Faculty of Bioscience Engineering
Academic year 2015-2016
Experimental and modelling study of
pure-culture syngas fermentation for biofuels
production
Joren Vandecasteele
Promotor: Prof. dr. ir. Eveline Volcke and dr. Ramon Ganigué
Tutor: Md. Salatul Mozumder
Master’s dissertation submitted in partial fulfillment of the requirements for the degree of
Master in Bioscience Engineering: Chemistry and Bioprocess Technology
Faculty of Bioscience Engineering
Academic year 2015-2016
Experimental and modelling study of
pure-culture syngas fermentation for biofuels
production
Joren Vandecasteele
Promotor: Prof. dr. ir. Eveline Volcke and dr. Ramon Ganigué
Tutor: Md. Salatul Mozumder
Master’s dissertation submitted in partial fulfillment of the requirements for the degree of
Master in Bioscience Engineering: Chemistry and Bioprocess Technology
The author and supervisors give the permission to use this thesis for consultation and to copy
parts of it for personal use. Every other use is subject to the copyright laws, more specifically
the source must be extensively specified when using results from this thesis.
Ghent, 3 June 2016
The author,
Joren Vandecasteele
Promotors,
dr. Ramon Ganigué
Prof. dr. ir. Eveline Volcke
Preface
Performing my MSc thesis work at the Biosystems Control group and at Lequia was a journey
worthwhile taking however impossible without the support, guidance and insights of many
individuals. Hereby, I would like to use this opportunity to express my deepest appreciation
to everyone who supported and helped me during the study of this interesting topic.
First and foremost, I would like to express my special appreciation to my promotor dr. Ramon
Ganigué. I am extremely grateful for your trust in me by giving me the opportunity to carry
out my research at Lequia, in Girona. Many thanks for your guidance during my laboratory
experiments in Girona. Furthermore, I am eternally thankful for all the effort and patience
during this year and for encouraging my work. Somewhere, I also have to thank destiny to
bring you here at our faculty.
I would also like to thank Prof. dr. ir. Eveline Volcke for giving me the possibility to
complete my thesis at the Biosystems Control group and for the insightful comments and
encouragements that guided me in the right direction. To Md. Salatul Mozumder, I am
grateful for all your good suggestions that made my work a lot easier.
Further, I want to thank all the people of Lequia and in particular Sarah Ramió-Pujol for her
help with preparing the cultures. I would also like to express my gratitude to Angela Urrea,
Xavi Cases and Sandra Caro Romero for their company and assistance during my stay in
Girona.
Last but not least, I want to thank all my friends and family, because without their support
and believe in me I would not have come this far.
Joren Vandecasteele
Ghent, 3 June 2016
Table of contents
List of abbreviations
v
List of symbols
vi
Abstract
vii
Samenvatting
ix
1 Introduction and outline
1.1 Introduction: syngas fermentation as an alternative process for biofuels production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2 Literature review
2.1 Bioconversion of waste gases into valuable products . . . . .
2.1.1 Waste feedstock gasification and industrial off-gases
2.1.2 Syngas fermentation . . . . . . . . . . . . . . . . . .
2.2 Biochemistry of the biocatalysts . . . . . . . . . . . . . . .
2.2.1 Syngas fermenting bacteria . . . . . . . . . . . . . .
2.2.2 Wood-Ljungdahl pathway . . . . . . . . . . . . . . .
2.2.3 Energy conservation . . . . . . . . . . . . . . . . . .
2.3 Parameters affecting syngas fermentation . . . . . . . . . .
2.3.1 Temperature . . . . . . . . . . . . . . . . . . . . . .
2.3.2 pH . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.3 Fermentation medium . . . . . . . . . . . . . . . . .
2.3.4 Syngas partial pressure . . . . . . . . . . . . . . . .
2.3.5 Inhibitory compounds . . . . . . . . . . . . . . . . .
2.3.6 Bioreactor design . . . . . . . . . . . . . . . . . . . .
2.4 Commercialization of syngas fermentation . . . . . . . . . .
2.5 Conclusions and thesis objectives . . . . . . . . . . . . . . .
2.5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . .
2.5.2 Thesis objectives . . . . . . . . . . . . . . . . . . . .
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3 Lab-scale experiments of syngas fermentation
3.1 Experimental procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 Microorganism and culture medium . . . . . . . . . . . . . . . . . . .
3.1.2 Batch fermentation experiments . . . . . . . . . . . . . . . . . . . . .
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3.1.3 Determination of the volumetric mass transfer coefficient . . . . . . .
3.1.4 Analytical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.1 Experiment A - Batch fermentation of carbon dioxide and hydrogen .
3.2.2 Experiment B - Batch fermentation of carbon monoxide, carbon dioxide
and hydrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.3 Experiment C - Discontinuous fed-batch fermentation of carbon monoxide, carbon dioxide and hydrogen . . . . . . . . . . . . . . . . . . . . .
3.2.4 Determination of the volumetric mass transfer coefficients . . . . . . .
3.2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
25
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4 Modelling and simulation of syngas fermentation
4.1 Model development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Bioconversion reactions . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.2 Gas phase mass balances . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.3 Liquid phase mass balances . . . . . . . . . . . . . . . . . . . . . . . .
4.1.4 Mass transfer between gas and liquid phase . . . . . . . . . . . . . . .
4.2 Simulation procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.1 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.2 Model calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.3 Testing the goodness of the fit . . . . . . . . . . . . . . . . . . . . . .
4.3 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 Model calibration for biomass growth on carbon dioxide and hydrogen
4.3.2 Model calibration for biomass growth on carbon monoxide, carbon dioxide and hydrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
35
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5 Conclusions and recommendations
5.1 General conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Recommendations for future research . . . . . . . . . . . . . . . . . . . . . . .
65
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Bibliography
68
3.2
iv
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61
List of abbreviations
Abbreviations
Description
ABE
Acetyl-CoA
ACS
AOR
ATCC
ATP
CODH
CSTR
FBEB
Fd
FDH
NADH
NADPH
SLP
Syngas
THF
WLP
Acetone-butanol-ethanol
Acetyl coenzyme A
Acetyl-CoA synthase
Aldehyde oxidoreductase
American Type Culture Collection
Adenosine triphosphate
Carbon monoxide dehydrogenase
Continuous stirred tank reactor
Flavin-based electron bifurcation
Ferredoxin
Formate dehydrogenase
Nicotinamide adenine dinucleotide
Nicotinamide adenine dinucleotide phosphate
Substrate level phosphorylation
Synthesis gas
Tetrahydrofolate
Wood-Ljungdahl pathway
v
List of symbols
Symbol
Characterization
Unit
a
C
D
K
kH
kL
kL a
n
p
pi
R
ri
S
TR
V
UA
X
Y
α
θ
µ
ρj
φ
Specifc exchange area
Concentration
Diffusion coefficient
Saturation constant
Henry coefficient
Mass transfer coefficient
Volumetric mass transfer coefficient
Total number of moles in gas phase
Total pressure
Partial pressure of component i
Gas constant
Volumetric conversion rate of component i
Substrate
Transfer rate
Volume
Undissociated acetic acid
Biomass concentration
Yield coefficient
Inhibition coefficient
Parameter
Specific growth/production rate
Volumetric conversion rate of reaction j
Transformed parameter
m2 m−3
mol l−1
m2 s−1
mol l−1
mol l−1 atm−1
m3 m−2 h−1
h−1
mol
atm
atm
l atm K−1 mol−1
mol l−1 h−1
mol l−1
mol l−1 h−1
l
mol l−1
mol l−1
mol mol−1
subscripts/superscripts
ac
G
hy
L
I
max
opt
Acetate conversion
Gas phase
Hydrogenase
Liquid phase
Inhibition
Maximum
Optimum
vi
mol l−1 h−1
mol l−1 h−1
Abstract
The depletion of fossil fuel resources, coupled with the concerns over carbon dioxide emissiondriven global climate change have triggered the interest and development of more sustainable and environmentally friendly biomass-based processes. Syngas fermentation is a hybrid
thermochemical/biochemical technology, capable of converting the energy content of waste
feedstock into liquid biofuels and commodity chemicals in an efficient way. Furthermore, this
fermentation process can be applied to convert CO-rich industrial off-gases into biofuels and
other added-value products, which can be a very attractive way for reducing green house gas
emissions. The low production levels of biofuels, due to slow reaction rates, product inhibition
and mass transfer limitation, forms a barrier for the industrialization of syngas fermentation.
The scope of this thesis was focused on the production of acetate and ethanol by Clostridium
ljungdahlii on syngas (CO, CO2 and H2 ). C. ljungdahlii is an acetogenic bacteria, that under
anaerobic conditions can grow autotrophically on these gaseous substates and convert them
into organic acids (acetate) and alcohols (ethanol) through the Wood-Ljungdahl pathway.
The synthesis of acetate is known as acidogenesis. This acidogenic phase is growth-related
and is favored above the production of alcohols in favorable growth conditions. Production of
ethanol, which is desired during fermentation, happens in the solventogenic phase. This phase
takes place during unfavorable growth conditions, such as nutrient deficiency and lower pH.
Product inhibition, caused by high concentrations of acetic acid (undissociated), is another
factor which triggers ethanol production. Despite considerable research, it is still not clear
how exactly ethanol is produced during fermentation.
The objective of this master dissertation was to gain insights in the different reactions that
occur during syngas fermentation and translate the knowledge about this process into a
mathematical model. Up to now, little research has been focused on the development of
models, that can reproduce the outcome of this technology. In order to develop a macroscopic
model capable of describing this complex process, three different lab-scale batch experiments
were performed. These experiments consist of a CO2 and H2 batch fermentation, a syngas
batch fermentation and a discontinuous fed-batch fermentation on syngas. The proposed
model included six potential reactions. First of all, the growth of C. ljungdahlii, accompanied
with acetate production (acidogenesis), on all substrates was considered. Besides that, two
metabolic pathways towards the ethanol production (solventogenesis) were proposed. The
main focus lied on the prediction of the outcomes of the batch fermentation on carbon dioxide
and hydrogen.
Five models for the fermentation on CO2 and H2 were set up to test which model structure
could better describe the process. In Model A only biomass growth was considered. Model
B was further extended with the direct conversion of the substrates into ethanol. In the next
vii
model, the pathway towards the synthesis of ethanol was replaced with the re-assimilation of
acetate via acetyl-CoA into ethanol. Model D consisted of growth (with acetate production)
and the two different pathways for ethanol production. After calibration, it was concluded
from the simulation results that Model C was most capable of describing the experimental
data. Eventually, Model C was further extended with a sporulation term to slow down the
bioconversion of acetate, assuming that sporulation occurred at the end of the fermentation.
Due to the sporulation term Model E succeeded in deactivating the re-assimilation at the end
of the simulation, resulting in a decent fit between the simulation and the experimental data.
However, it cannot be concluded that sporulation really took place during fermentation.
viii
Samenvatting
De uitputting van fossiele brandstoffen, gekoppeld aan de bezorgdheid van de koolstofdioxide
emissie-gedreven klimaatsverandering heeft de interesse en de ontwikkeling van meer duurzame en milieuvriendelijke biomassa-gebaseerde processen opgewekt. Syngas fermentatie
is een hybride thermochemische/biochemische technologie, die het mogelijk maakt om de
energie-inhoud van afvalstromen in vloeibare biobrandstoffen en basischemicaliën op een efficiënte wijze om te zetten. Daarenboven kan dit fermentatieproces worden toegepast om
CO-rijke rookgassen om te zetten in biobrandstoffen en andere waardevolle producten, wat
een heel aantrekkelijke manier lijkt om broeikasgasemissies te reduceren. De lage productieniveaus van biobrandstoffen, te wijten aan de trage reactiesnelheden, product inhibitie en
de limitatie van massa transport, vormt een hinderpaal voor de industrialisering van syngas
fermentatie.
Deze masterproef is toegespitst op de productie van acetaat en ethanol door Clostridium ljungdahlii die syngas (CO, CO2 en H2 ) kan gebruiken als substraat. C.ljungdahlii behoort tot de
acetogens, die onder anaerobe omstandigheden autotroof kunnen groeien op deze gasvormige
substraten en ze converteren in organische zuren (acetate) en alcoholen (ethanol) via de
Wood-Ljungdahl pathway. De synthese van acetaat staat gekend als acidogenesis. Deze fase
is groei-gerelateerd en wordt geopteerd boven de productie van alcoholen in optimale groeiomstandigheden. De productie van ethanol, wat gewenst is tijdens de fermentatie, gebeurd in
de solventogenic fase. Deze fase vindt plaats in ongunstige groeicondities zoals een gebrek
aan voedingsstoffen en lagere pH. De product inhibitie, veroorzaakt door hoge concentraties
aan acetaat (ongedissocieerd), is een andere factor die aanleiding geeft tot de productie van
ethanol. Ondanks aanzienlijk veel onderzoek is het nog steeds niet duidelijk hoe ethanol
precies wordt geproduceerd tijdens de fermentatie.
De doelstelling van deze masterproef was het verwerven van inzichten in de verschillende
reacties die plaatsvinden tijdens syngas fermentatie en de kennis hiervan te vertalen in een
wiskundig model. Tot op heden, weinig onderzoek heeft zich opgelegd in de ontwikkeling
van modellen, die er in slagen de uitkomst van deze technologie te reproduceren. Om een
macroscopisch model te ontwikkelen die in staat is dit complex proces te beschrijven, werden
drie verschillende lab-scale batch-experimenten uitgevoerd. Deze experimenten bestaan uit
een CO2 en H2 batch-fermentatie, een syngas batch-fermentatie en een discontinue fed-batch
cultuur op syngas. Het voorgesteld model bevat zes potentiële reacties. Ten eerste, de groei
van C. Ljungdahlii, vergezeld met de productie van acetaat, werd in overweging genomen.
Daarnaast, werden twee metabolische routes voorgesteld voor de productie van ethanol. De
doelstelling was vooral toegelegd op het voorspellen van de resultaten afkomstig van de fermentatie met koolstofdioxide en waterstofgas. Vijf modellen voor de fermentatie van CO2 en
H2 werden opgesteld om na te gaan welk model het best in staat is om het proces te beschriix
jven. In Model A werd enkel de groei van biomassa in beschouwing genomen. Model B
werd verder uitgebreid met de directe conversie van de substraten in ethanol. In het volgende
model, werd de route, betreft de directe synthese van ethanol, vervangen door de re-assimilatie
van acetaat via acetyl-CoA in ethanol. Model D bestond uit celgroei (met de productie van
acetaat) en de twee verschillende manieren voor de productie van ethanol. Na calibratie,
kon geconcludeerd worden dat Model C het meest geschikt was om de experimentele data
te beschrijven. Tenslotte werd Model C verder uitgebreid met een term voor spoorvorming
om de omzetting van acetaat te vertragen, ervan uitgaande dat spoorvorming plaatsvond op
het einde van de fermentatie. Door de toevoeging van deze term, slaagde Model E erin de
re-assimilatie te deactiveren op het einde van de simulatie, wat een degelijke fit tussen de
simulatie en de experimentele data met zich mee bracht. Niettemin, is het geen zekerheid dat
spoorvorming effectief plaats vond tijdens de fermentatie.
x
Chapter 1
Introduction and outline
1.1
Introduction: syngas fermentation as an alternative process for biofuels production
The diminishing reserves of fossil resources, the adverse effects of global warming as a consequence of CO2 emission and dependency on foreign oil imports have led to an increasing
interest in renewable environmentally friendly energy resources (Lennartsson et al., 2014; Devarapalli and Atiyeh, 2015). The increasing deployment of technologies based on water, wind
and sunlight already make a considerable contribution for the demand of electricity. However, oil and other fossil fuels remain the primary source for the global energy demand and
worldwide production of chemicals and plastics. In order to deal with the consequences of
fossil fuels, the European Union has mandated member states that by 2020, 10% of transport
fuels should be derived from renewable sources (European Union, 2009).
Renewable biomass-based biofuels are a sustainable alternative for petroleum, coal and natural
gas as energy sources. Currently, biofuels are mainly produced from starch, sugar and seed-oil
based feedstock. First generation biofuels such as corn-based ethanol in the United States,
sugarcane-based ethanol in Brazil and rapeseed and soybean-based biodiesel in Europe have
been produced to meet the increasing energy demand (Naik et al., 2010; USDA, 2013; Sims
et al., 2010). Bioethanol, which accounted for 74% of the global biofuel production, has
an annual production up to 94 billion liters. (REN 21, 2015). However, the cost of the
crop-derived-carbohydrates is influenced by their value as primary human food or animal
feed which make them not cost-competitive with existing fossil fuels (Oakley, 2012). The
considerable ethical discussions around this subject triggered the so-called food-versus-fuel
debate (Lennartsson et al., 2014). The development of novel technologies that convert lower
cost and/or non-food based resources to biofuels is therefore of utmost importance.
The limitations of first generation feedstock are overcome by the development of second
generation technologies. Second generation biofuels are derived from lignocellulosic biomass
which do not compete for arable land (Munasinghe and Khanal, 2011). Second generation
biofuels are produced through two different conversion routes, namely a biochemical and thermochemical routes (Cherubini, 2010). The biochemical approach includes the pretreatment
(acid/alkaline hydrolysis, steam explosion or ammonia fiber explosion) of the lignocellulosic
components of the biomass to improve the availability for enzymatic hydrolysis to fermentable
1
Chapter 1. Introduction and outline
2
sugars, followed by the conversion of the sugars to ethanol using microorganisms. However,
this technology faces several challenges such as high pretreatment and enzyme costs and
the formation of inhibitory compounds such as hydroxymethylfurfural. The key obstacle of
lignocellulosic (cellulose, hemicellulose and lignin) fermentation is that it does not succeed
in converting the lignin fraction into bioethanol (Soetaert, 2013). On the other hand, the
thermochemical conversion includes the gasification of biomass into synthesis gas (syngas), a
gas mixture containing CO, CO2 and H2 as main components. Subsequently, the syngas is
converted to a range of liquid fuels and hydrocarbons over transition metal catalysts, which
is known as the Fischer-Tropsch process (Cherubini, 2010). The major drawbacks of the
chemical catalytic process are the intensive operation cost due to the high operation temperature and pressure, low catalytic specificity, inactivation of catalysts by toxic compounds,
requirement for specific substrate ratios and expensive metal catalysts (Chatterjee et al., 1996;
Bredwell et al., 1999; van Steen and Claeys, 2008).
An alternative approach for the production of biofuels is ’syngas fermentation’, a biotechnological process that is currently undergoing intensive research and development. This hybrid
thermochemical/biochemical process, capable of converting gas by biocatalysts into ethanol
and other added-value products, is a promising technology and is considered to be more attractive due to several benefits over the biochemical pathway and the Fischer-Tropsch process
(Mohammadi et al., 2011). A well-studied microorganism is Clostridium ljungdahlii, an acetogenic bacteria that under anaerobic conditions can grow either hetero -or autotrophically.
This model-organism is capable to convert syngas or CO-rich industrial off-gases (e.g. steel
mill off-gas) into organic acids (e.g. acetate) and alcohols (e.g. ethanol) through the WoodLjungdahl pathway (WLP), also known as the reductive acetyl-CoA pathway (Oakley, 2012).
Despite the considerable research, syngas fermenting plants are still at a pre-commercial
stage. Syngas fermentation faces numerous challenges to establish this bioconversion process
at commercial scale. Barriers such as redirecting the metabolic pathway towards ethanol
production, high product recovery costs, slow reaction rates and product inhibition prevent
the economic feasibility of the fermentation process. Furthermore, the mass transfer limitation due to the low solubility of the gaseous substrates is another major challenge for the
development of plants at full-scale (Cherubini, 2010). The aim of this master dissertation
is to gain insight in the different reactions that occur during syngas fermentation and the
process parameters (pH, product inhibition, gas pressure) that affect the performance of this
novel process, while developing a mathematical model, capable of describing this biochemical
process. It is important to highlight that currently little research has been done on developing
such model.
1.2
Outline of the thesis
The master dissertation starts with a literature review in which the pathway is discussed
properly followed by a summary of the most important process parameters that affect growth
