature reports for the production of hydrogen and

secondary metabolites of different T. Thermosaccharolyticum strains, giving high hydrogen

yields [48], [119], [120]. Therefore, this medium was used to evaluate the performance of the

commercial T. Thermosaccharolyticum ATCC 7956 for hydrogen and secondary metabolites

production.


Table 4-3. Composition of the Clostridium Thermohydrosulfuricum (CT) medium [121]

Table 4-4. Composition of the MB medium [47]

Component Concentration (g/L)

NH4Cl 1.0

K2HPO4 1.5

KH2PO4 0.75

MgCl2 x 6H2O 0.5

NaCl 2.0

KCl 0.2

Cysteine-HCL 0.5

Yeast extract 2.0

Tryptone 2.0

Sucrose 10

- Substrate type

Based on the datasheet of the Leibniz Instutite DSMZ, the microorganism grows using sucrose

as carbon source. However, based on the information provided in Table 4-2, the microorganism

can grow in a variety of substrates. Prior to utilization of acid and enzymatic hydrolysates for

growth and H2 production, a set of experimental runs were carried out using the synthetic

medium (MB) supplemented with analytical-grade sugars as carbon source to determine the

fermentative behavior of the T. thermosaccharolyticum ATCC 7956 (see, Table 4-5). Sucrose,

glucose, xylose, and a mixture of glucose and xylose were added at a fixed total sugar quantity

(10 g L-1). Subsequently, the hydrolysates from the acid and enzymatic pretreatments of both

raw materials were tested as carbon source for the production of hydrogen and secondary

metabolites using T. Thermosaccharolyticum ATCC 7956.

Components Concentration (g/L)

Tryptone 10

Yeast Extract 2

FeSO4 · 7H2O 0.2

Na2SO3 0.2

Na2S2O3 · 5H2O 0.08

Cysteine-HCL 0.001

Sucrose 10


Table 4-5. Pure substrate composition for the dark fermentation of T. Thermosaccharolyticum

ATCC 7956

Preliminary tests

Sucrose 100%

Glucose 100%

Glucose 75% - Xylose 25%



Xylose 100%

- Preinoculum

First, the bacteria was growth in agar plates using the CT medium in an incubator at 55°C and

subsequently, several colonies were collected with a handle and were transferred into tubes with

rubber stoppers that contained MB medium. In the tubes, 5 ml of the medium was poured in

anaerobic conditions (using nitrogen as sparging gas). Previously, the pH of the medium was

adjusted to 6.8 – 7.5 using NaOH 2N and HCL 2N. The tubes with the bacteria were incubated

in a water bath at 55°C for 3 days.

- Fermentation

Fermentation was carried out in 100 mL glass serum bottles with 50 mL medium and the pH

was adjusted between 6.5 and 7. The inoculum was added at 2%w/w in anaerobic conditions

using N2 as sparging gas. Fermentation procedure was performed at 60°C in a reciprocal

shaking bath at 120 rpm for 3 days. These conditions were adapted from different authors that

have worked with T. Thermosaccharolyticum strains in order to produce hydrogen [47], [115],

[117]. The experimental setup consisted of three serum bottles serially connected by hoses and

valves. The first flask contained the inoculum previously gassed with nitrogen. The second flask

was filled with water and was connected directly to the first flask. The function of the second

flask was to collect the gas that was produced in the fermentation; and the water in the second

flask was displaced to the third bottle. This works as an indicator of the amount of gas that was

produced in the fermentation. This procedure is normally called the water displacement method.

Figure 4-2 presents a schematic description of the dark fermentation experimental setup.


Figure 4-2. Flowsheet of the biomass gasification to produce hydrogen from wood residues

- Analytical Methods

Sugars and furan-based compounds from the acid hydrolysis and enzymatic saccharification

were quantified using High Performance Liquid Chromatography (HPLC) (Hitachi Elite

LaCrhome) with a column Transgenomic CARBOSep CHO-782 Pb. Deionized water was used

as mobile phase. The column oven and RID were maintained at 70°C, and the flow rate of the

mobile phase was fixed at 0.6 ml min-1. Prior to the quantification, the samples were centrifuged

at 10,000 rpm for 10 min. Then, the supernatant was filtered through an Agilent Nylon Filter

with a pore diameter of 0.2 µm into HPLC vials. Peaks were detected by the RI detector and

quantified based on the area and the retention time of the standards (glucose, xylose, galactose,

arabinose, mannose and furfural) procured from Merck, Sigma-Aldrich and Acros Organics.

The HPLC quantification was carried out in the Laboratorio de Intensificación de Procesos y

Sistemas Híbridos of the National University of Colombia at Manizales.

Volatile fatty acids (acetic and lactic acid) were quantified using HPLC (Shimadzu, Kyoto,

Japan) with a column C18 ODS hypersil. Phosphate buffer with pH=2.4 was used as mobile

phase. The column and UV detector were maintained at 40°C, and the flow rate of the mobile

phase was fixed at 0.7 ml min-1. The sample preparation was the same procedure mentioned in

the sugar and furan-based compounds quantification. Peaks were detected by the UV detector

and quantified based on the area and retention time of the standards. This procedure was carried

out in the Laboratorio of Cinéticas Enzimáticas y Equilibrios Químicos of the National

University of Colombia at Manizales.

Ethanol was measure using HPLC (Hitachi Elite LaCrhome) consisting of a pump, autosampler,

column oven, RID and organizer units. Transgenomic ICSep ORH-801 Column was used as

stationary phase with H2SO4 0,01N as mobile phase at a flowrate of 1 mL/min and temperatures

in the oven and detector of 70 and 45°C, respectively. This procedure was also carried out in the

Fermentation

Broth

Water

Displacement

Method

Sampling

Point

Gas

Gas

Shaking Bad


Laboratorio de Intensificación de Procesos y Sistemas Híbridos of the National University of

Colombia at Manizales.

The sugars concentration in the dark fermentation were measured based on the determination of

reducing sugars using the dinitrosalicylic acid (DNS) method, where the absorbance was

measured at different wave-lengths depending on the sugar concentration, following the

protocol proposed by Miller [122].

The amount of produced gases in the dark fermentation were measured by water displacement

method, where gaseous samples were collected in 250 ml flasks and then, they were analyzed in

a Portable Gas Analyzer (GASBOARD3100p, Wuhan, China). The calculation of the gaseous

species yield was performed considering the following equation (Eq. 4-6).

𝑌𝑔𝑎𝑠 =

𝑉𝑔𝑎𝑠 ∗ 𝐶𝑔𝑎𝑠 ∗𝜌𝑔𝑎𝑠

𝑀𝑊𝑔𝑎𝑠

(𝑆𝑓 − 𝑆𝑜) ∗𝑉𝑡𝑜𝑡𝑎𝑙

𝑀𝑊𝑠𝑢𝑏

(4-6)

Where 𝑉𝑔𝑎𝑠 is the volume of water that was displaced by the generated gas from the dark

fermentation, 𝐶𝑔𝑎𝑠 the concentration of the specie in the gaseous mixture, 𝜌𝑔𝑎𝑠 is the density of

the gaseous specie, 𝑀𝑊𝑔𝑎𝑠 is the molecular weight of the gas specie, (𝑆𝑓 − 𝑆𝑜) is the substrate

consumption in the dark fermentation, 𝑉𝑡𝑜𝑡𝑎𝑙 is the volume of fermentation medium and 𝑀𝑊𝑠𝑢𝑏

the molecular weight of the substrate.

4.6 Simulation Procedure

- Overview

In order to evaluate the performance of stand-alone and biorefinery ways to produce hydrogen,

six scenarios were proposed. Two scenarios are related to the production of hydrogen in a stand-

alone pathway from which only one product (hydrogen) was obtained. The remaining four

scenarios were evaluated considering the conceptual design of a biorefinery which is related to

three concepts: i) hierarchy, ii) sequence and iii) integration [123]. First corresponds to the

hierarchical decomposition of relevant elements in the biorefinery, such as feedstocks, products

and technologies. Then, the logical order of the technologies and products in the biorefinery

must be decided (sequence). And finally, the convenience of the integration of raw material,

processes or process streams is analyzed [124].

According to the hierarchy approach, the first step considers the selection of the main products

that are going to be targets for the biorefinery design. Biorefinery products can be categorized in

five groups: biofuels, bioenergy, biomolecules and natural chemicals, biomaterials, and food


product. Hydrogen, electricity, ethanol, acetic acid and butyric acid were selected as main

products addressing the hierarchy design to hydrogen, then to ethanol, VFA and finally,

electricity. Besides, the sequence of the technological routes was established according to the

well-known onion diagram, giving importance to the reaction stage [125]. Finally, the

integration of stream processes that can be used as energy sources in other processes was carried

out. Mass and energy balances were obtained using simulation procedures. The software used

for this purpose was the simulation tool Aspen Plus v8.0 (Aspen Technology, Inc, USA).

The effect of the hierarchy of products within a biorefinery was used to evaluate the economic

performance of the hydrogen production. The main objective of this procedure was to select the

scenarios, in a biorefinery way, that make the hydrogen production process more profitable

compared to those in a stand-alone pathway. Additionally, the energy efficiency of each

scenario, to support the selection of the most suitable process, was calculated. There must be a

balance between economic profitability and environmental impacts of the process to be

considered as sustainable. As a consequence, Green House Gases (GHG) assessment was

performed in order to calculate the amount of CO2 emitted by each scenario, especially

gasification scenarios. Since GHG evaluation only considers the effect of CO2 in the atmosphere

(which can be related to the global warming potential), another simulation tool that evaluates

the performance of the process in different impact categories was implemented. For this

purpose, the Waste Reduction Algorithm (WAR) developed by the Environmental Protection

Agency of the United States (EPA) was used to compare each scenario based on eight (8)

impact categories. A detailed description of the scenarios, the processes and the evaluation

parameters involve in the simulation procedure is presented below.

4.6.1 Scenarios

Six scenarios for gasification and dark fermentation were proposed to evaluate the effect of the

hierarchy decomposition of the products in the economic, energy and environmental assessment

of the hydrogen production. Stand-alone ways (the production of hydrogen as a single product

from gasification and dark fermentation) were selected as base cases for the comparison with

the remaining four scenarios in which, the conceptual design of a biorefinery was applied.

Table 4-6 presents the description of the six evaluated scenarios considering three products

(hydrogen, ethanol and electricity) for the gasification scenarios and four products (hydrogen,

ethanol, acetic acid and butyric acid) for the dark fermentation scenarios.


Table 4-6. Stand-alone and biorefinery scenarios for the hydrogen production

Technology Scenario Products Description

Gasification

Scenario 1 Hydrogen 100 % Raw material for syngas

production

Scenario 2a Hydrogen +

Electricity

50% of syngas intended for power

generation and the remaining for the H2

production.

Scenario 3b Hydrogen +

Electricity + Ethanol

70% raw material for syngas

production. The remaining 30% for

ethanol production.

Dark

Fermentation

Scenario 4 Hydrogen 100% raw material for hydrogen

production

Scenario 5c Hydrogen + Ethanol Additional ethanol separation

Scenario 6d Hydrogen +

Ethanol+ VFA

Additional acetic and butyric acid

separation

a Same distribution of raw material as in the scenario 1. b Same distribution of synthesis gas as in the scenario 2. c Same distribution of raw material as in the scenario 4. d Same distribution of raw material as in the scenario 5.

Scenarios 1 and 4 consider only the production of hydrogen from the stand-alone processes.

Two additional scenarios are proposed for the production of hydrogen, ethanol and electricity

through biomass gasification. Scenario 2 considers the use of 50% of the generated syngas in

the gasification for hydrogen production and the remaining 50% for the electricity generation,

using the syngas as fuel in a gas engine. Furthermore, scenario 3 considers the ethanol

production from a fraction (30%) of the feedstock to the process. The remaining 70% is used in

the gasification process for syngas production from which, 50% is used for hydrogen production

and the remaining 50% for electricity generation. Finally, scenarios 5 and 6 consider the

separation of the byproducts from the fermentation broth (ethanol and VFA) along with the

hydrogen production.

4.6.2 Process description

For all proposed scenarios, mass and energy balances were obtained using simulation

procedures aiming to calculate the requirements for raw material, utilities and energy needs.

Mathematical modelling of the concentration profiles using kinetic models was performed in

software packages such as Matlab (MathWorks, USA). Calculation of the technological routes

are based on kinetic models [107], [117], [126]–[129] (see, Annex C).


For the simulation of the biomass gasification, the Grayson-Streed thermodynamic model was

used to calculate the activity coefficients of the liquid phase since the model was developed for

systems with high H2 concentration. The Redlich-Kwong equation of state was applied to

describe the vapor phase. In the simulation of the dark fermentation, the Non Random Two

Liquids (NRTL) was used to analyze the behavior of the liquid phase and the Hayden

O´Connell equation of state was selected to describe the vapor phase [130]. Additional data

such as physical properties were obtained from the work of Wooley and Putsche [131].

4.6.3 Gasification

The process scheme used in the production of hydrogen through biomass gasification is

presented in the Figure 4-3. The simulation of the air gasification was divided in three stages:

pretreatment of the raw material (drying and milling), gasification (enrichment of the syngas)

and purification (membranes). The first part of the process involved the pretreatment of the

feedstock which consisted on drying the raw material to achieve a moisture content between 10

- 20%. Subsequently, the lignocellulosic residue was chipped to obtain a particle size between

0.5 and 1 cm. These conditions were selected for the simulation according to the requirements

of the gasifier from the experimental procedure.

The second stage was related to the chemical pathway of gasification that took place inside of

the reactor as shown in Eq. 4-7 to 4-14. The simulation of the downdraft gasifier was performed

splitting the reactor in three main processes: pyrolysis, combustion and reduction. Dried

biomass underwent into the devolatilization (pyrolysis), where the raw material was

decomposed into carbon, hydrogen, oxygen and ash according to the elemental analysis from

the experimental procedure. Then, all the components from pyrolysis zone went into the

combustion chamber, where they reacted with oxygen to produce CO2, CO, H2O and heat. The

produced char in the pyrolysis and the combustion zone passed to the reduction zone, where

char gasification took place to produce CO2, CO, H2 and CH4. Ash and the remaining char were

separated from the syngas using a cyclone. The operational conditions of the gasifier (e.g.

temperature, air/biomass ratio) were selected based on equilibrium and kinetic models of the

modelling of a downdraft gasifier. Annex D presents the equilibrium and kinetic models used in

the mathematical modelling of the downdraft gasifier and the effect of two process parameters

(moisture content and air/biomass ratio) in the synthesis gas composition and energy content.

Pyrolysis

𝐵𝑖𝑜𝑚𝑎𝑠𝑠 + ℎ𝑒𝑎𝑡 → 𝐶𝑠 + 𝑡𝑎𝑟 𝑔𝑎𝑠𝑒𝑠

(4-7)

Combustion

𝐶𝑠, 𝑡𝑎𝑟 𝑔𝑎𝑠𝑒𝑠 + 𝑂2 → 𝐶𝑂2 + 𝐻2𝑂 + ℎ𝑒𝑎𝑡 (4-8)


𝑡𝑎𝑟 𝑔𝑎𝑠𝑒𝑠 + ℎ𝑒𝑎𝑡 → 𝐶𝑂 + 𝐻2 (4-9)

Reduction

𝐶𝑠 + 𝐶𝑂2 ↔ 2𝐶𝑂 (4-10)

𝐶𝑠 + 𝐻2𝑂 ↔ 𝐶𝑂 + 𝐻2 (4-11)

𝐶𝑠 + 2𝐻2 ↔ 𝐶𝐻4 (4-12)

𝐶𝐻4 + 𝐻2𝑂 ↔ 𝐶𝑂 + 3𝐻2 (4-13)

𝐶𝑂 + 𝐻2𝑂 ↔ 𝐶𝑂2 + 𝐻2 (4-14)

In order to improve the hydrogen content in the generated gas, a catalyst adsorption was

proposed. Carbonation reaction was based on the conversion of carbon dioxide (CO2) into

calcium carbonate (CaCO3) using calcium oxide (CaO) as adsorbent. For this purpose, the

kinetic analysis for the capture and desorption of CO2 proposed by Nikulshina et al., [127] was

used. As mentioned in section 4.3, the presence of CaO in the biomass gasification can increase

the hydrogen and carbon monoxide content, and further reduce the tar content in the syngas.

For this reason, a coupled two bed reactors, using CaO as catalyst, were chosen to improve the

selectivity of the hydrogen in the synthesis gas. In the first reactor, the carbonation reaction took

place (Eq. 4-15). The calcination reaction involved the CO2 desorption using air in the second

reactor (Eq. 4-16).

𝐶𝑂2(g)+ 𝐶𝑎𝑂(𝑠) → 𝐶𝑎𝐶𝑂3(𝑠)

(4-15)

𝐶𝑎𝐶𝑂3(s)→ 𝐶𝑎𝑂(𝑠) + 𝐶𝑂2(𝑔) (4-16)

Finally, a further purification stage was required using hollow fiber membranes, which are

widely used in many gas separation industries [132]. Ideal selectivity, separation factor and H2

recovery were obtained from data reported in literature [132], [133]. Choi et al., [133] evaluated

the performance of hollow fiber membranes in the separation of H2/N2 and H2/CO mixtures for

hydrogen production, and consequently their application in the separation of gaseous mixtures

in thermochemical methods such as Steam Methane Reforming (SMR) from which hydrogen

concentration up to 30% can be obtained. First, the authors tested the permeance of the

membrane with pure gases (H2, N2, CO, CO2). Then, based on the selectivity of the membrane

for the pure components, two mixture (H2/N2 and H2/CO) were selected to evaluate the

hydrogen recovery in these systems. The operational conditions of the membrane were: 50°C,

feed/retentate pressure up to 6 bar and permeate pressure 1 bar. Based on the reported data, 85%

molar of hydrogen recovery and 72% molar of hydrogen purity were obtained in the

experimental procedures [133]. Therefore, these data were used in the simulation procedure of

the hydrogen purification using hollow fiber membranes. Previous treatment of the hydrogen

rich gas was required to reach the suitable pressure conditions inside the membrane to enhance

the mass transfer of the hydrogen. Maus et al., [134] examined the problems involve in


refueling vehicles woth compressed hydrogen. According to Maus et al., [134], the filling

pressure that must be supplied from the hydrogen storage location should be at levels between

35 to 70 MPa, in order to achieve an adequate volumetric storage density. For this reason, due

to the loss of energy after the membrane separation, the hydrogen stream must be compressed to

reach the required conditions for the filling station.

Figure 4-3. Flowsheet of the biomass gasification to produce hydrogen from wood residues

4.6.4 Electricity generation

The generated synthesis gas from the biomass gasification has a high energy content, which can

be used directly as fuel in a gas engine to produce electricity due to its high H2/CO ratio. An

internal combustion engine burns the gaseous fuel to produce electricity by means of a

generator. Normally, the efficiency of these devices is between 27 – 38% and they can provide

energy in form of electricity from 30 kWh to 60 MWh [27]. The simulation of the electricity

generation from the synthesis gas was based on the syngas mass flow, lower heating value

(LHV) of the gas and the efficiency of the internal combustion engine. As a result, the amount

of available energy that can be obtained from the synthesis gas was calculated.

Coffee Cut-StemsChipperDryer

Air

Absorber Desorber

Membrane

Exhausted Gas

H2-Rich Gas

Gasifier

Ash

Calcium OxidePurge

Carbon Dioxide

Hydrogen

Heating Requirements

Net Work

Purification and Conditioning

Gasification

Raw material conditioning


4.6.5 Ethanol Fermentation

Wood residues have high cellulose and hemicellulose content, from which fermentable sugars

for bioenergy production can be obtained. Due to high crystallinity and low biodegradability,

lignocellulosic biomass may require a pretreatment prior to fermentation processes [11]. For this

reason, a mild-acid pretreatment and enzymatic hydrolysis were proposed as methods for raw

material pretreatment. Acid hydrolysis with sulfuric acid (2% w/w) at 130°C and solid to liquid

ratio of 1:10 were used as conditions in the simulation procedure. Hydrolysis yields were

calculated based on the kinetic expression reported by Rafiqul et al., [107] considering the

operational conditions mentioned before. Xylose is the main fermentable sugar from the acid

hydrolysis that can be used as carbon source for the ethanolic fermentation. One problem

associated with the dilute-acid hydrolysis is the formation of toxic compounds such as acids,

furfural and phenolic compounds. Alkaline treatment with Ca(OH)2 is widely used in the

hydrolysates detoxification [111]. The simulation procedure of the alkaline treatment consisted

in the degradation of furan compounds and consequently, the formation of gypsum that was

separated from the hydrolysate by filtration. The chemical pathway of the overliming was

obtained from Purwadi et al., [113]. The unconverted fraction of cellulose from the acid

hydrolysis can be used to produce glucose by enzymatic saccharification. Enzymatic hydrolysis

of woody residues was simulated based on the kinetic expressions reported by Zheng et al.,

[110], Khodaverdi et al., [111], Kadam et al., [119]. This model correlates the degradation of

cellulose and cellobiose with the formation of glucose considering the enzyme charge (cellulase

and β-glucosidase). The concentration profiles of the enzymatic saccharification were calculated

in the software MATLAB (MathWorks, USA).

The fermentable sugars obtained from the pretreatment stage were converted into ethanol using

the bacteria Zymomonas mobilis at 30°C for 30 hours. This bacteria has the ability to degrade

hexoses and pentoses, as carbon source. Leksawasdi et al., [136] developed a two-substrate

model for the fermentation of glucose and xylose by the recombinant Zymomonas mobilis.

Concentration profiles of ethanol and fermentable sugars were calculated based on the

Leksawasdi model in the software MATLAB (MathWorks, USA). Then, the fermentation broth

with an ethanol concentration of 5 – 6 % by weight was sent to a downstream process, which

consisted of two distillation columns and molecular sieves. In the first column, the ethanol was

concentrated up to 60%. Then, the ethanol was concentrated until the azeotropic point (96 %wt).

Finally, a dehydration stage was required in order to obtain ethanol at 99.6 %wt using molecular

sieves [81]. The process scheme of the ethanol production using woody residues is presented in

Figure 4-4.


Figure 4-4. Flowsheet of the ethanol production from wood residues using Z. mobilis

4.6.6 Dark Fermentation

The production of hydrogen through dark fermentation involved various stages: pretreatment of

the raw materials, fermentation, hydrogen purification, ethanol and VFA separation. The

process scheme for hydrogen, ethanol, acetic acid and butyric acid production is presented in

Figure 4-5. The pretreatment of the raw material consisted mainly in physical and chemical

treatments. The same process for the physical treatment (drying and milling) in section 4.6.3

and the same for chemical treatment (acid and enzymatic hydrolysis) in section 4.6.5 were

considered in the simulation procedure of the dark fermentation. The hydrolysate from the acid

and enzymatic hydrolysis was used as carbon source for hydrogen production by the moderate

thermophile Thermoanaerobacterium thermosaccharolyticum. Cell growth and substrate

consumption was simulated based on the Monod model [117] and the products formation was

described based on the Luedeking–Piret model [137]. Hydrogen, carbon dioxide and other

metabolites (ethanol, acetic and butyric acid) were the main products from the dark

fermentation. The separation of hydrogen from the carbon dioxide was performed using coupled

porous and non-porous membranes, in order to enhance the hydrogen selectivity. Belafibako et

al., [138] evaluated the performance of porous and non-porous membranes for the hydrogen

separation of a fermentative process. During the fermentation, four gaseous species are

produced: water vapor, hydrogen, carbon dioxide and nitrogen. The direct separation of

hydrogen from CO2 and N2 seems to be too difficult; therefore, a two-step gas separation system

can be used to separate first, the mixture H2-N2 in a non-porous membrane and subsequently,

the mixture H2-CO2 in a porous membrane. At the end of the two-step separation system, a

hydrogen recovery percentage up to 75% can be reached [138]. Consequently, this data was

used in the simulation of the hydrogen purification.