and ethanol productivity (chapter 2). In chapter 3, the three performed lab-scale fermentation
processes by C. ljungdahlii are presented in the first section. In the second section, the results
of the experiments are discussed in detail. The development of the syngas fermentation model
is explained in chapter 4. The model consist of six different reactions that includes biomass
growth and acetate production from either CO or CO2 and H2 , and two different pathways
Chapter 1. Introduction and outline
3
towards the production of ethanol. Besides the development of the model, the simulation
procedure (sensitivity analysis and model calibration) is explained in this chapter. Chapter
4 shows also the calibration of five different models to simulate the experimental data from
the fermentation on carbon dioxide and hydrogen. Finally, the general conclusions and some
recommendations on future research are given in chapter 5.
Chapter 2
Literature review
The literature review first starts with a general overview of the syngas fermentation process and the potential sources of the gaseous substrates (section 2.1). The biochemistry of
the syngas fermenting bacteria is discussed in section 2.2. Section 2.3 deals with the most
import process parameters that influences cell growth and product formation, with ethanol
production in particular. An overview of the current leading companies in field of syngas
fermentation is given in section 2.4. Finally, a general conclusion of the literature review is
given together with the objectives of the master’s dissertation.
2.1
Bioconversion of waste gases into valuable products
In this section, first the different sources of the substrate gases for syngas fermentation are
discussed. The gases can either be derived from industrial off-gases or through the gasification
of waste feedstock. Subsequently, the overall process of syngas fermentation as a hybrid
technology is more discussed in detail.
2.1.1
Waste feedstock gasification and industrial off-gases
The valorization of anthropogenic waste through bioconversion to new added-value chemicals is an important contribution towards sustainability. Agricultural residues (e.g. corn
stover, etc.), industrial byproducts (e.g. sugarcane bagasse, seed cake) and municipal solid
waste contain lots of reusable carbon fractions. However, some of these wastes are poorly
biodegradable and cannot be easily converted to new valuable products by microorganisms
since they usually contain complex structures. Gasification is a mature technology to handle
the processing of these complex wastes (Drzyzga et al., 2015).
Gasification is the thermochemical conversion of carbonaceous biomass in the presence of
an oxidizing agent that takes place at high temperatures (500°C - 1400°C). The gasifying
medium is typically air, pure oxygen, steam or a mixture of these (Morrin et al., 2012). The
lignocellulosic structure (i.e. cellulose, hemicellulose and lignin) of the biomass has to go
through several stages before turning into a gaseous mixture called syngas. Unlike the conventional first generation technologies, which use food crops, or the biochemical technologies,
which only convert cellulose and hemicellulose but not lignin, the thermochemical approach
4
Chapter 2. Literature review
5
succeeds in converting the entire structure of the biomass into a homogeneous substrate. The
produced syngas contains mainly carbon monoxide (CO) and hydrogen (H2 ) with varying
amounts of carbon dioxide (CO2 ), methane (CH4 ), water vapor and a variety of impurities
such as hydrogen sulfide (H2 S), sulfur dioxide (SO2 ), ammonia (NH3 ), nitrogen (N2 ), hydrogen cyanide (HCN), carbonyl sulfide (COS), oxygen (O2 ), chlorine compounds, mono-nitrogen
oxides (NOx ), tars and ash. The composition of syngas depends on several factors such as
properties of the biomass, type and design of the gasifier, operation conditions like oxidizing
agent equivalence ratio and temperature and pressure of gasifier. Gasifier types involve fluidized bed and fixed bed (downdraft or updraft) gasifiers (Griffin et al., 2012; Kumar et al.,
2009; Xu et al., 2011).
In addition to syngas, industrial waste streams containing CO, CO2 and H2 can also be used
to produce biofuels and chemicals. Carbon monoxide is a low cost, energy rich byproduct
of partial combustion of coal, oil or other carbon compounds. For example, during the
production of iron ore and steel, significant quantities of CO are inevitably produced. Syngas
fermentation could be a sustainable solution to convert such CO-rich industrial off-gases into
bioethanol (Oakley, 2012). Also waste gases containing hydrogen can be used for syngas
fermentation. Hydrogen is as it happens a valuable byproduct of many chemical industries.
The largest sources are the production of chlorine, sodium chlorate and ethylene/styrene. A
lot of chemical manufacturers burn hydrogen but do not utilize the full potential of this gas.
So the bioconversion of H2 to ethanol for instance, would be a better solution (Ballard, 2011).
Several industrial processes also emit CO2 through chemical reactions. Carbon dioxide is the
primary greenhouse gas emitted through human actions. In order to reduce CO2 emissions
and further global warming, CO2 derived from the production of metals such as iron and
steel and the production of chemicals, can be used as a substrate gas for syngas fermentation
(EPA, 2015).
2.1.2
Syngas fermentation
Fermentation processes have been increasingly used for industrial production of chemicals,
pharmaceuticals, detergents, bioplastics and biofuels. At the moment, industrial biotechnology is one of the most promising, innovative approaches towards lowering greenhouse gas
emissions. Fermentation of renewable raw materials is considered as an important technology
to reduce the dependency on products derived from fossil resources (Formenti et al., 2014).
Most fermentation metabolites are traditionally produced from sugar substrates derived from
food crops such as corn, sugar cane and sugar beet. Instead of using sugars as carbon source
which serve as the primary source for human food and animal feed, certain anaerobic bacteria
have the capability to use inorganic carbon, such as industrial waste gases or syngas, as substrate and convert them into valuable products. Ethanol is one of the most desirable product
of the syngas fermentation process and is mainly used as additive to gasoline. Ethanol blended
with transportation fuels at typical ratios (E10, E15 and E20) acts as an oxygenating agent
improving the combustion efficiency and reducing the emission of air pollutants (Abubackar
et al., 2011; Oakley, 2012). Butanol is another valuable alternative for liquid transportation
fuels. Butanol-gasoline blends have no restrictions and because of the higher energy content
more research is going in the direction of butanol fermentation (Ndaba et al., 2015). Besides ethanol and butanol, other byproducts such as 2,3-butanediol, acetic acid and butyric
acid can also be produced through syngas fermentation and can be a valuable source for the
Chapter 2. Literature review
6
production of chemicals and plastics.
A schematic overview of biomass gasification integrated with syngas fermentation is shown in
Figure 2.1. This technology contains a gasification and clean-up step followed by fermentation
and downstream processing of the fermentation broth (Devarapalli and Atiyeh, 2015). After
gasification, as discussed above, the gas has to pass through a series of cleanup units, such
as cyclones, filters, wet scrubbers and catalytic crackers, to remove poisonous components
(Woolcock and Brown, 2013). The effects of the impurities on the bacteria are discussed in
section 2.3. The hot syngas is also passed through a heat recovery exchanger to recover the
heat. The heat can be used to produce high pressure steam to generate renewable power
(INEOS Bio, 2012). After treatment of the gas, the syngas fermentation process can finally
start. The cleaned substrate gas is compressed and fed to a fermentor along with fresh
media. Industrial waste gases can be fed directly to the fermentor. To supply the bacteria
with a sufficient amount of gas, the fermentor medium is agitated to improve the gas-liquid
transfer. Continuous stirred tank reactors (CSTR) are typically used for syngas fermentation.
However, research towards more cost-efficient bioreactor designs is rapidly increasing. The
process occurs at relatively low temperatures (35°C - 42°C), a pH range of 4 - 6 and anaerobic
conditions. This means whenever the bacteria are exposed to air they die and thus pose
no threat to humans and the environment outside the fermentor. During the fermentation
process exhaust gas (unconverted syngas) from the bioreactor is cleaned and combusted to
generate additional electricity (Griffin et al., 2012; INEOS Bio, 2012). Another possibility is
to recycle this tail gas to the fermentor. The fermentation broth is sent to the downstream
processing to recover ethanol or other valuable products. The bacteria are first removed
from the aqueous phase and subsequently recycled to the bioreactor to maintain high cell
concentrations. In other cases, the bacteria are for instance sent to an anaerobic digestion
system to produce biogas (Griffin et al., 2012). Cell separation can be accomplished by
centrifugation, membrane filtration or other equipments. The azeotrope mixture with acetic
acid as byproduct is then passed through a distillation column where a maximum of almost
96% ethanol is produced. The water at the bottom of the column is recycled back to the
fermentor. To obtain anhydrous bioethanol, which has the right capacities to blend with
gasoline, a molecular sieve is employed (Gaddy et al., 2003; INEOS Bio, 2012).
Chapter 2. Literature review
7
Figure 2.1: Process scheme of continuous syngas fermentation with product recovery (Mohammadi
et al., 2011).
2.2
Biochemistry of the biocatalysts
Among the different microorganisms, able of metabolizing syngas, acetogens have been of
prime interest due to their ability of producing biofuels. In this section the Wood-Ljungdahl
pathway, the metabolic pathway of acetogens, is discussed together with the production of
acetate and ethanol. To give an impression of how difficult it is to understand this pathway
a general introduction is given about their energy conservation.
2.2.1
Syngas fermenting bacteria
Syngas as a building block for the synthesis of various biofuels and chemicals can be metabolized by a diverse range of microorganisms. These organisms, including phototrophic bacteria,
acetogenic bacteria, aerobic carboxydotrophs and methanogenic bacteria have been isolated
from numerous habitats such as terrestrial soils, marine sediments, feces, and even termite
guts (Karnholz et al., 2002; Latif et al., 2014; Drzyzga et al., 2015). The best-studied microorganisms that are able to synthesize multi-carbon organics are predominantly acetogens. Acetogens are a group of obligate anaerobic bacteria that can grow either chemoorganotrophically
on organic carbon or chemolithotrophically on CO, CO2 and H2 and ferment them through
the reductive acetyl-CoA pathway with acetate as their main product. Most acetogens are
gram-positive bacteria and are for a greater part Clostridium and Acetobacterium species.
Among the acetogens, Acetobacterium woodii, Alkalibaculum bacchi, Butyribacterium methylotrophicum, Clostridium aceticum, C. ljungdahlii, Clostridium thermoaceticum, Clostridium
autoethanogenum, Clostridium ragsdalei and Clostridium carboxidivorans have been most investigated (Munasinghe and Khanal, 2011; Mohammadi et al., 2011; Michael T. Madigan,
John M.Martinko, David A. Stahl, 2012). Acetogenic bacteria, such as A. woodii, are only
able to produce acetate while other acetogens are also capable of producing other products,
such as ethanol, butyrate, butanol and 2,3-butanediol. In Figure 2.2 an overview is given of
the different products and the microorganisms capable of producing that product. In order to
find an adequate acetogen, several factors need to be considered such as the growth and pro-
8
Chapter 2. Literature review
duction rate, the tolerance towards poisonous gas components, product yield and suitability
for metabolic engineering.
2.2.2
Wood-Ljungdahl pathway
The acetogens depend on the Wood-Ljungdahl pathway, also known as the acetyl-CoA pathway, to convert inorganic carbon into biomass and products. This non-cyclic pathway, which
is shown in Figure 2.2, was first discovered by Wood and Ljungdahl and is restricted to anaerobes. The WLP uses CO, CO2 and H2 as a source of energy and carbon with acetyl-CoA
as intermediate. During fermentation of syngas, electrons are obtained from the oxidation of
hydrogen, catalyzed by hydrogenase, or from the oxidation of CO to CO2 , catalyzed by CO
dehydrogenase (CODH) (Schuchmann and Müller, 2014). The acetyl-CoA pathway consists of
two separate branches: the eastern branch (methyl branch) and the western branch (carbonyl
branch). The methyl branch contains several steps, where one molecule of CO2 is reduced by
a sequence of different enzymatic reactions to the methyl group of acetyl-CoA. Carbon dioxide
can either directly been taken or can be produced by the conversion of carbon monoxide. In
the carbonyl branch either CO is derived from CO2 or CO is directly taken as the source for
the carbonyl group to synthesize acetyl-CoA. Acetyl-CoA is then either integrated in cellular
biomass or converted to metabolic products (Ragsdale and Pierce, 2008). Acetogens are able
to produce acetate and ethanol according to the following overall stoichiometric reactions:
0
4CO + 2H2 O −−→ 1CH3 COOH + 2CO2
∆G◦ = −175 kJ/mol
6CO + 3H2 O −−→ 1CH3 CH2 OH + 4CO2
∆G◦ = −224 kJ/mol
2CO2 + 4H2 −−→ 1CH3 COOH + 2H2 O
2CO2 + 6H2 −−→ 1CH3 CH2 OH + 3H2 O
0
0
∆G◦ = −95 kJ/mol
◦0
∆G = −104 kJ/mol
(2.1)
(2.2)
(2.3)
(2.4)
It is important to note that in case only CO is utilized to produce acetate a carbon efficiency
of only 50% will be achieved. This will even be lower for the formation of ethanol. The
carbon efficiency would be higher if the electrons are derived from H2 and CO is used as the
carbon source. However, the activity of the enzyme hydrogenase is inhibited in the presence
of CO. This means that CO in each case is consumed as both carbon and electron source.
See section 2.3 to learn more about the inhibition of hydrogenase.
Eastern branch
In the first step the thermodynamically unfavorable conversion of CO2 to formate is catalyzed
by formate dehydrogenase (FDH) (Ragsdale and Pierce, 2008). The formate undergoes then a
reaction with tetrahydrofolate (THF), which generates formyl-THF. This ATP-dependent reaction is catalyzed by 10-formyl-THF synthase. A enzyme cyclohydrolase is responsible for the
further conversion of the intermediate into methenyl-THF, by subtracting a water molecule.
In the next step the methenyl group is reduced to methylene by 5,10-methylene-THF dehydrogenase that uses either NADH or NADPH as reductant (Schuchmann and Müller, 2014).
Finally, 5,10 methylene-THF is reduced to methyl-THF. This reduction is catalyzed by a
oxygen-sensitive enzyme, called 5,10-methylene-THF reductase, that contains an iron-sulfur
Chapter 2. Literature review
9
cluster and uses ferredoxin (Fd) as electron donor (Ragsdale and Pierce, 2008; Clark and
Ljungdahl, 1984). However some assume NADH is the reductant in this enzymatic reaction
(Schuchmann and Müller, 2014). The last step of the eastern branch compromises the transfer
of the methyl group to the cobalt site bound to the corrinoid iron-sulphur protein (Co-FeSP), to form an organometallic and inactive methyl-Co(III) intermediate. This reaction is
catalyzed by the B12 -dependent methyltransferase (Ragsdale, 2008).
Western branch
In the other branch, a carbonyl group is formed which is then bound with the methyl group
to synthesize acetyl-CoA. The enzyme CODH plays an important role in the western branch
of the pathway. It catalyzes the reduction of CO2 to CO, which represents the largest thermodynamic barrier in the WLP (Schuchmann and Müller, 2014). However, this will only be
the case if CO is not available in the medium. The reaction is given by the following equation
(Eq. 2.5):
CO2 + 2H+ + 2e− −−→ CO + H2 O
(2.5)
The enzyme CODH is classified in two groups: (i) monofunctional CODH and (ii) bifunctional CODH. The first class of CODH catalyzes the oxidation of CO to CO2 , which can be
incorporated in the eastern branch (Ragsdale, 2008; 2004). This delivers the necessary reducing equivalents for the different reduction steps in the WLP and is similar to the water-gas
shift reaction. The second class is an association between CODH and ACS (acetetyl-CoA
synthase), which reduces CO2 to CO that provides the carbonyl group and ACS serves as the
catalyst for the synthesis of acetyl-CoA from the carbonyl group, CoA and the methyl group
of the methylated Co-FeS-P (Ragsdale, 2008; Menon and Ragsdale, 1996). As mentioned
before, the carbon monoxide molecule can also directly been taken from the medium, in case
CO is the only carbon source available. The mechanism of acetyl-CoA synthesis involves
several organometallic intermediates, the reaction sequence of this mechanism is described by
Ragsdale (2008).
The precursor acetyl-CoA
The produced acetyl-CoA molecule can subsequently be used for the formation of a whole
range of products. Acetogens all contain the genes to assimilate acetate, but not all of them
have the capacity to also produce other metabolites. In this thesis the focus especially lies
on acetogens capable of producing acetate and ethanol. The description of the reaction
mechanisms of other important metabolites represented in Figure 2.2 are beyond the scope
of this thesis.
The generated acetyl-CoA is an ideal precursor for the synthesis of acetate, which is produced
by the action of two enzymes: prosphotransacetylase and acetate kinase (Figure 2.2). In this
reaction one mol of ATP is formed and the released CoA molecule is recycled (catalytic
function). In the presence of acetate kinase, acetate-phosphate is converted to acetate and
ADP is phosphorylated to ATP (Ljungdhal, 1986). The fixation of the acetyl-group in acetate
is called acidogenesis. This acidogenic phase is associated with the growth phase and is favored
above the formation of alcohols in favorable growth conditions (nutrients available, optimal
pH and temperature).
Chapter 2. Literature review
10
In total, there is no net ATP produced in the WLP, which means there is another way to
generate energy (Schuchmann and Müller, 2014).
In addition, several anaerobic bacteria have demonstrated to produce ethanol from syngas
(Maddipati et al., 2011; Abubackar et al., 2012; Cotter et al., 2009a; Sakai et al., 2005).
These bacteria exhibit a biphasic behaviour. This has a good resemblance with heterotrophic
bacteria, such as Clostridium acetobutylicum and Clostridium beijerinckii, which are characterized by the so called acetone-butanol-ethanol (ABE) fermentation (Haus et al., 2011).
The conversion of acetyl-CoA to ethanol is known as solventogenesis. This phase is benefit during unfavorable growth conditions, such as low temperature, nutrient deficiency and
lower pH. Product inhibition, caused by high concentrations of acetic acid (undissociated),
is likely another factor which contributes to ethanol production. A bifunctional acetaldehyde/ethanol dehydrogenase was discovered to form acetaldehyde. The reduction of acetyl-CoA
to acetaldehyde is followed by the conversion to ethanol by ethanol dehydrogenase (Figure
2.2). Both enzymes are dependent of the electron carrier NADH. However, it is not certain
if acetyl-CoA is directly converted into ethanol or if acetate is re-assimilated via acetyl-CoA
into ethanol. Most likely the re-assimilation will proceed during fermentation, as this reaction results in a decrease of the inhibitor acetate and a lower pH. Research has also indicated
that some microorganisms, like C. ljungdahlii, contain genes encoding an aldehyde oxidoreductase (AOR). This respective enzyme is capable of catalyzing the reduction of acetate to
acetaldehyde. The further reduction to ethanol can be catalyzed by a monofunctional alcohol
dehydrogenase or by the bifunctional (Köpke et al., 2010; Bertsch and Müller, 2015). Several
researchers reported that consumption of acetic acid is associated with ethanol production
(Younesi et al., 2005; Maddipati et al., 2011). They mentioned that during the solventogenic
phase the acetate concentration declined together with an increase of pH and ethanol. The
presence of the enzyme AOR could be the reason for this phenomenon. However, this will
probably happen via acetyl-CoA, as mentioned before, since the direct conversion of acetate
into ethanol via acetaldehyde requires a Fd molecule as reducing agent. A genomic analysis
of C. ljungdahlii during fermentation on CO and CO2 revealed that there was a significant
up-regulation of the gene aor1 (gene of aldehyde oxidoreductase) in the mid-log phase. This
indicates that an alternative pathway of acetate re-assimilation to ethanol exists (Xie et al.,
2015). Klasson et al. (1992b) recommended that the production of ethanol is non-growth
associated, as the accumulation of ethanol results in a net consumption of ATP which does
not support the growth of the microorganisms. However, it is still not clear that ethanol is
actually a secondary metabolite. Various experiments haven proven that ethanol formation
also occurred during exponential growth (Cotter et al., 2009b; Kundiyana et al., 2010). This
would mean that ethanol production is mixed-growth associated. Obviously, more research
has to be performed to understand the mechanisms governing the switch between acid and
solvent production since the synthesis of ethanol is desired above the formation of acetate.
The influence of different operational conditions on ethanol production is described in section
2.3.
Chapter 2. Literature review
11
Figure 2.2: Schematic overview of the Wood-Ljungdahl pathway of acetogens including the formation
of important products (Daniell et al., 2012).
Chapter 2. Literature review
2.2.3
12
Energy conservation
ATP is the universal energy carrier which transports chemical energy within cells to support
the metabolic processes. During heterotrophic growth ATP is generated by substrate level
phosphorylation (SLP). Although SLP is involved in the WLP, the pathway yields no net
ATP. Hence, energy conservation in acetogens under autotrophic conditions has to rely on
a chemiosmotic ion gradient-driven phosphorylation to drive the ATP synthesis. This mode
of energy conservation couples an exergonic reaction to the translocation of ions across the
membrane. As a result, an electrical and/or ion gradient is settled across the membrane which
is the driving force for ATP synthesis achieved by a membrane-bound ATP synthase. At first,
there were two distinct differences in how the chemiosmotic mechanisms were established in
acetogens (Schuchmann and Müller, 2014). Organisms such as Moorella thermoacetica or C.
aceticum possess cytochromes and quinones to generate a proton gradient while A. woodii
establishes a Na+ gradient in order to generate ATP by a Na+ F1 F0 ATP synthase (Köpke
et al., 2010; Müller, 2003). However, the discovery of C. ljungdahlii led to the classification
of a third group. In experiments with media containing varying sodium concentrations no
change in growth was observed when C. ljungdahlii was grown in these media (Köpke et al.,
2010). The contrary was proven by Winner and Muller (1989), where no growth of A. Woodii
was observed with sodium concentrations below 2.5 mM. Besides that, C. ljungdahlii cannot
translocate protons across the membrane via cytochromes and quinones because these bacteria
do not dispose of these compounds.
C. ljungdahlii contains flavin-based enzymes that couples exergonic redox reactions with endergonic redox reactions. This process is called flavin-based electron bifurcation (FBEB)
(Buckel and Thauer, 2013). An example is the Rnf complex which plays an important role
in pumping protons extracellular for energy conservation during autotrophic growth (Tremblay et al., 2013). This Fd:NAD+ oxidoreductase catalyzes the oxidation of reduced Fd with
NAD+ as electron acceptor. The conversion of the negative ferredoxin molecule delivers an
electrochemical proton potential which in turn drives the phosphorylation of ATP (Figure
2.3) (Buckel and Thauer, 2013). Since Fd is required to obtain energy in the form of ATP,
the conversion of acetate via AOR will most likely only take place when Fd is in excess. It can
be expected that especially during the conversion of CO2 and H2 no re-assimilation of acetate
will happen via AOR, as Fd is necessary to convert CO2 into CO, which is needed in the
carbonyl branch. Recently, also soluble FBEB systems were identified in acetogens. A bifurcating [FeFe] hydrogenase which uses Fd and NADH as electron donors through the oxidation
of hydrogen has been identified. Studies also revealed the presence of a similar Nfn complex
of C. kluyveri in C. ljungdahlii (Nagarajan et al., 2013). This complex reduces NADP with
the oxidation of 1 mol of NADH and 1 mol of reduced Fd (Figure 2.3). Furthermore, it also
has been proposed that methylene-THF-reductase catalyzes an electron bifurcation reaction
in C. ljungdahlii (Köpke et al., 2010). Because of the existence of these protein complexes,
several combinations are possible that can occur during fermentation. A review of the energy
metabolism of model acetogens are presented in Schuchmann and Müller (2014) and Bertsch
and Müller (2015).
Without a doubt, the energy conservation of the acetogens is a subject that further has to
be figured out. Through better understanding of the energetic features that happen during
fermentation of CO, CO2 and H2 it will be more clear how to predict the carbon and electron
flow in the pathway and which reactions actually take place. Metabolic models are an useful
Chapter 2. Literature review
13
tool to get a better comprehension of the elements regulating the metabolic mechanisms
and the energy conservation. The first genome-scale metabolic model of C. ljungdahlii was
reconstructed by Nagarajan et al. (2013). The metabolic network of the enzymatic reactions
that take place in the organism was primarily reconstructed from the information identified
in its genome. Analysis of the metabolic model disclosed that FBEB plays a critical role in
energy conservation during autotrophic growth. Simulations revealed also that autotrophic
growth with H2 as electron source is infeasible when the bifurcating hydrogenase is NADHspecific. Such models can give an insight into the metabolic capabilities of acetogens and
could be an aid in the developing metabolic engineering strategies.
Figure 2.3: Overview of FBEB systems identified in acetogens that conserve energy during syngas
fermentation (Latif et al., 2014).
2.3
Parameters affecting syngas fermentation
The production of solvents such as butanol and ethanol are associated with co-production
of acetate. Bacteria prefer to produce acetate because they get more energy per carbon of
that product. Other metabolites (i.e. ethanol, butanol, 2,3-butanediol) are only produced
under certain conditions. The challenge is to control the operational conditions in order to
maximize the production of the desired product.
The efficiency of the whole process is affected by various process parameters, such as pH,
temperature, gas and medium composition, reducing agents, bacterial species and bioreactor
design have an effect on cell growth, product distribution and production rate. Therefore,
it is necessary to optimize fermentation by adjusting these process parameters in order to
Chapter 2. Literature review
14
improve the product yield and productivity. In particular understanding the conditions that
lead to solventogenesis is a major challenge to completely understand and it is essential for
future process control. Below, different factors with their findings from literature are discussed
within the scope of alcohol production.
2.3.1
Temperature
Temperature is an important parameter for fermentation processes. The temperature affects
microorganisms in two opposed ways. As temperature raises, chemical and enzymatic reactions proceed at greater rates and growth occurs faster. As a rule of thumb, rate constants of
chemical reactions double by increasing the temperature by 10°C. However, beyond a certain
temperature cell components can be irreversibly damaged (Michael T. Madigan, John M.Martinko, David A. Stahl, 2012). Besides the effect on substrate utilization, the temperature has
a major impact on the solubility of the syngas components in aqueous broths. According
to Henry’s Law, the solubility of CO, CO2 and H2 increases with decreasing temperature.
Ramió-Pujol et al. (2015) tested the influence of the temperature on C. carboxidivorans P7.
A lower growth rate was observed at 25◦ C. However, the culture incubated at 37◦ C was not
able to prevent ”acid crash”, a phenomenon stated by Maddox et al. (2000). A fast growth
rate is compatible with a fast accumulation of undissociated organic acids, which contributes
to inhibition of solventogenesis and thus results in low alcohol concentrations. In another
study, the effect of temperature on ethanol production of C. ragsdalei was examined. They
incubated the cultures at three different temperatures. Incubations performed at 42◦ C showed
unfavorable circumstances for cell growth, which obviously resulted in low ethanol and acetate concentrations. Compared to its optimum temperature of 37◦ C, C. ragsdalei’s growth
and ethanol production reached the highest concentrations under particular conditions at a
temperature of 32◦ C. The increased solubility of the gases due to the lower temperature could
be a reasonable explanation since this improves the gas-liquid mass transfer rates and consequently enhance the availability of the substrates (Kundiyana et al., 2011). Generally, syngas
fermentation uses C. ljungdahlii and C. carboxidivorans, which are mesophiles, as biocatalysts. Their optimum temperature ranges between 37◦ C and 40◦ C. However, thermophiles
such as M. thermoacetica and M. thermoautotrophica have a favorable growth temperature
between 55◦ C and 80◦ C. Although thermophilic conditions result in an decrease of solubility, it has to be noticed that higher temperatures result in increasing mass transfer due to
low viscosity (Munasinghe and Khanal, 2011). A summary of the optimum temperatures of
syngas fermenting bacteria are given in Table 2.1.
2.3.2
pH
The medium pH is found to have a strong influence in regulating substrate metabolism.
Parameters such as internal pH, membrane potential and proton-motive force are also altered
due to the fermentation pH. Numerous experiments have proven that pH affects the product
selectivity. As for temperature, microorganisms are only active in an narrow range of pH.
Table 2.1 gives an overview of the optimal pH of acetogenic bacteria. Decreasing pH leads
to cell growth reduction and affects thus the overall productivity of the process. However,
in acetogenic bacteria a shift from acidogenic to solventogenic phase takes action, in which
ethanol production is favored above the production of organic acids. The accumulation of
15
Chapter 2. Literature review
Table 2.1: An overview of the optimum temperature and pH of most important acetogens.
Microorganism
Acetobacterium woodii
Topt (◦ C)
30
pHopt
7-7.2
Acetogenum kivui
Alkalibaculum bacchi
Butyribacterium methylotrophicum
Clostridium aceticum
66
37
37
30
6.4
8.0-8.5
Clostridium autoethanogenum
37
5.8-6
Clostridium carboxidivorans
Clostridium coskatii
Clostridium ljungdahlii
38
37
37
6.2
5.85
6
Clostridium ragsdalei
Moorella thermoaceticum
37
55-60
5.5-6
6.8
Moorella thermoautotrophicum
58
6.1
8.5
Reference
Balch et al. (1977),
Genthner and Bryant (1987)
Leigh et al. (1981)
Allen et al. (2010)
Grethlein et al. (1991)
Sim et al. (2008),
Braun et al. (1981)
Abrini et al. (1994),
Kopke et al. (2011)
Liou et al. (2005)
James A. Zahn (2012)
Tanner et al. (1993),
Daniell et al. (2012)
Lewis et al. (2010)
Fontaine et al. (1942),
Drake and Daniel (2004)
Savage et al. (1987)
acidic organic products lowers the pH, which as result ceases the synthesis of acids and
triggers solvent production (Abubackar et al., 2012). The switch to solventogenesis, which
desired in syngas fermentation, is postulated as a survival strategy for the low external pH.
Products like acetic acid or butyric acid are weak organic acids, which permeates through the
cytoplasmic cell membrane in undissociated form and decrease the internal pH by carrying
H+ ions. Besides a pH-fall in the cytoplasm, the anions derived from the lipophylic acids can
disrupt the activity/function of essential cel components with inhibition as a result (Debevere
et al., 2011). DNA damage, inhibition of metabolic reactions and altering the cel membrane
by released anions are several examples of the toxicity of undissociated acids (Sakai et al.,
2005).
In contrast to other microorganisms, acetogens are unable to maintain their intracellular pH at
approximately constant level. As an alternative, they keep more or less a constant pH gradient
across the membrane (Gottwald and Gottschalk, 1985). However, a major obstacle in driving
the metabolism towards solvent production by lowering the pH, is the reduced productivity.
Cotter et al. (2009b) verified that lowering the initial medium pH from 6.8 to 5.5 had a
negative effect on the ethanol production of C. ljungdahlii. To prevent this drawback, Richter
et al. (2013) thought of two-stage continuous system to improve the ethanol productivity of
C. ljungdahlii. The first phase or growth phase (CSTR) was operated at pH 5.5 to preserve an
optimal growth of C. ljungdahlii while producing acetate. In the second phase (bubble column
reactor) the pH was lowered to a range of 4.4 - 4.8 to trigger solventogenesis. The reactor
in stage two was provided with a cell recycle system to maintain high cell concentrations (10
g l−1 ). After 1517 h of operation the ethanol concentrations of reactor 1 and 2 were 0.529
g l−1 and 19.707 g l−1 , respectively. The results indicate that the shift of pH improved solvent
production tremendously. Besides the high concentration, the ethanol productivity (stage
two) is also promising. A productivity of 0.374 g l−1 h was observed, which is closing in
Chapter 2. Literature review
16
the distance between the yeast-based commercial bioethanol plants (1.25 - 3.75 g l−1 h−1 ).
Although, the external pH is obviously a crucial factor for the metabolic change towards the
solventogenic phase, further research is a necessity to get a better understanding of how to
anticipate exactly in this process.
2.3.3
Fermentation medium
Besides, a sufficient amount of carbon and energy sources, microorganisms need vitamins,
mineral nutrients and trace metals to maintain a high metabolic activity. Additionally, the
medium is often supplemented with yeast extract to provide nitrogenous compounds (Mohammadi et al., 2011). As mentioned before, there is a hypothesis that considers that the
production of solvents is favored above acid production by acetogenic bacteria in non-growth
conditions (James L., 1992). Hence, nutrient limitation would enhance ethanol production.
Such case was studied by James L. (1992), by lowering the initial concentration of yeast extract (0.005%, 0.01%, 0.05%) in the medium, a molar product ratio of ethanol/acetate of 0.11
was reached while at higher concentrations (0.1% and 0.2%) a ratio of approximately 0.05
was achieved. However, an experiment performed by Cotter et al. (2009a) illustrated that
media without yeast extract resulted in resting C. ljungdahlii cultures with limited metabolic
activity and low concentrations of ethanol and acetate. Although, nutrient limitation induces
metabolic shift towards solventogenesis, it can cause significant loss in cell viability and result
in reduced product formation. So a minimum concentration of for example yeast extract is
necessary to provide the required nutrients. Since yeast extract, with an industrial price of
9.2 $ kg−1 , is an expensive component several alternatives such as corn steep liquor (CSL)
and cotton seed extract have been tested. Clostridium strain P11 or C. ragsdalei exhibited
an 60% increase of ethanol from 6.1 to 9.6 g l−1 in an medium containing respectively 1
g l−1 yeast extract and 20 g l−1 CSL (Maddipati et al., 2011).
The addition of reducing agents in liquid media of Clostridium species has shown to enhance
solventogenesis (Rao and Mutharasan, 1987). Reducing agents are added to reduce the redox
potential en to prevent inhibition of oxygen (Mohammadi et al., 2011). By lowering the
redox potential an altered electron flow is caused, which directs the carbon flow to alcohol
production (Klasson et al., 1992b). Reducing agents acts as electron carriers that through a
redox reaction are oxidized and donate the electrons to biological carriers such as NAD+ and
NADP+ (Babu et al., 2010). Since solventogenesis requires high levels of NADH, as discussed
in section 2.2, reducing agents will enhance the production of alcohols . As a matter of
fact, the activity of enzymes like aldehyde dehydrogenase and alcohol dehydrogenase will be
increased (Devarapalli and Atiyeh, 2015). Klasson et al. (1992b) observed that by adding small
quantities (30, 50 and 100 ppm) of reducing agents such as sodium thioglycolate, ascorbic
acid, menthyl viologen and benzyl viologen a higher production of ethanol was accomplished.
However, the outcome of the experiments with 100 ppm were growth-limited cultures. The
most common reducing agents used are cysteine-HCl and sodium sulfide (Abubackar et al.,
2011).
Trace metals act as cofactors or coenzymes that are necessary to provide catalysis performed
by enzymes. Saxena and Tanner (2011) studied the effect of trace metals (Co2+ , Cu2+ , Fe2+ ,
Mn2+ , Mo6+ , Ni2+ , SeO4 – and WO4 – ) on cell growth, enzyme activities and ethanol and
acetate production by C. ragsdalei. The depletion of several metals such as Fe2+ , Ni2+ and
Co2+ can inactivate the metalloenzymes present in the acetyl-CoA pathway.
Chapter 2. Literature review
2.3.4
17
Syngas partial pressure
The effect of the partial pressure of different gas components plays an important role in the
overall process efficiency. The availability of the substrate gases CO and H2 , which have
a low solubility, can be improved by applying higher partial pressures. It has been proven
experimentally that increasing the partial pressure of CO (pCO ) has a major effect on the cell
growth. In a syngas fermentation with C. carboxidivorans P7, increasing pCO from 0.35 to 2.0
atm raised the cell concentration from 0.20 to 1.08 g l−1 after 72 h, respectively. Furthermore,
only with the experiments conducted at high pCO (1.35 and 2.0 atm) acetate consumption
was accompanied by ethanol production (Hurst and Lewis, 2010). An explanation for this
may be due to the utilization of excess electrons, coming from the oxidized CO, for conversion
of acetate into ethanol. Another effect of increasing the pressure of CO is the inhibition of
the enzyme hydrogenase. Kim et al. (1984) conducted an experiment with C. acetobutylicum
in a glucose containing medium which was exposed to different partial pressures of CO.
The growth rate as the activity of hydrogenase were slow down in cultures with a higher
concentration of CO in the headspace. Skidmore (2010) found out that an partial pressure of
0.084 atm CO already 90% of the hydrogen uptake inhibited. The effect of syngas impurities
on hydrogenase is discussed in the next subsection. Younesi et al. (2005) conducted an
experiment on C. ljungdahlii where the headspace consisted of an initial syngas composition
of 10% CO2 , 15% Ar, 20% H2 , 55% CO at various total pressures. Since hydrogenase is
inhibited by the presence of carbon monoxide, consumption of CO2 and H2 only occurred
after CO depletion. Carbon dioxide is essential for the western branch of the pathway, in
which CO2 acts as the precursor for the methyl-group of acetyl-CoA. If CO is the sole carbon
source, the reducing power is delivered via the water-gas shift reaction catalyzed by CODH.
The rate of this reaction will increase with increasing pCO and decrease with increasing partial
pressure of CO2 (pCO2 ) (Abubackar et al., 2011). However, the effect of combined CO and
CO2 feed was examined using Butyribacterium methylotrophicum as microorganism. The
fermentation with the gas mixture (70% CO and 30% CO2 ) showed a higher growth rate and
a higher acetate concentration compared to the fermentation with CO as only carbon source
(Heiskanen et al., 2007). This might indicate that a direct uptake of CO2 in the methyl
branch instead of first oxidizing CO enhances the production of acetate.
2.3.5
Inhibitory compounds
Removal of syngas contaminants depends upon the effect of the impurity on the syngas
application. In case of fermentation, autotrophs are able to grow on CO, CO2 and H2 .
However, biomass-derived syngas also contains several components that can inhibit the cell
growth. Alongside the substrate gases, the following impurities can also be found in gasified
biomass: H2 S, SO2 , NH3 , N2 , HCN, COS, O2 , chlorine compounds, NOx , tars and ash.
For instance tars, defined as condensable organic compounds, promote cell dormancy and alter
the distribution of acetate and ethanol production in C. carboxidivorans P7 cultures (Ahmed
et al., 2006). A common method for tar removal is catalytic cracking (secondary method) at
high temperatures that provides additional syngas by tar reforming (Kumar et al., 2009). In
the study performed by Ahmed et al. (2006), hydrogenase was still inhibited after filtration
of the tars. Potential inhibitors of hydrogenase are O2 , CO, acetylene and NO. If the enzyme
hydrogenase is inhibited, electrons are obtained from CO via CODH. This is inefficient for
18
Chapter 2. Literature review
the product formation, since CO is partly consumed for electrons with decrease of carbon
conversion efficiency as result. Another experiment with C. carboxidivorans P7 confirmed
that NO is an inhibitor of hydrogenase. This non-competitive inhibitor stimulates ethanol
production but suppresses cell growth. The reason of increasing ethanol production is due to
the stimulation of alcohol dehydrogenase by NO. It was reported that concentrations below
40 ppm had no effect (Xu et al., 2011; Ahmed and Lewis, 2007).
The nitrogen content of the biomass will determine the amount of NH3 , HCN, and NOx that
will be formed during gasification. These unfavorable contaminants are capable of poisoning
the biocatalysts. Studies investigating the effects of ammonium reported that increasing
concentrations of NH4+ substantially inhibited cell growth. The cause of the inhibition is the
increasing osmolarity. Furthermore, even the activity of hydrogenase is restricted at low levels
of NH4+ . Despite the negative effects, ammonium is kept as an source of nitrogen for the
bacteria (Daniell et al., 2012; Xu et al., 2011). Removal of nitrogen contaminants is mainly
accomplished by the utilization of wet scrubbers, where the ammonia is absorbed in water.
Hot gas cleaning focuses on the decomposition of ammonia. This is done by oxidation in the
presence of catalysts that selectively oxidize the nitrogen components and thereby avoiding
undesired reactions (Kumar et al., 2009; Hu et al., 2012; Woolcock and Brown, 2013).
The fact that acetogens are anaerobes makes the presence of oxygen a critical obstacle. Very
low levels of oxygen are able to poison the microbial catalysts, especially iron containing
enzymes are inhibited. However, many species are capable to grow in microoxic conditions.
C. ljungdahlii tolerates low concentrations of oxygen (8%) and other contaminants (NOx ).
Research in these conditions showed higher ethanol and lower acetate concentrations due to
unfavorable growth conditions. Despite the higher ethanol concentration, in view of production rate this is undesirable (Whitham et al., 2015; Karnholz et al., 2002). Although the
chance of syngas containing oxygen is minimal, oxygen can be removed by sending the syngas
trough a palladium-based catalyst (Daniell et al., 2012).