H2SO4 2%

Acid Hydrolysis

Filter

DetoxificationFilter

Ca(OH)2

Enzyme

CaSO4

Filter

AutoclaveFermentationWater

Enzymatic Saccharification

Distillation

Rectification

Molecular sieves

ETHANOL

Filter

Nutrients

Z. mobilis

CO2Biomass

StillageWaterResidual Solid Fraction

Coffee Cut-Stems

Cellulose rich-solid

Water

C5–C6 fractions


Figure 4-5. Flowsheet of the hydrogen, ethanol, acetic acid and butyric acid production through

dark fermentation of wood residues.

The separation of ethanol and VFA’s (acetic and butyric acid) was simulated based on different

separation process schemes. The downstream process for ethanol separation from the

fermentation broth followed the same procedure described in section 4.6.5. Acetic and butyric

acid are the main VFA in the fermentation broth, the separation of these two metabolites was

performed based on the aqueous two-phase system using calcium chloride [139], [140].

Aqueous two-phase partition is a liquid-liquid extraction method broadly used in the separation

of inorganic molecules and biomolecules [140]. The introduction of a salt affects the boiling

point, the solubility of the liquid components and the equilibrium composition of the vapor

phase. Wu et al., [140] evaluated the separation of butyric acid by an aqueous two-phase

system. A solution of butyric acid-water-salt and a simulated fermentation broth, consisting of

butyric-acetic acid, were used in the experimental runs. Separation yield of the system butyric

acid-water reached a recovery percentage up to 87%. From the simulated fermentation broth,

the concentration of butyric acid raised 2-3 times the initial concentration of the butyric-acetic

acid mixture. Furthermore, Wu et al., [139] studied the extraction and separation of butyric acid

from a clostridium tyrobutyricum fermentation broth using a Polyethylene Glycol

(PEG)/sodium sulfate aqueous two-phase system. As a result, an extraction yield up to 92% can

be obtained using a solution of 9.6% Na2SO4 (w/w) in order to induce the salting effect in the

fermentation broth and a solution of 26% PEG (w/w) to form the aqueous two-phase solution.

The aqueous two-phase process scheme (see, Figure 4-5) and its operational conditions were

adapted from Wu et al., [139], Wu et al., [140] and Lefranc et al., [141]. First, NasSO4 was

mixed with the fermentation broth at 36°C to salt out the cell protein, sugars and nitrogen

Wood Residues

Acid Hydrolysis

Solid

Residue

Liquor

Detoxification

Calcium

Hydroxyde

Xylose

Calcium Sulfate

Water

Enzymatic Hydrolysis

Enzyme

Lignine

Dark Fermentation

T. Thermosaccarolyticum

Hydrogen

Biomass

Byproducts

Glucose

Stillage

Waste Water

Ethanol

Molecular Sieves

Sodium Sulfate

Sugars

N2 compounds

PEG

Sodium Sulfate

Sugar

N2 compounds

PEG

rich-phase

Iodine

PEG

VFA

Acetic Acid

Butyric Acid

Sulfuric Acid


compounds. Then, the liquid fraction was separated from the precipitate by filtration.

Subsequently, the broth was added into PEG from which an aqueous and organic phase were

formed. The organic phase contained most of the butyric acid, acetic acid and PEG. Iodine

solution (in a ratio 1:2 of iodine: PEG) was added to the organic phase to precipitate the PEG.

The filtrate from the solution contained butyric acid and acetic acid, which were submitted to an

atmospheric distillation system at 118°C to separate the butyric acid.

4.7 Analysis of the proposed scenarios

In this section, a detailed description of the economic, energetic and environmental assessment

of the proposed scenarios is presented. These analyzes were used as decision criteria for the

selection of the suitable scenarios for hydrogen production, considering as main parameters:

economic profitability, energy efficiency and the environmental potential impact of the

evaluated processes.

4.7.1 Economic Assessment

A basic equipment mapping adapted to the economic conditions (tax rate, interest of return,

operator and supervisor wages, among others) in Colombia was performed to determine the

operating costs of the proposed scenarios including the raw material, utilities, labor and

maintenance, general plant and administrative costs. Additionally, the depreciation of the

equipment was evaluated considering a project life of 10 years. The mass and energy balances

from the simulation procedure were used as a starting point for the economic assessment using

the software Aspen Process Economic Analyzer v8.0 (Aspen Technology, Inc, USA). Raw

material and utilities costs are the parameters that have the greatest influence in the production

costs. Biomass costs were evaluated considering two parameters: the biomass purchase cost

(depending on the location of the collection centers) and the transportation cost of biomass from

the collection center to the processing plant. The utilities costs are linked to the heating and

cooling requirements of the process, which are obtained from the energy balance in the software

Aspen Energy Analyzer (Aspen Technology, Inc., USA).

The transportation cost of the raw material is limited by the biomass availability and the

distance between the collection center and processing plant. Biomass availability influences

directly the process scale of the proposed scenarios. The amount of raw material that can be

used in the processing plant was calculated as the sum of the maximum capacity of each

collection center. The feedstock availability in each department define the collection center

capacity. However, some departments have higher biomass availability than others because of

the large cultivation area of the crop. These departments have higher contribution to the total


amount of available biomass. However, as mentioned in chapter 3, fractions above the 50% of

the total biomass production in each department cannot be used as raw material in the

processing plant due to the logistics issues.

Another important parameter in the evaluation of the economic performance of the hydrogen

production is the distance between the collection centers and the processing plant. The hydrogen

production through gasification (scenario 1) is the process scheme with the lower raw material

(biomass and reagents) requirements in comparison to other scenarios. Wood residues and

calcium oxide are the main inputs of this scenario. It was considered that the most important

parameter in the economic behavior of the hydrogen production was the biomass cost; therefore,

the purchase cost of CaO was not deeply analyzed and its price was obtained from different

databases such as ICIS Princing and Alibaba [142], [143]. Hence, the effect of the biomass cost

in the economic profitability of the hydrogen production was assessed considering that the base

case for the evaluation was the scenario 1. The region or department that evidence the lowest

hydrogen production cost was selected as suitable zone for the plant. The results from this

analysis can be extrapolated to the other scenarios and thus, the economic performance of each

one was calculated based on the selected location in the scenario 1.

Subsequently, the economic profitability in terms of the Net Present Value (NPV) was assessed

in the different proposed scenarios. Net Present Value (NPV) represents the amount of money,

which must be available at the present time considering the profit on the project, pay-off

investment, and normal interest on the investment [144]. The production cost of hydrogen and

further by-products (electricity, ethanol, acetic acid and butyric acid) were determined and the

influence of the process scale (plant capacity) in the production costs was assessed.

Additionally, the effect of the fluctuations in the market price of the main products and reagents,

in the economic profitability of the proposed scenarios were evaluated. The main data used in

the economic assessment of the proposed scenarios is presented in Table 4-7.

Table 4-7. Utilities, reagents and products market prices

Component Price Units

Sulfuric Acid 0.094a USD/Kg

Sodium Hydroxide 0.35 a USD/Kg

Calcium Hydroxide 0.05 a USD/Kg

Calcium Oxide 0.062 a USD/Kg

Sodium Sulfate 0.077 a USD/kg

Fuel Ethanol 0.68c USD/L

Acetic Acid (86%w/w) 0.595 a USD/kg


Butyric Acid (94%w/w) 0.2 a USD/kg

Hydrogen 4.47b USD/Kg

Water 1.252 d USD/m3

Electricity 0.1d USD/kWh

Iodine 30 a USD/kg

Polyethylene glycol 2 a USD/kg

Propane (Refrigerant) 2.94 a USD/kg

High P. Steam (105 bar) 9.86 d USD/ton

Mid P. Steam (30 bar) 8.18 d USD/ton

Low P. Steam (3 bar) 1.57 d USD/ton

a Prices based on Alibaba International Prices [142]

b Based on hydrogen price projections [145]

c Ethanol price based on statistics of the Biofuels National Federation [146]

d Prices adapted to the Colombian context [82], [147]

4.7.2 Energy Analysis

The net energy balance is a sustainability parameter that represents the relation between the

produced and required bioenergy in the evaluated scenarios, based on the amount of energy that

can supply the bioenergy products in order to mitigate the energy requirements of the processes.

The net energy balance (𝑬𝒏) of the process involves two components: first, the energy content

of the bioenergy products in each scenario (i.e. hydrogen (𝑬𝑯𝟐), ethanol (𝑬𝑬𝒕𝑶𝑯) and electricity

(𝑬𝒘), among others) and the energy needs of the process (i.e. heating requirements (𝑬𝒉𝒆𝒂𝒕𝒊𝒏𝒈),

power supply (𝑬𝒑𝒐𝒘𝒆𝒓), among others). Positive values indicate that the process is energically

sustainable, since the energy content of the products can fulfil the energy requirements of the

process. Negative values means that the energy requirements are higher than the produced

energy and thus, the process is energetically deficient.

𝐸𝑛 = ∑ 𝐸𝑜𝑢𝑡𝑝𝑢𝑡𝑠 − ∑ 𝐸𝑖𝑛𝑝𝑢𝑡𝑠 (4-16)

Considering the main bioenergy products from the evaluated scenarios and their respective

energy requirements, Eq. (4-16) can be expressed as

𝐸𝑛 = 𝐸𝐻2+ 𝐸𝐸𝑡𝑂𝐻 + 𝐸𝑤 − 𝐸ℎ𝑒𝑎𝑡𝑖𝑛𝑔 − 𝐸𝑝𝑜𝑤𝑒𝑟 (4-17)

The energy content of the products can be calculated from the amount of hydrogen and ethanol

that was produced in the simulation procedure and the heating value of each product. The

heating value is the energy released as heat when a compound undergoes complete combustion.

The energy content of the products can be calculated with the following expression.


𝐸𝑝𝑟𝑜𝑑𝑢𝑐𝑡 = �̇�𝑝𝑟𝑜𝑑𝑢𝑐𝑡 ∗ 𝐿𝐻𝑉𝑝𝑟𝑜𝑑𝑢𝑐𝑡 (4-18)

Where �̇�𝒑𝒓𝒐𝒅𝒖𝒄𝒕 is the mass flow of the product in kg/h and 𝑳𝑯𝑽𝒑𝒓𝒐𝒅𝒖𝒄𝒕 is the heating value of

the product in MJ/kg. The amount of produced energy from the synthesis gas, which is used as

fuel in a gas engine, can be calculated considering the engine efficiency (ƞ) as mentioned in

section 4.6.4, the syngas flux �̇�𝒔𝒚𝒏𝒈𝒂𝒔 and the heating value of the syngas. The heating value of

the syngas is the sum of the heating value of the gaseous species by the mass fraction of the

specie. The equation that describes the electricity generation from the synthesis gas is shown

below.

𝐸𝑤 = �̇�𝑠𝑦𝑛𝑔𝑎𝑠 ∗ 𝐿𝐻𝑉𝑠𝑦𝑛𝑔𝑎𝑠 ∗ 𝜂𝑒𝑛𝑔𝑖𝑛𝑒 (4-19)

The heating requirements (𝑬𝒉𝒆𝒂𝒕𝒊𝒏𝒈) were taken from the pinch evaluation of the process using

the educational software Hint, which uses the pinch methodology to calculate the exchanger

network of a process [148]. The software Hint calculates the energy requirements of the process

in terms of the heating and cooling utilities. In the other hand, the amount of power (𝑬𝒑𝒐𝒘𝒆𝒓)

required in the process was calculated from the energy balance in the simulation procedure.

Additionally, the energy performance of the scenarios were evaluated based on two criteria.

First, the energy efficiency of the process considering as only product the hydrogen (Eq. 4-20)

and secondly, the energy efficiency taking into account all the bioenergy products along with

the energy requirements (𝑬𝒏) (Eq. 4-21).

𝜂 =𝐸𝐻2

�̇�𝑏𝑖𝑜𝑚𝑎𝑠𝑠 ∗ 𝐿𝐻𝑉𝑏𝑖𝑜𝑚𝑎𝑠𝑠 (4-20)

𝜂 =𝐸𝑛

�̇�𝑏𝑖𝑜𝑚𝑎𝑠𝑠 ∗ 𝐿𝐻𝑉𝑏𝑖𝑜𝑚𝑎𝑠𝑠 (4-21)

Where �̇�𝒃𝒊𝒐𝒎𝒂𝒔𝒔 is the mass flow of biomass (kg/h) and 𝑳𝑯𝑽𝒃𝒊𝒐𝒎𝒂𝒔𝒔 is the heat of combustion

of the raw material (MJ/kg).

The lower heating value of the raw material can be calculated from the higher heating value

(HHV) of biomass fuels on dry basis as shown in Eq. (4-22). HHV was determined

experimentally or as a function of the proximate analysis as described in Annex A.

𝐿𝐻𝑉 = 𝐻𝐻𝑉 − ℎ𝑓𝑔 (𝐹𝑤𝑎𝑡𝑒𝑟

𝐹𝑓𝑒𝑒𝑑𝑠𝑡𝑜𝑐𝑘)

(4-22)

Where 𝒉𝒇𝒈 is the entalphy of vaporization of water (approx. 2.26 MJ/Kg) [149]. The ratio

(𝑭𝒘𝒂𝒕𝒆𝒓

𝑭𝒇𝒆𝒆𝒅𝒔𝒕𝒐𝒄𝒌⁄ ) is the relation between the moisture content of biomass and the amount of

feedstock.


4.7.3 Environmental Assessment

Two methodologies were used to evaluate the environmental performance of the hydrogen

production through gasification and dark fermentation in the different proposed scenarios. First,

the Green House Gases (GHG) balance was used to calculate the CO2 emissions and then, the

potential environmental impact (PEI) was evaluated using the Waste Reduction Algorithm

(WAR).

Green House Gases (GHG) balance is the net amount of greenhouse gases emitted by a process

or product, considering all emissions and sinks along a supply chain [150]. The activities and

processes involved in the calculation of the GHG emissions are limited, depending on the

system boundaries. In this study, only the gate-to-gate process boundary (i.e, the transformation

of raw material into products) was considered.

In the other hand, the Waste Reduction Algorithm WAR evaluates processes in terms of

potential environmental impacts. The PEI balance is a quantitative indicator of the

environmental friendliness or unfriendliness of a process [151]. WAR algorithm evaluates the

PEI in terms of eight categories: Human toxicity by ingestion (HTPI), human toxicity by dermal

exposition or inhalation (HTPE), aquatic toxicity potential (ATP), Global warming (GWP),

Ozone depletion potential (ODP), Photochemical oxidation potential (PCOP) and acidification

Potential (AP).


5. Results and Discusion

5.1 Overview

In this chapter the main results of this thesis are presented. First, the results from the

physicochemical characterization of the Pinus Patula (PP) and Coffee Cut-Stems (CCS) in

terms of the chemical composition, proximate and elemental analysis are presented. Then, the

experimental results from the gasification and dark fermentation procedures are evaluated and

discused. The gasification of both raw material involved two components: a normal and a

catalyzed downdraft gasification as described in sections 4.2 and 4.3. Additionally, some

experimental runs were performed in a fluidized-bed gasifier as part of a short research stay

conducted at the Maryland University as can be noticed in Annex B. Subsequently, the results

from the dark fermentation are presented based on the experimental procedure described in

section 4.5. The results from the experimental procedure were used as starting point in the

simulation of the stand-alone and biorefinery scenarios using both raw material. The results

from the simulation procedure were compared in terms of the productivity, economic

profitability, energy efficiency and environmental performance of the evaluated scenarios.

5.2 Physicochemical characterization of lignocellulosic

biomass

Both raw material were characterized in terms of the chemical composition, proximate and

elemental analysis to evaluate the potential use of these residues in the production of hydrogen.

The results related to the characterization of the feedstock have great importance to understand

the mechanisms of the chemical conversion, and further optimization of the lignocellulosic

residues.

There are hundreds of reports on the chemical composition, proximate and elemental analysis of

different biomass. Comparing the data from one author to another, it evidences that the

analytical procedures are different from lab to lab, and a complete description of the procedure

is not provided. For example, many reports do not describe if the samples were pre-extracted


with some solvents before analysis. Others do not follow a published procedure, so comparison

of data is not suitable. Nevertheless, data reported in literature related to the chemical

characterization of PP and CCS was compared with those obtained in this thesis.

- Pinus Patula (PP)

The results from the physicochemical characterization of PP are presented in Table 5-1. The

high hollocellulose content of the raw material suggests the potential use of this residue for the

production of different platforms such as fermentable sugars that can be used to produce

hydrogen through fermentative processes, and synthesis gas to produce directly bioenergy

and/or hydrogen through thermochemical methods. However, due to its high lignin content, this

residue requires a prior pretreatment stage in order to increase the biodegradability of

lignocellulosic biomass to obtain fermentable sugars able to produce bioenergy and biochemical

products. The wood extractives are mainly composed of resins, which are molecules of high

molecular weight (12 – 80 carbon bonds). They are brittle, friable and aromatic substances,

insoluble in water and soluble in alcohol, ether and other organic solvents. The main application

of these oleoresins are in the chemical, pharmaceutical, paper, soap, paint, rubber, adhesives and

insecticide industry [152]. The ash content is the remaining inorganic part of wood that cannot

be combusted. For Pinus Patula, it is below 1%. A high ash content makes the wood material

less desirable as fuel, because a considerable fraction cannot be converted into energy [153].

Table 5-1. Physicochemical characterization of Pinus Patula

Moisture Content (%wt) 9.21

Chemical Composition (%wt dry)

Cellulose 44.78

Hemicellulose 23.75

Lignin 20.22

Extractives 11.0

Ash 0.25

Proximate Analysis (%wt dry)

Volatile Matter 82.14

Fixed Carbon 17.64

Ash 0.23

Elemental Analysis (%wt dry)

Carbon 49.78

Hydrogen 6.03

Oxygen 44.19

HHVa (MJ/kg) 18.48

a HHV = Higher Heating Value


The proximate and elemental analysis are widely used to describe the behavior of the gaseous,

liquid and solid components that are obtained through thermochemical methods. From the

proximate analysis, the amount of gaseous species (volatile matter) can be calculated and the

solid fraction (fixed carbon), mainly char, can be determined. The high volatile matter content

of the PP evidences its high potential to produce bioenergy through the direct conversion of the

raw material. Char and ash are the main solid residues obtained from a thermochemical process,

which constitute the fixed carbon and ash fractions in the proximate analysis; therefore, low

content of char and ash are desirable since greater proportion of wood is converted in bioenergy.

The calorific value of wood is related to its chemical composition and varies between 17 and

20 MJ/kg for oven-dried wood [153]. Major elements contributing to the calorific value are

carbon, hydrogen, nitrogen, oxygen and sulphur. Elemental analysis can be used to describe

biomass fuels, to determine their calorific values [154] and their expected impact on the

environment. Table 5-2 summarizes the physicochemical characterization of some types of

wood reported in literature. It is evidence that the results from every characterization varies

depending on the evaluated conditions and the type of residue.

Table 5-2. Physicochemical characterization of different types of wood reported in literature.

a Chemical composition of the biomass was evaluated in dry and extractives-free basis. b The chemical composition of the wood was not determined by authors

Chemical

Composition (%wt)

Softwood

(Average) [155] Hardwood

(Average) [155]

Block wood [98]

Humidity NRa NRa 12.16

Cellulose 45.8 45.2 NRb

Hemicellulose 24.4 31.3 NRb

Lignin 28.0 21.7 NRb

Extractives NRa NRa NRb

Ash 1.7 2.7 NRb


Volatile Matter 70.0 72.3 83.32

Fixed Carbon 28.1 25.0 14.59

Ash 1.7 2.7 2.09


Carbon 52.1 48.6 46.90

Hydrogen 6.1 6.2 6.07

Oxygen 41.0 41.1 43.99

HHV (MJ/Kg) 20.0 18.8 18.26


- Coffee Cut-Stems (CCS)

Table 5-3 presents the results of the physicochemical characterization of CCS. High

holocellulose (cellulose and hemicellulose) and low lignin content provide a suitable scenario

for the production of fermentable sugars aiming to produce bioenergy and/or biochemicals

because of the high biodegradability of this coffee residue [84]. On the other hand, CCS has

higher ash content than PP, which affects its calorific value. Despite this fact, its energy content

is very high and it can be used directly as fuel in combustion processes or as mentioned before,

in the bioenergy and/or biochemicals production. The behavior of the proximate and elemental

analysis for both raw material (PP and CCS) remains almost unchanged as can be compared in

Table 5-1 and 5-3, whereas their calorific value differ appreciably. There is not enough

information about CCS reported in literature; however, some authors have studied the

production of different bioernergy and/or biochemical from CCS and thus, the characterization

of CCS is performed [82], [156].

Table 5-3. Physicochemical characterization of Coffee Cut-Stems

a HHV = Higher Heating Value

a Chemical composition was not determined by authors

This Thesis Ortega et al., [156]

Moisture Content (%wt) 8.7 11.44

Chemical Composition (%wt dry)

Cellulose 40.39 NRb

Hemicellulose 34.01 NRb

Lignin 10.13 NRb

Extractives 14.18 NRb

Ash 1.27 NRb


Volatile Matter 82.15 83.14

Fixed Carbon 16.78 14.57

Ash 1.07 2.29


Carbon 48.21 51.2

Hydrogen 5.61 5.6

Oxygen 45.81 43.2

HHV (MJ/Kg) 18.25 17.52


5.3 Experimental Procedure

In this section, a detailed description of the results obtained from the gasification and dark

fermentation of both raw material is presented. The gasification procedure involves two

components: First, the gasification was carried out using CCS and PP as raw material and air as

gasifying agent. Subsequently, the results from the normal gasification were compared with the

gasification using an adsorbent (CaO) aiming to improve the hydrogen selectivity in the

synthesis gas. On the other hand, the results from the dark fermentation procedure involve the

production of fermentable sugars from both raw material through different treatment methods

such as acid hydrolysis and enzymatic saccharification, the formation of hydrogen and other

metabolites using different substrate sources i.e. pure sugars (glucose and xylose) and

hydrolysates from the pretreatment methods.

5.3.1 Gasification

The results from the gasification procedure are divided in two sections. First, the concentration

profiles of the generated gaseous species are presented for different experimental runs in the

pilot-scale gasifier. Then, the comparison of the gasification performance without catalyst and

with different catalyst loads is assessed based on the syngas composition.

5.3.1.1 Air Gasification of CCS and PP

- Coffee Cut-Stems (CCS)

Figure 5-1 presents the results from the CCS gasification for three experimental runs: CCS-1,

CCS-2 and CCS-3. It can be evidence that the concentration profiles of the three experimental

runs (CCS-1, CCS-2 and CCS-3) changed throughout the gasification time, especially in the

amount of generated hydrogen, carbon monoxide and carbon dioxide. An average gasification

temperature (850°C) was kept constant in all the experimental procedures. However, the

gasification time varied in each experimental run: CCS-1 was monitored for 90.5 min, CCS-2

for 104.9 min whereas CCS-3 for 64.6 min. The portable gas analyzer is able to detect the

composition of the synthesis gas every 5 seconds, store it and then, plot the composition of the

synthesis gas vs the gasification time (see, Figure 5-1). The difference between the gasification

times from one run to the other is related to the conversion of the wood materials to the

synthesis gas (see, Table 5-4). The highest CCS conversion to syngas (88.2%) was obtained in

the experimental run CCS-2, from which an average hydrogen, carbon monoxide and methane

concentration of 15.6, 14.4 and 3.1% was determined, respectively. However, the highest

hydrogen (18.9%) and carbon monoxide (16.9%) concentration were generated from the

experimental run CCS-1, which had a lower CCS conversion (70.8%). The experimental run


CCS-3 had the lowest biomass conversion to syngas, which it was evidenced in the high amount

of remaining char and ash from the gasification procedure accounting for the 34.7% of the total

CCS fed to the gasifier.

Table 5-4. Global mass balance of the experimental gasification runs using CCS as raw material

Experimental

Run

Conversion to

syngas

CCS-1 70.8%

CCS-2 88.2%

CCS-3 65.5%

Figure 5-1. Concentration profiles of the different gaseous species obtained from the pilot-scale

gasification of CCS.

CCS-1

CCS-2

CCS-3


The most important fuel species in the syngas are hydrogen, carbon monoxide and methane.

High concentration of hydrogen and carbon monoxide were obtained from the downdraft

gasification of CCS using air as gasifying agent as presented in Table 5-5. The hydrogen

content varies from 15 – 20% and the carbon monoxide from 11 – 18%, based on the data

obtained from the three experimental runs. According to this, the generated syngas has a great

energy potential to be used as fuel in a gas engine for the electricity generation because of the

high H2/CO ratio. Despite that the methane content is relative low (2 – 4%), it can contribute to

the relative high calorific value of the synthesis gas as can be observe in Table 5-5. Despite the

high hydrogen content, the yield of syngas is relative low compared to other literature reports

depending on the syngas concentration. Lv et al., [43] evaluated the production of hydrogen-rich

gas using air and oxygen/steam as gasifying agents in a downdraft gasifier. As a result, the

synthesis gas yield between 0.88 and 0.91 Nm3 kg biomass was obtained using air as gasifying

agent and pine wood as feedstock.

Table 5-5. Composition and calorific value of the generated syngas from CCS

Syngas Components Composition (%mol)

Hydrogen 15 -20 %

Carbon Monoxide 11 – 18%

Methane 2 – 4%

Calorific Value (Mj/kg) 4 – 5.3

Gas flux (Nm3/h) 6.77 – 10.37

Gas Yield (Nm3/kg wood) 0.62 – 0.94

- Pinus Patula (PP)

Figure 5-2 presents the concentration profiles of the main gaseous species of the synthesis gas

from the PP gasification. As in the case of the CCS gasification, three experimental runs using

PP as raw material with an average temperature of 850°C were carried out. The gasification

time using PP as raw material was lower than the CCS gasification: PP-1 was monitored for

62.5 min, PP-2 for 47.1 min and PP-3 for 62.6 min. The process performance was evaluated

based on the conversion of PP to syngas and the amount of remaining char and ash (see, Table

5-6). The highest conversion of the three experimental runs was obtained in the PP-1 (74.5%)

and the lowest in the PP-2 (54.5%). The use of PP as raw material for syngas production has a

low conversion compared to the gasification using CCS. Despite the similar trend of the

proximate analysis in both raw material, the amount of remaining char and ash in the PP case is

higher than in the CCS case. Another posible explanation related to the difference in the

gasification time and the wood conversion could be the density of both materials. Hardwood

(CCS) has higher density than softwood (PP); therefore, the produced charcoal lasts longer and


light better. In this sense, the longest gasification times and the highest conversion were

obtained using CCS as raw material.

Table 5-6. Composition and calorific value of the generated syngas from PP

Experimental

Run

Conversion

to Syngas

PP-1 74.5%

PP-2 54.5%

PP-3 68.2%

Figure 5-2. Concentration profiles of different gaseous species obtained from the pilot-scale

gasification of PP.

PP-1

PP-2

PP-3


Moderate hydrogen and carbon monoxide content was obtained from the PP gasification. The

hydrogen concentration varies from 13 – 17% and in the same way, the carbon monoxide from

13 – 16%. When comparing the composition of the PP syngas with that obtained from CCS,

higher hydrogen and carbon monoxide content were obtained from the CCS gasification

because of the high charcoal reactivity in the CCS case, which implies its great potential to be

implementated as fuel in the energy generation. This can be also evidenced in the calorific value

of the generated syngas. Whereas the CCS syngas has an energy content between 4 to 5.3

Mj/kg, PP syngas has a lower energy content ranging between 3.5 and 4.3 Mj/kg as evidenced

in Table 5-7. However, the syngas yield using PP (1.30 Nm3 kg-1 wood) as feedstock is higher

than in the CCS case (0.94 Nm3 kg-1 wood). Despite the syngas yield, there must be a tradeoff

between the heating value and the flux of the generated syngas in order to obtain the highest

energy efficiency in the gasification procedure.

Table 5-7. Composition and calorific value of the generated syngas from CCS

Syngas Components Composition (%mol)

Hydrogen 13 – 17%

Carbon Monoxide 13 – 16%

Methane 2 – 4%

Calorific Value (Mj/kg) 3.5 – 4.3

Gas Flux (Nm3/h) 10.94 – 14.31

Gas Yield (Nm3/kg wood) 0.99 – 1.30

5.3.1.2 Effect of CaO in the downdraft gasification

Tables 5-8 and 5-9 present the results of the CCS and PP gasification using CaO. Different

adsorbent loads were used to evaluate the effect of CaO in the syngas composition as mentioned

previously in section 4.3. Two experimental runs (with 10 and 20%wt. CaO) for CCS and one

(10%wt CaO) for PP were carried out. Higher CaO loads and more PP experimental runs were

not considered in this thesis due to the unavailability of wood chips (both for CCS and PP).

By increasing the CaO load, the hydrogen and carbon dioxide content raises while the carbon

monoxide content is reduced as evidenced in Tables 5-8 and 5-9. The use of CaO as adsorbent

in the biomass gasification improves the hydrogen content in the syngas due to the promotion of

the Water Gas-Shift (WGS) reaction (Eq. 5-1) [106]. This behavior can be also evidenced in the

reduction of the CO to produce more hydrogen in the WGS reaction. Higher content of CO2 was

obtained because of the CO2 release from CaO. This can be explained since the gasification

procedures were carried out at high temperatures (850°C), which is unfavorable for the CO2

absorption, in terms of thermodynamic equilibrium, because of the exothermic nature of CaO

carbonation reaction [157]. In this sense, the reverse reaction (calcination) is favored, which


causes more CO2 content in the synthesis gas [158]. Another drawback of the use of high

temperatures in the air gasification is the reduction in the hydrogen and carbon monoxide

content due to the high reactivity of char with air at a high equivalence ratios (ER) [159]. On the

other hand, the concentrations of CH4 remained constant at different CaO loads. As a result,

quicklime had no apparent enhancing effect on the air gasification of small molecular

hydrocarbons such as methane [159].

𝐶𝑂 + 𝐻2𝑂 ↔ 𝐶𝑂2 + 𝐻2 (5-1)

Table 5-8. Effect of the CaO in the synthesis gas composition from CCS gasification

Syngas Composition (%mol) Without CaO 10%wt. CaO 20%wt. CaO

Hydrogen 13.49 14.52 15.7

Carbon Monoxide 15.98 12.36 10.83

Methane 2.89 2.32 2.85

Carbon Dioxide 11.15 12.69 14.12

Energy Content (Mj/kg) 3.85 3.38 3.5

Table 5-9. Effect of the CaO in the synthesis gas composition from PP gasification

Syngas Composition (%mol) Without CaO 10%wt. CaO

Hydrogen 13.95 15.81

Carbon Monoxide 14.95 13.41

Methane 2.69 3.21

Carbon Dioxide 10.39 13.1

Energy Content (Mj/kg) 3.7 3.94

As can be evidenced from the results in Tables 5-8 and 5-9, the variation in the hydrogen

content from the gasification with and without CaO is relative low for both raw material, which

implies that hydrogen is obtained mainly from the thermal cracking of biomass. Additionally,

increasing the CaO load resulted in improvement of the calorific value of the generated syngas;

however, the difference between the energy content of the syngas in the experimental runs is

relative low. This can be explained since high CaO loads increase the hydrogen content but

decrease the carbon monoxide content, and subsequently, changing the energy content of the

syngas. Based on the previous statements, a novel and more highly efficient catalyst/adsorbent

should be used to obtain higher H2 yields. Although, the implementation of CaO as adsorbent to

increase the hydrogen production using air gasification can improve the economic profitability

of the process because of the high availability and low purchase price of CaO.


5.3.2 Dark Fermentation

The main results of the dark fermentation procedure were divided in two sections: the

pretreatment of both raw material (acid hydrolysis, detoxification and enzymatic

saccharification) and the fermentation of pure sugars and hydrolysates from the pretreatment

stage. Fermentable sugars from the pretreatment stage were quantified in terms of glucose,

xylose, galactose, arabinose, mannose and furfural. Additionally, the detoxification yield and

sugar degradation in the detoxification procedure were calculated.

Sugars yield, gaseous species content and secondary metabolites concentration from the dark

fermentation process were determined based on different analytical methods. Pure sugars

(sucrose, glucose and xylose), acid and enzymatic hydrolysates were used as substrate in the

hydrogen production. The produced hydrogen from the dark fermentation was measured using

the water displacement method and a portable gas analyzer. Secondary metabolites were

quantified using High Performance Liquid Chromatography (HPLC) and the determination of

reducing sugars concentration was performed using the dinitrosalicylic acid (DNS) method.

5.3.2.1 Acid Hydrolysis

One of the main advantages of the dilute-acid hydrolysis is that the hemicellulose fraction of the

raw material can be easily and selectively extracted to obtain a xylose-rich hydrolysate, which

can be used as substrate for different bioconversion processes [107]. Table 5-10 presents the

results from the dilute-acid hydrolysis in terms of the evaluated monosaccharides and furfural.

After the mild pretreatment, the monosaccharides yield in the PP and CCS hydrolysate have no

similar trend. In the PP case, the sugars yield follows the order mannose > glucose > xylose >

galactose > arabinose. On the other hand, the sugars yield for CSS follows the order xylose >

mannose > glucose > galactose > arabinose. High content of hexoses were obtained from the

acid hydrolysis of PP whereas high content of pentoses were obtained from CCS hydrolysis.

Sykes et al., [160] developed the hemicellulose characterization of eight lignocellulosic raw

materials considering softwood, hardwood and agroindustrial residues as raw material.

Softwood (PP) has the potential to produce high amount of glucose, galactose, arabinose and

mannose; meanwhile hardwood (CCS) can be used to obtain high content of glucose and xylose,

mainly. From Table 5-10, it is evidenced that the CCS hydrolysate has the highest xylose

concentration (10.72g/100g substrate) in comparison to the PP hydrolysate (3.44g/100g

substrate), since CCS has higher hemicellulose content and thus, xylose can be obtained as main

fermentable sugar. However, higher hexoses (glucose, mannose and galactose) content were

obtained from the PP hydrolysate due to the low hemicellulose content of the lignocellulosic

material. Small amounts of arabinose were detected in the hydrolysates after dilute-acid


treatment, which indicates that the pentoses were sensitive to the acid hydrolysis conditions and

had been degraded to furfural [109].

Table 5-10. Sugars yield after the dilute-acid hydrolysis

Sample

Glucose

(g/100 g

substrate)

Xylose

(g/100 g

substrate)

Galactose

(g/100 g

substrate)

Arabinose

(g/100 g

substrate)

Mannose

(g/100 g

substrate

Furfural

(g/L)

PP 4.96±0.03 3.44±0.03 2.31±0.04 1.02±0.03 8.55±0.05 0.53±0.01

CCS 2.30±0.07 10.72±0.06 1.44±0.10 0.73±0.1 2.71±0.12 0.55±0.01

Based on the results from the sugars yield of the acid hydrolysis, the monosaccharides

conversion was evaluated considering the potential concentration of fermentable sugars that can

be obtained from both raw material (see, Eq. 4-4). The potential concentration of fermentable

sugars was calculated considering the polymer concentration (cellulose and hemicellulose,

mainly) in the raw material based on Eq. 4-5. Table 5-11 presents the conversion of the main

monosaccharides that constitute the cellulose and hemicellulose matrix in the dilute-acid

hydrolysis of both raw material.

Table 5-11. Monosaccharides conversion in the dilute-acid hydrolysis

Sample

Glucose

Conversion

(%)

Xylose

Conversion

(%)

Galactose

Conversion

(%)

Arabinose

Conversion

(%)

Mannose

Conversion

(%)

PP 19.92±0.13 13.79±0.12 81.52±1.47 85.43±2.57 96.33±0.57

CCS 10.22±0.30 47.73±0.25 35.51±2.57 43.12±5.80 21.36±0.91

The type and amount of reaction byproducts are also important, because some compounds can

limit the viability of hydrolysates as fermentation media. In this sense, special attention must be

taken with the production of furfural, because this byproduct can cause inhibitory or toxic

effects in microorganisms. One of the drawbacks of using sulfuric acid as hydrolysis agent is

the formation of these toxic compounds; therefore, an additional pretreatment method should be

implemented aiming to remove totally or partially these compounds from the hydrolysate.

5.3.2.2 Detoxification

After the dilute-acid treatment, the liquid fraction was submitted to overliming process, which

involves the addition of Ca(OH)2 to remove the produced toxic compounds in the acid

hydrolysis. Table 5-12 presents the yields of monosaccharides and furfural after the overliming

process. The furfural content in the hydrolysate was reduced for both raw material, when

comparing to the results of the dilute-acid hydrolysis (see, Table 5-10). However, the highest

detoxification yield was obtained for CCS hydrolysate (73.3%) in contrast to detoxification


yield of 45.8% in the PP hydrolysate as presented in Table 5-13. Despite the high reduction of

furfural after the overliming process, degradation of sugars was also evidenced. Higher sugars

degradation was presented in the PP hydrolysates than CCS as observed in Table 5-14. Among

the quantified sugars, arabinose and galactose were the monosaccharides that evidenced the

highest degradation yield since they were the sugar fractions with the lowest production yield

from the acid hydrolysis.

Table 5-12. Sugar and furfural content of the hydrolysate after overliming with Ca(OH)2

Sample

Glucose

(g/100 g

substrate)

Xylose

(g/100 g

substrate)

Galactose

(g/100 g

substrate)

Arabinose

(g/100 g

substrate)

Mannose

(g/100 g

substrate)

Furfural

(g/L)

PP 3.93±0.09 2.64±0.07 1.64±0.05 ND 7.42±0.22 0.29±0.01

CCS 2.0±0.01 9.46±0.05 1.18±0.01 0.56±0.0 2.32±0.01 0.15±0.01

Table 5-13. Detoxification yield from PP and CCS hydrolysates

Sample Yield (%)

PP 45.78±0.84

CCS 73.3±0.09

The results obtained in this thesis are in concordance with other detoxification studies of

different lignocellulosic residues. Martinez et al., [161] studied the effect of the Ca(OH)2

addition in the production of ethanol from sugarcane bagasse hydrolysates. Furan compounds

reduction up to 69% and sugar degradation up to 17% were obtained at 60°C, pH 10 and 30

min. Horváth et al., [112] evaluated the effect of overliming in Spruce wood hydrolysates in

order to produce ethanol using S. Cerevisiae. As a result, the reduction of furan compounds was

enhanced to 59% and the sugar degradation was increased up to 15%. In this study, higher pH

value (pH 11) and detoxification time (3 hours), but lower temperature (30°C) were used in the

detoxification procedure, which makes the process uneconomically since longer times are

required to obtain low furan yields.

Table 5-14. Monosaccharides degradation after the detoxification procedure

Sample Glucose (%) Xylose (%) Galactose (%) Arabinose (%) Mannose (%)

PP 20.75±1.65 23.18±1.94 29.09±2.43 100.0±0.0 13.28±2.83

CCS 13.0±3.11 11.96±0.94 18.01±5.78 22.63±11.3 14.41±3.50


5.3.2.3 Enzymatic Saccharification

The solid fraction from the dilute-acid hydrolysis was submitted to the enzymatic

saccharification using Celluclats 1.5L and a multienzyme complex Viscozyme. Table 5-15

presents the monosaccharides yield and the furfural concentration of the PP and CCS

hydrolysates after the enzymatic saccharification. The hydrolysates gave none or very low

yields of pentoses (e.g. xylose) since the main role of the acid pretreatment is to solubilize

hemicellulose from biomass and to make cellulose more accessible for cellulases [162]. Glucose

and mannose have the highest yields from the enzymatic saccharification owing to the fact that

the enzymes used in the procedure were able to degrade the cellulose fraction. On the other

hand, higher content of glucose and mannose was obtained from the CCS hydrolysate in

comparison to PP case. Despite the high lignin content of the CCS (approx. 40%), the acid

pretreatment has a positive effect in the delignification of the raw material, increasing the

available cellulose to be degraded by cellulase enzymes. The hexose sugars from PP have a

relative high yield despite the observation of Soudhamn et al., [162], which showed that the acid

pretreatment did not benefit the subsequent enzymatic hydrolysis of softwood substrates. The

furfural concentration in both hydrolysates was very low since most of the remaining furfural in

the solid fraction, after the acid pretreatment, was washed prior the enzymatic saccharification.

Table 5-15. Sugar and furfural content after the enzymatic hydrolysis

Sample

Glucose

(g/100 g

substrate)

Xylose

(g/100 g

substrate)

Galactose

(g/100 g

substrate)

Mannose

(g/100 g

substrate)

Furfural

(mg/L)

PP 6.77±0.63 ND 0.17±0.04 3.56±0.37 4.91±0.22

CCS 7.56±1.25 0.20±0.01 0.17±0.04 3.51±0.30 3.33±0.45

Normark et al., [109] evaluated the effect of different acid pretreatment conditions in the

enzymatic saccharification of Scott Pine. The glucose yield obtained from the enzymatic

hydrolysis at mild conditions (2%wt H2SO4, 180°C and 6 min) varies from 0.03 to 0.06 g g-1

substrate depending of the evaluated Scott Pine fraction. Mannose was the second highest

hexose fraction that was obtained from the enzymatic hydrolysis with a yield between 0.007 and

0.009 g g-1 substrate. Low yield of pentose fractions (xylose and arabinose) were obtained

because of the hemicellulose solubilization in the acid pretreatment.

The glucose and mannose yield in this thesis are higher than those reported by Normark et al.,

[109]. This can be explained since the acid hydrolysis in this thesis was carried out with a

sulfuric acid concentration of 2%wt., 115°C and a reaction time of 3 hours. In contrast, Norwark

et al., [109] used higher temperature and lesser reaction time. High hydrolysis temperatures can

enhance the formation of toxic compounds due to the decomposition of pentose sugars to furan


compounds. Longer reaction times can also increase the formation of toxic compounds, but the

formation of pentose and hexose fractions is also enhanced. There should be a trade-off between

the temperature and the hydrolysis time in order to obtain the highest concentrations of sugars

and the lowest concentration of toxic compounds. As a consequence, the conditions used in this

thesis were selected.

5.3.2.4 Dark Fermentation

First, the production of the hydrogen and secondary metabolites using pure sugars (sucrose,

glucose and xylose) as substrate was assessed. Then, the behavior of the T.

Thermosaccharolyticum ATCC 7956 using acid and enzymatic hydrolysates (from the previous

pretreatment stages) as substrates in the hydrogen production was evaluated. The main products,

secondary metabolites and sugaras were monitored based on the procedure described in section

4.5.4.

- Pure sugars

From the phenotypic chart, it was evidenced that the T. Thermosaccharolyticum ATCC 7956 is

able to degrade different carbohydrates such as glucose, xylose, arabinose, mannose, galactose

and sucrose. Table 5-16 presents the sugar conversion in a set of dark fermentation experiments

using different substrates as carbon source. Sucrose had the highest sugar consumption, which it

is in agreement with other reports [117]. The microorganism prefered glucose over xylose, since

the dark fermentation using 100% glucose had a sugar conversion of 52%, whereas the

experimental test using a mixture of glucose and xylose (50% - 50%) had a sugar conversion of

35%.

Table 5-16. Sugar consumption of different experimental tests of the dark fermentation

Experimental Sets % Substrate Consumption

Sucrose 100% 90%

Glucose 100% 52%

Glucose – Xylose 75% - 25% 33%



Cao et al., [120] evaluated the performance of the T. Thermosaccharolyticum strain W16 in the

production of hydrogen using as raw material corn stover hydrolysates. Prior to the utilization of

corn stover in the production of hydrogen, various experimental tests using analytical-grade

sugars (glucose, xylose and arabinose) were carried out. When xylose was the carbon source in

the medium, consumption of this sugar was somewhat slower than consumption of glucose.


Glucose and xylose conversion were 73 and 69%, respectively. In contrast, lower conversion of

sugars were obtained using the T. Thermosaccharolyticum ATCC 7956 in this thesis;

nevertheless, the microorganism was able to degrade more easily sucrose and glucose than other

sugars (pentoses).

The composition of the produced gaseous species in the dark fermentation was analyzed

through a portable gas analyzer that was connected directly to the sample instrument. The

composition of the gaseous species from the different experimental tests are presented in Table

5-17. The portable gas analyzer (GASBOARD 3100-P) uses a Thermal Conductivity Detector

(TCD) in order to quantify the concentration of hydrogen in a sample with high accuracy;

however, the equipment was initially design to measure continuous flowing samples (i.e.

gasification) with a minimum flow rate of 1 L min-1. Since the dark fermentation was carried

out in a batch operation regime and the sample was collected with a 60 mL-syringe, the portable

gas analyzer was not able to detect the hydrogen concentration in the sample. There are many

reports of the production of hydrogen using T. Thermosaccharolyticum strains through dark

fermentation of pure sugar and hydrolysates; however, the analytical method used to quantify

the composition of hydrogen in the generated gas is Gas Chromatography equipped with a

thermal conductivity detector (TCD). In most of the cases, GC allows to determine the

concentration of hydrogen in mM (mmol of H2 per L) [47], [115], [119], [120]. Another factor

that may influence the detection of hydrogen from the gaseous mixture is related to the

concentration of this gaseous specie in the generated gas. According to reported data, the

hydrogen production rate (HPR) using T. Thermosaccharolyticum W16 and different substrates

(pure sugars and hydrolysates) varies between 0.25 to 0.31 liters of H2 per liter-hour. As a

comparison, the hydrogen production rate of the commercial strain T. Thermosaccharolyticum

ATCC 7956 was calculated considering a total volume of 60 mL (volume of the syringe used in

the sampling procedure) with a HPR between 0.015 to 0.018 liters of H2 per liter-hour, which it

is lower than the reported HPR for the isolated strain (T. Thermosaccharolyticum W16) and

therefore, the equipment used for the detection of the gaseous species was not able to determine

the composition of hydrogen. Besides, the headspace of the fermentation bottle was filled with

nitrogen to ensure anaerobic conditions; therefore, the hydrogen concentration could be diluted

and thus, its detection was not possible using the portable gas analyzer.

Despite that hydrogen was not detected using the portable gas analyzer, the carbon dioxide

composition from the generated gas was measured, supporting the idea that the microorganism

was able to growth in the fermentation medium, except in the medium containing the mixture

glucose – xylose (25% - 75%) (see, Table 5-17). The carbon dioxide yield of the five evaluated

medium was calculated with the same expression used to determine the hydrogen yield (see,

Eq. (4-6)). It can be evidenced that an increment in the xylose fraction as substrate, the amount


of carbon dioxide in the dark fermentation is enhanced. This can be explain since the

microorganism can also degrade xylose in order to produce higher concentration of carbon

dioxide than using glucose as substrate. However, this behavior does not mean that hydrogen

production is linked to the degradation of xylose by the microorganism, although many authors

have evaluated the production of hydrogen from this substrate [47], [115], [117].