The capability to tolerate certain concentrations of contaminants depends on the species.
Most biocatalysts are in comparison with chemical catalysts resistant to the poisoning of
sulfur compounds and have a higher tolerance to other impurities. Moreover, these sulfur
compounds act as a reducing agent to keep the environment anoxic (Kim and Chang, 2009).
However, further research and strain engineering is necessary to improve ethanol production
in presence of contaminants. By optimizing these acetogens the cleanup section would be far
more robuster and would result in lower operation costs.
2.3.6
Bioreactor design
The mass transfer of the gaseous substrates to the liquid phase is a rate-limiting step in
syngas fermentation. This limitation has a greater impact in comparison with the usual
aerobic processes, as the solubility of the different gases is much lower than that of oxygen.
Low mass transfer leads to a restricted availability of substrate which consequently leads to a
lower productivity (Bredwell et al., 1999). The mass transport rate is given by the following
expression:
dC
= kL a(C ∗ − CL )
dt
(2.6)
Chapter 2. Literature review
19
where kL (m s−1 ) is the mass transfer coefficient and a (s−1 ) the specific exchange area. It is
very difficult to measure both kL and a separately in a fermentation, therefore the two parameters are combined in the term kL a, the volumetric mass transfer coefficient. The parameter
illustrates the agitation capacity, it depends on the design and operating conditions of the
fermentor. The concentration gradient in equation 2.6 is considered as the driving force of
the mass transfer. CL is the dissolved gas concentration and C∗ represents the saturation
concentration of a component at the gas/liquid phase, assumed to be in equilibrium with his
gas phase as expressed by Henry’s Law (Garcia-Ochoa and Gomez, 2009; Soetaert, 2013).
Transport of syngas to the fermentation medium can be increased by either increasing the
volumetric mass transfer coefficient or by increasing the driving force. Since stirred tank
reactors are most employed for syngas fermentation, a common approach to achieve a higher
volumetric mass transfer rate is to increase the impeller rate. The increasing agitation speed
enhances the breakup of the bubbles, which results in a higher interfacial area for mass transfer. However, this is not economically feasible for further upscaling and commercialization
because of the high power requirements. Furthermore, the high rational speed can be harmful
for shear-sensitive microorganisms (Bredwell and Worden, 1998). The driving force can be
increased by using higher partial pressures, however high concentrations of carbon monoxide
could be inhibitory.
Other methods, such as high gas flow rates, innovative impeller design, advancement in baffle
design, other reactor configurations (e.g. CSTR, bubble columns, packed columns, trickle bed
reactors, membrane reactors etc.) and micro-bubble dispersers, have also been examined to
enhance the gas-liquid mass transfer (Drzyzga et al., 2015; Munasinghe and Khanal, 2010).
The coefficient kL a is a reliable parameter to compare the mass transfer rates between different bioreactor designs. Bredwell and Worden (1998) evaluated the effect of micro-bubble
sparging on the volumetric mass transfer coefficient. They determined that after initiating
micro-bubble sparging the kL a for CO underwent a six-fold increase. Another study demonstrated that increasing the gas flow rate in a CSTR resulted in a higher CO conversion.
However, if the gas flow rate is beyond the cell’s kinetic requirements the conversion efficiency would remain constant (Younesi et al., 2006). Recently, nanoparticles were used to
gain more bioethanol in syngas fermentation by C. ljungdahlii. The use of silica nanoparticles covered with hydrophobic functional groups such as methyl and isopropyl enhanced the
solubility of the substrate gases. It was reported that these nanoparticles at a concentration
of 0.3wt% led to an increase of 166.1% of ethanol (Kim et al., 2014).
In Table 2.2 an overview is given of fermentation experiments operated at different conditions.
From the table it can be noticed that in most cases either pure CO or syngas (CO, CO2 and
H2 ) is fed to the fermentor, while experiments on CO2 and H2 are left out. Since syngas is
the main product of waste feedstock gasification it is only natural to use this gas mixture in
research. Furthermore, carbon monoxide is also a waste product of several industries, such
as steel manufacturers, that be fed directly into a fermentor. Besides the use of different
gas substrates, also a lot of different bioreactor configurations are applied. Next to the
conventional stirred bioreactors, also bubble column and membrane bioreactors are applied
to overcome the mass transfer limitations. Overall, it is clear from the experiments that the
obtained ethanol concentrations are still to low.
a
d
c
b
The
The
The
The
Batch with
continuous feed
Batch
Batch with
continuous feed
Batch with
continuous feed
Bubble column
reactor
Monolith
biofilm reactor
Two CSTR
in serie
Batch
CSTR
Batch
Batch with
continuous feed
Batch
Butyribacterium methylotrophicum
Clostridium aceticum
Clostridium autoethanogenum
Clostridium autoethanogenum
Clostridium carboxidivorans
Clostridium carboxidivorans
Clostridium ljungdahlii
Clostridium ljungdahlii
Clostridium ljungdahlii
Clostridium ljungdahlii
Clostridium ragsdalei
Moorella sp. HUC22-1
CO2 :H2
[20:80]
CO:CO2 :H2 :N2
[20:15:5:60]
CO:CO2 :H2 :Ar
[55:10:20:15]
CO:CO2 :H2 :Ar
[55:10:20:15]
180
360
120
-
24
-
Syngasb
CO:CO2 :H2 :N2
[20:20:5:55]
-
-
-
168
120
432
Fermentation
time (h)
CO:CO2 :H2 :N2
[20:15:5:60]
CO:CO2 :H2 :N2
[20:15:5:60]
CO
[100]
CO
[100]
CO:H2 :Ar
[78:4:18]
CO
[100]
Gas substrate
(v/v %)
pH is not controlled.
composition is not known.
pH controlled in the first reactor (growth phase).
pH controlled in the second reactor (non-growth phase).
Bioreactor
configuration
Microorganism
1.15
0.74
0.21
6.1a
6.3a
2.34
6.8a
-
0.611
6.8a
-
6a
-
-
6a
5 - 7c and
4 - 4.5d
0.188
0.288
0.75
0.55
Cell dry weight
(g l−1 )
4.75
6
8.5
6
pH
Ethanol: 0.0598
Acetate: 3.012
Ethanol: 9.6
Acetate: 3.4
Ethanol: 0.55
Acetate: 1.3
Ethanol: 6.50
Acetate: 5.43
Ethanol: 0.306
Acetate: 0.145
Ethanol: 3
Acetate: -
Ethanol: 4.89
Acetate: 3.05
Ethanol: 3.20
Acetate: 2.35
Ethanol: 0.649
Acetate: 1.668
Ethanol: 0.907
Acetate: 0.910
Ethanol: Acetate: 1.23
Ethanol: 0.16
Acetate: 1.60
Products
(g l−1 )
Sakai et al. (2005)
Maddipati et al. (2011)
Younesi et al. (2005)
Mohammadi et al. (2012)
Kim et al. (2014)
Klasson et al. (1992b)
Shen et al. (2014)
Shen et al. (2014)
Abubackar et al. (2012)
Abubackar et al. (2015)
Sim et al. (2008)
Grethlein et al. (1991)
References
Table 2.2: Productivity of various biocatalysts with different operational conditions.
Chapter 2. Literature review
20
Chapter 2. Literature review
2.4
21
Commercialization of syngas fermentation
Several companies are currently pursuing commercialization of syngas fermentation to produce biofuels. Among these companies, LanzaTech, Coskata and INEOS Bio are capable of
operating large facilities for high ethanol production.
Since 2013, INEOS Bio succeeded in producing cellulosic ethanol at commercial scale in
Vero Beach, Florida. This is the first commercial facility in the world using gasification and
fermentation to convert biomass waste, such as municipal solid waste, yard waste, untreated
wood and construction and demolition debris, to bioethanol and renewable electricity (INEOS
Bio, 2012). Unfortunately, soon after operation of the plant they had to shut it down due to
the high sensitivity of the microorganisms to hydrogen cyanide in syngas. In order to prevent
further intoxication of the microorganisms, INEOS Bio had to install an additional cleanup
system (scrubbers) to reduce the HCN concentrations below 1 ppm (Jim Lane, 2014). This
joint venture project between INEOS Bio and New Planet Energy planned to have annual
output of eight million gallons bioethanol and 6 MW of generated power (INEOS Bio, 2012).
LanzaTech, founded in 2005, is a pioneer of commercializing technologies that converts carbonrich waste gases into biofuels and chemicals. At this point, LanzaTech has successfully exhibited bioethanol production at a pilot plant in Glenbrook, New Zealand and recently started
with two pre-commercial facilities in China, each producing 100 000 gallons a year (LanzaTech Inc., 2015). In 2014, they also started the operation of a demonstration plant in Taiwan,
where steel flue gas is converted into ethanol with a capacity of 100 kg d−1 . Not long ago,
LanzaTech announced to establish Europe’s first steel mill off-gas based fermentation process
to bioethanol at commercial scale, located in at ArcelorMittal’s steel plant in Ghent, Belgium. This project is a result of the partnership between LanzaTech, Primetals Technologies
and ArcelorMittal. It is projected to operate at full scale in 2018 with a total capacity of
47 000 tons of ethanol, where every ton of bioethanol reduces ArcelorMittal’s CO2 emissions
by 2.3 tons (ArcelorMittal, 2015). This is one of the many partnerships LanzaTech has in
which they provide innovative carbon capture solutions. Besides bioethanol production, they
also intend to provide a route to capture carbon form waste gases, produced by industries
such as steel manufacturing, oil refining and chemical production, and sequester them into
high-quality products, such as 2,3-butanediol which can be used to produce nylon and rubber.
Acetogens are known being able to produce only acetate, butyrate, 2,3-butanediol, butanol
and ethanol. At laboratory scale a lot of research is going towards increasing their range of
products. LanzaTech is working with Global Bioenergies to make an artificial pathway which
allows the bacteria to produce isobutylene from waste gases (LanzaTech Inc., 2016).
Coskata Inc. is an American company founded in 2006 which is focused on the bioconversion
of woodchips and natural gas into bioethanol. They dispose of an semi-commercial plant
which achieves 100 gallons ethanol per dry ton of wood. This company has also isolated and
patented a new Clostridium species, C. coskatii (James A. Zahn, 2012).
Chapter 2. Literature review
2.5
2.5.1
22
Conclusions and thesis objectives
Conclusions
Syngas fermentation has the potential to become a sustainable alternative for first and second
generation processes. One of the great benefits of syngas fermentation is that is able to capture industrial waste gases and transform them into energy rich biofuels and chemicals. The
production of biofuels and biochemicals through the bioconversion of syngas, derived from
non-food based feedstocks, and waste gases can also provide a solution for the increasing
energy demand and be an aid in reducing greenhouse gas emissions. Although this technology seems a promising alternative in the near future, in order to scale it up and make this
biotechnological process economical feasible significant efforts have to be made.
It is clear from section 2.3 that many factors, such as pH, temperature, reducing agents,
bioreactor configuration and gas composition, affect the productivity of the fermentation
process. However, to clear the path towards commercialization a better understanding of the
pathway is required to promote the formation of the desired products. Strain improvement
by means of metabolic engineering and synthetic biology can be an aid to overcome the low
production rates and yields.
2.5.2
Thesis objectives
The overall objective of the master thesis is to get a better understanding of this novel gasto-liquid technology, called syngas fermentation. Experimental analysis is a good way to gain
insight into new or poorly understood biosystems and to achieve more knowledge about the
behaviour of the system. The downside of this approach is that it is often very time consuming. Translation of the biosystem in a mathematical model is an useful way to reduce
the number of lab experiments and to save money. This brings us to the goal of the master’s
dissertation. The objective is to translate the knowledge about syngas fermentation into the
development of a bioprocess model. In order to build a mathematical model, several experiments had to be performed to obtain valuable experimental data for model calibration. The
mathematical model has to be capable of capturing the complexity of syngas fermentation
and the interaction between physical, chemical and biological processes. It should be able
to describe the process dynamics (products, substrate and biomass) and be a realistic representation of the fermentation process, to use eventually for scenario analysis, optimization
and control. Modelling this biotechnological process brings also the opportunity to understand which reactions actually could take place during syngas fermentation and which process
parameters have an influence on these reactions.
Chapter 3
Lab-scale experiments of syngas
fermentation
This chapter first gives a detailed explanation of the procedure that was applied for the three
batch-scale experiments. Subsequently, the results of three fermentations processes are more
discussed in detail.
3.1
3.1.1
Experimental procedure
Microorganism and culture medium
The acetogenic bacterium, Clostridium ljungdahlii DSM13528T was used in this study as
biocatalyst. The cultures were obtained from the German Collection of Microorganisms and
Cell Cultures (DSMZ). The bacteria were grown anaerobically on a Clostridial modified ATCC
(American Type Culture Collection) 1754 medium. The liquid medium used for culturing
differed from the original formulation in: (i) all soluble carbon sources, i.e. yeast extract,
fructose and NaHCO3 , were excluded and (ii) 2-(N-morpholino)ethanesulfonic acid (MES)
was used as pH buffer. The basal DSMZ 879 medium contained (per liter): 1 g NH4 Cl, 0.1 g
KCl, 0.2 g MgSO4 · 7 H2 O, 0.8 g NaCl, 0.1 g CaCl2 · 2 H2 O, 100 mM MES, 10 ml reducing
agent, 1 ml trace elements and 1 ml Wolfe’s vitamin solution. 1 mg of resazurin (10 mg/L)
was added as an indicator of anaerobic conditions and the pH of the medium was adjusted
to 6 with NaOH (1 mM). The prepared medium was dispensed into several anaerobically
butyl rubber sealed glass bottles. The glass bottles were boiled and degassed with nitrogen
before they were autoclaved at 121°C for 15 min. After cooling down, the MES buffer and
reducing agent from sterile anaerobic stock solutions were added to the liquid medium inside
an anaerobic chamber (gas mixture N2 :H2 :CO2 [90:5:5], Coy Lab Products, Michigan, USA).
The initial stock cultures were incubated in the 125 ml serum bottles with 25 ml of modified
ATCC 1754 medium and the headspace was flushed and pressurized to 100 kPa with syngas
consisting of CO, CO2 , H2 and N2 [32:8:32:28]. The cultures were kept active by weekly
transfer a 4% inoculum into new serum bottles.
23
Chapter 3. Lab-scale experiments of syngas fermentation
3.1.2
24
Batch fermentation experiments
In total three different lab-scale batch experiments were performed in order to obtain experimental data for model calibration. The fermentation experiments were performed in 25
ml anaerobic glass tubes containing 6 ml of prepared modified medium (free of organic carbon). Gas impermeable butyl rubber septum and aluminum crimp seals were used to seal
the tubes. Before inserting the medium, all the tubes were flushed with nitrogen gas for at
least 1 min to create an anaerobic atmosphere and were autoclaved at 121°C for 15 min.
Thereafter, the tubes were filled with 0.6 ml of exponentially growing inoculum, creating a
headspace volume of 18.4 ml. All activities with inoculum and prepared medium were done
in an anaerobic chamber. Note that for each experiment the pH was not kept constant during
the fermentation. This made it possible to see how the pH would change in function of the
acid concentration and if the decline of the pH had an effect on the process.
Experiment A - Batch fermentation of carbon dioxide and hydrogen
So far, most experiments on syngas fermentation either use syngas (CO, CO2 and H2 ) or
carbon monoxide only as substrate for their bacteria. These set-ups are predominately focused
on the consumption of CO. By feeding the culture with only CO2 and H2 it can give better
insights in how the culture responds in these circumstances. In this way the data can be used
to calibrate the specific parameters for these substrates. In the first set-up, the tubes were
flushed with substrate gas containing CO2 and H2 [20:80] and pressurized to a final pressure
of 2.5 atm. The gas was only injected at the beginning of the batch experiment. Since there is
not an internal standard available to determine the total pressure changes in the glass tubes,
a pressure transducer was used to measure the total pressure in the headspace.
The tubes were incubated in an orbital shaker Stuart incubator SI500 (Bibby scientific Ltd.,
OSA, UK) at the optimal temperature of 37°C and 150 rpm. The cultures were placed horizontally in the incubator to enhance the gas-liquid mass transfer. In order to present reliable
results, each experiment was done in triplicate. During the experiments three independent
tubes were taken for the determination of the gas composition, optical density, pH and acetate
and ethanol concentration at appropriate intervals. Gas samples (500 µl) were collected in
gastight syringes and injected in the gas valve of the gas chromatograph. The liquid samples
were filtered with a 0.2 µm membrane filter (nylon, Millipore, Germany) to remove the cells
and stored in a 2 ml vial (Agilent, borosilicate glass, 9 mm cap) at 4°C. The pH of the
medium was measured using a BASIC 20 pH meter (Crison, Spain). The cell densities of the
cultures were calculated by analyzing the optical density (absorbance) of the samples using a
UV-2501(PC) spectrophotometer (Shimazdu Corporation, Japan) at 600 nm. A calibration
curve was used to determine the cell dry weight. As all experiments were accomplished in
triplicate, it made it easier to detect outliers, experiments that did not have the same behaviour as the rest. Even a slight difference between the injection of medium and bacteria
could contribute to the variance of the results. The same can be said about the injection of
the gas. The outliers were excluded from the data set.
Chapter 3. Lab-scale experiments of syngas fermentation
25
Experiment B - Batch fermentation of carbon monoxide, carbon dioxide and
hydrogen
A similar procedure as the above described experiment was used in the second experiment.
In the second set-up the tubes were flushed with syngas containing CO, CO2 , H2 and N2
[32:8:32:28] and pressurized to a final pressure of 2.5 atm. These were incubated and monitored as in Experiment A. In this study, nitrogen gas was here used as internal standard to
calculate the total pressure in the headspace, since this inert gas is neither consumed nor produced by C. ljungdahlii. The same experimental procedure was used to analyze the different
aspects of the fermentation.
Experiment C - Discontinuous fed-batch fermentation of carbon monoxide, carbon dioxide and hydrogen
As third experiment, a discontinuous fed-batch fermentation was performed. In comparison
with experiment B, where syngas was injected at the start of the experiment, the headspace
of the tubes were flushed with fresh syngas periodically once a day to ensure replenishment
of gas substrates. In this way the fermentation of CO, CO2 and H2 was prolonged. The
same fermentation conditions were applied in this experiment, which means an incubation
temperature of 37°C , an agitation of 150 rpm and the gas was each time pressurized to a
pressure of 2.5 atm.
3.1.3
Determination of the volumetric mass transfer coefficient
To complete the mass balances of the model, an experiment was proposed to determine the
mass transfer coefficient of the tubes. The method used was a variation of the dynamic method
in which the increase of dissolved gas is measured over time (Garcia-Ochoa and Gomez,
2009). The anaerobic glass tubes filled with 0.066 l medium were flushed and pressurized to
a pressure of 1.8 atm (below the detection limit) with pure CO2 . To establish the same mass
transport, the agitation was performed at the same conditions (37◦ C and 150 rpm) like the
conducted experiments. As the uptake rate is unknown, inoculum was not used and thus
the experiment was performed without biological consumption of gas. In order to calculate
CL,CO2 , a transducer was used to measure the pressure of the headspace. The measured
pressures were expressed in mV, so a calibration curve was used to convert them into atm.
The pressure was measured each 20 seconds until a steady state was reached.
During the incubation the pressure dropped because of CO2 dissolving in the liquid medium.
The dissolved CO2 concentration was calculated by subtracting the initial pressure with the
measured pressure and using the ideal gas law to calculate the concentration in mol l−1 . The
saturation concentration was computed by using Henry’s law. To determine kL a Eq. 2.6 was
used. The integration of this equation can be expressed as:
ln(C ∗ − CL ) = −kL a · t + Cst
where Cst is the interception with the y-axis.
(3.1)
Chapter 3. Lab-scale experiments of syngas fermentation
3.1.4
26
Analytical methods
Gas analysis
In order to determine the gas substrate consumption, the gas phase was analyzed using a
gas chromatograph (Agilent 7890A GC system, Agilent Technologies, Spain) equipped with
a fused zeolite capillary column (HP-Molesieve, 30 m x 0.53 mm x 50 µm) and a thermal
conductivity detector (TCD) using helium as carrier gas. Each time the composition of CO,
CO2 , H2 and N2 (% vol) in gas samples was measured. The injector and detector temperatures
were set at 115°C and 275°C, respectively. The oven temperature was initially kept at 45°C for
6 min, and subsequently increased following a ramp of 8°C min−1 until a temperature of 70 is
reached. Then the column was gradually increased from 70°C to 130°C and from 130°C to
220°C at a rate of 5°C min−1 and 35°C min−1 , respectively. Finally, the temperature is
maintained at 220°C for 5 min.
Liquid analysis
Concentrations of the volatile acids and alcohols in the liquid samples were measured using
the same gas chromatograph but through another column. The gas chromatograph equipped
with a fused-silica capillary column (DB-FFAP, 30 m x 0.32 mm x 0.5 µm) and a flame
ionization detector (FID) with helium as carrier gas. The injector and detector temperatures
were set at 250°C and 275°C, respectively. The oven temperature was initially maintained
at 40°C for 1 min, and subsequently increased 5°C min−1 until 70°C. Then the column was
gradually increased from 70°C to 180°C and from 180°C to 250°C at a rate of 10°C min−1 and
35°C min−1 , respectively. Finally, the temperature was held at 250°C for 5 min. The liquid
samples were acidified beforehand and crotonic acid was used as internal standard.
3.2
Experimental results
This section presents the results of the three experiments. First, the fermentation with only
carbon dioxide and hydrogen is described. Secondly, the results of the batch fermentation
by C. ljungdahlii with syngas (CO, CO2 and H2 ) are discussed. Last of all, the fermentation
whereby the headspace was flushed with fresh syngas each time after a certain period is
described in section 3.2.3.
3.2.1
Experiment A - Batch fermentation of carbon dioxide and hydrogen
First of all, a batch fermentation was conducted where only carbon dioxide and hydrogen
were supplemented to the headspace of the tubes. In this way, it was possible to see how
the bacteria would behave in the presence of these two substrates. The absence of carbon
monoxide prevented the inhibition of hydrogenase and made it possible to directly take up
hydrogen along with carbon dioxide. C. ljungdahlii exhibited a typical growth curve with
an exponential growth phase (0 - 51 h), and a stationary phase (51 - 91.5 h) where the
biomass concentration stayed constant (Figure 3.1a). In this fermentation process a maximum
biomass concentration of 25.75 mg l−1 was measured at the end of the exponential phase.
Chapter 3. Lab-scale experiments of syngas fermentation
27
The concentration of acetate increased with the cell dry weight, since the acetate formation
is associated with the growth phase (Figure 3.1b). The production of acetate reached a peak
after 51 h and gradually decreased during the stationary phase. A downfall of acetate at 68.75
h was observed, the reason may be due to the conversion of acetate to ethanol to prevent
further inhibition of undissociated acetic acid or to overcome further decrease of pH (Fig.
3.1a). The re-assimilation of acetate resulted in an increase in pH of the fermentation broth
(Fig. 3.1b). A maximum acetate concentration of 0.885 ± 0.069 g l−1 was reached at 51 h.
Regarding the concentration of ethanol, it’s value increased up to a maximum of 0.778 g l−1 .
Both products reached a steady state after around 70 when CO2 was completely consumed.
The formation of ethanol started in a later stage of the logarithmic growth phase and finished
at the end of the stationary phase, as is shown in Figure 3.1b. There is a premise that the
production of alcohols over organic acids by acetogens is promoted in non-growth conditions
(Mohammadi et al., 2011). Nutrient limitation, high undissociated acetic acid concentration
or low pH are one of the factors that could induce the solventogenesis. It is hard to derive
some information from the results why exactly the ethanol production started after 30 h.
Mohammadi et al. (2012), who performed a syngas fermentation with C. ljungdahlii, noticed
a metabolic shift from the acidogenic phase to the solventogenic phase at a pH around 5. In
another experiment with C. ragsdalei a switch to ethanol production was observed during the
stationary phase when the pH was around 4.7 (Maddipati et al., 2011). The solventogenesis
in this experiment could already be observed at a pH of 5.48. So most likely there are
other factors that contribute to the ethanol production. As can be noticed from Figure 3.1a,
both ethanol and acetate concentrations changed during the stationary phase, probably by
the conversion of acetate, but in a later phase of stationary phase the conversion of acetate
reached a steady state even when there was still hydrogen available. The sporulation of the
bacteria could be a reason for this phenomenon. When the microorganisms sporulate, the
bacteria are deactivated which means they are no longer capable of performing any reactions.
According to Klasson et al. (1991) and Lee et al. (2008) increased solventogenesis is associated
with sporulation. It is feasible that the onset of solventogenesis in this experiment is related
with the sporulation. The total amount of carbon dioxide and hydrogen in the liquid and
gas phase is shown in Figure 3.1c. When looking at the consumption rate, 0.8871 mmol of
H2 were consumed while only 0.363 mmol of CO2 were consumed during the first 70 hours.
Summation of the total mol of carbon brought by the biomass, acetate and ethanol equals
to 0.369 mmol. Thus only a small gap can be found between carbon dioxide and the total
amount of carbon in products and cell dry weight.
Most of the research in literature is focused on syngas (CO, CO2 and H2 ) or CO fermentation.
Unfortunately, almost no research is done about the fermentation of CO2 and H2 by acetogens.
Sakai et al. (2005) studied the growth of a thermophilic bacterium, Moorella sp. HUC22-1,
in 125 ml serum bottles containing a gas mixture of CO2 and H2 [80:20] and incubated at
a temperature of 55°C. After approximately 180 h of batch fermentation 3.012 g l−1 acetate
and 0.21 g l−1 of biomass was obtained. Even though the fermentation took twice as lang, a
cell dry weight ten times the one of C. ljungdahlii was achieved. Despite the high cell yield,
only 0.0598 g l−1 of ethanol was produced. Furthermore, the synthesis only started after the
pH reached a value of 4.5. In comparison with mesophilic bacteria, like C. ljungdahlii, much
lower ethanol concentrations are achieved.
28
Chapter 3. Lab-scale experiments of syngas fermentation
(a)
(b)
(c)
Figure 3.1: Growth and production of Clostridium ljungdahlii on CO2 and H2 . Mean values and
standard errors of triplicates are shown. (a) Cell dry weight (mg l−1 ) of biomass (blue
dots) and pH (green diamonds). (b) Concentration (g l−1 ) of acetate (blue dots) and
ethanol (green diamonds). (c) Consumption and production of the different syngas components (mmol) during batch fermentation. CO2 (blue dots) and H2 (green diamonds).
3.2.2
Experiment B - Batch fermentation of carbon monoxide, carbon dioxide and hydrogen
The next fermentation process was also a batch fermentation, but instead of supplying the
headspace with CO2 and H2 , the Hungate tubes were filled with syngas. Under strict autotrophic conditions, both CO and H2 serve as an energy and electron source for cell growth
and product formation. Figure 3.2a presents the cell dry weight (mg l−1 ) and the change of
pH of C. ljungdahlii during uptake of the syngas mixture. Exponential growth of C. ljungdahlii was observed from 0 to 49.5 h before entering the stationary phase (49.5 - 96.5 h). A
maximum concentration of 59.90 ± 5.30 mg l−1 was reached at the end of the experiment. In
comparison with the growth on CO2 and H2 , the culture fed with syngas reached a cell dry
Chapter 3. Lab-scale experiments of syngas fermentation
29
weight 2.40 times higher than observed in previous experiment. This is because CO is more
energetic as it has a lower potential than H2 (Schuchmann and Müller, 2014). The pH of the
medium kept decreasing during the experiment from 6.05 to 5.04. The main reason of this
decline is the production of acetate. A maximum concentration of 2.160 ± 0.053 g l−1 was
achieved. The production of acetate started only after 25.5 h (Figure 3.2b). An initial lag
phase could be an explanation for the delay of acetate production by the culture. Besides
the accumulation of acids, ethanol was also produced, as shown in Figure 3.2b. In contrast
to the acetate production, the production of ethanol was low with a maximum concentration
of 0.257 g l−1 . Alcohol production of ABE fermenters typically happens at a pH ranging between 4.5 and 5 (Haus et al., 2011). This happens when their metabolic state shifts from the
acidogenic phase to the solventogenic phase to prevent further decrease of the pH, which else
would cause death or cell damage. This is also the case for acetogenic bacteria, both Klasson
et al. (1992b) and Mohammadi et al. (2012) observed a raise in ethanol concentration when
pH was switched to 4.5. The results of this experiment however show no increase of ethanol.
The ethanol concentration remained at a constant level. In other words, the culture failed
to switch to solventogenesis. This phenomenon has been also reported in other experimental
studies (Mohammadi et al., 2014; Ramió-Pujol et al., 2015). Like already mentioned in section 2.3, a fast accumulation of acids can cause a low alcohol production. The fast rate of
production of undissociated acetic acid could be an explanation for the low content of ethanol.
The depletion of the favored carbon monoxide and the low availability of hydrogen could be
another reason for the low ethanol concentrations. The formation of alcohols requires more
reducing power in comparison with the synthesis of acids. Exactly the same was observed
by Younesi et al. (2005). They saw only an increase of ethanol once the total pressure of
syngas was above 1.6 atm, thus when enough hydrogen was available. The experiments with
pressures between 0.8 and 1.4 atm resulted in ethanol concentrations of around 0.15 g l−1 .
This is comparable with the concentrations obtained in this experiment. Differences in acetate concentration could not be observed, the concentrations fluctuated around 1.1 g l−1 .
Figure 3.2c presents the total amount of moles of each component in both gas and liquid
phase. During the exponential phase consumption of CO came together with production of
CO2 (Figure 3.2c). CO can be reduced to CO2 by the action of CODH, providing electrons
and protons. The conversion of CO ended after 40 h, at that point a maximum amount of
CO2 was reached. Inhibition of the hydrogenase enzyme plays a role in the low consumption
of H2 . Carbon monoxide inhibits the hydrogenase activity and thus the utilization of H2 by
the organism. As long as CO was present in the fermentation broth the uptake by hydrogenase was reduced. After the depletion of CO a higher rate of H2 consumption was noticed
accompanied with a decrease of CO2 . Consumption of CO2 was ceased after H2 was no longer
available for C. ljungdahlii.
30
Chapter 3. Lab-scale experiments of syngas fermentation
(a)
(b)
(c)
Figure 3.2: Growth and production of Clostridium ljungdahlii on CO, CO2 and H2 . Mean values
and standard errors of triplicates are shown. (a) Cell dry weight (mg l−1 ) of biomass
(blue dots) and pH (green diamonds). (b) Concentration (g l−1 ) of acetate (blue dots)
and ethanol (green diamonds). (c) Consumption and production of the different syngas
components (mmol) during batch fermentation. CO2 (blue dots), CO (green diamonds)
and H2 (red squares).
3.2.3
Experiment C - Discontinuous fed-batch fermentation of carbon monoxide, carbon dioxide and hydrogen
During the last experiment the headspace of each tube was replaced almost every 24 h with
fresh syngas to assure the growth of the culture. Figure 3.3c shows the profiles of the three
gaseous substrates during the fermentation process. The headspace was flushed regularly to
bring the partial pressures of the gas components to their initial levels. In this experiment, C.
ljungdahlii experienced a lag phase of almost 20 h. After the initial lag phase, a fast growth
rate was observed, which resulted in complete depletion of carbon monoxide before syngas
was even depleted. This also resulted in an almost complete consumption of hydrogen at
64.75 h. This was also indicated by the major increase in cell mass concentration during the
Chapter 3. Lab-scale experiments of syngas fermentation
31
first 50 h (Figure 3.3a). From then on the cell dry weight steadily increased to a maximum
value of 115 ± 22 mg l−1 (Fig. 3.3a). The decline of CO and H2 after refilling the headspace
at 112.25 h gives the impression that the consumption of both gases happened at a similar
rate. However, CO was likely depleted first, atlhough data points are missing between the
two injections, it cannot give a clear view of the consumption profile of both gases. It is
even possible that both substrates already reached zero before analyzing the headspace. It
can be noticed that after 160 h the consumption rate decreased. After a fermentation time
of 207.5 h a pH of 4.435 ± 0.049 was reached (Figure 3.3a). The profiles of pH and acetate
are both inline with each other. Two times the pH suddenly increased during fermentation,
along with an increase of ethanol and a decrease of acetate. A shift between the acidogenic
and solventogenic phase due to unfavored conditions could be an explanation. The synthesis
of ethanol started in the exponential growth phase after 40 h of fermentation (Figure 3.3b),
at a pH of 5.18. In comparison with the fermentation on CO2 and H2 the ethanol production
started at a lower pH. The maximum concentrations of ethanol and acetate were 4.706 and
4.722 g l−1 , respectively (Fig. 3.3b). Comparable acetate concentrations have been reported
by Ramió-Pujol et al. (2015) with C. carboxidivorans under the same growth conditions (3.49
g l−1 ). A similar experiment was conducted by Maddipati et al. (2011). The fermentation
was performed in 250 ml bottles, whereby the headspace was replaced every 24 h with syngas
composed of CO, CO2 , H2 and N2 [20:15:5:60] at 2.35 atm. The bottles were inoculated with
C. ragsdalei, also known as Clostridium strain P11. A maximum acetic acid concentration
of 2.6 g l−1 was reached after 144 h (end of exponential growth phase). However, here the
concentration of acetate kept increasing till the end of the fermentation. The supply of enough
substrate could be a possible reason, as in the experiment of Maddipati et al. (2011) nitrogen
gas accounted for 60% of the gas value. The shift towards solventogenesis also started at a
pH around 5.2. Ethanol concentrations of around 1.7 g l−1 were obtained at the end of the
fermentation. The use of different Clostridium species could also be a reason for the lower
product concentrations.
32
Chapter 3. Lab-scale experiments of syngas fermentation
(a)
(b)
(c)
Figure 3.3: Growth and production of Clostridium ljungdahlii on CO, CO2 and H2 . Mean values
and standard errors of triplicates are shown. (a) Cell dry weight (mg l−1 ) of biomass
(blue dots) and pH (green diamonds). (b) Concentration (g l−1 ) of acetate (blue dots)
and ethanol (green diamonds). (c) Consumption and production of the different syngas
components (mmol) during fermentation. After a certain time, the tubes were refilled to
their original pressure. CO2 (blue dots), CO (green diamonds) and H2 (red squares).
3.2.4
Determination of the volumetric mass transfer coefficients
The concentration of carbon dioxide in the liquid medium was calculated after each 20 seconds.
Figure 3.4 shows the increasing trend of CL,CO2 . The concentration reached a steady-state
after approximated 380 s. The concentration reached 79.55% of the saturation concentration
at a pressure of 1.801 atm.
Chapter 3. Lab-scale experiments of syngas fermentation
33
Figure 3.4: The concentration of carbon dioxide in the liquid phase.
Plotting ln(C ∗ − CL ) against time gives a straight line with a slope equal to −kL aCO2 (Figure
3.5). The value for kL aCO2 can subsequently be derived from the gained equation. The
coefficient is equal to 0.00728 s−1 or 26.20 h−1 . The determination of the volumetric mass
transfer coefficients for the other gases is explained in section 4.1.4.
Figure 3.5: Determination of volumetric mass transfer coefficient for CO2 in horizontal tube at
37 and 150 rpm.
3.2.5
Conclusions
The fermentation of syngas was carried out in 0.025 l tubes by C. ljungdahlii under three
different reactor conditions. A shift towards solventogenesis was observed in the fermentation
of CO2 and H2 with an ethanol/acetate ratio of 0.9972 at the end of the fermentation. A
maximum ethanol concentration of 0.778 g l−1 was reached. Considering the pH, which was
Chapter 3. Lab-scale experiments of syngas fermentation
34
relatively high, factors at microscopic level have to be taken into consideration in order to
explain why solventogenesis began so early. Sporulation could be an explanation for the
early production of ethanol, since solventogenesis in some cases is related with spore-forming.
Complete sporulation of the culture could also be the reason why the conversion of acetate
reached a steady state in a later phase of the stationary phase. In contrast to the fermentation
on CO2 and H2 , no metabolic switch towards the solventogenic phase was observed during
the batch fermentation on CO, CO2 and H2 . This was remarkable because the pH was lower
and higher acetate concentrations were achieved in comparison with Experiment A. Besides
a lower availability of hydrogen as reducing power, other factors play definitely a role in the
shift to solventogenesis. This fermentation process resulted in low ethanol concentrations of
around 0.257 g l−1 . The cell yield on hydrogen, YH2 , was 0.18 g cell mol−1 H2 , while the yield
on carbon monoxide, YCO , was equal to 0.68 g cell mol−1 CO. The difference between both
yields may be due to the higher ATP synthesis per mol CO consumed. The last experiment,
which was a discontinuous fed-batch fermentation on syngas, behaved in a similar way as
Experiment A. However, in Experiment C solventogenesis was activated at higher acetate
concentrations in comparison with Experiment A.
Chapter 4
Modelling and simulation of syngas
fermentation
This chapter first presents the development of the syngas fermentation model. Subsequently,
the different simulation/modelling methods which will be used for the simulation study, such
as sensitivity analysis, parameter estimation, are described in more detail. In the last section,
different versions of the model were evaluated on the two batch experiments, in which the
main focus lies on the fermentation of CO2 and H2 .
4.1
Model development
The complexity of syngas fermentation by acetogens, such as C. ljungdahlii, makes it challenging to develop an accurate mathematical model that is able to describe this process. To
date, several attempts have been made to develop a mathematical model that is able to capture the complexity of this process (Chen et al., 2015). However, in most cases only research
on the determination of growth rates or affinity constants of substrates is performed. In
this section, the development of such model is discussed. The proposed model consist of six
reactions: growth and acetate production, ethanol production and the conversion of acetate
into ethanol using different substates. Next to the description of the different bioconversion
reactions, the mass balances are also described in detail.
4.1.1
Bioconversion reactions
Biomass growth on carbon monoxide
The first process concerns the biomass growth on carbon monoxide. Up to now, you only find
the stoichiometry of the conversion of the substrates into acetate, but there is no mention to
growth. So to fill in this gap it is considered that the growth of C. ljungdahlii is accompanied
with the by-production of acetate (acidogenesis). This is based on the fact that synthesis
of acetate is growth-related. The stoichiometric reaction 4.1 describes the growth on carbon
monoxide. This reaction is based on equation 2.1, but with the incorporation of biomass.
The stoichiometric values were calculated based on the value for cell yield of carbon monoxide
35
36
Chapter 4. Modelling and simulation of syngas fermentation
(Table 4.3). The formula for biomass was derived from the results obtained from the elemental
analysis of C. ljungdahlii performed by Serveis Tècnics de Recerca (2013).
39.91CO + 19.04H2 O + 0.24NH3 −−→ 1CH1.81 O0.58 N0.24 + 9.23CH3 COOH + 19.44CO2
(4.1)
The kinetic expression for the specific growth rate on CO, µ1 , is given by equation 4.2.
µ1 = µmax
·
1
CCO
KCO + CCO +
2
CCO
KI,CO
·
KI,U A
KI,U A + CU A
(4.2)
A Haldane kinetic is used to describe the substrate limitation at low concentrations and inhibition at high levels of CO, which was recommended by Younesi et al. (2005) and Mohammadi
et al. (2014). Younesi et al. (2005) investigated ethanol and acetate production of C. ljungdahlii with various syngas (CO, CO2 and H2 ) pressures. Here the researchers proposed a
Haldane equation for the growth of the bacteria, in which they only incorporated CO as substrate. The aim of Mohammadi et al. (2014) was to determine the biokinetic parameters for
C. ljungdahlii grown on CO and H2 as substrates. From the obtained data, the researchers
determined the kinetics of CO uptake rate using a Haldane kinetic which accommodates CO
inhibition. Now to come back to the Haldane expression of equation 4.2, if there is no CO
limitation (CO > KCO ), the culture can grow unlimited. However, if the concentration of
carbon monoxide in the medium exceeds KI,CO , the growth will be inhibited.
The last term describes the inhibition of growth by undissociated acetic acid (U A), where
KI,U A is the inhibition constant. Concentrations of U A as high as KI,U A will lead to the
halving of the growth rate. The reason to choose U A and not the total amount of acetate is
based on the research performed by Wangt and Wang (1984). They studied the production
of acetic acid by Moorella thermoaceticum in batch fermentations. The researchers observed
that the bacteria were able to produce 56 g l−1 of acetate, in a pH-controlled (6.9) fermentor.
While on the other hand only a maximum concentration of 15.3 g l−1 was obtained, when the
pH of the fermentor was not controlled and decreased to a minimum of around 5.4. Further
experimental studies have shown that not the ionized acetate ion but the undissociated acetic
acid was the main inhibitor. Complete growth inhibition was reported at concentration