Table 5-17. Concentration of the gaseous species in the fermentation medium

Experimental Sets CO2 Yield (mol

CO2/mol sugar)

Sucrose 100% 0.703

Glucose 100% 0.980

Glucose – Xylose 75% - 25% 1.083

Glucose – Xylose 50% - 50% 1.209

Glucose – Xylose 25% - 75% ND

Based on the results obtained from the gaseous phase, it was decided to evaluate the liquid

fraction of the fermentation it was evidenced that the microorganism was able to produce acetic

acid, lactic acid, ethanol and butanol. Some authors have studied the production of ethanol and

other metabolites (acetic acid, lactic acid and butyric acid) using Thermoanaerobacterium

species. Wang et al., [163] evaluated the production of cellulosic ethanol using a combination of

two strains C. thermocellum and C. thermosaccharolyticum. From the behavior of the C.

thermosaccharolyticum with xylose as carbon source, it was obtained as metabolic products

ethanol (14.5 g L-1), lactic acid (16 g L-1) and acetic acid (10 g L-1). Chimtong et al., [164]

performed the characterization of an isolated thermophilic anaerobic bacterium, T.

thermosaccharolyticum strain NOI-1. As fermentation products, ethanol (19.34 mM), acetic

acid (13.48 mM) and butyric acid (0.33 mM) were obtained. Based on the results presented in

Table 5-18 and that reported in literature, it can be considered that the main metabolic products

from the dark fermentation are acetic acid, lactic acid, ethanol and butyric acid. In this thesis,

the concentration of acetic acid, lactic acid and ethanol in the fermentation media were

measured as observed in Table 5-18. According to the results from the metabolites

quantification by HPLC, it is noteworthy that high concentration of acetic acid was dertermined

in the fermentation broth. In general, the H2 yield is directly influenced by the state of reduction

of the fermented energy source, and tends to be inversely related to the total quantity of the

reduced end products in the fermentation medium [165]. Based on this statement, the hydrogen

production using T. Thermosaccharolyticum ATCC 7956 was reduced due to most of the energy

source was directed to produce acetic acid instead hydrogen. Decreasing the amount of acetic

acid in the fermentation medium, could improve the hydrogen production [166].


Table 5-18. Concentration of secondary metabolites in the fermentation medium

Experimental Sets Acetic Acid (mM) Lactic Acid (mM) Ethanol (mM)

Sucrose 100% 166.2 64.4 4.67

Glucose 100% 483.5 13.6 0

Glucose – Xylose 50% - 50% 222.6 16.1 0

- Hydrolysates

In this section, three carbon sources were evaluated: Two hydrolysates obtained from the

enzymatic saccharification of CCS and PP, and one hydrolysate from the acid hydrolysis of PP.

Table 5-19 presents the conversion of sugars using the T. Thermosaccharolyticum ATCC 7956

in the three evaluated hydrolysates. The substrate conversion in both enzymatic hydrolysates is

very similar; however, the sugars conversion from the acid hydrolysate is relative low compared

to the enzymatic hydrolysates. Despite the detoxification process of the acid hydrolysate, some

toxic compounds could remain in the liquid fraction and thus, they could inhibit the substrate

consumption by the microorganism. On the other hand, the substrate consumption of the

hydrolysates, in this thesis, is lower than those reported by different authors in literature. Ren et

al., [48] evaluated the performance of the T. Thermosaccharolyticum W16 for the production of

biological hydrogen using corn stover hydrolysates after enzymatic saccharification. A substrate

consumption of 60% using corn stover hydrolysate was reached. This result is relative higher

than those obtained in this thesis (58% for PP and 53% for CCS). This can be explained since

the initial substrate concentration of the evaluated hydrolysates were lower than the reported

corn stover hydrolysate. In this thesis, an initial total reducing sugar content of 2.83 g L-1 and

2.57 g L-1 of PP and CCS hydrolysate were used, respectively. In contrast, an initial corn stover

hydrolysate concentration of 9.9 g L-1 was employed in the experimental tests reported by [48].

Cao et al., [120] evaluated the production of hydrogen using the hydrolysate from the acid

hydrolysis pretreatment as carbon source through dark fermentation. An overall substrate

consumption of 82% was obtained, which is higher than that obtained in this thesis (41%).

Table 5-19. Sugar consumption of different experimental tests of the dark fermentation

Experimental Sets % Substrate Consumption

Enzymatic Hydrolysis PP 58%

Enzymatic Hydrolysis CCS 53%

Acid Hydrolysis PP 41%

As mentioned before in the dark fermentation of pure sugars, the hydrogen content in the

generated gas was not detected. However, the carbon dioxide and nitrogen composition were

measured and thus, the CO2 yield was calculated as presented in Table 5-20. The highest CO2

yield was obtained from the CCS enzymatic hydrolysate and the lowest from the PP acid


hydrolysate. These results are in concordance with those presented in Table 5-19, since low

sugar conversion is linked to low substrate uptake by the microorganism and hence, its growth

is inhibited (based on the assumption that CO2 can be linked to the bacteria growth). When

comparing the CO2 yield from the fermentation with pure sugars and hydrolysates, it is

evidenced that high content of carbon dioxide was obtained from the fermentation with pure

sugars (Table 5-17). This can be explained since the formation of carbon dioxide was linked to

the consumption of xylose, and the concentration of xylose in the hydrolysates was very low.

The PP and CCS enzymatic hydrolysates (Table 5-15) are mainly composed of glucose,

whereas the PP acid hydrolysate is composed of mannose, glucose and xylose (Table 5-12).

Table 5-20. Concentration of the gaseous species in the fermentation medium

Experimental Sets CO2 Yield (mol

CO2/mol sugar)

Enzymatic Hydrolysis PP 0.101

Enzymatic Hydrolysis CCS 0.456

Acid Hydrolysis PP 0.049

The same trend of the fermentation with pure sugars was evidenced in the case of the acid and

enzymatic hydrolysates. The concentration of acetic acid from the dark fermentation using the

acid and enzymatic hydrolysates as carbon source was lower than the media containing only

glucose as substrate (see, Table 5-21). Due to the presence of different monosaccharides in the

acid and enzymatic hydrolysates, the performance of the dark fermentation was hindered. This

behavior can be also evidenced in the concentration of lactic acid, which decreases when

hydrolysates are used as carbon source in the dark fermentation.

Table 5-21. Concentration of secondary metabolites in the fermentation medium

Experimental Sets Acetic Acid (mM) Lactic Acid (mM) Ethanol (mM)

Enzymatic Hydrolysis PP 286.29 6.95 0

Enzymatic Hydrolysis CCS 281.64 7.5 0.43

Acid Hydrolysis PP 274.19 5.6 0.43

Despite that the hydrogen was not detected in the dark fermentation procedure, high

concentration of acetic acid was produced in the fermentation broth. It is noteworthy that an

acetic acid concentration of 29.01 g L-1 from the fermentation with pure sugars and a

concentration of 17.18 g L-1 from the fermentation of the enzymatic hydrolysate was obtained in

the experimental procedure. Several authors have estudied the production of acetic acid from the

anaerobic bacteria Clostridium Thermoaceticum using mainly glucose as carbon source.


Schwartz et al., [167] studied the production of acetic acid using the anaerobic bacteria in a pH-

controlled batch fermentation at acidic pH. From the fermentation procedure, acetic acid

concentrations of 20 g L-1 at pH 7 and 15 g L-1 at pH 6 were obtained. Wang et al., [168] studied

also the production of acetic acid using the C. Thermoaceticum. The maximum acetic acid

concentration was 37 g L-1 at pH 7. In comparison to the results of the acetic acid production

using the C. Thermoaceticum, relative high concentration of acetic acid was obtained from the

dark fermentation of different substrates (glucose and hydrolysates) as carbon sources using as

microorganism T. Thermosaccharolyticum ATCC 7956. When using glucose as substrate, the

concentration of acetic acid was 29.01 g L-1, which is relative close to the results reported by

Wang et al., [168]. An average acetic acid concentration between 16.45 to 17.18 g L-1 was

obtained from the dark fermentation using acid and enzymatic hydrolysates as substrate,

respectively. It is a very interesting result since the commercial strain T.

Thermosaccharolyticum ATCC 7956 is able to produce acetic acid in high concentrations which

gives an insight of the implementation of different process schemes to produce other

metabolites using the commercial strain.

5.4 Stand-alone and biorefinery pathways for hydrogen

production: Simulation approach

In this section, the techno-economic, energetic and environmental assessment of the stand-alone

and biorefinery pathways to produce hydrogen through gasification and dark fermentation was

developed. Each feedstock (PP and CCS) was evaluated based on the six proposed scenarios.

Finally, the result of the assessment of each feedstock was compared aiming to determine the

suitable lignocellulosic biomass for hydrogen production through different stand-alone and

biorefinery pathways.

5.4.1 Pinus Patula

The simulation results of the proposed scenarios using Pinus Patula as feedstock are presented

in this section. First, the productivities and yields of the different process schemes were

calculated. Then, the economic profitability of the process considering the PP supply chain was

assessed. Subsequently, the energy performance of the stand-alone and biorefinery schemes

were evaluated and compared. Finally, the environmental assessment of each process in terms

of the GHG balance and the potential environmental impact was performed.


5.4.1.1 Overall performance

From the simulation procedure of the evaluated scenarios, the mass and energy balances were

obtained as presented in Table 5-22. The highest hydrogen productivity and yield were obtained

when the feedstock was used directly to generate synthesis gas, from which hydrogen was

separated. Scenarios 2 and 3 involved the use of fractions of the feedstock aiming to produce

different bioenergy products (ethanol and electricity) under the biorefinery concept. From these

approaches, it is evidenced that the hydrogen productivity decreases since not all raw material is

destined for hydrogen production. In scenario 2, 50% of the synthesis gas was used to generate

electricity through a gas engine, which can be used to supply the internal energy demand of the

process and the surplus can be sold to the national grid. Meanwhile, scenario 3 considers the

production of bioethanol (25% of the feedstock) and also the electricity generation from the

synthesis gas. Despite the low electricity yield, it was enough to supply the energy requirements

of the biorefinery scheme, especially when considering the ethanol separation and purification,

which are high energy consumption processes. It is noteworthy that the bioethanol yield is

higher than other reported yields for different lignocellulosic biomass such as sugarcane bagasse

[147], coffee cut-stems [82], olive stone [169], among others.

Table 5-22. Overall performance of the stand-alone and biorefinery pathways for hydrogen

production using PP as feedstock.

Scenarios Productivitya Yieldsa

Value Units Value Units

Scenario 1 8.54 Ton H2/day 0.040 Ton H2/ton PP


10.54 MW 4,248.5 MJ/ton PP


43,108.44 Liters Ethanol/day 201.16 Liters Ethanol/ton PP

3.13 MW 1,263.63 MJ/ton PP


Scenario 5b 7,540.38 Liters Ethanol/day 35.19 Liters Ethanol/ton wood

Scenario 6c 32.68 Ton Acetic Acid/day 0.153 Ton Acetic Acid/ton PP

16.10 Ton Butyric Acid/day 0.075 Ton Butyric Acid/ton PP


a Calculated based on 214.3 Ton Pinus Patula/day

b Hydrogen productivity has the same value than scenario 4

c Hydrogen and ethanol productivity have the same values than scenario 4 and 5, respectively.

The scenarios that involve the dark fermentation of lignocellulosic biomass presented low

hydrogen yields compared to gasification processes. This behavior is mainly related to the

production regime; thermochemical technologies operate in a continuous mode, whereas

biochemical processes operate in a batch mode and therefore, the overall performance of the

process in terms of productivity is hindered. Ethanol, acetic acid and butyric acid are obtained

as main secondary metabolites in the fermentation broth and in order to provide an added value

to these bioenergy products, the separation of these metabolites from the fermentation broth in

scenario 5 and 6 was considered. The ethanol productivity in scenario 3 is higher than in the

scenario 5. The main reason is related to the fact that the ethanol, in the dark fermentation, is

separated from the fermentation broth as byproduct, which could increase the added-value of

hydrogen. Meanwhile, ethanol is the main product from the metabolic pathway of the

microorganism (Z. mobilis) in the scenario 3 and it is not considered as a by-product of the

process. The separation and valorization of the acetic and butyric acid from the fermentation

broth could enhance the economic profitability of the dark fermentation and reduces the

potential environmental impact that these organic acids could generate if they are emitted

directly into the environment.

5.4.1.2 Techno-economic assessment


In chapter 3, a detailed description of the PP supply chain was developed considering three

departments (Caldas, Antioquia and Cundinamarca) as the potential processing plant locations

in Colombia. Based on this statement, the economic performance of the gasification process to

produce hydrogen as main product was assessed aiming to select the suitable collection and

plant location between the three departments. The main parameters that influence the plant

location are the PP purchase price and the freight cost in each department. The suitable location

was selected based on two economic parameters: the hydrogen production cost and the Net

Present Value (NPV) of the scenario 1 in each department. Figures 5-3 and 5-4 present the

results of the economic assessment of the scenario 1 in terms of the hydrogen production cost

and NPV for each department, respectively. The most outstanding result of both figures is the

effect of the process scale (processing plant capacity) in the hydrogen production cost and NPV

of the process. From figure 5-3, it is evident that as the process scale increases, the hydrogen

production cost decreases until it reaches a point in which the effect of the process scale has no

a direct influence in the hydrogen production cost. Moreover, it is noteworthy that the


processing plant capacity also affects the economic profitability of the process. At low process

scale (biomass availability < 50 MT year-1), the hydrogen production through gasification is not

profitable as evidenced by the negative values of the NPV in all departments (see, Figure 5-4).

However, at high plant capacity (above 50 MT year-1), the process starts to show positive values

of the NPV.

Figure 5-3. Effect of the biorefinery location and plant capacity in the hydrogen production cost

On the other hand, there is no significant difference between the hydrogen production cost and

the NPV of the process between the three selected departments. Nevertheless, Antioquia seems

to be the suitable department for the location of the biorefinery due to the lower hydrogen

production cost and higher NPV. Additionally, Antioquia is the department with the highest PP

cultivated area with a PP yield of 20 m3 Ha-1 year-1 and therefore, the amount of residues that

can be obtained from the transformation of the roundwood is larger compared to other

departments (See, Table 3-2). Despite the higher PP residue purchase price (10.4 USD ton-1) in

comparison to other departments such as Caldas (7.1 USD ton-1), the large availability of

biomass in Antioquia reduces the transportation costs since the collection center can be also

located in the same department and thus, reducing the amount of biomass from other collection

centers.


Figure 5-4. Effect of the biorefinery location and plant capacity in the Net Present Value of the

process

- Economic assessment of the evaluated scenarios

- Gasification

Figure 5-5 presents the effect of the plant capacity in the hydrogen production cost for the three

scenarios that involve gasification. From the sensibility analysis, it is noteworthy that the

hydrogen production cost in the scenario 1, at low process scale, is higher than the scenario 2.

However, at high plant capacity, the production cost is lower than in scenario 2. Scenario 2

involves the production of electricity and hydrogen, where 50% of the synthesis gas is used to

produce electricity, which has a lower added-value in comparison to hydrogen. This behavior

evidences that the production of other energetic products, that have a low added-value, does not

improve the economic performance of the process when the feedstock is distributed among

different processes. However, this behavior was not evidenced in the scenario 3, which produces

hydrogen, ethanol and electricity. The contribution of the ethanol to the economic performance

of the scenario 3 is limited by the production cost and volume. High volume and low production

costs are required in order to implement the ethanol as a byproduct of the biorefinery scheme. In

this sense, the high productivity of ethanol (43,108 L day-1) and low production cost (0.268

USD L-1) improve the profitability of the scenario 3, which have direct influence in the

hydrogen production cost since the ethanol represents the highest contribution to the total sales

of the products in the biorefinery. Moreover, the generated electricity from the synthesis gas, in

the scenario 3, is used to supply the internal energy requirements of the process. From the

scenario 3, a hydrogen production cost between 1,650 and 1,750 USD ton-1 was calculated,

considering a plant capacity of 60 MT year-1. Higher plant capacities were not evaluated

because of the seasonal availability of lignocellulosic biomass.


Figure 5-5. Effect of the plant capacity in the hydrogen production for the three scenarios that

involve gasification

Figure 5-6. Effect of the plant capacity in the NPV for the three gasification scenarios

Based on the results from the hydrogen production cost in each scenario, the NPV was

calculated considering a hydrogen market price of 4,400 USD ton-1 [145]. Figure 5-6 presents

the effect of the plant capacity in the NPV of the three gasification scenarios. At low scale, the

scenarios 1 and 2 have negative values of NPV due to the high hydrogen production cost.

However, high process scale (> 50 MT year-1) could improve the economic performance of the

scenarios. Scenario 3 seems to be the most promising process scheme for the integral production

of hydrogen through gasification under a biorefinery concept, with the highest economic

profitability of the three evaluated scenarios.


Based on the previous statement, the effect of the market price variations for PP and the main

products of the scenario 3 (hydrogen, ethanol and electricity) in the NPV of the biorefinery were

evaluated considering a low scale biorefinery (1.8 ton h-1) and a mid-scale biorefinery (8.9 ton

h1). Figure 5-7 presents the behavior of the NPV in the scenario 3 when a low scale biorefinery

is considered. The parameters that have a strongly influence in the NPV are the ethanol and

hydrogen prices since a reduction of 40% and 80% in the market price, respectively, which may

turn the process non-profitable. However, according to the National Biofuels Federation

(Fedebiocombustibles), this behavior is unlikely since the ethanol market price in the last 6

years has been between 0.54 and 0.78 USD L-1. In the hydrogen case, the scenario is almost the

same, since the hydrogen market price will vary between 9,400 and 4,400 USD ton-1, based on

hydrogen production costs projections [145]. The integrated biorefinery would give negative

NPV values if the ethanol and hydrogen reach a selling price below 0.34 USD L-1 and 1,100

USD ton-1, respectively. PP and electricity market price do not affect considerably the economic

performance of the biorefinery. The ethanol market price has the strongest influence in the NPV

behavior because of the high production volume and thus, it has the highest contribution to the

economic allocation of the scenario.

Figure 5-7. Sensibility analysis of the economic performance of the low scale biorefinery

Figure 5-8 presents the behavior of the NPV in the scenario 3 considering a mid-sacle

biorefinery. It is evidenced that neither the market price of raw material or products have a

significant effect on the economic performance of the biorefinery. The ethanol market price

could affect the NPV of the scenario 3 if a reduction above 90% of the selling price is reached.


Figure 5-8. Sensibility analysis of the economic performance of the mid-scale biorefinery

The total production cost of a process scheme was calculated based on different parameters that

influenced, positive or negative, the economic performance of the process. Raw material costs,

utilities costs, capital depreciation and maintenance costs are the most important parameters that

have the highest contribution to the total production cost as presented in Figure 5-9. Scenario 3

has the highest raw material costs since it involves the ethanol production, which requires

additional reagents such as sulfuric acid and calcium hydroxide for the pretreatment of the

lignocellulosic biomass. On the other hand, scenarios 1 and 2 have similar raw material costs

due to these scenarios involve the transformation of the feedstock to synthesis gas and then, the

production of hydrogen (scenario 1) and electricity (scenario 2). Scenario 1 has the highest

utilities costs since this process scheme does not consider a cogeneration or power generation

plant in order to supply the energy requirements of the synthesis gas conditioning stage prior to

the membrane system, which is a high energy consumption process. Scenario 2 has the highest

contribution to the capital depreciation and maintenance costs. Despite that the scenario 3

requires a higher number of equipment for the ethanol production, scenario 2 has the highest

capital costs. This behavior is associated with the direct equipment cost of the generator used in

the electricity production. Higher synthesis gas flux is destined for the electricity generation in

the scenario 2 in comparison to scenario 3; therefore, large capacity of the generator is required

and thus, its contribution to the capital depreciation and maintenance costs is higher.



gasification scenarios

- Dark fermentation

The production of hydrogen through dark fermentation is not feasible because of the high

production cost that varies between 32,000 and 18,000 USD ton-1, which is a consequence of the

low productivity of hydrogen from batch processes and the hindered exploitation of secondary

metabolites from the fermentation broth. Figure 5-10 presents the effect of the process scale in

the hydrogen production cost of the dark fermentation scenarios. When the separation of ethanol

from the fermentation broth is considered along with the hydrogen production in the scenario 5,

the hydrogen production cost is reduced and the profitability of the process in terms of the NPV

is enhanced. However, due to the low volume of ethanol in comparison to the scenario 3, the

NPV of the scenario 5 is similar to the scenario 4. This gives the insight that it is not enough the

separation of one product from the fermentation broth and leave the remaining secondary

metabolites without any valorization. Based on this statement, the valorization of all the

metabolites in the fermentation broth was considered in the scenario 6. In this scenario, a

hydrogen production cost between 4,000 and 6,200 USD ton-1 and a small economic margin

were calculated. The main drawback to obtain low production costs and positive economic

margins is the fact that high process scales are required (> 80% of the plant capacity) (see,

Figure 5-11), which it is not possible since the biorefinery scheme requires a continuous and

sustainable feedstock supply and due to the seasonal availability of the lignocellulosic biomass,

the production of hydrogen may be hindered.


Figure 5-10. Effect of the plant capacity in the hydrogen production cost of the dark

fermentation scenarios

Based on the previous statement, the effect of the market price variations of PP and main

products of the scenario 6 (hydrogen, ethanol, acetic acid and butyric acid) in the NPV of the

biorefinery was evaluated considering a low scale biorefinery (1.8 ton h-1) and a mid-scale

biorefinery (8.9 ton h1). Figure 5-12 presents the behavior of the NPV in the scenario 6 when a

low scale biorefinery is considered. The integrated production of hydrogen through dark

fermentation is a non-profitable process when the selling price variations in the PP, hydrogen,

ethanol and butyric acid are considered. However, if the acetic acid price increases almost

100%, the hydrogen production can yield a small profit margin. An acetic acid market price of

1,190 USD ton-1 is required in order to turn the process profitable, but this scenario is unlikely

since 75% of the acetic acid is produced by the carbonylation of methanol, which is the cheapest

and highly productive process for the acetic acid production.


Figure 5-11. Effect of the plant capacity in the economic performance of the dark fermentation

scenarios

Figure 5-12. Sensibility analysis of the economic performance of the low scale dark

fermentation biorefinery

Figure 5-13 presents the behavior of the NPV in the scenario 6 considering a mid-scale

biorefinery. By increasing the process scale, the biorefinery profitability is enhanced. As

mentioned in Figure 5-12, the acetic acid market price has the strongest influence in the NPV of

the process scheme. In this case, an increment of 20% in the acetic acid selling price is required

in order to turn the process profitable. Despite this behavior, the profitability of the hydrogen

production through dark fermentation is lower than the gasification scenarios.


Figure 5-13. Sensibility analysis of the economic performance of the mid-scale dark


Figure 5-14. Contribution of the economic parameters in the dark fermentation scenarios

The main economic parameters that have the highest contribution to the total production cost of

hydrogen are the raw material and utilities costs. The scenarios 4 and 5 have the same raw

material costs since both are the same process, but the scenario 5 involves the separation of

ethanol from the fermentation broth, which is the most energy consumption process as

evidenced in Figure 5-14. On the other hand, the scenario 6 has the highest raw material costs

since it involves the production of hydrogen along with the separation of ethanol, acetic and

butyric acid. The separation of acetic and butyric acid from the fermentation broth requires

additional reagents such as polyethylene glycol, iodine solution and sodium sulfate, which

increase the raw material costs in this scenario. Scenario 5 has the highest utilities costs since

the the largest energy energy sink of the process is located in the separation and purification of


ethanol. Scenario 6 involves the separation of ethanol, acetic and butyric acid from the

fermentation broth; nevertheless, the utilities costs are lower than in scenario 5. The Pinch

methodology was used to develop the energy integration of the biorefinery scenarios and based

on the results of the analysis, the heating and cooling requirements of the scenario 5 are higher

than in scenario 6.