between 0.04 and 0.05 mol l−1 . In order to determine the amount of U A, both the pH and
the total amount of acetate, CA , have to be known. The pH was implemented as an input
and CA is a state variable in the model. The concentration of undissociated acetic acid can
subsequently be determined as follows:
CU A = CA −
10(pH−pKa) · CA
10(pH−pKa) + 1
(4.3)
where pKa is the logarithmic acid dissociation constant. The pKa value for acetic acid is
equal to 4.77 at 37°C (Ramió-Pujol et al., 2015).
Chapter 4. Modelling and simulation of syngas fermentation
37
Biomass growth on carbon dioxide and hydrogen
In the absence of carbon monoxide, C. ljungdahlii can also grow on the inorganic substrates
carbon dioxide and hydrogen. Equation 4.4 presents the stoichiometric reaction of growth on
CO2 and H2 . A similar procedure as above described was used to form this stoichiometric
reaction. The stoichiometric values were calculated based on the value of cell yield of hydrogen
(Table 4.3).
147.06H2 + 73.55CO2 + 0.24NH3 −−→ 1CH1.81 O0.58 N0.24 + 36.27CH3 COOH + 74.20H2 O
(4.4)
In the earlier study, performed by Mohammadi et al. (2014), the microbial growth rate was
described by a dual-substrate model. An additive combination of Luong and Monod kinetics
was chosen among several combinations. The model included a maximum inhibitory CO
concentration at which no growth is manifested. The drawback of this kinetic growth model
is that CO2 is not taken into account, since the consumption of H2 , which serves as an energy
source, is accompanied with the consumption of CO2 . Furthermore, they did not account for
the inhibition of hydrogenase, since this enzyme is very sensitive in the presence of carbon
monoxide. In accordance to this dual-substrate model, the microorganisms would be able to
consume H2 when CO is in the liquid medium. This of course is not possible.
In the proposed model, the consumption of carbon monoxide and hydrogen is separated.
The specific growth rate for the growth on CO2 and H2 consist of two Monod kinetics,
which describe the substrate limitation effect of both gaseous substrates. The growth rate
includes also the inhibition effect of CO on the hydrogenase enzyme and product inhibition
by undissociated acetic acid. The kinetic expression for the specific growth is described by
equation 4.5:
µ2 = µmax
·
2
hy
KI,CO
CCO2
CH 2
KI,U A
·
· hy
·
KCO2 + CCO2 KH2 + CH2 K
K
I,U A + CU A
I,CO + CCO
(4.5)
KH2 and KCO2 are the saturation constants for hydrogen and carbon dioxide, respectively.
These constants serve as a representation of the affinity for the substrates. The lower the
hy
value of the parameter, the higher the affinity. The parameter KI,CO
is the carbon monoxide inhibition constant for the enzyme hydrogenase. When no carbon monoxide is present
hy
(CO << KI,CO
), the growth will not be inhibited. However, if CO is available the growth on
CO2 and H2 is strongly reduced, as a consequence of hydrogenase inhibition. As a result there
will only be consumption of carbon monoxide. When the carbon monoxide is depleted, there
will be a shift towards the consumption of carbon dioxide and hydrogen. KI,U A represents
the UA inhibition constant for growth on CO2 and H2 by C. ljungdahlii. In this way the cells
are not capable of producing acetate in an unlimited way.
Direct conversion of substrates into ethanol
The production of ethanol is less straightforward than the synthesis of acetate during the
exponential phase. Several researchers, such as Klasson et al. (1992b), stated that the pro-
38
Chapter 4. Modelling and simulation of syngas fermentation
duction of ethanol is non-growth associated, since ethanol production does not result in a
positive net production of ATP. However, in a review were they discussed the bioenergetic
constraints for syngas fermentation by A. woodii quite the opposite was noticed. From their
calculations a negative energy balance was observed if hydrogen was used as electron source.
This would lead to the production of undesired byproducts like acetate. But if CO was picked
as an electron source the synthesis of ethanol would yield ATP. However if ethanol production
from carbon monoxide results in a positive balance of ATP, then should the bacteria produce
ethanol at the start of their growth and not in a later phase when for example the pH is really
low. Furthermore, it is possible that calculating the energy balance for C. ljungdahlii would
have an other outcome since these bacteria use a different chemiosmotic mechanism (Bertsch
and Müller, 2015).
For this model it is assumed that ethanol production is not related with biomass production.
The production of ethanol is considered to take place through two different metabolic routes,
either directly from acetyl-CoA or through the conversion of acetate into ethanol via acetylCoA. This subsection deals with the first approach. The stoichiometric reaction 2.2 is taken
for the ethanol formation with carbon monoxide as substrate. Product inhibition is not
incorporated in the kinetic expression due to the low ethanol concentrations. The specific
ethanol production rate is expressed by equation 4.6.
µ3 = µmax
·
3
CCO
KCO + CCO +
2
CCO
KI,CO
·
CU A
KU A + CU A
(4.6)
The external pH is most certainly one of the key factors driving the solventogenesis. The
solventogenic phase is regarded as a survival strategy in response to a decreasing pH. Other
factors, such as high acid concentrations and nutrient limitation, also induce a metabolic shift
towards solventogenesis. Nevertheless, the effect of pH is never taken into account in metabolic
models or kinetic expressions. In contrast to syngas fermentation, pH-dependent models
for ABE fermentation are already developed. The metabolic pathway of ABE fermentation
contains a switch between acidogenesis and solventogenesis as well. Millat et al. (2011)
for example developed an ordinary differential equation model that combines the metabolic
network with the regulation of the enzymes required for the solvent production. Because of the
incorporated gene regulation, the model was competent to give an accurate representation of
the pH-induced switch. The model was based on C. acetobutylicum, the first fully sequenced
Clostridium species and a model-organism for solventogenesis. Here, the concentration of
UA is chosen to get a shift towards ethanol production. In fact, this is a combination of the
pH of the medium and the total amount of acetate (Eq. 4.3). In response to an increasing
concentration of U A, the bacteria will produce ethanol and this will lead to a competition
between acetate and ethanol production. This activation expression is represented by the
last term of equation 4.6. If the concentration reaches the value of the parameter KU A the
reaction will be at the half of his maximum rate.
Next to the ethanol production from CO as substrate, the bacteria are also able to synthesize
ethanol from CO2 and H2 . The stoichiometric reaction for the second process is based on
equation 2.4. The kinetic expression for ethanol production from carbon dioxide and hydrogen
Chapter 4. Modelling and simulation of syngas fermentation
39
is represented by equation 4.7:
µ4 =
µmax
·
4
hy
KI,CO
CCO2
CH 2
CU A
·
· hy
·
KCO2 + CCO2 KH2 + CH2 K
KU A + CU A
I,CO + CCO
(4.7)
This process will only take place if the the carbon monoxide concentration is far below the
hy
value of the inhibition constant of hydrogenase (CO << KI,CO
). Just like previous kinetic
expression (Eq. 4.6), the accumulation of ethanol is activated by the rising undissociated
acetate concentrations in the liquid medium.
Conversion of acetate into ethanol
In comparison with previous reactions, in which ethanol is produced directly from CO/CO2 ,
ethanol can also be produced by the re-assimilation of acetate via acetyl-CoA. It is most
reasonable that this reaction would happen during fermentation, as a means to reduce the
inhibition of pH and acetate. Despite that many researchers have noticed the re-assimilation
of acetate into ethanol, this process has never been used in any model. Both CO and H2 can
serve as an electron source to reduce acetate. Note, that some bacteria also contain genes to
produce AOR, an enzyme that is capable of converting acetic acid into acetaldehyde, which
is subsequently converted into ethanol. However, this reaction is excluded from the model.
To reduce acetate to ethanol, the water-gas shift reaction can be used, in which the conversion
of CO into CO2 delivers the necessary reducing power. The stoichiometric reaction, in which
CO serves as electron source, is represented by equation 4.8:
1CH3 COOH + 2CO + 1H2 O −−→ 1CH3 CH2 OH + 2CO2
(4.8)
The expression for the specific ethanol production rate for this conversion reaction is given
as followed:
CCO
CU A
µ5 = µmax
·
· ac
(4.9)
2
5
CCO
KCO + CCO + KI,CO KU A + CU A
The kinetic expression consists of a Haldane kinetic, that describes the limitation and inhibition of CO, and a Monod kinetic for acetate with KUacA as the saturation constant. The
conversion rate, µ5 , responds to the increasing concentration of U A.
Acetate can also be reduced by hydrogen, in which the enzyme hydrogenase delivers the
reducing power. The stoichiometric reaction of this process is given by equation 4.10
1CH3 COOH + 2H2 −−→ 1CH3 CH2 OH + 1H2 O
(4.10)
The expression for the specific conversion rate of acetate is given by the following equation:
µ6 =
µmax
·
6
hy
KI,CO
CH 2
CU A
·
·
KH2 + CH2 KUacA + CU A K hy + CCO
I,CO
(4.11)
The kinetic expression consists of a Monod kinetic for U A with KUacA as the saturation constant. This reaction will only proceed if there is no CO present in the fermentation broth.
Chapter 4. Modelling and simulation of syngas fermentation
40
hy
As long as the concentration is not reduced to zero (CO << KI,CO
) the microorganism will
not be able to reduce acetate to ethanol.
Sporulation
As mentioned in chapter 3, sometimes sporulation can occur in batch fermentation processes.
According to Drake et al. (2008), sporulation of acetogens could be an aid in their in situ
survival. This phenomenon however results in deactivation of the bacteria, which lead to the
fact that the biological activity suddenly ceases. In case spore-forming would happen during
fermentation, the following term can be added to the expressions of the six reaction rates:
X α
1−(
)
(4.12)
Xmax
This term accounts for the inhibitory effect of the biomass concentration itself. Xmax is
the maximum biomass concentration which can be reached at which the specific rate is zero,
assuming that the bacteria are completely sporulated. The parameter α is defined as an index
of the inhibitory effect that accounts for the deviation of the reaction rates. The term for
the deactivation was based on the model of Mozumder et al. (2014), in which high biomass
concentrations negatively affected the biomass growth rate.
Summary
The stoichiometry of the biological conversion reactions are summarized in a matrix notation
in Table 4.1. In this stoichiometric matrix, only the discussed reactions, which affect biomass
growth, acetate and ethanol production, are considered. Other processes, such as maintenance
and decay, are not considered for the model. Production and consumption of water is also
not considered in the stoichiometric matrix. The excess amount of nitrogen, in the form
of ammonium chloride, at the disposal of C. ljungdahlii makes it unnecessary to include a
state variable for nitrogen in the model. The first row represents the different components
that are relevant for the different processes. The left column contains the different processes,
as mentioned above. Each element within the matrix represents a stoichiometric coefficient,
vij . The indexes i and j refer to the components and processes, respectively. A negative
stoichiometric coefficient refers to the consumption of that specific component while a positive
sign indicates production. The biological process rates, ρj , for each reaction are summarized
in Table 4.2.
The yield coefficients Y for each reaction j are summarized in Table 4.3. A fixed biomass
composition of CH1.81 O0.58 N0.24 with a molecular weight of 26.49 g mol−1 is assumed in
order to convert mass into mol. The cell yields of both substrates were calculated from the
experimental data (chapter 3) according to equation 4.13:
Yi =
dX
dS
(4.13)
in which X represents the biomass concentration and S the substrate concentration of either
CO or H2 . The values of the other four yield coefficients are based on the stoichiometric
reactions, which were mentioned before.
41
Chapter 4. Modelling and simulation of syngas fermentation
Table 4.1: Stoichiometry of growth and product formation by C. ljungdahlii.
Component → i
j Process ↓
1 Biomass growth on CO
1
CO
mol l−1
−1
Y1
−1
Y3
−2
Y5
4
X
mol l−1
− 0.0175
−1
Y2
5
A
mol l−1
1
0.25
Y1
− 0.5
1
0.25
Y2
− 0.5
0.67
Y3
6
E
mol l−1
1
−0.33
Y4
4 Ethanol production from CO2 and H2
5 Conversion of acetate into ethanol (CO)
0.5
Y1
3
H2
mol l−1
−( 0.5
Y2 + 0.0175)
2 Biomass growth on CO2 and H2
3 Ethanol production from CO
2
CO2
mol l−1
−1
Y4
1
2
Y5
−2
Y6
6 Conversion of acetate into ethanol (H2 )
−1
Y5
1
−1
Y6
1
Table 4.2: Process kinetics of growth and ethanol production by C. ljungdahlii.
j Process ↓
Process rate ρj [mol l−1 h−1 ]
1 Biomass growth on CO
ρ1 = µ1 · X = µmax
·
1
CCO
CCO
2
+CCO
2 Biomass growth on CO2 and H2
ρ2 = µ2 · X = µmax
· KCO
2
3 Ethanol production from CO
ρ3 = µ3 · X = µmax
·
3
2
2
CCO
CCO
2
+CCO
ρ4 = µ4 · X = µmax
· KCO
4
5 Conversion of acetate into ethanol (CO)
ρ5 = µ5 · X = µmax
·
5
2
2
CCO
2
C2
CO
I,CO
CH
2
+CH
2
2
2
C2
CO
I,CO
hy
KI,CO
K
hy
KI,CO
+CCO
A
· KI,U AI,U
+CU A · X
UA
· KU C
·X
A +CU A
·
2
hy
KI,CO
hy
KI,CO
+CCO
UA
· KU C
·X
A +CU A
A
· K acCU+C
·X
UA
UA
A
· K acCU+C
·
UA
UA
·
2
CH
2
+CH
· KH
KCO +CCO + K
ρ6 = µ6 · X = µmax
· KH
6
A
· KI,U AI,U
+CU A · X
CH
2
+CH
· KH
KCO +CCO + K
4 Ethanol production from CO2 and H2
6 Conversion of acetate into ethanol (H2 )
K
C2
CO
I,CO
KCO +CCO + K
hy
KI,CO
hy
KI,CO
+CCO
·X
The parameters that have been described above are summarized in Table 4.3. Only a small
fraction of the kinetic parameters were found in literature because there is not much research
towards modelling of syngas fermentation. The stoichiometric coefficients are considered to be
known and will not be adjusted during model calibration. As a consequence only the kinetic
parameters will be considered in the model calibration. The values in bold will be used as
initial values for sensitivity analysis. Furthermore, they will be used as a starting point for
the model calibration, but by trial and error other initial values will be selected to obtain a
better fit between the experimental results and the model predictions. The carbon monoxide
inhibition constant, KI,CO , is in the model assumed to be equal for each process. This is also
applied to the carbon monoxide inhibition constants for hydrogenase. From table 4.3 it is
hy
clear that the CO inhibition constant for hydrogenase, KI,CO
, is very small, which means that
the oxidation of hydrogenase, and thus the reactions that acquire hydrogen, only can take
place if CO is completely consumed. To simplify the model the affinity or saturation constants
Chapter 4. Modelling and simulation of syngas fermentation
42
were considered to be same for each reaction. The parameter KCO ,with a value of 7.8.10−5
mol l−1 of carbon monoxide, is the smallest affinity constant. This implies that the bacteria
have a higher affinity for CO than for the other substrates, such as U A, CO2 and H2 . This
can be explained by the fact that the consumption of carbon monoxide delivers more energy
through the chemiosmotic ion gradient-driven phosphorylation. Note that a difference is made
between the U A saturation constants KUacA and KU A . The reason for this is because they
each have another purpose in the model. U A in the specific acetate conversion rates, µ5 and
µ6 , functions as a substrate for the process, while it in the ethanol production rates, µ3 and
µ4 , serves as activator or regulator to trigger the production of ethanol. The fact that there
are no values available for the four maximum specific ethanol production rates in literature,
it was difficult to set up an initial value for these parameters, since there is no reference. To
fill in this gap, some assumptions were made. First, the actual ethanol production rate was
calculated from Experiment A, which can be assumed to be equal to the process rates ρ4
and ρ6 (Table 4.2). This was done by calculating the amount of ethanol that was produced
from the second till the second-last data point, assuming that only in that period ethanol was
produced, and dividing by that time period. Subsequently, µmax was calculated by dividing
the production rate by the average biomass concentration in that period, assuming there
was no substrate limitation. Finally, a value of 0.39 mol l−1 h−1 was gained. All maximum
specific ethanol production or acetate conversion rates are assumed to be equal to that value
(Table 4.3). The parameter Xmax was determined from the experimental data from the batch
fermentation on CO2 and H2 , assuming that at the end of the experiments all bacteria were
sporulated.
43
Maximum specific growth rate from CO2 and H2
Maximum specific ethanol production rate from CO
Maximum specific ethanol production rate from CO2 and H2
Maximum specific acetate conversion rate from CO
Maximum specific acetate conversion rate from H2
CO saturation constant
CO2 saturation constant
H2 saturation constant
UA saturation constant for ethanol production
UA saturation constant for acetate conversion
CO inhibition constant
CO inhibition constant for hydrogenase
UA inhibition constant
Maximum biomass concentration before total sporulation
Inhibition coefficient
KCO2
KH2
KU A
KUacA
KI,CO
hy
KI,CO
KI,U A
Xmax
α
Maximum specific growth rate from CO
Cell yield of carbon monoxide
Cell yield of hydrogen
Ethanol yield of carbon monoxide
Ethanol yield of hydrogen
Ethanol yield of acetate (CO)
Ethanol yield of acetate (H2 )
Description
µmax
2
µmax
3
µmax
4
µmax
5
µmax
6
KCO
µmax
1
Parameters
Y1
Y2
Y3
Y4
Y5
Y6
Yield coefficients
Symbol
0.195
0.022
0.04
0.042
0.39
0.39
0.39
0.39
0.000078
0.00069
0.00022
0.00022
0.0003
0.0005
0.0005
0.002
0.00048
0.000000007
0.0062
0.0009631
1
0.0257
0.0068
0.167
0.167
1
1
Value
mol l−1
mol l−1
mol l−1
mol l−1
mol l−1
mol l−1
mol l−1
mol l−1
mol cell (mol cell)−1 h−1
mol ethanol (mol cell)−1 h−1
mol ethanol (mol cell)−1 h−1
mol ethanol (mol cell)−1 h−1
mol ethanol (mol cell)−1 h−1
mol l−1
mol cell (mol cell)−1 h−1
mol cell (mol CO)-1
mol cell (mol H2 )−1
mol ethanol (mol CO)−1
mol ethanol (mol H2 )−1
mol ethanol (mol acetate)−1
mol ethanol (mol acetate)−1
Unit
Table 4.3: Overview of parameter values.
Mohammadi et al. (2014)
Younesi et al. (2005)
Klasson et al. (1992a)
Sakai et al. (2005)
Assumed in this study
Assumed in this study
Assumed in this study
Assumed in this study
Younesi et al. (2005)
Mohammadi et al. (2014)
Assumed in this study
Skidmore et al. (2013)
Mohammadi et al. (2014)
Assumed in this study
Assumed in this study
Younesi et al. (2005)
Mohammadi et al. (2014)
Ragsdale and Ljungdahl (1984)
Sakai et al. (2005)
Determined from experimental data
Assumed in this study
Determined from experimental data
Determined from experimental data
Stoichiometric equation
Stoichiometric equation
Stoichiometric equationy
Stoichiometric equation
Reference
Chapter 4. Modelling and simulation of syngas fermentation
4.1.2
44
Gas phase mass balances
Rather than sparging the gaseous substrates through the liquid medium, the gas was injected
in the headspace. The gas phase or headspace is assumed to be perfectly mixed. It is also
assumed that no reactions occur in the gas phase. The small amounts of volatile components
in the gas phase, such as ethanol, are assumed to be negligible. The amount of water present in
the gas phase due to evaporation, leading to dilution of the gas phase components, is neglected
as well. The gas phase mass balances for carbon dioxide, carbon monoxide, hydrogen and
nitrogen (indicated as component i) are expressed by equation 4.14.
d(VG · CG,i )
∗
= −kL ai · (CL,i
− CL,i ) · VL
dt
(4.14)
The volume of the headspace VG is assumed to be constant. Because of the continuous
transport between the gas and liquid phase, the pressure in the headspace has to be determined
every time step. A possible way to calculate the total pressure is by first calculating the total
amount of moles present in the gas phase.
X
nG = VG ·
CG,i
(4.15)
i
The total pressure in the gas phase, p, is then calculated from the ideal gas law (Eq. 4.16).
The temperature is assumed to be constant, thus ignoring the heat, absorbed or released in
the reactions.
nG · R · T
p=
(4.16)
VG
Subsequently, the partial pressure of each gas can be calculated using Raoult’s law:
pi =
VG · CG,i
·p
nG
(4.17)
Note that the partial pressure of N2 stays constant during the fermentation, as there is neither
production nor consumption.
4.1.3
Liquid phase mass balances
The mass balances of the different components in the fermentation broth are composed of two
contributions: gas-liquid interphase transport and biological conversion. The components are
transported between the gas and liquid phase, in this case carbon monoxide, carbon dioxide,
hydrogen and nitrogen gas. Since N2 is an inert gas, no mass balance at all is included. The
liquid phase mass balance for each individual component i (CO, CO2 , H2 , X, A and E) can
be written as follows:
d(VL · CL,i )
∗
= kL ai · (CL,i
− CL,i ) · VL + ri · VL
dt
(4.18)
The term ri [mol l−1 h−1 ] is known as the conversion rate of each component i and can be
calculated from the process rates, ρj , from Table 4.2 and the stoichiometric coefficients, vi,j ,
45
Chapter 4. Modelling and simulation of syngas fermentation
from Table 4.1, according to equation 4.19:
ri =
6
X
vi,j · ρj
(4.19)
i=1
Assuming that VL is constant the mass balance is reduced to:
dCL,i
∗
= kL ai · (CL,i
− CL,i ) + ri
dt
(4.20)
Taking into account that there is no physical transport of biomass and products (ethanol and
acetate), the liquid mass balances of these three components only consist of the conversion
rate.
 dC
X