5.4.1.3 Energy Analysis

From the simulation procedure, the energy balance of the proposed scenarios was evaluated.

The results from this analysis were represented in Sankey diagrams in order to achieve a better

description of the inputs and outputs involve in the simulation of each scenario. Figures 5-15

and 5-16 describe the energy balance of the gasification and dark fermentation scenarios

through Sankey diagrams, respectively.

From the Sankey diagrams, the inputs of the gasification scenario involve the potential energy

content of the feedstock and the energy requirements of the process. The outputs consider the

main products from the gasification scenarios such as hydrogen, electricity, ethanol and heating

utilities (low and high pressure steam). Moreover, the outputs of the Sankey diagrams represent

the energy losses of the process, which refers to the amount of energy in the inputs that is not

converted into the respective products in each scenario. According to Figure 5-15, the highest

energy losses in the conversion of the feedstock to bioenergy products were determined in the

scenario 1. Scenario 3 has the lowest energy losses due to the integrated production of different

bioenergy products such as hydrogen, ethanol and electricity along with the steam generation,

which could reduce the heating requirements of the process. Additionally, the surplus of

electricity can be sold to the national grid, which could improve the economic profitability of

the biorefinery scheme.

Scenarios that involve the production of hydrogen through dark fermentation have high energy

requirements in terms of electricity and heating utilities (lower and high pressure steam) as

observed in Figure 5-16. The highest energy losses of the three evaluated schemes were

obtained in the scenario 5, which involves the production of hydrogen along with the separation

of ethanol from the fermentation broth. This can be explained due to the high energy

consumption of the ethanol separation and purification stage. Despite that scenario 6 also

involves the separation of ethanol from the fermentation broth, the energy losses were the

lowest for the three dark fermentation scenarios due to the energetic valorization of the

secondary metabolites (acetic and butyric acid).


Figure 5-15. Sankey diagram of the energy balance for the gasification scenarios H2

Scenario 1

Pinus Patula

49.53 MW

Power 4.15 MW11.86 MW H2

LP Steam 0.85 MW

HP Steam 16.96 MW

Energy Losses

24.1 MW

Scenario 2Pinus Patula

49.53 MW

6.02 MW H2

Power 10.54 MW

HP Steam 13.01 MW

Energy Losses

19.96 MW

Scenario 3Pinus Patula

49.53 MW

Energy Losses

9.95 MW

HP Steam 11.62 MW

Ethanol

11.36 MW H2

4.37 MW

Power 12.23 MW


Figure 5-16. Sankey diagram of the energy balance for the dark fermentation scenarios

Based on the results of the simulation procedure, two energy efficiencies were calculated. The

first energy efficiency considers only the hydrogen as the main product of the evaluated

scenarios and the second energy efficiency involves the hydrogen along with the other

bioenergy products from each scenario. Figure 5-17 presents the energy efficiencies of the

evaluated scenarios considering hydrogen as the only energetic product. The highest energy

Scenario 4

Pinus Patula

49.53 MW

HP Steam 5.93 MW

1.21 MW H2

Energy Losses

59.5 MW

LP Steam 4.99 MW

Power 0.26 MW

Scenario 5

Pinus Patula

49.53 MW

1.32 MW H2

Ethanol 1.99 MW

Energy Losses

78.01 MW

HP Steam 13.09 MW

LP Steam 18.43 MW

Power 0.27 MW

Scenario 6

Pinus Patula

49.53 MW

Energy Losses

56.78 MW

Acetic Acid 5.52 MW

Ethanol

2.03 MW H2

1.21 MW

Butyric 5.52 MWHP Steam 3.03 MW

LP Steam 17.34 MW

Power 0.26 MW


efficiency was calculated for the gasification scenarios due to the high hydrogen productivity in

contrast to the dark fermentation scenarios, where the highest energy efficiency was 2.65%.

Among the gasification scenarios, scenario 1 has the highest energy efficiency due to the fact

that the feedstock is directly converted into synthesis gas and subsequently to hydrogen.

However, energy efficiency of the processes is very low when considering only hydrogen as

product, with a maximum value of 23.94%. The process design under the biorefinery concept

can improve the energy efficiency of the process due to the variety of bioenergy products (i.e.

electricity and ethanol) that can be obtained from the evaluated scenarios, as observed in Figure

5-18. When the energy analysis involves all the bioenergy products from each scenario as

represented in the Sankey diagrams (see, Figure 5-15 and 5-16), the overall energy efficiencies

of the scenarios is increased.

Figure 5-17. Energy efficiency considering hydrogen as the only product of the evaluated

scenarios

As evidenced in Figure 5-18, the energy efficiency of the scenario 1 increases from 23.94% to

59.89%, the scenario 2 increases from 12.16% to 59.83% and the scenario 3 from 8.83% to

61.57%. These results highlight the importance of the design of integrated biorefineries to

obtain different added-value products. In this case, the scenario 3 has the highest energy

efficiency due to the integral production of hydrogen, ethanol and electricity, which improve the

overall process efficiency. Regardless the evaluated gasification scenario, the energy efficiency

of the biomass gasification varies between 50 – 60%, which is in agreement with the literature

reports [5], [170]. On the other hand, the valorization of the secondary metabolites in the dark

fermentation improves the energy efficiency of the process. It is evidenced in Figure 5-18 that

the energy efficiency of the dark fermentation increases from 2.44%, considering only hydrogen

as main product in the scenario 4, to 27.01% in the scenario 6 where the hydrogen production

along with the separation of ethanol, acetic and butyric acid were considered. Ruggeri et al.,

[171] evaluated the production of energy from the dark anaerobic fermentation considering the


net energy balance aiming to determine when a process is energetically sustainable. From the

results, it is suggested the possibility to recover the fatty volatile acids from the fermentation

broth and giving them an energy valorization in order to enhance not only the energy efficiency,

but also the economic performance of the dark fermentation.

Figure 5-18. Energy efficiency considering all the possible products from the evaluated

scenarios

A process aimed at producing energy needs to produce more energy than the energy necessary

to run the process itself in order to be energetically sustainable [171]. Based on this statement,

the Net Energy Value (NEV) was used as key parameter in the determination of the net energy

balance between the energy expended to produce hydrogen and other by-products, and the

amount of energy gained from the bioenergy products. Figure 5-19 presents the net energy

balance of the evaluated scenarios. At first sight, the gasification scenarios have positive values

of NEV that evidences the high energy content in the bioenergy products in comparison to the

required energy to produce them. In contrast, dark fermentation scenarios have negative values

of NEV; however, when the valorization of the secondary metabolites was considered, the net

energy balance almost reaches zero. Scenario 3 has the highest NEV value because of the

integrated production of different bioenergy products as presented in Figure 5-15. The worst

scenario, in terms of the energy sustainability, is the scenario 5 because of the high energy

requirements in the separation and purification of ethanol. Regardless that scenario 6 also

involves the ethanol purification along with the acetic and butyric acid separation, the scenario 6

has the lowest NEV due to the valorization of the secondary metabolites in the fermentation

broth.


Figure 5-19. Net energy balance of the evaluated scenarios

5.4.1.4 Environmental Assessment

The main emissions generated from thermochemical processes are related to the gaseous species

that are emitted directly into the atmosphere. In this sense, the GHG balance was used as

methodology in the determination of the CO2 emissions from the gasification scenarios. Figure

5-20 presents the emissions of kilogram CO2-equivalent per kilogram of feedstock in the

gasification and dark fermentation scenarios. All the evaluated scenarios has negative values,

which gives an insight of the low CO2 emissions and thus, the mitigation of these emissions.

However, the gasification scenarios evidence the lowest CO2 mitigation potential because of the

emissions of carbon monoxide from the hydrogen purification stage and the flue gas from the

electricity generation. Scenario 3 has the lowest CO2 emissions, which can be explained since a

fraction of the feedstock is intended in the production of ethanol instead synthesis gas, reducing

notoriously the CO2 emissions of the biorefinery scheme.

On the other hand, GHG balance has no significant effect on the dark fermentation scenarios as

evidenced in Figure 5-20 since the main residues from the fermentation scenarios are: solid

wastes (cellulose, hemicellulose, lignin and ash), sulfuric acid, gypsum and volatile fatty acids,

which could affect not only the emissions into the atmosphere, but also emissions into water and

land sources. In this sense, other environmental impact categories were evaluated for the dark

fermentation scenarios such as acidification, photochemical oxidation and human toxicity using

the Waste Reduction Algorithm (WAR) that evaluates the potential environmental impact of the

processes if the outlet streams were submitted directly into the atmosphere. As mentioned

before in Chapter 3, the software evaluates the process in eight impact categories; however,

three categories were selected to evaluate the dark fermentation scenarios: human toxicity by


dermal exposition or inhalation (HTPE), photochemical oxidation potential (PCOP) and

terrestrial toxicity potential (TTP). Figure 5-21 presents the potential environmental impact of

the dark fermentation scenarios based on the three selected impact categories. Due to the

valorization of the secondary metabolites from the fermentation broth, the potential

environmental impact of the dark fermentation scenarios decrease from the scenario 3 to 6.

Figure 5-20. GHG balance of the evaluated scenarios

The impact categories that have the highest contribution to the total potential environmental

impact are TTP and PCOP. The lethal-dose that produce death in 50% of rats by oral ingestion

(LD50) is used as an estimate for the TTP [172]. The LD50 for ethanol is 7,060 mg/kg, which is

the highest dose in comparison to the other by-products from the fermentation broth: acetic acid

and butyric acid with LD50 of 3310 and 2000 mg/kg, respectively. As a consequence, the

separation of ethanol and the valorization of the other secondary metabolites in the scenario 6

reduce the TTP in comparison to the scenario 1. The PCOP or smog formation potential is

determined by comparing the rate at which a unit mass of chemical reacts with hydroxyl radical

(OH·) to the rate at which a unit mass of ethylene reacts with OH· [172]. The main components

involve in the smog formation are the volatile organic compounds (VOC’s), which are

categorized in alkanes, olefins, alkines, aromates, alcohols, aldehydes and hydrocarbons.

Ethanol and acetic acid are some of the volatile organic compounds that are included in the dark

fermentation scenarios; therefore, their contribution to the environmental potential impact is

reflected in scenario 4, where their valorization is not considered. Ethanol seems to be the

component that has the highest contribution to the PCOP based on the PEI reduction from

scenario 4 to 5, which is approximately 64%. In the scenario 6, this behavior is more evident

due to a reduction in the PCOP of 99.6% from the scenario 4.


Figure 5-21. Environmental assessment of the dark fermentation scenarios

5.4.2 Coffee Cut-Stems (CCS)

In this section, techno-economic, energetic and environmental assessment of the hydrogen

production through gasification and dark fermentation using CCS as feedstock was developed.

First, the overall performance of the proposed scenarios in terms of the productivity and yields

of each process was evaluated. Subsequently, the selection of the suitable plant location based

on the economic performance of each scenario was carried out. Then, the selected plant location

was used in the economic assessment of the proposed scenarios. The energy analysis was

carried out considering the energy efficiency and the Net Energy Value (NEV) of each scenario.

Finally, GHG emissions and the Potential Environmental Impact (PEI) were used as key

parameters in the environmental assessment of the evaluated scenarios.

5.4.2.1 Overall Performance

Among the gasification scenarios, the scenario 1 has the highest hydrogen production yield

because of the direct use of the generated synthesis gas to produce hydrogen (see, Table 5-23).

Despite the low productivity of the other two gasification scenarios, high yields (> 0.038 ton H2

ton-1 CCS) are obtained in comparison to the dark fermentation scenarios (0.005 ton H2 ton-1

CCS). Due to the batch operation regime of the dark fermentation processes, low productivities

and yields were calculated. Electricity is generated as by-product in the scenarios 2 and 3 taking

advantage of the high energy content of the synthesis gas. The highest electricity generation is

evidenced in the scenario 2 due to the use of 50% of the generated syngas in the CCS

gasification in the electricity generation. Bioethanol was evaluated as one of the by-products

from the biorefinery scheme due to the high lignocellulosic content in the CCS. Scenario 3


involves the production of ethanol along with hydrogen and electricity. Higher bioethanol yields

were determined from the gasification scenarios in comparison to the dark fermentation

scenarios. A yield of 202.21 L ethanol ton-1 CCS was calculated in the scenario 3, whereas a

yield of 47.19 L ethanol ton-1 CCS was obtained in the scenarios 5 and 6. The main results from

the overall performance of the dark fermentation scenarios are related to the fact that the

valorization of the different by-products from the fermentation broth could improve the

economic profitability of the process.

Table 5-23. Productivity and yields of the CCS scenarios

Scenarios Productivitya Yieldsa

Value Units Value Units

Scenario 1 35.76 Ton H2/day 0.106 Ton H2/ton CCS


13.04 MW 3,325.98 MJ/ton CCS


68,490.34 Liters Ethanol/day 202.21 Liters Ethanol/ton CCS

9.00 MW 2,294.84 MJ/ton CCS


Scenario 5b 15,983.91 Liters Ethanol/day 47.19 Liters Ethanol/ton CCS

Scenario 6c 57.54 Ton Acetic Acid/day 0.170 Ton Acetic Acid/ton CCS

28.34 Ton Butyric Acid/day 0.084 Ton Butyric Acid/ton CCS

a Calculated based on 338.70 Ton CCS/day b Hydrogen productivity has the same value than scenario 4 c Hydrogen and ethanol productivity have the same values than scenario 5

5.4.2.2 Techno-economic assessment


In chapter 3, a detailed description of the CCS scenario in Colombia was performed considering

three departments (Huila, Antioquia and Caldas) as the largest coffee producers. According to

this, the economic performance of the gasification process (scenario 1) to produce hydrogen as

main product was evaluated in order to select the suitable collection and plant location between

the three departments. In contrast to the PP case, the main parameter that influences the location

of the processing plant is the transportation cost. The CCS purchase price was not considered as

a key parameter in the economic profitability of the process since the coffee market price is

regulated by the Federación Nacional de Cafeteros (FNC) and, at national level, this price is


stable with slight changes from region to region. Hence, the CCS purchase price has no

significant variation between the three selected departments. However, these changes have not

significant effect in the economic viability of the process. Therefore, the coffee price was not

considered as a parameter that influences the location of the processing plant, but an average

coffee price was selected aiming to determine the CCS purchase price as mentioned in Chapter

3. From the three selected departments, Huila has the largest coffee plantations per hectare

(154,980 Ha), the highest coffee yield per hectare (1.2 ton Ha-1) and thus, the highest CCS

production (109,688 Ton year-1). For this reason, Neiva (capital city of Huila) was selected as

the processing plant location due to the high availability of raw material in this department,

which reduces the raw material requirements from other collection centers (Antioquia and

Caldas).

- Economic assessment of the evaluated scenarios

- Gasification

Figure 5-22 presents the effect of the plant capacity in the hydrogen production cost of the

gasification scenarios. Low process scales involve higher hydrogen production costs in

comparison to high process scales that involve lower hydrogen production costs. At low plant

capacity, the hydrogen cost in the scenario 1 is higher than in the scenario 2; however, the

hydrogen production cost is lower in the scenario 1 than in scenario 2 at high plant capacities.

This can be explained since 50% of the generated synthesis gas is intended for the production of

electricity, and the remaining is submitted to the hydrogen separation stage. Due to the low

added-value of the electricity in comparison to hydrogen, the economic profitability of the

scenario 2 decreases at high plant capacities. Due to the integral production of different by-

products (hydrogen, electricity and ethanol) under a biorefinery scheme, scenario 3 has the

lowest hydrogen production cost (1,702.4 USD ton-1).

Figure 5-22. Effect of the plant capacity in the hydrogen production cost of the gasification

scenarios.


This behavior is reflected in the economic profitability of the scenario 3 as presented in Figure

5-23. Despite that scenario 3 has the lowest hydrogen production cost, it is noteworthy that the

economic profitability of the scenario 1 is relative high; however, the implementation of an

integrated biorefinery could improve even more the feasibility of the process, as in scenario 3.

Figure 5-23. Effect of the plant capacity in the economic profitability of the gasification

scenarios

Despite the low process scale, the economic profitability of the integrated production of

hydrogen in the scenario 3 is relative high, as observed in Figures 5-24 and 5-25. The effect of

the market price variations of CCS and main product (hydrogen, ethanol and electricity) in the

NPV of the biorefinery was evaluated considering a low scale biorefinery (2.82 ton h-1) and

mid-scale biorefinery (14.1 ton h-1). The only parameter that may adversely affect the economic

profitability of the scenario 3 is the ethanol market price. However, an ethanol selling price

below 0.17 USD L-1 (a reduction of above 60% of the market price) is required in order to turn

the process non-profitable. Based on the current ethanol selling price, this behavior is very

difficult to be evidenced.

Figure 5-24. Sensibility analysis of the economic performance of the low-scale gasification

biorefinery



biorefinery. It is evidenced that neither the raw material and products mrket price has a

significant effect on the economic profitability of the biorefinery. The ethanol market price has

the greatest influence in the NPV of the biorefinery; however, a reduction above 80% of the

ethanol selling price is required to turn the process non-profitable.

Figure 5-25. Sensibility analysis of the economic performance of the mid-scale gasification

biorefinery

The economic parameters that influence the most the total production cost of the gasification

scenarios are the raw material costs, utilities costs and capital depreciation (see, Figure 5-26).

Scenarios 1 and 2 have similar raw material costs since the processes involve the same amount

of reagents. In contrast, the scenario 3 requires additional reagents such as sulfuric acid and

calcium hydroxide for the raw material pretreatment due to the production of ethanol. The

energy requirements of the gasification scenarios are related to heating utilities (steam) and

electricity. Scenario 1 has the highest utilities costs because of the energy needs in the hydrogen

separation and purification. In contrast, the utilities costs of the scenario 2 are lower than in

scenario 1 due to the integrated production of electricity from the synthesis gas in the scenario 2

that would help to meet the energy requirements of the process and the surplus can be sold to

the national grid. The capital depreciation costs were calculated based on the basic sizing of the

equipment in each scenario. The scenario 2 and 3 have the highest capital depreciation costs,

which are related to the high direct costs of the electricity generator (scenario 2) and the

additional costs of the ethanol processing plant (scenario 3).



gasification scenarios

- Dark Fermentation

The same behavior presented in the PP case for the economic profitability of the hydrogen

production through dark fermentation is evidenced in the CCS case. Figures 5-27 and 5-28

present the effect of the process scale in the hydrogen production cost and economic

profitability of the dark fermentation scenarios. It is evidenced that the valorization of secondary

metabolites from the fermentation broth reduces the hydrogen production costs and increases

the profitability of the process. The lowest hydrogen production cost was calculated for the

scenario 3 (4,861.45 USD ton-1) along with the production of ethanol (952.24 USD ton-1), acetic

acid (657.4 USD ton-1) and butyric acid (220.98 USD ton-1). Despite the valorization of these

by-products, the NPV is negative due to the high raw material and utilities costs in comparison

to the main products profit margins. It is noteworthy that the process scale has a different

influence in the economic profitability of the dark fermentation in comparison to the

gasification scenarios. The economic profitability of the gasification scenarios increases when

the process scale increases (see, Figure 5-23). In contrast, the NPV of the dark fermentation

scenarios reduces when the process scale increases. This behavior of the NPV in the dark

fermentation scenarios is strongly influenced by the valorization of the secondary metabolites

and the utilities costs.


Figure 5-27. Effect of the plant capacity in the hydrogen production cost in the dark

fermentation scenarios

Figure 5-28. Effect of the plant capacity in the NPV of the dark fermentation scenarios

Based on the previous statement, the effect of the market price variations of CCS and main

products of the scenario 6 (hydrogen, ethanol, acetic acid and butyric acid) in the NPV of the

biorefinery was evaluated considering a low scale biorefinery (2.82 ton h-1) and a mid-scale

biorefinery (14.1 ton h1). Figure 5-29 presents the behavior of the NPV in the scenario 6 when a

low scale biorefinery is considered. The integrated production of hydrogen through dark

fermentation is a non-profitable process when the selling price variations in the CCS, hydrogen,

ethanol and butyric acid are considered.


Figure 5-29. Sensibility analysis of the economic profitability of the low scale dark


Figure 5-30. Sensibility analysis of the economic performance of the mid-scale dark



biorefinery. The acetic acid market price has the strongest influence in the NPV of the process

scheme. However, an almost 100% increment in the acetic acid market price is required in order

to yield a small profit margin in the hydrogen production through dark fermentation. An acetic

acid market price of 1,190 USD ton-1 is required in order to turn the process profitable, but this

scenario is unlikely since 75% of the acetic acid is produced by the carbonylation of methanol,

which is the cheapest and highly productive process for the acetic acid production.


Figure 5-31. Contribution of the main economic parameters to the total production costs of the

dark fermentation scenarios.

Figure 5-31 presents the influence of the economic parameters in the total production costs of

the dark fermentation scenarios. Scenario 6 involve the separation and valorization of the

metabolites in the fermentation broth, which is reflected in the low negative values of NPV. The

highest utilities costs were determined in the scenario 5 because of the high energy requirements

in the separation and purification of ethanol and thus, the economic profitability of the scenario

5 is lower than in scenario 4, which only involves the production of hydrogen. Scenario 6 has

the highest raw material costs due to the additional reagents used in the formation of the two

aqueous phase system in the separation of acetic and butyric acid. Moreover, the separation of

acetic acid from butyric acid increases the capital costs of the scenario.

5.4.2.3 Energy Analysis

The net energy balance of the proposed scenarios from the simulation procedure were described

using Sankey diagrams as presented in Figures 5-32 and 5-33. The inputs of the Sankey

diagram are related to the potential energy content of the feedstock and energy requirements.

The main products from each scenario along with other by-products such as steam and

electricity are the outputs of the Sankey diagrams. Additionally, the energy losses of the

process, based on the energy balance, were also described in these diagrams. The highest energy

exploitation of the gasification scenarios was obtained in the scenario 3 due to the integral

production of different bioenergy products under the biorefinery scheme. This behavior cannot

be evidenced in the dark fermentation scenarios due to the high energy requirements of the

scenarios that involve the separation and purification of the secondary metabolites from the

fermentation broth.


Figure 5-32. Sankey diagrams for the gasification scenarios

Scenario 1

Coffee Cut-Stems

78.29 MW

Power 6.73 MW19.54 MW H2

LP Steam 2.73 MW

HP Steam 26.32 MW

Energy Losses

36.43 MW

Scenario 2Coffee Cut-Stems

78.29 MW

9.77 MW H2

Power 13.04 MW

HP Steam 22.76 MW

Energy Losses

32.72 MW

Scenario 3Coffee Cut-Stems

78.29 MW

Energy Losses

4.29 MW

HP Steam 43.91 MW

Ethanol

18.05 MW H2

6.76 MW

Power 5.28 MW


Figure 5-33. Sankey diagrams for the dark fermentation scenarios

As mentioned in the PP case, the energy efficiency of the scenarios was evaluated based on two

criteria: the energy efficiency only considering hydrogen as the main product of each scenario

and the energy efficiency considering all the bioenergy products. The energy efficiency of the

evaluated scenarios considering hydrogen as the only product is presented in Figure 5-35. The

gasification scenarios have the highest energy efficiencies due to the high productivity of

hydrogen in comparison to the dark fermentation scenarios, from which an energy efficiency up

Scenario 4

Coffee Cut-Stems

78.29 MW

HP Steam 13.77 MW

2.13 MW H2

Energy Losses

106.5 MW

LP Steam 16.24 MW

Power 0.29 MW

Scenario 5

Coffee Cut-Stems

78.29 MW

2.8 MW H2

Ethanol 3.97 MW

Energy Losses

151.7 MW

HP Steam 29.93 MW

LP Steam 49.90 MW

Power 0.32 MW

Scenario 6

Coffee Cut-Stems

78.29 MW

Energy Losses

113.51 MW

Acetic Acid 9.72 MW

Ethanol

4.18 MW H2

2.13 MW

Butyric 8.13 MWHP Steam 11.88 MW

LP Steam 47.21 MW

Power 0.29 MW


to 3.58% was reached. Among the gasification schemes, the scenario 1 has the highest energy

efficiency due to the direct production of hydrogen from the synthesis gas. Nevertheless, the

energy efficiency of the gasification and dark fermentation scenarios is very low when

considering only hydrogen as main product.

Figure 5-34. Energy efficiency of the evaluated scenarios considering only hydrogen as product

Therefore, the energy efficiency of the scenarios taking into account all the bioenergy products

was calculated and the results are presented in Figure 5-36. The energy efficiency of the

scenario 1 was improved from 24.96% to 62.06% and the scenario 2 from 12.48% to 58.20%,

whereas the energy efficiency of the scenario 3 reached 94% due to the integrated production of

hydrogen, ethanol, electricity and steam under the biorefinery scheme (see, Figure 5-33). On

the other hand, the efficiency of the dark fermentation scenarios was improved when the

valorization of the secondary metabolites was considered, reaching an energy efficiency of

30.86% in the scenario 6.

Figure 5-35. Energy efficiency of the evaluated scenarios considering all the bioenergy

products


Despite the energy efficiency, the relation between the required energy in a process scheme and

the amount of produced energy from the main products can be also used as a decision criteria of

the sustainability of different process schemes. In this sense, the Net Energy Value (NEV) was

used along with the energy efficiency as key parameters in the assessment of the sustainability

of the proposed scenarios. Figure 5-37 presents the net energy balance of the evaluated

scenarios. Positive NEV were obtained from the gasification scenarios, which is related to the

high valorization of the bioenergy products in comparison to the energy requirements of the

process. In contrast, the dark fermentation scenarios have negative NEV because of the high

energy requirements as shown previously in the Sankey diagrams (see, Figure 5-34). The

highest NEV of the gasification scenarios was calculated for the scenario 3 due to the lower

energy losses and the production of different bioenergy products. The worst scenario, from the

energetic point of view, is the scenario 5 that involves the separation and purification of ethanol.

From these results, it is noteworthy that the energy efficiency of a process is highly influenced

by the productivity of the main bioenergy products, and since the dark fermentation scenarios

have lower productivities, the sustainability of these schemes cannot be accomplished.

Figure 5-36. Net energy balance of the evaluated scenarios

5.4.2.4 Environmental Assessment

From the evaluation of the GHG emissions and the potential environmental impact (PEI) using

the WAR algorithm, it was evidenced a similar trend as in the PP case. The scenarios 2 and 3

have the highest GHG emissions due to the generation of electricity, which is related to the flue

gas realease into the atmosphere after the combustion of the synthesis gas. Since the highest

synthesis gas fractions is intended for the production of electricity in the scenario 2, the

emissions of the flue gas have a greater environmental potential impact than scenario 3. This


flue gas has a strongly influence in the Global Warming Potential (GWP) and thus, in the

environmental performance of the gasification scenarios as observed in Figure 5-38. The effect

of the secondary metabolites from the fermentation broth in the GHG balance was not

evidenced since the main residues from the fermentation are compounds such as sulfuric acid,

gypsum, ethanol and secondary metabolites, which should be analyzed based on other impact

categories that reflect their environmental impact. In this sense, the Waste Reduction algorithm

(WAR) was used in the evaluation of the environmental performance of the dark fermentation

scenarios based on different impact categories, being the most important: human toxicity by

dermal exposition or inhalation (HTPE), photochemical oxidation potential (PCOP) and

terrestrial toxicity potential (TTP). The highest potential environmental impact (PEI) was

calculated for the scenario 4; however, the valorization of the secondary metabolites improved

the environmental efficiency of the dark fermentation scenarios; therefore, the PEI of the

scenario 6 is lower than scenarios 4 and 5 (see, Figure 5-39). The impact category with the

highest contribution to the total environmental impact was the TTP, which is related to the final

disposition of ethanol and the remaining secondary metabolites based on the lethal-dose of each

component. The LD50 for ethanol is 7,060 mg/kg which is the highest dose in comparison to

other by-products from the fermentation broth: acetic acid and butyric acid with LD50 of 3310

and 2000 mg/kg, respectively. For this reason, the valorization of the secondary metabolites

reduces the TTP impact from the scenario 5 to scenario 6. The PCOP is influenced by the

presence of ethanol, acetic and butyric acid in the fermentation broth, which in the presence of

UV radiation, produce smog. Due to the valorization of VFA’s from the fermentation broth, the

smog formation potential is reduced from the scenario 4 to scenario 6.

Figure 5-37. GHG balance of the gasification and dark fermentation scenarios


Figure 5-38. Environmental Potential Impact (PEI) of the dark fermentation scenarios


6. General comparison of stand-alone and

biorefinery pathways for hydrogen production

One of the main purposes of this thesis was to stablish if the production of hydrogen in a stand-

alone way from lignocellulosic biomass can be a feasible route for future industrial production

levels. In this way the results from the techno-economic assessment of stand-alone and

biorefinery ways to produce hydrogen evidenced that the production of a product portfolio from

different fractions of the feedstock or the valorization of by-products enhanced the profitability

of the process schemes.

In the gasification scenarios, the selected products in the biorefinery schemes influenced the

NPV of the process. In this sense, low added-value products (e.i. electricity) do not improve the

economic performance of the evaluated scenarios. In contrast, high-volume production of

ethanol enhanced the NPV of the biorefinery scheme and thus, reduced the hydrogen production

costs (see, Figure 5-7 and 5-24). When considering only hydrogen as main product, the stand-

alone pathway had the highest energy efficiency of the evaluated scenarios since high

productivities were obtained (see, Figures 5-18 and 5-35). Nevertheless, when the integral

production of different bioenergy products was considered, the energy efficiency of the

biorefinery schemes increased up to 50% in the PP case and 90% in the CCS case (see, Figures

5-19 and 5-36). In contrast, the GHG emissions of the stand-alone production of hydrogen

through gasification were lower than the biorefinery schemes because of the generated

emissions from the combustion of the syngas to produce electricity (Scenario 2) and the energy

requirements (mainly, steam) in the bioethanol production (Scenario 3) (see, Figures 5-21 and

5-38).

On the other hand, the valorization of by-products from the dark fermentation reduced the

hydrogen production costs and increased the NPV of the integrated biorefinery scheme due to

the additional profits from the sale of the secondary metabolites (see, Figures 5-12 and 5-29).

The energy efficiency of the dark fermentation scenarios was very low; however, the high


energy content of the secondary metabolites, especially ethanol, and their valorization in the

biorefinery scenarios increased the energy efficiency of the process from approximately 2%

(stand-alone way) to 30% (see, Figures 5-19 and 5-36). Moreover, the disposal of the

secondary metabolites had the highest contribution to the total environmental impact of the dark

fermentation; although their valorization in the biorefinery scheme mitigated the environmental

impact up to 90% (see, Figures 5-22 and 5-39).

So it is very understandable that the stand-alone ways for hydrogen production are not yet the

solution, and different approaches should be included such as the integrated biorefinery

pathways. There are few information reported in literature related to the transformation of

biomass in hydrogen, especially using biochemical methods. Some author have evaluated the

economic assessment of the hydrogen production through thermochemical pathways

(especially, gasification) using biomass. Bowen et al., [173] evaluated the production of

hydrogen from three lignocellulosic residues: sugarcane bagasse, switchgrass and nutshell at

different process scales. From the economic assessment, high process scale improve the

hydrogen production cost. For bagasse with a flowrate between 400 to 1600 dry tonnes day-1,

the hydrogen production costs range from 0.92 USD kg-1 (high scale) to 1.23 USD kg-1 (low

scale). Parthasarathy et al., [5] analyzed the hydrogen production costs of different technologies

from thermochemical to biochemical methods. A hydrogen production cost of 5.52 USD kg-1

was calculated for the biochemical methods. The authors highlight the necessity to study the

effect of the production scale and its influence in the hydrogen production costs and the energy

efficiency of the process.


7. Conclusions

The results of this thesis revealed the potential use of lignocellulosic residues (Pinus Patula and

Coffee Cut-Stems) for the production of bioenergy through thermochemical (gasification) and

biochemical (dark fermentation) methods. Air gasification seems to be a promising method for

the production of bioenergy in the Non-Interconnected Zones (NIZ) in Colombia due to its

technological maturity and high productivity. The hydrogen content in the gasification

procedure was improved using CaO, which gives an insight of the potential use of this cheap

and high available adsorbent in the production of a high energy content syngas. The dark

fermentation seems to be a very interesting process for the production of a multiproduct

portfolio; however, its potential application is hindered by its productivity and batch operation

regime. The commercial strain T. Thermosaccharolitycum ATCC 7956 did not report

production of hydrogen; however, high concentration of acetic acid was determined in the

fermentation broth which it is an interesting result for the implementation of different process

schemes to produce other metabolites using this commercial strain.

Pinus Patula (PP) and Coffee Cut-Stems (CCS) are two of the most available wood residues in

Colombia that can be used in the production of bioenergy because of their high lignocellulosic

content. Colombia is considered as a country suitable for forestry and agriculture due to its soil

conditions, weather, geographical location, among other factors. PP is the most cultivated

reforestation specie in Colombia with high productivity and easy adaptation to different climatic

and geographical conditions. On the other hand, Colombia is the fourth largest coffee producer

in the world and thus, the main wood residue (CCS) of the crop is highly available after the

“Zoqueo” process every 5-6 years. Despite the high availability of these wood residues, their

supply chain is not well stablished in Colombia. The residues of the processing of furniture,

pulp and paper, and ply wood are not well exploited since they are used directly in combustion

processes for cooking and heating. Similar trend is observed in the CCS case, where the wood

residue is left in the ground or use by the farmers for heating and cooking after the “Zoqueo”.


The high availability and lignocellulosic content of these wood residues evidence their high

energy potential in the production of bioenergy multiproduct portfolio.

The stand-alone and biorefinery pathways for hydrogen production were evaluated in order to

determine the most important parameters that influence the economic, energetic and

environmental performance of the evaluated scenarios. Parameters such as the biomass supply

chain, products portfolio and the process scale are the most important variables that must be

considered to improve the profitability of the hydrogen production. Thermochemical methods

such as a gasification have the highest economic yields and energy efficiencies; nevertheless,

the implementation of this method in a biorefinery approach can further increase the

profitability of the process due to the wide range of products that can be obtained from the same

raw material. For both raw materials, the best scenario for the hydrogen production through

gasification was the scenario 3, which considers the production of hydrogen along with ethanol

and electricity. The hydrogen production cost in this scenario varied between 1.6 – 2.3 USD kg-1

in the PP case, and 1.6 – 2.0 USD kg-1 in the CCS case. These results are in agreement with the

values reported by Parthasarathy et al., [5]. However, the hydrogen production cost should be

improved aiming to be competitive to the traditional technologies such as steam methane

reforming (SMR), from which a production cost of 0.75 USD kg-1 with an energy efficiency of

83% is obtained. The parameter that has the highest influence in the total production cost of the

scenario 3 is the raw material costs that involve the biomass purchase and transportation costs.

In this sense, a well-stablished supply chain of the feedstock coupled to the biorefinery scheme

are required in order to obtain competitive processes. From the energetic point of view, both

feedstocks had similar energy content; however, when they are used in the integrated production

of bioenergy in a biorefinery pathway, the CCS case presented the highest energy performance

accounting to an energy efficiency of 94% in comparison to the PP case (62%). Despite the high

energy efficiency, the GHG emissions of the gasification scenarios in the CCS was higher than

the PP case.

Biochemical processes require more research not only in terms of productivity, but also in the

proper valorization of secondary metabolites from the fermentation broth. The best scenario for

hydrogen production through dark fermentation considers the separation of ethanol, acetic and

butyric acid from the fermentation broth along with the production of hydrogen. Due to the

valorization of the secondary metabolites, the hydrogen production cost is reduced, the global

energy efficiency is increased and the potential environmental impact is also reduced compared

to the stand-alone production of hydrogen. A hydrogen production cost of 4.3 USD kg-1 in the

PP case and 4.8 USD kg-1 in the CCS case were obtained. For both lignocellulosic residues, an

energy efficiency in the range of 27 – 30% was reached. The impact categories that contribute


the most to the total environmental impact were the human toxicity by dermal exposition or

inhalation (HTPE), photochemical oxidation potential (PCOP) and terrestrial toxicity potential

(TTP).

8. Recommendations

As part of the research path, there are many conclusions and reflections that are reflected in

future research projects. Some of the recommendations that the authors suggest for the

development of future works in relation to this topic are:

To use different catalyst such as Nickel, Iron or Cobalt in order to increase the remotion

of CO2 from the synthesis gas and also evaluate the location of the catalyst in the

gasifier (fixed bed).

To evaluate the production of hydrogen using T. Thermosaccharolyticum isolated

strains

To determine the suitable growth conditions and substrate type for the production of

hydrogen using the commercial strain T. Thermosaccharolyticum.

To assess the implementation of different process schemes to produce different

metabolites using the commercial strain T. Thermosaccharolyticum.


A. Approach for the calculation of the

elemental analysis and calorific value

Experimental methods to estimate the heating value and elemental analysis of biomass

compounds are time consuming as well as expensive, and have higher possibilities of

experimental errors. For these reasons, several authors have developed correlations, based on

experimental data, to estimate the heating value and elemental analysis of biomass. Shen et al.,

[99] developed various correlations for the determination of the carbon, hydrogen, oxygen and

nitrogen content in biomass. A large number of biomass data (137 materials) such as husk,

wood, straw, saw dust, food wastes, among others from literature reports were collected. Some

of these data were used for the correlation derivation and validation. The experimental

correlations used in the determination of the carbon, hydrogen and oxygen content, based on the

proximate analysis, are presented in Eq.A-1 to A-3.

𝐶 = 0.635𝐹𝐶 + 0.460𝑉𝑀 − 0.095𝐴𝑆𝐻 (𝑤𝑡. %) (A-1)

𝐻 = 0.059𝐹𝐶 + 0.060𝑉𝑀 − 0.010𝐴𝑆𝐻 (𝑤𝑡. %) (A-2)

𝑂 = 0.340𝐹𝐶 + 0.469𝑉𝑀 − 0.023𝐴𝑆𝐻 (𝑤𝑡. %) (A-3)

Where C, H and O are abbreviations of carbon, hydrogen and oxygen, respectively.

Additionally, FC, VM and ASH are also abbreviations of fixed carbon, volatile matter and ash,

respectively. According to the authors, these correlations have an applicability range as

summarized below.

Fixed Carbon 9.2% ≤ 𝐹𝐶 ≤ 32.79%

Volatile Matter 57.2% ≤ 𝑉𝑀 ≤ 90.6%

Ash 0.1% ≤ 𝐴𝑆𝐻 ≤ 24.6%

Carbon 36.2% ≤ 𝐶 ≤ 53.1%

Hydrogen 4.7% ≤ 𝐻 ≤ 6.61%

Oxygen 31.37% ≤ 𝑂 ≤ 48.0%


Nhuchhen et al., [97] proposed a new approach of linear and non-linear correlations for the

calculation of the higher heating value. Different published data of proximate analysis and

higher heating values were collected for the correlations derivation. Subsequently, the

developed correlations were validated using experimental higher heating values and proximate

analysis, and also with data reported in literature. The authors developed 20 linear and 4 non-

linear correlations with their respective standard deviation. Two correlations (1 linear and 1

non-linear) were selected since they have the lowest standard deviation and therefore, these

correlations were used in the calculation of the higher heating value.

𝐻𝐻𝑉 = 19.2880 − 0.2135 (𝑉𝑀

𝐹𝐶) − 1.9584 (

𝐴𝑆𝐻

𝑉𝑀) + 0.0234 (

𝐹𝐶

𝐴𝑆𝐻)

(A-4)

𝐻𝐻𝑉 = 20.7999 − 0.3214 (𝑉𝑀

𝐹𝐶) + 0.0051 (

𝑉𝑀

𝐹𝐶)

2

− 11.2277 (𝐴𝑆𝐻

𝑉𝑀)

+ 4.4953 (𝐴𝑆𝐻

𝑉𝑀)

2

− 0.7223 (𝐴𝑆𝐻

𝑉𝑀)

3

+ 0.0383 (𝐴𝑆𝐻

𝑉𝑀)

4

+ 0.0076 (𝐹𝐶

𝐴𝑆𝐻)

(A-5)

Tables A-1 and A-2 present the results of the experimental and correlation approach of the

elemental analysis and higher heating value of PP and CCS, respectively. Despite the nitrogen

content, the elemental analysis of the experimental and correlation approach have similar values

in terms of carbon, hydrogen and oxygen. Nitrogen content in the experimental determination

was quantified from the microanalyzer; however, the correlation approach does not consider this

element due to its low contribution to the biomass composition. On the other hand, the higher

heating value for both feedstocks has different values from the experimental and correlation

approach. Based on the results of the experimental and correlation approach, it can be concluded

that both procedures accurate describe the elemental composition and energy content of

lignocellulosic raw material.

Table A - 1. Results of the experimental and correlation approach in the PP elemental analysis

and calorific value

Pinus Patula Experimental data Correlation approach

Carbon (%) 49.78 49.23

Hydrogen (%) 6.03 6.00

Oxygen (%) 44.19 44.76

Nitrogen (%) 0.37 NRa

HHV (kJ/kg) 18,484 20,109b

19,974c

a Nitrogen content not calculated from the correlation approach b Linear correlation c Non-linear correlation


Table A - 2. Results of the experimental and correlation approach in the CCS elemental

analysis and calorific value

Coffee Cut-Stems Experimental data Correlation approach

Carbon (%) 48.21 49.09

Hydrogen (%) 5.61 6.02

Oxygen (%) 45.81 44.89

Nitrogen (%) 0.00 NRa

HHV (kJ/kg) 18,246 18,856b

19,324c

a Nitrogen content not calculated from the correlation approach b Linear correlation c Non-linear correlation


B. Gasification of Wood chips: Effect

of the temperature and gasifying agent

Short Internship in the Maryland

University, College Park, USA

Overview

Recently, the main focus of the research group directed by the professor Ashwani K. Gupta is

related to the clean conversion of biomass, wastes and low grade fuels to clean hydrogen-rich

syngas and liquid fuels. Therefore, the work that is presented below is related to the evaluation

of some parameters such as the gasifying agent (steam and carbon dioxide) flow rate and

reaction temperature in the syngas production, especially the effect of these parameters in the

hydrogen and carbon monoxide ratio. Steam gasification is a well-known technology that is

commonly used in the production of hydrogen due to its high hydrogen selectivity; however, the

high production of tar is one of the main drawbacks of this technology. On the other hand, the

gasification using carbon dioxide as gasifying agent has not been widely studied; although, it

has a lot of applications because of the fact that most of the processes generates carbon dioxide,

mainly the thermochemical technologies such as coal gasification, and therefore, this

greenhouse gas can be used as raw material in the production of high added-value bioenergy

products and reducing the GHG emissions into the atmosphere. The main drawback of this

thermochemical method is that an external heat source is required to maintain the gasification

temperature because the heat is not supplied by the partial combustion of biomass as in the case

of the air, oxygen or steam gasification [174].

Experimental Procedure

The experimental procedure was developed in the Combustion laboratory in the Maryland

University using a laboratory scale gasifier which consists, mainly, of a boiler to produce steam

according to the stoichiometric reaction between hydrogen and oxygen, a steam heater in order

to preheat all the gaseous and vapor species that undergoes into the reactor chamber containing

the wood sample. The gases from the gasification, first, goes into five bottles in which samples


are measured at different time intervals (0.5, 1, 2, 3 and 4 minutes). Subsequently, the behavior

of the gasification after first 5 minutes of the reaction was measured using a gas

chromatography. Nitrogen was used as carrier gas at constant flow (2.1 LPM) and it was used to

determine the flow rates of different gaseous species of the gasification [175].

In this study, carbon dioxide, steam and a mixture of these at different ratios were used as

gasifying agents in order to determine the performance of the gasification in terms of the H2/CO

ratio and total energy production. The results from the gasification with different gasifying

agents were compared with that from the pyrolysis of wood chips aiming to evaluate the

differences in the syngas composition and energy yield of both technologies. Yellow wood was

used as feedstock for the gasification and pyrolysis processes, and the elemental

characterization of this raw material was reported by Ahmed et al., [35]. Table B-1 presents the

elemental analysis and the calorific value of the Yellow wood used in the pyrolysis and

gasification procedures.

Table B - 1. Elemental composition of the Yellow wood.

Elemental

Analysis Carbon Hydrogen Oxygen Nitrogen Sulfur Chloride

LHV

[Mj/kg]

Yellow

Woodchips 52.6 7 40.1 0 0 0 22.3

Table B-2 summarizes the different conditions that were evaluated in the carbon dioxide

gasification in order to determine the suitable operation conditions at which the best H2/CO

ratios and energy production can be obtained. For this reason, five flow rates of carbon dioxide

were evaluated keeping constant the steam flow rate. Besides, four temperatures in the pyrolysis

and gasification case were assessed.

Table B - 2. Experimental conditions used in the evaluation of wood pyrolysis and gasification

Conditions Values Units

CO2 flow rate 2.7 3.65 5.4 6.25 8.1 g/min

Steam flow rate 4.42 g/min

CO2/Steam ratios 0.61 1.22 1.31 g CO2/ g H2O

Nitrogen flow rate (Inert gas) 2.1 LPM

Reactor Temperatures 700 800 900 1000 °C

Wood Sample 35 g of wood chips


Results and Discussion

Effect of the temperature in the hydrogen flow rate

Figures B-1, B-2 and B-3 present the effect of the temperature in the hydrogen flow rate profile

of the pyrolysis, steam gasification and CO2 gasification, respectively. The highest hydrogen

flow rate in the pyrolysis was obtained at a temperature of 1000°C in the first 2 minutes

(approximately 0.22 g per min); however, the hydrogen rate decreases rapidly until it reaches

zero at approximately 5 minutes. When the pyrolysis was carried out at lower temperature

(700°C), the hydrogen flow rate (0.05 g per min) is not as high as the pyrolysis at 1000°C, but

the reaction rate takes more time to reach zero (approximately 20 min). Therefore, the best

hydrogen production performance in the pyrolysis process can be obtained at 700°C. In the case

of the steam and dry gasification, the highest hydrogen flow rate was obtained at 1000°C in the

first two minutes of the process. Nevertheless, the CO2 gasification reaches the zero flow rate in

5 min, whereas the steam gasification took approximately 20 min to reach zero hydrogen flow

rate. Slow reaction kinetics of char gasification, using steam as gasifying agent, leads to

increase the gasification reaction time. The best hydrogen performance profile for both

gasifying agents was obtained at 900°C with higher reaction time in the steam gasification

(approximately 40 min), which means that the char-steam reaction is more favorable at this

condition.