 dt = rX = (µx1 + µx2 ) · X
µe3
µe4
dCA
0.25
(4.21)
dt = rA = [( Yx1 − 0.5) · (µx1 + µx2 ) − Ye3 − Ye4 ] · X

 dCE
dt = rE = (µe1 + µe2 + µe3 + µe4 ) · X
In a similar way, the conversion rates of carbon monoxide, carbon dioxide and hydrogen
can be calculated by the summation of the product of the process rate and their respective
stoichiometric coefficient.

µx1
µe1
2µe3

rCO = (− Yx1 − Ye1 − Ye3 ) · X
rCO2 = [( Y0.5
− 0.0175) · µx1 − ( Y0.5
+ 0.0175) · µx2 +
x1
x2


µx2
µe2
2µe4
rH2 = (− Yx2 − Ye2 − Ye4 ) · X
0.67µe1
Ye1
−
0.33µe2
Ye2
+
2µe3
Ye3 ] · X
(4.22)
The liquid mass balances of the gaseous substrates and nitrogen gas can subsequently be
composed by combining the transfer rate with the conversion rates of equation 4.22.
 dC
L,CO
∗

= kL aCO · (CL,CO
− CL,CO ) + rCO

dt


dC
L,CO2

∗

= kL aCO2 · (CL,CO − CL,CO2 ) + rCO2
dt
2
(4.23)
dCL,H
∗
2

=
k
a
·
(C
−
C

L
H
L,H2 ) + rH2
L,H2
dt
2



 dCL,N2 = k a · (C ∗
L N2
L,N − CL,N2 )
dt
2
The calculation of the volumetric mass transfer coefficients will be discussed in next section.
4.1.4
Mass transfer between gas and liquid phase
During fermentation, the substrate gases carbon monoxide, carbon dioxide and hydrogen and
inert nitrogen gas are transported from the gas phase to the liquid phase. The transfer rate
from the gas phase to liquid phase is given by the following expression:
∗
T Ri = kL ai · (CL,i
− CL,i )
(4.24)
The volumetric mass transfer coefficients of carbon monoxide, hydrogen gas and nitrogen gas
(component i) are related to the corresponding mass transport coefficient for carbon dioxide,
derived from an experiment, according to the following equation:
46
Chapter 4. Modelling and simulation of syngas fermentation
s
kL ai = kL aCO2 ·
Di
DCO2
(4.25)
in which Di is the diffusion coefficient of that respective component. This relationship is
∗ represents
only valid in case the liquid interphase is turbulent (De Heyder et al., 1997). CL,i
the saturation concentration of a component at the gas/liquid phase, which is assumed to
be in equilibrium with the prevailing concentration of is component as expressed by Henry’s
law. The saturation concentration is represented by the partial pressure of the gas (pi [atm])
multiplied with Henry’s coefficient (kH,i [mol l−1 atm−1 ]).
∗
CL,i
= pi · kH,i
(4.26)
Temperature has a significant effect on the solubility of the gases, this is translated into
the Henry coefficients. The temperature dependence of the Henry coefficient of each can
be described with the van ’t Hoff equation, according to Sander (2015). This equation was
used to calculate the coefficients for the temperature of the incubator (310.15 K). The Henry
coefficients of the gases are summarized in Table 4.4.
Table 4.4: Henry’s coefficients for the different gases at 37°C [mol l−1 atm−1 ].
Symbol
kH,CO
kH,CO2
kH,H2
kH,N2
4.2
Description
Henry’s coefficient
Henry’s coefficient
Henry’s coefficient
Henry’s coefficient
for
for
for
for
carbon monoxide
carbon dioxide
hydrogen
nitrogen
Value
8.30 10−4
2.45 10−2
7.32 10−4
5.48 10−4
Simulation procedure
The syngas fermentation model was implemented in Matlab-Simulink (R2015b) to carry out
the simulation work described in this thesis. Below the different methods that were used in
the simulation part of the thesis are described in more detail.
4.2.1
Sensitivity analysis
Before the parameters were estimated, a sensitivity analysis was performed in order to determine the most sensitive parameters. For the development of this model, a local sensitivity
analysis of the different parameters around their initial values was performed in order to identify which parameters are the most sensitive. The parameters that have a significant influence
on the model output variables, were subsequently regarded in the parameter estimation. Besides that, parameters that were not found in literature, due to the absence of models that
are able to describe syngas fermentation, were also calibrated in most cases (Nopens, 2014).
The sensitivity function was defined as the partial derivative of the variable, y, to the parameter, θ, which was determined numerically applying the finite difference method assuming
local linearity (Nopens, 2014):
Chapter 4. Modelling and simulation of syngas fermentation
∂y(t)
y(t, θ + ∆θ) − y(t, θ)
= lim
∆θ→0
∂θ
∆θ
47
(4.27)
Notice, that here a forward difference was used, i.e. a function with a positive perturbation
∆θ for the parameter θ. The perturbation is defined as (Nopens, 2014):
∆θ = ξ · θ
(4.28)
in which ξ denotes the perturbation factor. As this model is non-linear, this numerical
approximation is only valid if the perturbation is taken very small. The practice of the
difference method with big perturbation factors will result in inaccuracies. On the other
hand, factors that are too low could lead to numerical errors. A typical value of 10−4 was
selected as perturbation factor. The sensitivity function discussed by equation 4.27 is called
a absolute sensitivity function. The value of this depends on the value of the variable and the
considered parameter. As a consequence, the sensitivity functions of the different parameters
cannot be compared. To overcome this, a total relative sensitivity function of variable i
towards parameter j was defined as (Nopens, 2014):
Si,j (t) =
∂y(t) θ
·
∂θ y(t)
(4.29)
The relative sensitivity allows to compare all combinations of sensitivity functions of all chosen
variables and parameters. To rank the parameters based on the overall sensitivity over the
considered time period, the average of Si,j (t) was calculated for each parameter θj for the
different variables i as follows (Nopens, 2014):
P ∂y(t)
∂θ
δi,j =
4.2.2
θ
· y(t)
n
(4.30)
Model calibration
In order to find the optimal parameter values for the model, a parameter estimation or model
calibration has to be performed. To select the right parameters for the model calibration, a
sensitivity analysis was performed, as described in section 4.2.1.
In view of parameter estimation, an objective function is composed to obtain the best possible
fit between the model predictions and the experimental data. The objective function used in
the model is given by the average relative deviation (Eq. 4.31) (Ganigué et al., 2010):
Pn
J(θ) =
i=1
|ybi −yi (θ)|
ybi
n
(4.31)
in which ybi represents the experimental data of the outputs, in this case the acetate and
ethanol concentration and the cell dry weight, while yi (θ) represents the model predictions
for a given parameter set θ. The average relative deviation makes it possible to compare the
deviations of the different model predictions. During model calibration, a search algorithm or
minimization algorithm is used to find the parameter set which results in the lowest objective
Chapter 4. Modelling and simulation of syngas fermentation
48
function. The function fminsearch was used in Matlab to minimize the objective function
J(θ). This function makes use of the ’Nelder mead simplex’ estimation algorithm (Islam
Mozumder et al., 2015).
In order to prevent that the search algorithm get lost in not relevant regions of the parameter
space, constraints are set for the parameters. The algorithm used in fminsearch automatically
searches in the parameter space between -∞ and +∞. This means that during minimization
of the objective function J(θ) the algorithm can get lost in the negative part of the parameter
space, with negative parameters as a result. To implement these parameter constraints, the
parameters were transformed according to the following equation (Nopens, 2014):
φ = tan(
π 2θ − θmax − θmin
)
2 θmax − θmin
(4.32)
The solution in the original parameter space is subsequently obtained by the inverse transformation (Nopens, 2014):
1
atan (φ)
θ = (θmax + θmin )(θmax − θmin )
2
π
(4.33)
In this way it is possible for the function fminsearch to search for an optimal parameter value
in the parameter space between -∞ and +∞, but the parameter θ will all ways be higher
than θmin and lower than θmin (Nopens, 2014).
4.2.3
Testing the goodness of the fit
There is no independent experimental dataset available to validate the model. However,
in order to compare the different versions of the model the Nash-Sutcliffe model efficiency
coefficient was used. The coefficient is able to describe the accuracy of the model outputs
(Nopens, 2014):
Pn
(yi − ybi )2
E = 1 − Pi=1
n
m
i=1 (yi − yi
(4.34)
Where yi stands for the model outputs, ybi is the experimental data and yim is the arithmetic
mean of the model prediction. The Nash-Sutcliffe efficiency, E, ranges from -∞ to 1. If E is
equal to one, model outcome is a perfect match with the observed data. In case E is equal
to zero, the model outcomes are as accurate as the mean of the experimental data. When E
is negative, the observed mean is a better predictor that the model itself (Nopens, 2014).
Chapter 4. Modelling and simulation of syngas fermentation
4.3
49
Simulation results
To develop a model capable of describing the syngas fermentation process and to better
understand the different reactions that happen during fermentation, several models were
considered. The evaluation and calibration of the proposed models was divided in two sections.
In the first section only the fermentation on carbon dioxide and hydrogen was considered. In
this way, it was easier to understand how C. ljungdahlii grows on these substrates without the
presence of carbon monoxide. The calibration of the different models that were proposed was
performed on the experimental data set described in section 3.2.1. In the next section, the
whole model was tested on the fermentation of syngas (CO, CO2 and H2 ). The experimental
data described in section 3.2.2 was selected for the calibration of the model.
4.3.1
Model calibration for biomass growth on carbon dioxide and hydrogen
To describe biomass growth on carbon dioxide and hydrogen, five different models (A-E)
were considered and calibrated on the experimental data, described in section 3.2.1. The
simulation outcomes from the calibrated models were compared with each other to identify
which model is most capable of describing the fermentation process.
Model A - biomass growth on carbon dioxide and hydrogen
In this model only biomass growth and the production of acetate was taken into account. The
specific biomass growth rate on carbon dioxide and hydrogen, µ2 , is represented by equation
4.35. The production of ethanol from carbon dioxide and hydrogen and the conversion of
acetate into ethanol were not considered is this model because the first goal was to fit the
biomass growth with the experimental data.
µ2 = µmax
·
2
hy
KI,CO
CCO2
CH 2
KI,U A
·
· hy
·
KCO2 + CCO2 KH2 + CH2 K
KI,U A + CU A
I,CO + CCO
(4.35)
All parameters considered in this model are defined in table 4.5 with their initial values found
in literature. The half saturation constant of carbon dioxide, KCO2 , is here assumed to be
equal to the half saturation constant of hydrogen, KH2 , because so far only kinetic parameters
for CO and H2 are discussed in literature for acetogens. The choice of which parameters should
be calibrated is for the biggest part dependent on whether or not the kinetic parameters are
available in literature. Besides that, the influence of the parameters on the output variables
is also important.
50
Chapter 4. Modelling and simulation of syngas fermentation
Table 4.5: Initial parameter values considered in the model.
Parameter
µmax
2
KCO2
KH2
hy
KI,CO
KI,U A
Initial value
0.042
0.00022
0.00022
7.10−9
0.0062
Reference
Sakai et al. (2005)
Assumed in this study
Skidmore et al. (2013)
Ragsdale and Ljungdahl (1984)
Sakai et al. (2005)
A sensitivity analysis was performed using a perturbation factor of 10−4 . The resulting δvalues (as defined in section 4.2) of the parameters are listed in Table 4.6 for biomass, acetate
and ethanol.
Table 4.6: Initial parameter values considered in the model.
Parameter
µmax
2
KCO2
KH2
hy
KI,CO
KI,U A
δ-value biomass
0.6918
0.0215
0.1048
0.0000
0.0818
δ-value acetate
0.4305
0.0143
0.0665
0.0000
0.0579
δ-value ethanol
0.0000
0.0000
0.0000
0.0000
0.0000
It is clear from the sensitivity analysis that the parameter µmax
is the most sensitive. On the
2
hy
other hand, the parameter KI,CO has no influence on the model simulations, this of course is
due to the fact that in this fermentation no carbon monoxide is available. From Figure 3.1b
it is clear that in the first 30 h of the fermentation only biomass and acetate production took
place, so in other words only equation 4.35 is considered to take place in the first 30 h. So here
the objective was to calibrate the model for the first three data points of the experimental
results. In this way the behaviour of the bacteria could be observed without the production
of ethanol. Parameter µmax
was chosen to estimate the optimal value that would result in
2
a model capable of predicting the experimental data. To avoid the searching algorithm to
get lost in irrelevant areas of the parameters space, the parameters to be estimated were
transformed according to equation 4.32. This was especially used to avoid that the searching
algorithm would pick negative values for the parameters. The optimal value for µmax
after
2
model calibration is given in Table 4.7.
Table 4.7: Initial and optimal value for the kinetic parameters used in the model calibration.
Parameter
µmax
2
Initial value
0.042
Optimal value
0.0375
With the optimal parameter value in Table 4.7, obtained after model calibration, the model
was simulated and compared with the experimental data. The graph with model output of
acetate, ethanol and biomass is given in Figure 4.1a. The simulation outcome of the two
substrate gases in function of time is illustrated in Figure 4.1b.
51
Chapter 4. Modelling and simulation of syngas fermentation
(a)
(b)
Figure 4.1: Results of model calibration. Comparison between simulation outcome and experimental
data for acetate, ethanol and biomass. (b) Results of model calibration. Comparison
between simulation outcome and experimental data for carbon dioxide and hydrogen in
the gas phase.
From Figure 4.1a, it can be seen that the experimental cell dry weight is predicted quite well.
High concentrations of undissociated acetic acid are probably the cause of the slow grow
rate in comparison with the experimental data. On the other hand, the model overestimated
the prediction of the acetate concentrations. This can be explained by the fact that during
fermentation a part of the acetate is actually converted into ethanol. This can also be derived
from Figure 4.1b where also the concentration of hydrogen in the gas phase is overestimated.
However, assuming that ethanol production is directly produced from acetyl-CoA, then it is
also possible that the production of ethanol competes with acetate production and in this
way reduces the acetate concentration and increases the consumption of hydrogen as ethanol
requires more hydrogen.
Model B - biomass growth and ethanol production from carbon dioxide and
hydrogen
In this model the production of ethanol from carbon dioxide and hydrogen was considered,
next to the biomass growth and the production of acetate, which already was taken into
account in the previous model. The specific biomass growth rate, µ2 , and the specific ethanol
production rate, µ4 , is represented by equation 4.36. The conversion of acetate to ethanol
was not considered in this model.

CCO
CH
2

· KCO +C2CO · KH +C
·
µ2 = µmax
2
H
2
2
2
2
CCO
CH

2
µ4 = µmax
· KCO +C2CO · KH +C
·
4
H
2
2
2
2
hy
KI,CO
hy
KI,CO +CCO
hy
KI,CO
hy
KI,CO
+CCO
K
A
· KI,U AI,U
+CU A
(4.36)
UA
· KU C
A +CU A
All parameters considered in this model are defined in Table 4.8 with their initial values found
in literature. To simplify the model, the affinity constants of carbon dioxide and hydrogen
52
Chapter 4. Modelling and simulation of syngas fermentation
considered in µ4 were assumed to be equal to the constants corresponding with the specific
growth rate µ2 . This is also valid for the CO inhibition constant for hydrogenase. However,
in this case it does not really matter since carbon monoxide is not present in the headspace.
Furthermore, parameter KU A , which is the UA affinity constant, was assumed to be equal
to 0.0005. This parameter could not be found in literature for acetogens like C. ljungdahlii,
that is why a decent value for this parameter was considered.
Table 4.8: Initial parameter values considered in the model.
Parameter
µmax
2
KCO2
KH2
hy
KI,CO
KI,U A
µmax
4
KU A
Initial value
0.042
0.00022
0.00022
7.10−9
0.0062
0.39
0.0005
Reference
Sakai et al. (2005)
Assumed in this study
Skidmore et al. (2013)
Ragsdale and Ljungdahl (1984)
Sakai et al. (2005)
Assumed in this study
Assumed in this study
Subsequently, a sensitivity analysis was performed using a perturbation factor of 10−4 . The
resulting δ-values of the parameters are listed in Table 4.9 for biomass, acetate and ethanol.
Table 4.9: Initial parameter values considered in the model.
Parameter
µmax
2
KCO2
KH2
hy
KI,CO
KI,U A
µmax
4
KU A
δ-value biomass
0.6721
0.0183
0.0922
0.0000
0.08071
0.1011
0.0161
δ-value acetate
0.4127
0.0116
0.0560
0.0000
0.0565
0.0524
0.0124
δ-value ethanol
0.0923
0.0169
0.0798
0.0000
0.0669
0.1795
0.1692
From Table 4.9 it is clear that again µmax
is the most sensitive parameter. Furthermore,
2
max
the parameters KH2 , KI,U A and µ4
are also quite sensitive. However, including too many
parameters in a model calibration is not recommended. Parameter µmax
has a big influence
4
on the ethanol production. Furthermore, synthesis of ethanol by acetogens has never been
used in any mathematical model, which leads to the fact that this parameter cannot be found
in literature. The same can be said about parameter KU A . From previous model calibration,
not a large change in parameter value was established for µmax
. The growth but also ethanol
2
production strongly depends on the value of KI,U A and KH2 . Therefore, the parameters
KI,U A , µmax
and KH2 were selected for the subsequent model calibration. The optimal values
4
for KI,U A , µmax
and KH2 after model calibration are given in Table 4.10.
4
53
Chapter 4. Modelling and simulation of syngas fermentation
Table 4.10: Initial and optimal value for the kinetic parameters used in the model calibration.
Parameter
KH2
KI,U A
µmax
4
Initial value
0.00022
0.0062
0.39
Optimal value
0.00052
0.0031
0.77
The simulation results of acetate, ethanol and biomass, after model calibration, are presented
in Figure 4.2a. The simulation outcome of carbon dioxide and hydrogen is shown in Figure
4.2b.
(a)
(b)
Figure 4.2: Results of model calibration. Comparison between simulation outcome and experimental
data for acetate, ethanol and biomass. (b) Results of model calibration. Comparison
between simulation outcome and experimental data for carbon dioxide and hydrogen in
the gas phase.
From Figure 4.2b, it can be observed that the model simulations of both substrate gases
fit the experimental data quite good. Comparing the simulated and experimental acetate
concentrations, illustrated in Figure 4.2a, it is clear that the model predicts the experimental
data good for the first 50 h. From then on the acetate concentration keeps increasing and
reaches a steady state (E = 0.6085), due to the depletion of carbon dioxide. It can also be
observed that the model was not able do describe the experimental results of ethanol (E =
0.0061). For this model calibration a value of 0.0005 was assumed for the parameter KU A ,
which is relative low compared with the maximum concentration of undissociated acetic acid
(± 0.00035) during this simulation. This is one of the reasons why ethanol production already
can be noticed at the beginning of the simulation. Increasing the value of this parameter would
prevent solventogenesis at the start of the simulation, but to be able to follow the increase
of ethanol around the time of 20 h, a very high and unrealistic value for the parameter
µmax
would be necessary. Furthermore, the model does also not succeed in predicting the
4
experimental results of biomass (underestimated), which is reflected by the Nash-Sutcliffe
efficiency coefficient (E = −0.5416 for biomass). As a result of the competition for the same
substrate (CO2 ) by the two processes (biomass growth and ethanol production), the ethanol
54
Chapter 4. Modelling and simulation of syngas fermentation
and cell dry weight are underestimated. It can be concluded that there has to be an other
way to produce ethanol, as there is not enough carbon dioxide available to synthesis both
acetate and ethanol.
Model C - biomass growth and conversion of acetate into ethanol
In contrast to Model B, in which ethanol was produced from CO2 and H2 , ethanol is produced
by the reduction of acetate with hydrogen as the source of reducing power. It was expected
that including this reaction the amount of acetate would decrease and that the ethanol concentration would further increase. The specific biomass growth rate, µ2 , and the specific
acetate conversion rate, µ6 , is represented by equation 4.37.
hy
KI,CO
hy
KI,CO +CCO
2
2
2
hy
KI,CO
CU A
ac
KU A +CU A K hy +CCO
I,CO

CCO
CH
2

· KCO +C2CO · KH +C
·
µ2 = µmax
2
H
2
CH

2
µ6 = µmax
· KH +C
·
6
H
2
2
K
A
· KI,U AI,U
+CU A
(4.37)
·
All parameters considered in this model are defined in Table 4.11 with their initial values found
in literature. In contrast to previous model, where undissociated acetic acid was considered
as an activator for the ethanol production, in this model it is defined as a substrate. That
is also why parameter KUacA is considered as another parameter in comparison with KU A .
The fact that the conversion of acetate to ethanol never has been taken into account in any
mathematical model for acetogens, means that the parameters KUacA and µmax
has to be
6
estimated. The same initial value for KUacA was chosen, as the one for KU A . The initial value
for µmax
was based on the experimental data, as discussed in section 4.1.1. The hydrogen
6
saturation constant of the specific acetate conversion rate is also assumed to be equal to the
saturation constant of rate µ2 .
Table 4.11: Initial parameter values considered in the model.
Parameter
µmax
2
KCO2
KH2
hy
KI,CO
KI,U A
µmax
6
KUacA
Initial value
0.042
0.00022
0.00022
7.10−9
0.0062
0.39
0.0005
Reference
Sakai et al. (2005)
Assumed in this study
Skidmore et al. (2013)
Ragsdale and Ljungdahl (1984)
Sakai et al. (2005)
Assumed in this study
Assumed in this study
A sensitivity analysis was performed to get an idea of their impact on the state variables. The
resulting δ-values of the parameters are listed in Table 4.12 for the output variables biomass,
acetate and ethanol.
It can be noticed that parameter µmax
is again the most sensitive parameter, followed by the
2
ac
parameter µmax
.
The
parameters
K
I,U A , KH2 and KU A have a smaller impact but still worth
6
to mention. The impact of KI,U A is about equal for all three state variables while the δ-value
for ethanol is large for the parameters KUacA and KH2 . The parameters KH2 , KI,U A and µmax
6
55
Chapter 4. Modelling and simulation of syngas fermentation
Table 4.12: Initial parameter values considered in the model.
Parameter
µmax
2
KCO2
KH2
hy
KI,CO
KI,U A
µmax
6
KUacA
δ-value biomass
0.5780
0.0182
0.0290
0.0000
0.0625
0.2772
0.0598
δ-value acetate
0.4166
0.0137
0.0662
0.0000
0.0500
0.0052
0.0020
δ-value ethanol
0.5448
0.0159
0.2203
0.0000
0.0552
0.6869
0.17834
were used in the model calibration. The reason to not choose µmax
is because, as mentioned
2
before, the value of µmax
already
results
in
a
good
fit
with
the
experimental
results. KUacA
2
max
is also kept constant because µ6
already has a big influence on the simulation outcome.
To avoid the searching algorithm to get lost in irrelevant areas of the parameters space, the
parameters to be estimated were transformed according to equation 4.32. The optimal values
for KH2 , µmax
and KI,U A after model calibration are given in Table 4.13.
6
Table 4.13: Initial and optimal value for the kinetic parameters used in the model calibration.
Parameter
KH2
µmax
6
KI,U A
Initial value
0.00022
0.39
0.0062
Optimal value
0.00026
0.61
0.0104
Results of the simulated model are presented in Figures 4.13 and 4.3b.
(a)
(b)
Figure 4.3: Results of model calibration. Comparison between simulation outcome and experimental
data for acetate, ethanol and biomass. (b) Results of model calibration. Comparison
between simulation outcome and experimental data for carbon dioxide and hydrogen.
Chapter 4. Modelling and simulation of syngas fermentation
56
Compared to previous model, a better fit can be noticed between the simulated and experimental ethanol concentrations (Fig. 4.3a). The fact that there is no competition for carbon
dioxide between the two processes makes it possible to produce further acetate and biomass
without the limitation of carbon dioxide. Furthermore, the overproduction of acetate, which
was predicted in Model A, is reduced due to the bioconversion of acetate into ethanol. However, at the end of the fermentation the ethanol concentration is overestimated and the acetate
concentration is underestimated compared to the experimental profiles. This is also reflected
in the prediction of hydrogen in the gas phase (Fig. 4.3b). The consumption of hydrogen
is bigger than it should be in the last 50 h. The Nash-Sutcliffe efficiencies of both acetate
and ethanol are 0.7761 and 0.9343, respectively. A higher value for the inhibition constant,
KI,U A , is another reason for the almost perfect fit that can be noticed between the biomass
simulation outcome and the experimental results of the biomass (E = 0.9603).
Model D - biomass growth and the two metabolic pathways for ethanol production
In this section, both metabolic routes towards ethanol production (de novo from CO2 and
via acetate re-assimilation) were included in the model. Ethanol can either be synthesized by
the conversion of acetate or can be directly produced from CO2 and H2 . The reaction rates
that take place in this model are summarized by equation 4.38.

hy
CCO
CH
KI,CO
K A

2

µ2 = µmax
· KCO +C2CO · KH +C
·
· KI,U AI,U
hy
2

+CU A
H2
K
+C

CO
2
2
2
I,CO


hy
CCO
CH
K
2
UA
µ4 = µmax
· KCO +C2CO · KH +C
· hy I,CO · KU C
4
H
A +CU A
K

2
2
2
2
I,CO +CCO


hy

CH
KI,CO

CU A
2
µ6 = µmax
·
·
·
ac
hy
6
KH +CH
K +CU A
2
2
UA
(4.38)
KI,CO +CCO
All parameters considered in this model are defined in Table 4.14 with their initial values
found in literature. As this is a combination of Model B and C, most parameters calibrated
in these models were also selected to calibrate and to get a model capable of predicting the
experimental data as good as possible.
Table 4.14: Initial parameter values considered in the model.
Parameter
µmax
2
KCO2
KH2
hy
KI,CO
KI,U A
µmax
4
KU A
µmax
6
KUacA
Initial value
0.042
0.00022
0.00022
7.10−9
0.0062
0.39
0.0005
0.39
0.0005
Reference
Sakai et al. (2005)
Assumed in this study
Skidmore et al. (2013)
Ragsdale and Ljungdahl (1984)
Sakai et al. (2005)
Assumed in this study
Assumed in this study
Assumed in this study
Assumed in this study
Subsequently, a sensitivity analysis was performed using a perturbation factor of 10−4 . The
resulting δ-values of the parameters are listed in Table 4.15 for biomass, acetate and ethanol.
57
Chapter 4. Modelling and simulation of syngas fermentation
Table 4.15: Initial parameter values considered in the model.
Parameter
µmax
2
KCO2
KH2
hy
KI,CO
KI,U A
µmax
4
KU A
µmax
6
KUacA
δ-value biomass
0,5727
0.0151
0.0080
0.0000
0.0659
0.0821
0.0200
0.2932
0.0657
δ-value acetate
0.4019
0.01117
0.0564
0.0000
0.0495
0.0541
0.0134
0.0072
0.0027
δ-value ethanol
0.3461
0.0186
0.1869
0.0000
0.0139
0.2619
0.0880
0.4249
0.1135
From Table 4.15 it is clear that µmax
, KH2 , µmax
and µmax
are now the most sensitive
2
4
6
parameters (total δ-value above 0.25). However, it is not recommend to calibrate too many
parameters. From experience of previous calibrations, parameters KH2 , µmax
and µmax
were
4
6
max
max
selected to estimate the optimal values. The optimal values for KH2 , µ4 and µ6 are given
in Table 4.16.
Table 4.16: Initial and optimal value for the kinetic parameters used in the model calibration.
Parameter
KH2
µmax
4
µmax
6
Initial value
0.00022
0.39
0.39
Optimal value
0.000044
0.070
0.45
With optimal parameters values in Table 4.16, obtained after model calibration, the model was
simulated and compared with experimental data. The graph with model output of acetate,
ethanol and biomass is given in Figure 4.4a. The simulation outcome of the two substrate
gases is illustrated in 4.4b.
58
Chapter 4. Modelling and simulation of syngas fermentation
(a)
(b)
Figure 4.4: Results of model calibration. Comparison between simulation outcome and experimental
data for acetate, ethanol and biomass. (b) Results of model calibration. Comparison
between simulation outcome and experimental data for carbon dioxide and hydrogen.
It is clear from the model predictions, shown in Figure 4.4, that there is not a distinct difference with Model C. This can also be observed from the optimal values that were calibrated.
In comparison with model C, a better prediction can be noticed for biomass (E = 0.9941),
but this results in an overestimation of the acetate concentration (E = 0.6919) as both variables are related (Fig. 4.4a). However, it is obvious that the direct conversion of CO2 and
H2 into ethanol, represented by the rate µ4 , does not really partake during the fermentation.
No further benefits arise from including the specific ethanol production rate µ4 and thus will
be excluded from the model. This is consistent with the theory, since the direct production
of ethanol is not so favorable for the bacteria, as it does not provide any energy, while the
conversion of acetate to ethanol has the advantage to diminish the inhibition of high acetate
concentrations and a low pH.
Model E - Model C with sporulation term
From previous simulation results it can be concluded that most likely only biomass growth
and re-assimilation of acetate take place during fermentation of CO2 and H2 . However, at
end of the simulation a further decrease of acetate and an increase of ethanol can be noticed
(Fig. 4.3a), which is contrary to the experimental results. To prevent this of happening, the
specific acetate conversion rate, µ6 , was extended with a deactivation term. As discussed in
chapter 3, sporulation of the bacteria can take place in the stationary phase in typical batch
experiments. This results in deactivation of the bacteria which will lead that reactions such as
ethanol production will cease. The specific biomass growth rate, µ2 , and the specific acetate
conversion rate, µ6 , are represented by equation 4.39. It was expected that at the end of the
fermentation the conversion of acetate into ethanol would slow down.
59
Chapter 4. Modelling and simulation of syngas fermentation