Figure B - 1. Effect of the temperature in the hydrogen flow rate of the wood chips pyrolysis


Figure B - 2 Effect of the temperature in the hydrogen flow rate of the wood chips steam

gasification

Figure B - 3. Effect of the temperature in the hydrogen flow rate of the wood chips CO2

gasification

Aiming to improve the performance of the dry gasification, different mixtures of CO2 and steam

as gasifying agent were tested in the gasification of wood chips. Three CO2/steam ratios were

selected as presented in Table B-2. For this purpose, the effect of two temperatures in the

hydrogen flow rate were evaluated: 900°C and 1000°C. Figure B-4 presents the effect of the

temperature in the hydrogen flow rate for three CO2/steam ratios. As expected, the use of carbon

dioxide along with steam as gasifying agent improve the hydrogen flow rate in comparison to

the results obtained in the dry gasification of wood chips. At a CO2 rate of 5.4 g min-1 and

900°C, the maximum hydrogen flow rate in the dry gasification was 0.092 g min-1, whereas the

same CO2 flowrate mixed with steam generated a hydrogen flow rate of 0.12 g min-1. According


to this, the use of steam in the dry gasification improves the hydrogen production performance

along with the reaction extend.

Figure B - 4. Effect of the temperature and the CO2/steam ratio in the hydrogen flow rate in the

wood chips gasification. CO2/steam ratios: (A) 1.31 (B) 1.22 and (C) 0.61 g CO2/g steam


Effect of the H2/CO ratio

From the gaseous species that are generated in the gasification of wood chips, hydrogen and

carbon monoxide are considered the most important fuel components of the synthesis gas since

a high H2/CO ratio could increase the energy efficiency of the process. Figure B-5 presents the

behavior of the H2/CO ratio at different temperatures in the three main evaluated processes:

pyrolysis, carbon dioxide and steam gasification. In the pyrolysis, the highest ratio was obtained

at 700°C and the concentration of hydrogen decreases as the temperature increases. The main

reaction that takes place in the dry gasification is the Boudouard reaction [174], from which the

main product is carbon monoxide. According to this, the H2/CO ratio decreases as the

gasification temperature increases. In the steam gasification, the highest H2/CO ratio was

obtained at 900°C and subsequently, the ratio decreases as the temperature increases. From the

three evaluated processes, steam gasification has the highest H2/CO ratio due to high influence

of the water-gas shift reaction, which uses the carbon monoxide to produce more hydrogen.

Figure B - 5. Effect of the temperature in the H2/CO ratio of the three evaluated processes:

pyrolysis, dry and steam gasification.

The H2/CO ratio was also evaluated considering different CO2/steam ratios. Figure B-6 presents

the behavior of the H2/CO ratio in terms of the temperature and the CO2/steam mixtures. Two

main results were obtained from this evaluation: First, a mixture with high amount of carbon

dioxide (CO2/steam = 1.31) generates a low H2/CO ratio (0.08 g H2/g CO), whereas a low

carbon dioxide content (CO2/steam = 0.61) in the gasifying agent produces higher content of

hydrogen. From Figure B-5, the H2/CO ratio of the dry gasification is 0.006, whereas the

H2/CO ratio in Figure B-6, using the same CO2 flow rate (5.4 g min-1) mixed with steam, is


0.087. Therefore, the conversion of wood chips into a mixture of hydrogen and carbon

monoxide is increased when the gasifying agent is a mixture between CO2 and steam.

Figure B - 6. Effect of the temperature and the CO2/steam mixture in the H2/CO ratio in the

wood gasification. CO2/steam ratios: ratio 1 (0.61), ratio 2 (1.22) and ratio 3 (1.31)

Energy yield

Table B-3 summarizes the results of all the experimental procedures developed in the

University of Maryland based on the amount of hydrogen and carbon monoxide that was

obtained from each of the experimental runs. When steam was used as gasifying agent, either

alone or mixed, high hydrogen content can be obtained and thus, this technology is commonly

used to produce hydrogen. On the other hand, dry gasification has a low hydrogen content;

nevertheless, the amount of carbon monoxide that is generated from the Boudouard reaction is

high. Therefore, the main application of this technology may be in the generation of electricity

through the combustion of the generated syngas in a gas engine. Moreover, CO2 generates high

environmental impacts to the Global Warming; therefore, its valorization aiming to generate

electricity seems to be a promising option, especially in zones where the electricity supply is

limited. Based on the previous statements, the energy yield of the three main evaluated

processes is presented in Figure B-7. It is evidenced that the amount of energy that can be

obtained from the dry gasification is higher than steam gasification and pyrolysis since most

wood chips are converted into high amounts of carbon monoxide (with low hydrogen content),

which can be used as fuel in gas engine to produce electricity. It is noteworthy that the energy

yield of the dry gasification increases with the temperature, which confirms the effect of the

Boudouard reaction in the carbon monoxide production.


Figure B-7. Effect of the temperature in the energy yield of the three evaluated processes

Table B-3. Amount of hydrogen and carbon monoxide obtained in the experimental runs

Process Amount of Hydrogen (g) Amount of Carbon Monoxide (g)

Temperature: 700°C

Pyrolysis 0.318 5.65

Dry Gasification 0.106 8.98

Steam Gasification 0.425 2.73

Temperature 900°C

Pyrolysis 0,32 6.9



CO2/steam = 1.31 1.25 15.25

CO2/steam = 1.22 1.21 13.85

CO2/steam = 0.61 1.34 10.06

Temperature 1000°C

Pyrolysis 0.29 8.51



CO2/steam = 1.31 0.90 13.11

CO2/steam = 1.22 1.19 12.16

CO2/steam = 0.61 1.31 13.82


Final Remarks

Currently, new efforts have been developed aiming to reduce the amount of GHG emissions

into the atmosphere, especially related to CO2. The use of carbon dioxide as gasifying agent

seems to be one of the solutions that can be implemented in order to reduce the GHG emission

to the environment and simultaneously, its valorization in an integrated process. A synthesis gas

with high carbon monoxide content and high energy yield was generated from the CO2

gasification, which can be used in the generation of electricity through gas engines. However,

the hydrogen content in the syngas from the dry gasification is very low. In this sense, a mixture

of gasifying agents should be used to improve the hydrogen content in the synthesis gas. In this

research stay, different CO2/steam mixtures were tested in order to enhance the productivity and

energy efficiency of the dry gasification. Higher H2/CO ratios and energy efficiencies were

obtained when carbon dioxide was mixed with steam as gasifying agents.

The main objective of this short-internship in the Maryland University was related to the use of

different gasifying agents (carbon dioxide and steam) to produce a synthesis gas with high

calorific value and H2/CO ratio that can be used as an alternative for the production of

bioenergy in Colombia, mostly the dry gasification using CO2 as gasifying agent.


C. Kinetic Models

Table C-1. Kinetic models used in the simulation procedure of the stand-alone and biorefinery

pathways to produce hydrogen through gasification and dark fermentation. Technologies (1)

Acid Hydrolysis, (2) Enzymatic Saccharification, (3) Gasification, (4) Dark Fermentation and

(5) Ethanol Fermentation

Technology Kinetic Model Parameters Conditions Ref

(1)

Xylan to Xylose

[𝑋] =𝑘1[𝑋𝑛𝑝]

𝑘2 − 𝑘1(𝑒−𝑘1𝑡 − 𝑒−𝑘2𝑡) + [𝑋𝑜]𝑒−𝑘2𝑡

Cellulose to Glucose

[𝐺] = [𝐺𝑝](1 − 𝑒−𝑘3𝑡)

Furfural formation

[𝐹] = [𝐹𝑝](1 − 𝑒−𝑘4𝑡)

Acetic acid formation

[𝐴] = [𝐴𝑝](1 − 𝑒−𝑘5𝑡)

[𝑋𝑛𝑝] = potential concentration of xylan

[𝐺𝑝], [𝐹𝑝] 𝑎𝑛𝑑 [𝐴𝑝] = potential concentration of

glucose, furfural and acetic acid.

[𝑋𝑛𝑝] = 20.75 g xylose/l

𝑘1=0.03522 min-1

𝑘2=0.00381 min-1

[𝐺𝑝]= 8.02 g/l

𝑘3=0.01134 min-1

[𝐹𝑝]= 1.81 g/l

𝑘4=0.0045 min-1

[𝐴𝑝]= 4.55 g/l

𝑘5=0.04188 min-1

Sulfuric acid

concentration:

2wt.%

Temperature:

121°C

[107]

(2)

Cellulose to Cellobiose

𝑟1 =𝑘1,𝑟𝐶𝐸1𝐵

𝑅𝑠𝐶𝑠

1 +𝐶𝐺2

𝐾1𝐼𝐺2+

𝐶𝐺𝐾1𝐼𝐺

+𝐶𝑋𝑦

𝐾1𝐼𝑋𝑦

Cellulose to glucose

𝑟2 =𝑘2,𝑟(𝐶𝐸1𝐵

+ 𝐶𝐸2𝐵)𝑅𝑠𝐶𝑠

1 +𝐶𝐺2

𝐾2𝐼𝐺2+

𝐶𝐺𝐾2𝐼𝐺

+𝐶𝑋𝑦

𝐾2𝐼𝑋𝑦

Cellobiose to glucose

𝑘1𝑟 = 22.3 g·mg-1·h-1

𝑘2𝑟 = 7.18 g·mg-1·h-1

𝑘3𝑟 = 285.5 g·mg-1·h-1

𝐸𝑎1= 𝐸𝑎2

= 𝐸𝑎3= -23190

J·mol-1

𝐾1𝐼𝐺2 = 0.015 g/l

𝐾1𝐼𝐺 = 0.1 g/l

𝐾1𝐼𝑋𝑦 = 0.1 g/l

Temperature:

50°C [129]


𝑟3 =𝑘3,𝑟𝐶𝐸2𝐹

𝐶𝐺2

𝐾3𝑀 (1 +𝐶𝐺

𝐾3𝐼𝐺+

𝐶𝑋𝑦

𝐾3𝐼𝑋𝑦) + 𝐶𝐺2

Enzyme adsorption

𝐶𝐸𝑖𝐵=

𝐸𝑖𝑚𝑎𝑥𝐾𝑖𝑎𝑑𝐶𝐸𝑖𝐹

𝐶𝑠

1 + 𝐾𝑖𝑎𝑑𝐶𝐸𝑖𝑓

Enzyme

𝐶𝐸𝑖𝑇= 𝐶𝐸𝑖𝐹

+ 𝐶𝐸𝑖𝐵

Substrate Reactivity

𝑅𝑠 =𝐶𝑠

𝑆𝑜

Temperature dependence

𝑘𝑖𝑟 = 𝑘𝑖𝑟(𝑇𝐼) exp (−𝐸𝑎𝑖

𝑅𝑇)

𝐶𝐺2[Cellobiose], 𝐶𝐺[Glucose], 𝐶𝑋𝑦[Xylose],

𝐶𝑠[Cellulose]

𝐾2𝐼𝐺2 = 132.0 g/l

𝐾2𝐼𝐺 = 0.04 g/l

𝐾2𝐼𝑋𝑦 = 0.2 g/l

𝐾3𝐼𝑋𝑦 = 201 g/l

𝐾3𝑀 = 24.3 g/l

𝐸1𝑚𝑎𝑥 = 0.06 g/g

𝐸2𝑚𝑎𝑥 = 0.01 g/g

𝐸𝑎,1 = 111600 J·mol-1

𝐾1𝑎𝑑 = 0.4 g/g

𝐾2𝑎𝑑 = 0.1 g/g

(3)

Gasification based on the elemental analysis of

the raw material, stoichiometric approach using

free Gibbs energy minimization method.

Involved components

H2, CO2, CO, CH4, N2, O2, H2O, CxHyOz

Chemical Approach

Gasification

temperature:

850 °C

Pressure:

1 atm

[100]

(4)

Substrate Uptake

𝑑𝑆

𝑑𝑡= −𝑢𝑚𝑎𝑥 ∗ 𝐼 ∗ (

𝑆

𝑆 + 𝐾𝑠) ∗ 𝑋

pH inhibition

𝐼 = exp (−3 ∗ (𝑝𝐻 − 𝑝𝐻𝑢

𝑝𝐻𝑢 − 𝑝𝐻𝑖)

2

)

Biomass Growth

𝑑𝑋

𝑑𝑡= −𝑌𝑥/𝑠 ∗

𝑑𝑆

𝑑𝑡

Products formation

𝑑𝐻2

𝑑𝑡= −𝑌1 ∗

𝑑𝑆

𝑑𝑡

𝑑𝐴𝑐

𝑑𝑡= −𝑌2 ∗

𝑑𝑆

𝑑𝑡

𝑑𝐵𝑢𝑡

𝑑𝑡= −𝑌3 ∗

𝑑𝑆

𝑑𝑡

𝑢𝑚𝑎𝑥 = 0.31 h-1

𝐾𝑠 = 1.47 g/l

𝑌𝑥/𝑠 = 0.135 g/g

𝑌1 = 0.0127 g/g

𝑌2 = 0.1928 g/g

𝑌3 = 0.1278 g/g

𝑌4 = 0.1597 g/g

𝑌5 = 0.0521 g/g

𝑝𝐻𝑢 = 7

𝑝𝐻𝑖 = 4

Temperature:

60°C

pH = 6.25

Microorganism:

Thermoanaer

obacterium

thermosacch

arolitycum

[119]


𝑑𝐶𝑂2

𝑑𝑡= −𝑌4 ∗

𝑑𝑆

𝑑𝑡

𝑑𝐸𝑡𝑜𝐻

𝑑𝑡= −𝑌5 ∗

𝑑𝑆

𝑑𝑡

𝑑𝐻2

𝑑𝑡 [Hydrogen],

𝑑𝐴𝑐

𝑑𝑡 [Acetic Acid],

𝑑𝐵𝑢𝑡

𝑑𝑡 [butyric

acid], 𝑑𝐶𝑂2

𝑑𝑡 [Carbon dioxide],

𝑑𝐸𝑡𝑜𝐻

𝑑𝑡 [Ethanol]

(5)

Glucose consumption

𝑅𝑠1 = 𝛼𝑞𝑠𝑚𝑎𝑥1 (𝐶𝑠1

𝐾𝑠𝑠1 + 𝐶𝑠1) (1

−𝐶𝑝 − 𝑃𝑖𝑠1

𝑃𝑚𝑠1 − 𝑃𝑖𝑠1) (

𝐾𝑖𝑠1

𝑘𝑖𝑠1 + 𝐶𝑠1) 𝐶𝑥

Xylose consumption

𝑅𝑠2

= (1 − 𝛼)𝑞𝑠𝑚𝑎𝑥2 (𝐶𝑠2

𝐾𝑠𝑠2 + 𝐶𝑠2) (1

−𝐶𝑝 − 𝑃𝑖𝑠2

𝑃𝑚𝑠2 − 𝑃𝑖𝑠2) (

𝐾𝑖𝑠2

𝑘𝑖𝑠2 + 𝐶𝑠2) 𝐶𝑥

Ethanol production

𝑅𝑝 = [𝛼𝑞𝑝𝑚𝑎𝑥1 (𝐶𝑠1

𝐾𝑠𝑝1 + 𝐶𝑠1

) (1

−𝐶𝑝 − 𝑃𝑖𝑝1

𝑃𝑚𝑝1 − 𝑃𝑖𝑝1

) (𝐾𝑖𝑝1

𝑘𝑖𝑝1 + 𝐶𝑠1

)

+ (1 − 𝛼)𝑞𝑝𝑚𝑎𝑥2 (𝐶𝑠2

𝐾𝑠𝑝2 + 𝐶𝑠2

) (1

−𝐶𝑝 − 𝑃𝑖𝑝2

𝑃𝑚𝑝2 − 𝑃𝑖𝑝2

) (𝐾𝑖𝑝2

𝑘𝑖𝑝2 + 𝐶𝑠2

)] 𝐶𝑥

Biomass Growth

𝑅𝑥

= [𝛼𝑢𝑚𝑎𝑥1 (𝐶𝑠1

𝐾𝑠𝑥1 + 𝐶𝑠1) (1

−𝐶𝑝 − 𝑃𝑖𝑥1

𝑃𝑚𝑥1 − 𝑃𝑖𝑥1) (

𝐾𝑖𝑥1

𝑘𝑖𝑥1 + 𝐶𝑠1)

+ (1 − 𝛼)𝑢𝑚𝑎𝑥2 (𝐶𝑠2

𝐾𝑠𝑥2 + 𝐶𝑠2) (1

−𝐶𝑝 − 𝑃𝑖𝑥2

𝑃𝑚𝑥2 − 𝑃𝑖𝑥2) (

𝐾𝑖𝑥2

𝑘𝑖𝑥2 + 𝐶𝑠2)] 𝐶𝑥

𝛼 = 0.65

𝑢𝑚𝑎𝑥1 = 0.31 𝑢𝑚𝑎𝑥2 = 0.1

𝐾𝑠𝑥1 = 1.45 𝐾𝑠𝑥2 = 4.91

𝑃𝑚𝑥1 = 57.2 𝑃𝑚𝑥2 = 56.3

𝑘𝑖𝑥1 = 200 𝑘𝑖𝑥2 = 600

𝑃𝑖𝑥1= 28.9 𝑃𝑖𝑥1= 26.6

𝑞𝑠𝑚𝑎𝑥1 = 10.9 𝑞𝑠𝑚𝑎𝑥2 = 3.27

𝐾𝑠𝑠1 = 6.32 𝐾𝑠𝑠2 = 0.03

𝑃𝑚𝑠1 = 75.4 𝑃𝑚𝑠2 = 81.2

𝐾𝑖𝑠1 = 186 𝐾𝑖𝑠2 = 600

𝑃𝑖𝑠1 = 42.6 𝑃𝑖𝑠2 = 53.1

𝑞𝑝𝑚𝑎𝑥1 = 5.12 𝑞𝑝𝑚𝑎𝑥2 = 1.59

𝐾𝑠𝑝1 = 6.32 𝐾𝑠𝑝2 = 0.03

𝑃𝑚𝑝1 = 75.4 𝑃𝑚𝑝2 = 81.2

𝐾𝑖𝑝1 = 186 𝐾𝑖𝑝2 = 600

𝑃𝑖𝑝1 = 42.6 𝑃𝑖𝑝2 = 53.1

Temperature:

35°C

Microorganism:

Zymomonas

mobilis

[136]

D. Mathematical modelling of biomass

gasification for hydrogen production

Introduction

Mathematical modeling of gasification processes has high complexity; however, different

mechanisms have been proposed to simulate this technology in different reaction zones. Depending

on the complexity of the mathematical model, different procedures have been assessed [8], [9],

[126], [176]–[178]. Different mathematical models can be used to describe the behavior of a

downdraft gasifier. Among these, the kinetic models predict the progress and product composition

at different positions along a reactor, whereas an equilibrium model predicts the maximum

achievable yield of a desired product from a reacting system. The neural network model predicts

the gas yield and composition from gasification processes. It does not require the mathematical

description of the phenomena involved in the process and therefore, it might be useful in the

simulation and up-scaling of complex biomass gasification processes. However, its main limitation

is the amount and quality of the provided data. The main objective of these models is to study the

thermochemical pathways involve in the gasification and evaluate the influence of the major

process variables such as moisture content, air / biomass ratio, reaction temperature, composition

and energy content of the produced gas [179].

The chemical pathway of a gasification can be divided in three zones as previously described in

section 4.6.3. In the pyrolysis and combustion zones, it is not possible to describe the concentration

profile of the gas species, for this reason, a thermodynamic equilibrium model was used to

determine the maximum product yields in each zone. The reduction zone is the most important

stage in the gasification since the synthesis gas is generated there. Given the importance of the

reduction zone, a kinetic model was used to describe the concentration profile of the gas mixture.

The aim of this section is to evaluate the effect of two process parameters (moisture content and

equivalence ratio (ER)) in the synthesis gas production from two wood residues in Colombia


(Coffee Cut-Stems and Pinus Patula). Thermodynamic equilibrium and kinetic models were used

to carry out the mathematical modelling of the synthesis gas production in a three-zone downdraft

gasifier. The effect of the major process variables such as moisture content and the air / biomass

ratio in the composition and energy content of the generated gas were also assessed. The effect of

the evaluated parameters was used as guideline in the energetic and exergetic assessment aiming to

determine the suitable process conditions for the gasification simulation.

Model description

Figure D-1 describes the three zones in which a downdraft gasifier can be divided. Biomass is feed

in the top of the equipment, whereas the gasifying agent (in this case, air) undergoes from the

bottom to the top of the gasifier. Given this behavior, the first reaction that takes places is the

pyrolysis, followed by the combustion and then, the reduction. In this section, the thermodynamic

and kinetic models used in each zone of the downdraft gasifier are described. Some assumption of

the model are:

- The model evaluates only the reactions that take place inside the reactor to produce the

synthesis gas.

- The model predicts the production of H2-rich synthesis gas; therefore, additional separation

equipment must be incorporated in order to separate the hydrogen. However, in the

mathematical modelling were not considered.

- Heat losses are directly related to the gasifier temperature. The air/biomass ratio and the

energy content of the feedstock (in terms of Higher Heating Value, HHV) govern the

behavior of the reactor. For this reason, the heat losses may be calculated as the product

between the air/biomass ratio and the HHV. The heat losses are ruled for the maximum

temperature in each zone, from which the heat transfer into the environment is going to be

distribute between the zones [126].

Pyrolysis zone model

The mathematical modelling of the pyrolysis zone is governed by the following assumptions:

50% of the hydrogen presents in the biomass (𝐶𝐻𝑚𝑂𝑛) is converted into methane.

Due to absence of air, the elemental balance for oxygen does not take into account the

contribution of air.

The products from the pyrolysis are C, H2, CO, CO2, CH4, H2O


The biomass conversion to the previously selected products in this zone is defined by the

stoichiometric reaction Eq. D-1.

𝐶𝐻𝑚𝑂𝑛 + 𝑤𝐻2𝑂 → 𝑥𝑝1𝐶 + 𝑥𝑝2𝐻2 + 𝑥𝑝3𝐶𝑂 + 𝑥𝑝4𝐶𝑂2 + 𝑥𝑝5𝐶𝐻4 + 𝑥𝑝6𝐻2𝑂 (D-1)

Where 𝐶𝐻𝑚𝑂𝑛 is the chemical representation of biomass (dry basis). The m and n subscripts were

calculated from the elemental analysis of the raw material. The 𝑥𝑝𝑛 fractions represent the number

of moles in each one of the pyrolysis products.

From the Eq. D-1, the mass balance (elemental balance C, H, O) can be obtained as follows

- Balance for C

𝑥𝑝1 + 𝑥𝑝3 + 𝑥𝑝4 + 𝑥𝑝5 = 1 (D-2)

- Balance for H

2 ∗ 𝑥𝑝2 + 4 ∗ 𝑥𝑝5 + 2 ∗ 𝑥𝑝6 = 𝑚 + 2 ∗ 𝑤 (D-3)

- Balance for O

𝑥𝑝3 + 2 ∗ 𝑥𝑝4 + 𝑥𝑝6 = 𝑛 + 𝑤 (D-4)

Figure D-1. Scheme of a downdraft gasifier using wood residues as feedstock

Combustion zone model

The combustion zone modelling based on the thermodynamic equilibrium model has the following

assumptions.

The resulting hydrogen fraction in the pyrolysis zone are completely oxidized to produce

water due to its high combustion rate [126].


Oxygen fraction will be totally consume in the char oxidation because of the large reaction

area available for the O2 adsorption in the high reactive char from the pyrolysis zone.

Methanation (𝐶 + 2𝐻2 → 𝐶𝐻4) reaction is developed in the char surface, where the

equilibrium is reached. According to this, the equilibrium constant related to the number of

moles of hydrogen and methane can be calculated as follows.

𝑘1 =𝑥𝑝5

𝑥𝑝22

(D-5)

The main products in the combustión zone are C, CO, CO2, CH4, H2O y N2.

Global reaction in the oxidation zone can be describe by the following stoichiometric reaction.

𝑥𝑝1𝐶 + 𝑥𝑝2𝐻2 + 𝑥𝑝3𝐶𝑂 + 𝑥𝑝4𝐶𝑂2 + 𝑥𝑝5𝐶𝐻4 + 𝑥𝑝6𝐻2𝑂 + 𝑎(𝑂2 + 3.76𝑁2)

→ 𝑥𝑐1𝐶 + 𝑥𝑐2𝐶𝑂 + 𝑥𝑐3𝐶𝑂2 + 𝑥𝑐4𝐶𝐻4 + 𝑥𝑐5𝐻2𝑂 + 𝑥𝐶6𝑁2

(D-6)

Where 𝑎 is the number of moles of oxygen per mol of raw material and can be calculate from the

elemental balance for oxygen. The fractions 𝑥𝐶𝑛 represent the number of moles of the main

products in the oxidation zone.

From the general stoichiometric reaction, the elemental balances (C, H, O y N) were calculated.

- Balance for C

𝑥𝑝1 + 𝑥𝑝3 + 𝑥𝑝4 + 𝑥𝑝5 = 𝑥𝑐1 + 𝑥𝑐2 + 𝑥𝑐3 + 𝑥𝑐4 (D-7)

- Balance for H

2 ∗ 𝑥𝑝2 + 4 ∗ 𝑥𝑝5 + 2 ∗ 𝑥𝑝6 + 2 ∗ 𝑎 = 𝑥𝑐2 + 4 ∗ 𝑥𝑐4 + 2 ∗ 𝑥𝐶5 (D-8)

- Balance for O

𝑥𝑝3 + 2 ∗ 𝑥𝑝4 + 𝑥𝑝6 = 𝑥𝑐2 + 2 ∗ 𝑥𝐶3 + 𝑥𝑐5 (D-9)

- Balance for N

(3.76 ∗ 2)𝑎 = 2 ∗ 𝑥𝑐6 (D-10)

Reduction zone model

Unlike the pyrolysis and combustion zone, kinetic models were used to simulate the reduction

zone. After all the oxygen, present in the air, is consume in the combustion zone, and the pyrolysis

products are also decomposed into low-molecular weight compounds; the remaining produced char


in this two zones undergoes into the reduction zone, where five reactions describe the formation of

six gaseous species.

Boudouard Reaction 𝐶𝑠 + 𝐶𝑂2 → 2𝐶𝑂 (D- 11)

Water – Gas Reaction 𝐶𝑠 + 𝐻2𝑂 → 𝐶𝑂 + 𝐻2 (D-12)

Methanation Reaction 𝐶𝑠 + 2𝐻2 → 𝐶𝐻4 (D-13)

Steam Reforming reaction 𝐶𝐻4 + 𝐻2𝑂 → 𝐶𝑂 + 3𝐻2 (D-14)

Water – Gas Shift Reaction 𝐶𝑂 + 𝐻2𝑂 → 𝐶𝑂2 + 𝐻2 (D-15)

The kinetic model uses Arrhenius type-equations which are function of the temperature as

presented in Table D-1 [176].

Table D-1. Kinetic rate equation for the reduction zone

Reaction Kinetic rate

Boudouard Reaction 𝑟1 = 𝑛𝐴1 exp (−𝐸1

𝑅𝑇) (𝑃𝐶𝑂2

−𝑃𝐶𝑂

2

𝐾1) (D-18)

Water – Gas Reaction 𝑟2 = 𝑛𝐴2 exp (−𝐸2

𝑅𝑇) (𝑃𝐻2𝑂 −

𝑃𝐶𝑂 ∗ 𝑃𝐻2

𝐾2) (D-19)

Methanation Reaction 𝑟3 = 𝑛𝐴3 exp (−𝐸3

𝑅𝑇) (𝑃𝐻2

2 −𝑃𝐶𝐻4

2

𝐾3) (D-20)

Steam Reforming reaction 𝑟4 = 𝑛𝐴4 exp (−𝐸4

𝑅𝑇) (𝑃𝐶𝐻4

∗ 𝑃𝐻2𝑂 −𝑃𝐶𝑂 ∗ 𝑃𝐶𝐻4

2

𝐾4) (D-21)

Water – Gas Shift Reaction 𝑟5 = 𝑛𝐴5 exp (−𝐸5

𝑅𝑇) (𝑃𝐶𝑂2

∗ 𝑃𝐻2−

𝑃𝐶𝑂 ∗ 𝑃𝐻2𝑂

𝐾5) (D-22)

The activation energy and frequency factor values were taken from reported data in literature for

each gas specie and reaction involve in the reduction zone [9], [177], [178]. 𝑛 is the char reactivity

factor. It indicates the degree of burn-off and number of active sites on char and its particle size

[9]. The value of CRF may vary between 10 and 10,000 and sometimes exponentially or linearly,

for the present models it is taken as 100. Table D-2 summarizes the equilibrium constant,

activation energy and frequency factor used for each reaction involve in the reduction zone.


Table D-2. Activation energy data [9].

Reaction number 𝑨𝒊 (𝟏𝒔⁄ ) 𝑬𝒊 (𝒌𝑱

𝒎𝒐𝒍⁄ )

D-18 36.16 77.39

D-19 15.17𝑥104 121.62

D-20 4.189𝑥10−3 19.21

D-21 7.301𝑥10−2 36.15

D-22 2.842𝑥10−2 32.84

The equilibrium constant is a temperature dependent factor and it is calculated based on the Gibbs

function. The equilibrium constant for the five reactions that are involved in the reduction zone

were calculated as follows [9].

𝐾𝑒𝑞,1 = 𝑒−(2

𝑔𝐶𝑂° (𝑇)𝑅𝑇

−𝑔𝐶𝑂2

° (𝑇)

𝑅𝑇−

𝑔𝐶° (𝑇)𝑅𝑇

)

(D-23)

𝐾𝑒𝑞,2 = 𝑒−(


+𝑔ℎ2

° (𝑇)

𝑅𝑇−

𝑔𝐻2𝑂° (𝑇)

𝑅𝑇−


)

(D-24)

𝐾𝑒𝑞,3 = 𝑒−(

𝑔𝐶𝐻4° (𝑇)

𝑅𝑇−2

𝑔𝐻2° (𝑇)

𝑅𝑇−


)

(D-25)

𝐾𝑒𝑞,4 = 𝑒−(


+3𝑔ℎ2

° (𝑇)

𝑅𝑇−

𝑔𝐻2𝑂° (𝑇)

𝑅𝑇−

𝑔𝐶𝐻4° (𝑇)

𝑅𝑇)

(D-26)

𝐾𝑒𝑞,2 = 𝑒−(


+𝑔𝐻2𝑂

° (𝑇)

𝑅𝑇−

𝑔ℎ2° (𝑇)

𝑅𝑇−

𝑔𝐶𝑂2° (𝑇)

𝑅𝑇)

(D-27)

Where

𝑔𝑖°(𝑇)

𝑅𝑇= 𝐴𝑔𝑖 + 𝐵𝑔𝑖𝑇 + 𝐶𝑔𝑖𝑇2 + 𝐷𝑔𝑖𝑇3 + 𝐸𝑔𝑖𝑇4 +

𝐹𝑔𝑖

𝑇+ 𝐺𝑔𝑖 ln 𝑇 (D-28)

The values of the constants A, B, C, D, E, F and G were taken from Table D-3 for each gaseous

specie in a temperature range of 1000 – 5000 K [9].

Table D-3. Values of the constants for the Gibbs function calculation

Species Ag Bg Cg Dg Eg Fg Gg

CO -3,083139 -7,21E-04 9,38E-08 -8,49E-12 1,16E-15 -1,43E+04 -3,03E+00

CO2 5,409019 -1,57E-03 2,13E-07 -2,00E-11 2,78E-15 -4,90E+04 -4,45E+00

H2 4,346533 -3,50E-04 9,39E-09 7,69E-13 -2,64E-16 -8,35E+02 -2,99E+00

H2O -4,190672 -1,53E-03 1,46E-07 -1,00E-11 1,07E-15 -2,99E+04 -2,67215

CH4 -7,939917 -5,12E-03 6,45E-07 -5,65E-11 7,51E-15 -1,01E+04 -1,68E+00

𝐶𝑠 1,02E+01 -8,31E-04 1,11E-07 -1,08E-11 1,53E-15 -7,07E+02 -1,49E+00


The modelling procedure was divided in two sections: first, the pyrolysis and combustion zones

were modeled using the thermodynamic equilibrium model in the computational tool Engineering

Equation Solver (EES) (F-chart software, Wisconsin, USA). And finally, the reduction zone was

simulated in the software Matlab (MathWorks, USA) using Arrhenius-type kinetic models.

Energy Analysis

All processes must satisfy the first law of thermodynamics, which is related with the energy

conservation (Eq. 29).

∑ 𝐻𝑖𝑛 − ∑ 𝐻𝑜𝑢𝑡 = 𝑄𝑙𝑜𝑠𝑠 (D-29)

Where 𝐻 is the enthalpy of the reactants and products in the gasification process and 𝑄 is the

amount of energy lost as heat through the gasifier. Considering the chemical energy of the gas as

the main objective of the process, the gasification efficiency can be calculated as follows Eq. D-30.

𝜂 =�̇�𝑠𝑦𝑛𝑔𝑎𝑠 ∗ 𝐿𝐻𝑉𝑠𝑦𝑛𝑔𝑎𝑠

�̇�𝑏𝑖𝑜𝑚𝑎𝑠𝑠 ∗ 𝐿𝐻𝑉𝑏𝑖𝑜𝑚𝑎𝑠𝑠 (D-30)

Where �̇�𝑠𝑦𝑛𝑔𝑎𝑠 and �̇�𝑏𝑖𝑜𝑚𝑎𝑠𝑠 represent the mass flow of the synthesis gas and biomass,

respectively. 𝐿𝐻𝑉𝑠𝑦𝑛𝑔𝑎𝑠 and 𝐿𝐻𝑉𝑏𝑖𝑜𝑚𝑎𝑠𝑠 are the calorific values of the syngas and biomass,

respectively. The energy content of the biomass was calculated using lineal correlations based on

experimental data of the chemical composition of different raw materials.

Exergy Analysis

The exergy of a system is defined as the maximum shaft work that can be done by the composite of

the system and a specified reference environment. The reference environment is assumed to be

infinite, in equilibrium, and to enclose all other systems. Typically, the environment is specified by

stating its temperature, pressure, and chemical composition [180]. The corresponding exergy

balance of a steady-state process obeys the equation:

𝐸𝑥𝑏𝑖𝑜𝑚𝑎𝑠𝑠 + 𝐸𝑥𝑎𝑔𝑒𝑛𝑡 = 𝐸𝑥𝑠𝑦𝑛𝑔𝑎𝑠 + 𝐸𝑥𝑎𝑠ℎ ∗ 𝐼 (D-31)

Where 𝐸𝑥𝑏𝑖𝑜𝑚𝑎𝑠𝑠, 𝐸𝑥𝑎𝑔𝑒𝑛𝑡, 𝐸𝑥𝑠𝑦𝑛𝑔𝑎𝑠 y 𝐸𝑥𝑎𝑠ℎ represent the biomass, gasifying agent (air),

synthesis gas and ash exergy, respectively. 𝐼 is the irreversibility of the system. The exergy in a

stream can be calculated as the sum of its chemical 𝐸𝑥𝑐ℎ and physical exergy 𝐸𝑥𝑝ℎ.

𝐸𝑥 = 𝐸𝑥𝑐ℎ + 𝐸𝑥𝑝ℎ (D-32)


The kinetic and other types of exergies are neglected. The physical exergy of the gaseous material

is calculated as:

𝐸𝑥𝑝ℎ = ∑ 𝑥𝑖 ∗ 𝑒𝑥𝑖𝑝ℎ

(D-33)

Where 𝑥𝑖 and 𝑒𝑥𝑖𝑝ℎ

are the mole flow rate and physical exergy of component 𝑖, respectively. For

each component, the physical exergy is defined as:

𝑒𝑥𝑖𝑝ℎ

= (ℎ − ℎ𝑜) − 𝑇𝑜(𝑆 − 𝑆𝑜) (D-34)

Where (ℎ − ℎ𝑜) and (𝑆 − 𝑆𝑜) are calculated according to the following equations.

ℎ − ℎ0 = ∫ 𝐶𝑝 𝑑𝑇𝑇

𝑇𝑜

(D-35)

𝑆 − 𝑆0 = ∫𝐶𝑝

𝑇 𝑑𝑇

𝑇

𝑇𝑜

(D-36)

Where ℎ and 𝑆 are the specific enthalpy and entropy as function of the temperature T in a specified

state, respectively; ℎ𝑜 and 𝑆𝑜 denote the enthalpy and entropy in standard conditions (reference

environment) with temperature 𝑇𝑜 (298 K) and pressure (1 atm), respectively; and 𝐶𝑝 is the

constant pressure specific heat capacity, which can be calculated based on correlations proposed by

[181], as shown in Table D-4.

Table D-4. Heat Capacity at constant pressure for gaseous species involve in the gasification

Species 𝑪𝒑 (𝒌𝑱

𝒌𝒎𝒐𝒍 ∗ 𝑲)

𝑁2 𝐶𝑝 = 39.060 − 512.79 (𝑇

100)

−1.5

+ 1072.7 (𝑇

100) − 820.4 (

𝑇

100)

−3

𝑂2 𝐶𝑝 = 25.4 + 1.52𝑥10−2𝑇 − 0.7155𝑥10−5𝑇2 + 1.312𝑥10−9𝑇3

𝐻2𝑂 𝐶𝑝 = 32.24 + 0.1923𝑥10−2𝑇 + 1.055𝑥10−5𝑇2 − 3.595𝑥10−9𝑇3

𝐶𝑂 𝐶𝑝 = 28.16 + 0.1675𝑥10−2𝑇 + 0.5327𝑥10−5𝑇2 − 2.222𝑥10−9𝑇3

𝐶𝑂2 𝐶𝑝 = 22.26 + 5.981𝑥10−2𝑇 − 3.501𝑥10−5𝑇2 + 7.469𝑥10−9𝑇3

𝐻2 𝐶𝑝 = 29.11 − 0.1916𝑥10−2𝑇 + 0.4003𝑥10−5𝑇2 − 0.8704𝑥10−9𝑇3

𝐶𝐻4 𝐶𝑝 = 18.89 + 5.024𝑥10−2𝑇 + 1.269𝑥10−5𝑇2 − 11.01𝑥10−9𝑇3

On the other hand, the chemical exergy can be calculated from Eq. D-37:

𝐸𝑥𝑐ℎ = ∑ 𝑥𝑖 (𝑒𝑥𝑖𝑐ℎ + 𝑅𝑇𝑜

𝑥𝑖

∑ 𝑥𝑖) (D-37)


Where 𝑒𝑥𝑖𝑐ℎ is the standard chemical exergy of gaseous component 𝑖 (see, Table D-5); and 𝑅 is the

universal gas constant with a value of 8.314 kJ/kmol*K.

Table D-5. Specific enthalpy, entropy and standard chemical exergy values for the gaseous

components in the gasification

Component 𝒉𝒐 (𝒌𝑱 𝒌𝒎𝒐𝒍−𝟏) 𝒔𝒐 (𝒌𝑱 𝒌𝒎𝒐𝒍−𝟏𝑲−𝟏) 𝒆𝒙𝒊𝒄𝒉(𝒌𝑱 𝒌𝒎𝒐𝒍−𝟏)

N2 0 191.61 668

O2 0 205.03 3,970

H2O -228.58 188.72 9,500

CO -137.15 197.54 275,100

CO2 -394.37 213.68 19,870

H2 0 120.574 236,100

CH4 -74.85 186.16 831,650

Thermodynamic parameters such as the standard enthalpy, standard entropy and standard chemical

exergy of the species involve in the process were taken from reported data in literature [34] [181].

Table D-5 summarizes the values of the thermodynamic parameters for each gaseous component.

Finally, the biomass exergy was calculated based on the method propose by Szargut et al., [182].

𝐸𝑥𝑏𝑖𝑜𝑚𝑎𝑠𝑠 = 𝛽 ∗ 𝐿𝐻𝑉𝑏𝑖𝑜𝑚𝑎𝑠𝑠 (D-38)

Where the coefficient 𝛽 was calculated based on the elemental analysis, especially in the oxygen –

carbon and hydrogen – carbon ratios. And 𝐿𝐻𝑉𝑏𝑖𝑜𝑚𝑎𝑠𝑠 is the amount of energy that can be released

as heat from the raw material.

𝛽 =1.0414 + 0.0177 [

𝐻𝐶] − 0.3328 [

𝑂𝐶] {1 + 0.0537 [

𝐻𝐶]}

1 − 0.4021 [𝑂𝐶]

(D-39)

The exergetic efficiency is defined as the ratio between useful exergy outputs from the gasifier to

the necessary exergy input to the gasifier. In this work, the exergetic efficiency was calculated as

follows:

𝜂𝑒𝑥 =𝐸𝑥𝑠𝑦𝑛𝑔𝑎𝑠

𝐸𝑥𝑏𝑖𝑜𝑚𝑎𝑠𝑠 + 𝐸𝑥𝑎𝑖𝑟

(D-40)

Where 𝐸𝑥𝑠𝑦𝑛𝑔𝑎𝑠 is the exergy of the synthesis gas, 𝐸𝑥𝑏𝑖𝑜𝑚𝑎𝑠𝑠 is the exergy of the biomass and

𝐸𝑥𝑎𝑖𝑟 is the exergy of the gasifying agent.


Results

Effect of the moisture content in the concentration and calorific value of the generated gas

The moisture content in the raw material can generated a positive and negative effect in the

gasification process. The formation of hydrogen is favored since a greater number of moles of

hydrogen are presented by increasing the moisture content. However, in order to maintain the

operating conditions of the process, more energy is required; therefore, exothermic reactions are

favored rather than endothermic reactions, which affects the formation of CO and H2 [182].

Figures D-2, D-3 and D-4 present the effect of the moisture content of the Pinus Patula and

Coffee Cut-Stems in the concentration profiles of the gaseous species of the different zones of the

gasifier. In the pyrolysis zone, the number of moles of hydrogen increase as the number of moles

of water increase (moisture content of the raw material), whereas the number of moles of CO2 and

CO decrease (see, Figure D-2). Since the main gaseous products in the combustion zone are CO2,

CO, H2O and N2, increasing the number of moles of water reduces the concentration of the

remaining gaseous species from the combustion reaction (see, Figure D-3). The effect of the

moisture content of the raw material is evidenced in the reduction zone, where high number of

moles of hydrogen are generated because of the high amount of water (See, Figure D-4). Besides,

the effect of the moisture content in the heating value of the generated syngas was evaluated (see,

Figure 5). Hydrogen, carbon monoxide and methane are considered the most important fuel

species in the synthesis gas; if the hydrogen content increases in the syngas (due to the moisture

content), the heating value of the syngas also increases.

A


Figure D-2. Effect of the moisture content in the syngas composition in the pyrolysis zone

a) Pinus Patula and b) Coffee Cut-Stems

Figure D-3. Effect of the moisture content in the syngas composition in the combustion zone


B

A

B


Figure D-4. Effect of the moisture content in the syngas composition in the reduction zone


A

B

A


Figure D-5. Effect of the moisture content on the heating value of the synthesis gas

a) Pinus Patula b) Coffee Cut-Stems

Effect of the air/biomass ratio in the composition and heating value of the syngas

Equivalence ratio (ER) is defined as the ratio between the air–fuel of the gasification and the air–

fuel ratio for complete combustion [183]. Higher ER increases the amount of oxygen in the the

gasifier, which reduces the calorific value of the product gas. On the other hand, lower ER results

in higher calorific product gas; however, the tar yield is considerably higher. Generally,

combustion processes operate with ER > 0.5; whereas gasification processes the ER varies

between 0.2 and 0.4. Pyrolysis is a process that is carried out in absence of oxygen, for this reason,

the effect of ER on the composition of the gaseous species in this zone is negligible. In the

combustion zone, an increment in the supplied air increases the carbon dioxide content due to the

presence of more oxygen in order to reach a complete combustion condition (see, Figure D-6).

The low concentration of carbon dioxide compared to the high carbon monoxide content at low ER

values can be attributed to the fact that under oxygen deficient atmosphere and the presence of

highly reactive char, the partial oxidation of char is dominant [126]. In the reduction zone, high

content of carbon dioxide is obtained at ER values above 0.3 since most of the supplied air is

destined to produce heat and CO2 from the combustion zone (see, Figure D-7). Additionally, most

of the carbon dioxide is intended to produce carbon monoxide through the Boudouard reaction.

The amount of air that is supplied into the gasifier affects directly the heating value of the syngas

as presented in Figure D-8.

B


Figure D-6. Effect of the ER in the gas composition in the combustion zone

a) Pinus Patula b) Coffee Cut-Stems.

A

B

A


Figure D-7. Effect of the ER in the gas composition in the reduction zone


Figure D-8. Effect of the ER in the calorific value of the generated gas


B

A

B


Energy and exergy analysis

Based on the results of the effect of the ER in the syngas composition and heating value, it can be

concluded that high ER values decrease the hydrogen content and thus, the heating content of the

syngas. As a consequence, high ER values decrease the cold gas efficiency of the process (see,

Figure D-9). A lean mixture is considered when the biomass/air ratio is below 0.1 (ER < 0.1);

hence, these mixtures are more efficient but may cause engine damage. According to this, the

suitable ER varies between 0.25 and 0.3, which corresponds to an efficiency between 70 – 50%. It

is also evidenced that the highest energy efficiency was obtained using as raw material CCS in the

gasification procedure.

Figure D-9. Effect of the equivalence ratio (ER) in the energy efficiency of the gasification

Figure D-10. Effect of the equivalence ratio (ER) in the total syngas exergy


High air-to-fuel ratios favor the exothermic reactions that produce as main products CO2 and H2O.

The chemical exergy of these gases is lower than the chemical exergy of CO and H2; therefore, the

total exergy of the synthesis gas is lower and hence, decreasing the exergy efficiency of

conversion. The behavior of the total exergy of the generated gas is presented in Figure D-10.

Final Remarks

The mathematical modelling approach of the three-zone downdraft gasifier was used to evaluate

the behavior of the synthesis gas composition and heating value in terms of the moisture content

and the amount of supplied air. The moisture content of the biomass influences the energy

efficiency of the gasification procedure since additional energy is required to remove water from

the feedstock aiming to obtain a H2-rich syngas; therefore, a moisture content below 20% is

recommended. On the other hand, high biomass/air ratios increase the available oxygen for

combustion and thus, a flue gas with low hydrogen content is produced. Moreover, biomass/air

ratios between 0.25 and 0.3 are recommended in order to produce a high energy content syngas.

The effect of the ER in the energy and exergy efficiency of the air gasification was also evaluated.

High ER decreases the cold energy efficiency of the process because of the low energy content

syngas. Besides, the total exergy of the synthesis gas is also reduced since exothermic reactions are

more favorable at high ER values and hence, the content of CO2 and H2O is increased. From this

approach, it was evidenced that the composition of hydrogen and thus, the heating value of the

synthesis gas using as feedstock Coffee Cut-Stems (CCS) is higher than the case of Pinus Patula.

However, the relative high energy content of the syngas from both feedstocks can be used as an

indicator of the energy potential of these residues.

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