CCO
CH

2

· KCO +C2CO · KH +C
·
µ2 = µmax
2
H
2
2
2
2
CH

2
A

· KH +C
· K acCU+C
·
µ6 = µmax
6
H
UA
2
2
UA
hy
KI,CO
hy
KI,CO
+CCO
hy
KI,CO
hy
KI,CO
+CCO
·
KI,U A
KI,U A +CU A
· 1 − ( Xmax
X
· 1 − ( Xmax
X
)α
(4.39)
)α
All parameters considered in this model are defined in table 4.17 with their initial values
found in literature. The parameter Xmax is the maximum cell dry weight that can be reached
at which the specific acetate conversion rate is zero, assuming that at that specific point the
bacteria are completely sporulated (inactivated). The value for Xmax was determined from
experimental data.
Table 4.17: Initial parameter values considered in the model.
Parameter
µmax
2
KCO2
K H2
hy
KI,CO
KI,U A
µmax
6
KUacA
Xmax
α
Initial value
0.042
0.00022
0.00022
7.10−9
0.0062
0.39
0.0005
9.63058.10−4
1
Reference
Sakai et al. (2005)
Assumed in this study
Skidmore et al. (2013)
Ragsdale and Ljungdahl (1984)
Sakai et al. (2005)
Assumed in this study
Assumed in this study
Determined from experimental data
Assumed in this study
A sensitivity analysis was performed to get an idea of their impact on the state variables. The
resulting δ-values of the parameters are listed in Table 4.18 for the output variables biomass,
acetate and ethanol.
Table 4.18: Initial parameter values considered in the model.
Parameter
µmax
2
KCO2
KH2
hy
KI,CO
KI,U A
µmax
6
KUacA
Xmax
α
δ-value biomass
0.9059
0.0255
0.1082
0.0000
0.1042
0.1598
0.0547
0.4917
0.4168
δ-value acetate
0.4730
0.0135
0.0711
0.0000
0.0571
0.0080
0.0031
0.3595
0.2808
δ-value ethanol
0.1732
0.0035
0.1162
0.0000
0.0040
0.6070
0.2141
0.6140
0.4659
From the sensitivity analysis it can be noticed that the parameters µmax
, Xmax and α are
2
max
ac
the most sensitive, followed by the parameters µ6 , KH2 and KU A . First of all, parameters
KH2 and µmax
were selected for model calibration, based on the simulation of Model C.
6
60
Chapter 4. Modelling and simulation of syngas fermentation
Besides these two parameters, also α was chosen because there are no values available for this
constant in literature (sporulation) and µmax
, since the sporulation term has a big influence
2
on the growth. Xmax was kept constant because this value was derived from the experimental
results. The optimal values for µmax
, KH2 , µmax
and α after model calibration are given in
2
6
Table 4.19.
Table 4.19: Initial and optimal value for the kinetic parameters used in the model calibration.
Parameter
µmax
2
KH2
µmax
6
α
Initial value
0.042
0.00022
0.31
1
Optimal value
0.031
0.000011
0.59
98
With optimal parameters values in Table 4.19, obtained after model calibration, the model was
simulated and compared with experimental data. The graph with model output of acetate,
ethanol and biomass is given in Figure 4.5a. The simulation outcome of the two substrate
gases in function of time is illustrated in 4.5b.
(a)
(b)
Figure 4.5: Results of model calibration. Comparison between simulation outcome and experimental
data for acetate, ethanol and biomass. (b) Results of model calibration. Comparison
between simulation outcome and experimental data for carbon dioxide and hydrogen.
Compared to model C, the model was able to slow down the ethanol production at the end
of the fermentation due to the deactivation of the acetate conversion reaction (Fig. 4.3a).
This resulted in an perfect fit with the ethanol concentrations of the experimental data (E =
0.9828). Because the acetate conversion was inhibited at the stationary phase, no decrease
of acetate could be noticed at the end of the fermentation. Although, the simulation of
acetate could more or less follow the profile of the experimental data, no difference was
observed between Model C and E in the Nash-Sutcliffe efficiency coefficient for acetate (E =
0.7792). The downside of using this deactivation term is that the biomass growth was slowed
down in order to get a better fit between the model predictions of the products and the
61
Chapter 4. Modelling and simulation of syngas fermentation
experimental data. Compared to Model B and C, the fit between the simulated biomass and
the experimental data is not better (E = 0.9058 for biomass). Furthermore, again a small
underestimation of hydrogen in the gas phase can be recognized in Figure 4.5b. Overall, it can
be concluded that this model is most capable of describing the experimental data, because
in contrary to the other model structures, this model is more able to follow the trend of the
experimental data.
4.3.2
Model calibration for biomass growth on carbon monoxide, carbon
dioxide and hydrogen
In this section, both the consumption of CO and the consumption of CO2 and H2 were
included. The model was evaluated on the dataset of the batch fermentation on syngas. As
mentioned above, model E is most competent to predict the fermentation on CO2 and H2 . So
only biomass growth accompanied with acetate production and the conversion of acetate into
ethanol were considered. The optimal parameter values of model E were kept constant and
taken as initial values for the following simulation. Although some exceptions were made in
case the parameters also were included in the models concerning the consumption of carbon
monoxide. For example the parameter KI,U A , which was considered equal for both specific
growth rates.
In the model only biomass growth and the production of acetate was taken into account. The
reason why ethanol production was not considered is because the culture had an abnormal
behaviour, with very little ethanol production. The specific biomass growth rate on carbon
monoxide, µ1 , and the specific biomass growth rate on carbon dioxide and hydrogen, µ2 ,
are represented by equation 4.40. Production of ethanol, either by direct conversion of the
substrates or by conversion of acetate, is not taken into account.

max


µ1 = µ1 ·
K
CCO
C2
KCO +CCO + K CO
I,CO
A
· KI,U AI,U
+CU A
CCO
CH

2

· KCO +C2CO · KH +C
·
µ2 = µmax
2
H
2
2
2
2
hy
KI,CO
hy
KI,CO
+CCO
(4.40)
K
A
· KI,U AI,U
+CU A
The parameters considered in these equations are listed in table 4.20 with their initial values
found in literature. The initial values for the parameters used in the expression for the
specific biomass growth rate on carbon dioxide and hydrogen were selected from the model
that was most capable of predicting the experimental results from the batch fermentation
on CO2 and H2 . Note that parameter KI,U A is used in both growth rates. In this case the
original value for KI,U A was selected as initial value and was used in the model calibration.
Furthermore, in comparison with previous section, in which a model was selected that could
describe the fermentation on CO2 and H2 , also CO is available as substrate. This means that
hy
the parameter KI,CO
, the CO inhibition constant for hydrogenase, will have a tremendous
effect on the consumption of CO2 and H2 , since the reactions on CO2 and H2 will be inhibited
while CO is still in the fermentation broth.
62
Chapter 4. Modelling and simulation of syngas fermentation
Table 4.20: Initial parameter values considered in the model.
Parameter
µmax
1
KCO
KI,CO
KI,U A
µmax
2
KCO2
KH2
hy
KI,CO
Initial value
0.195
0.000078
0.002
0.0062
0.031
0.00022
0.000011
7.10−9
Reference
Mohammadi et al. (2014)
Younesi et al. (2005)
Younesi et al. (2005)
Assumed in this study
Model E
Model E
Model E
Ragsdale and Ljungdahl (1984)
A sensitivity analysis was performed using a perturbation factor of 10−4 . The resulting δvalues (as defined in chapter 4.2) of the parameters are listed in Table 4.21 for biomass,
acetate and ethanol.
Table 4.21: Initial parameter values considered in the model.
Parameter
µmax
1
KCO
KI,CO
KI,U A
µmax
2
KCO2
KH2
hy
KI,CO
δ-value biomass
0.4759
0.1350
0.1097
1.4183
1.0595
0.1817
0.0439
0.0564
δ-value acetate
0.3933
0.0633
0.0806
0.4409
0.3354
0.0520
0.0332
0.0242
δ-value ethanol
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
From the sensitivity analysis (Table 4.21) it can be concluded that KI,U A and µmax
are the
2
most sensitive parameters, followed by the parameters KCO , µmax
,
K
and
K
.
CO2
I,U A It can
1
hy
be noticed that parameter KI,CO does not have such a big influence on the state variables,
biomass and acetate. Because of this very low concentration the reactions with this inhibition
constant will only proceed if the concentration of CO is almost zero. So the increase of the
value for the inhibition constant with the perturbation factor will not result in a remarkable
hy
change in the simulation. In the model calibration, the initial value for the parameter KI,CO
hy
was chosen by trial and error until an optimal value was found. Next to KI,CO
, the parameters
max
µ1
and KI,U A were also selected for the model calibration. Parameters KCO and KI,CO
were kept constant to avoid that to many parameters would be calibrated from the same
hy
expression. The optimal values for µmax
, KI,U A and KI,CO
after model calibration are given
1
hy
in Table 4.22. Eventually the initial value for KI,CO
was set at 7.10−4 .
63
Chapter 4. Modelling and simulation of syngas fermentation
Table 4.22: Initial and optimal value for the kinetic parameters used in the model calibration.
Parameter
µmax
1
KI,U A
hy
KI,CO
Initial value
0.195
0.0062
0.0007
Optimal value
0.113
0.045
0.0000044
The optimal parameter values are shown in Table 4.22. The graph with model output of
acetate, ethanol and biomass is given in figure 4.6a. The simulations of carbon monoxide,
carbon dioxide and hydrogen are illustrated in 4.6b.
(a)
(b)
Figure 4.6: Results of model calibration. Comparison between simulation outcome and experimental
data for acetate, ethanol and biomass. (b) Results of model calibration. Comparison
between simulation outcome and experimental data for carbon dioxide and hydrogen in
the gas phase.
Comparing the simulated and experimental concentrations of biomass and acetate, illustrated
in figure 4.6a, an overestimation can be observed. The efficiency coefficients for biomass and
acetate are equal to 0.8411 and 0.8666, respectively. By taking the re-assimilation of acetate
into consideration, acetate would decrease due to the bioconversion and the competition for
the same substate, hydrogen or carbon monoxide. From figure 4.6b, it is clear that the
model succeeds in predicting the substrate concentrations in the gas phase quite well. The
efficiency coefficients for CO, CO2 and H2 are equal to 0.9829, 0.6466 and 0.9888, respectively.
However, the experimental concentrations of carbon dioxide at the end of the simulation are
underestimated by the model. This could probably be solved by incorporating the conversion
of acetate, since this reaction needs hydrogen to reduce acetate into ethanol. This means
that there will be less hydrogen available to produce biomass together with acetate. In other
words without hydrogen no carbon dioxide will be consumed. But also the consumption of
carbon monoxide results in an increase of carbon dioxide. It can also be noticed that a higher
value for the U A inhibition constant was needed to fulfill the simulation. So most likely, a
much higher value is more reasonable for this constant. Despite that the introduction of the
acetate conversion reaction could improve the simulation, this was not tested because the
Chapter 4. Modelling and simulation of syngas fermentation
64
culture behaved abnormally. The occurrence of an ”acid crash” could be a possibility for this
abnormal behaviour. This phenomenon sporadically takes place in typically pH-uncontrolled
batch fermentation experiments, where high amount of acetate instead of ethanol is produced.
Due to the high accumulation of undissociated acetic acid, the metabolic pathway of the
culture fails to switch from acidogenesis to solventogenesis. Although, acid crash might not be
the actual cause for the avoidance of the ethanol production, since the acetate concentrations
are not that high in this experiment (Mohammadi, 2014).
Chapter 5
Conclusions and recommendations
5.1
General conclusions
The main purpose of this master dissertation was to build the foundations of a mathematical
model that is capable of capturing the complexity of syngas fermentation. By doing so, a
better insight could be gained into the reactions that play a role during fermentation of the
gaseous substrates, CO, CO2 and H2 . Up to now, a few attempts have been made to develop
a mathematical model that can predict the outcome of this biotechnological process. To be
able to accomplish the development of a suitable model, three experiments with different
operational conditions were conducted. Because the focus lied on the production of ethanol,
also called the solventogenesis, a pure-culture of model-microorganism C. ljungdahlii was used
to perform the fermentation processes.
From the first batch experiment, in which only CO2 and H2 was injected, it can be concluded
that the bacteria were able to make a switch towards solventogenesis. However, this phase
only takes place in unfavorable growth conditions, to prevent further decrease of pH and
product inhibition, caused by high concentrations of acids. The results emphasize that most
likely, other factors also play a significant role in the changeover towards ethanol production.
An explanation could be the high amount of available reducing power in the form of hydrogen.
Sporulation of the culture could be another reason. This would also explain why the bacteria
fail to produce ethanol in the later stage of the stationary phase. Although the conditions
were less favorable, due to the lower pH and the higher acetate concentrations, no metabolic
shift from the acedogenic phase to the solventogenic phase could be observed in the batch
fermentation on syngas. The phenomenon ”acid crash” can have a part in this. Comparing the
growth of C. ljungdahlii in both batch fermentations points out that CO, as a source of energy
and reducing power, is more favorable above the consumption of H2 for gaining energy in the
form of ATP. The same conclusion can be derived from the proposed stoichiometric reactions
for growth. Less acetate has to be produced to gain the same amount of biomass in reaction
4.1 compared to reaction 4.4. The culture in the discontinuous fed-batch fermentation was,
in contrast to previous experiment, able to accumulate ethanol in the fermentation broth. It
is clear that the high acetate concentrations together with low pH activated solventogenesis
resulting in re-assimilation of acetate into ethanol.
To capture the complexity of this process, six different reactions were proposed in the model.
However, to evaluate and calibrate the kinetic expressions of these reactions in a proper way,
65
Chapter 5. Conclusions and recommendations
66
first the fermentation of carbon dioxide and hydrogen was simulated. In this way, it was
easier to comprehend how exactly C. ljungdahlii behaves in the presence of only these two
substrates. In total five different macroscopic models were taken into consideration to predict
the growth of the bacteria and the formation of the products (acetate and ethanol).
In Model A, biomass growth and acetate production (acidogenesis) were included. An overestimation of the experimental acetate concentrations was observed. It was concluded that
including ethanol production would improve the profile of acetate. Therefore, two different
pathways for ethanol production were examined. Model A was first extended by including
the direct conversion of CO2 and H2 via acetyl-CoA into ethanol, resulting in Model B. This
model did not succeed in describing acidogenesis and solventogenesis. The prediction of both
biomass and ethanol were underestimated at the end of the fermentation while acetate still
was overestimated. This can be explained by the competition of both reactions for the exact
same substrates, with carbon dioxide in particular. It was concluded that it was necessary
to include the re-assimilation of acetate into ethanol via acetyl-CoA into the model to prevent the low ethanol and high acetate concentrations at the beginning of stationary phase.
This led to the evaluation of Model C, in which biomass growth accompanied with acetate
production and acetate conversion was included. Compared to Model B, a better fit was
observed between the simulated and experimental ethanol concentrations. Because there was
no competition for carbon dioxide between the processes, it was possible to produce further
ethanol by converting acetate, as there was enough hydrogen available to reduce acetate.
Furthermore, bioconversion of acetate into ethanol reduced the overestimation of acetate.
The downside of this model was that the re-assimilation of acetate carried on until either
hydrogen or acetate were depleted. In Model D, both pathways for ethanol production (B
and C) were taken into account. It was clear from the model predictions that there was
not a distinct difference between Model C and D. This can be explained by the fact that
the re-assimilation pathway dominated over the synthesis from CO2 . However, it cannot be
concluded with absolute certainty that only re-assimilation took place. The fact that the
re-assimilation of acetate results in more favorable growth conditions due to less product
inhibition and a higher pH could be a reasonable explanation. Eventually, the expression
of the specific acetate conversion rate of Model C was extended with a deactivation term in
order to slow down the re-assimilation, resulting in model E. From the simulation results it
was concluded that Model E was capable to slow down the acetate conversion, assuming that
sporulation of the bacteria took action. Despite the perfect fit for ethanol, no improvements
were noticed in the simulation of biomass and acetate. Although Model E was not capable of
describing all variables in a decent way, it still was concluded that this model was most suited
for the simulation of this process, as in contrary to the other models, Model E was able to
simulate the profiles (trends) of the variables. From the simulation it cannot be concluded
that sporulation really took place during fermentation. If that actually is the case, search for
another term should be recommended.
Due to the unpredicted behaviour (no solventogenesis) of the culture in the batch fermentation
of syngas, the model was not extended with ethanol production. Here, only biomass growth
was considered. A good fit between the simulation and the experimental data of the three
substrate gases was observed. However, the model did not succeed in predicting the biomass
and acetate concentrations (overestimation). It was concluded that the incorporation of the
re-assimilation reaction would reduce this overestimation.
Chapter 5. Conclusions and recommendations
5.2
67
Recommendations for future research
In this thesis, the emphasis was put on the modelling of acetate and ethanol production by
pure cultures of C. ljungdahlii on CO2 and H2 . The model that has been developed can be a
good starting point to further build a more comprehensive model for syngas fermentation.
Beyond that, a few recommendations are given for future research on modelling syngas fermentation. The current model was developed following the simplest approach to reproduce
this complex fermentation process. Along the development of the model, undissociated acetic
acid, dependent on pH and the total amount of acetate, was considered as the inhibitor of
the growth of C. ljungdahlii and the trigger of ethanol production. Therefore, pH modelling
and its effects should be incorporated to the model in the future. Other factors, such as
the availability of reducing agents (NADH, NADPH and Fd) in their oxidized or reduced
form, have most certainly also an effect on the production of ethanol. These are aspects
already taken into account in genome-based metabolic models, which are used to elucidate
novel biological capabilities of syngas fermenting bacteria and the different facets of energy
conservation during autotrophic metabolism of acetogens. However, from the macroscopic
point of view, this would make the model far more complicated. Finally, in the present model
two metabolic pathways were considered towards the formation of ethanol. However, recent
research has also indicated that some bacteria contain AOR, an enzyme that converts acetate
via acetaldehyde into ethanol. To extend the model with this reaction, the model should go
deeper on the metabolic level, and thus the reducing agents should be taken into account.
All in all, syngas fermentation seems to be a very promising biotechnological application for
converting waste gases into valuable products. To make this process more economical feasible,
it is important to further investigate the affect of external process parameters, such as pH,
gas pressure and bioreactor configurations, on productivity. Eventually, macroscopic models
could be an useful tool for further process optimization. Future research efforts should also
focus on metabolic engineering in order to control the metabolic pathway in the direction
of solventogenesis. Increasing the range of products by introducing artificial pathways in
acetogens could be also a way to increase the interest in this process.
Bibliography
Abrini, J., Naveau, H., and Nyns, E.-J. (1994). Clostridium autoethanogenum, sp. nov., an
anaerobic bacterium that produces ethanol from carbon monoxide. Archives of Microbiology, 161(4):345–351.
Abubackar, H. N., Veiga, M. C., and Kennes, C. (2011). Biological conversion of carbon
monoxide: rich syngas or waste gases to bioethanol. Biofuels, Bioproducts and Biorefining,
5(1):93–114.
Abubackar, H. N., Veiga, M. C., and Kennes, C. (2012). Biological conversion of carbon
monoxide to ethanol: Effect of pH, gas pressure, reducing agent and yeast extract. Bioresource Technology, 114:518–522.
Abubackar, H. N., Veiga, M. C., and Kennes, C. (2015). Carbon monoxide fermentation to
ethanol by Clostridium autoethanogenum in a bioreactor with no accumulation of acetic
acid. Bioresource Technology, 186:122–127.
Ahmed, A., Cateni, B. G., Huhnke, R. L., and Lewis, R. S. (2006). Effects of biomassgenerated producer gas constituents on cell growth, product distribution and hydrogenase
activity of Clostridium carboxidivorans P7T. Biomass and Bioenergy, 30:665–672.
Ahmed, A. and Lewis, R. S. (2007). Fermentation of Biomass-Generated Synthesis Gas:
Effects of Nitric Oxide. Biotechnology and Bioengineering, 97(5):1080–1086.
Allen, T. D., Caldwell, M. E., Lawson, P. A., Huhnke, R. L., and Tanner, R. S. (2010).
Alkalibaculum bacchi gen. nov., sp. nov., a CO-oxidizing, ethanol-producing acetogen isolated from livestock-impacted soil. International Journal of Systematic and Evolutionary
Microbiology, 60(10):2483–2489.
ArcelorMittal (2015). ArcelorMittal, LanzaTech and Primetals Technologies announce partnership to construct breakthrough 87m biofuel production facility.
Babu, B. K., Atiyeh, H. K., Wilkins, M. R., and Huhnke, R. L. (2010). Effect of the Reducing
Agent Dithiothreitol on Ethanol and Acetic Acid Production by. Asabe, 3(1):19–35.
Balch, W. E., Schoberth, S., Tanner, R. S., and Wolfe, R. S. (1977). Acetobacterium, a
New Genus of Hydrogen-Oxidizing, Carbon Dioxide-Reducing, Anaerobic Bacteria. International Journal of Systematic Bacteriology, 27(4):355–361.
Ballard (2011). Utilizing By-Product Hydrogen and Fuel Cells for Distributed Power Generation - a California. Technical report, Ballad Power Systems,Inc.
68
Bibliography
69
Bertsch, J. and Müller, V. (2015). Bioenergetic constraints for conversion of syngas to biofuels
in acetogenic bacteria. Biotechnology and bioengineering, pages 1–12.
Braun, M., Mayer, F., and Gottschalk, G. (1981). Clostridium aceticum (Wieringa), a microorganism producing acetic acid from molecular hydrogen and carbon dioxide. Archives
of Microbiology, 128(3):288–293.
Bredwell, M. D., Srivastava, P., and Worden, R. M. (1999). Reactor design issues for synthesisgas fermentations. Biotechnology Progress, 15:834–844.
Bredwell, M. D. and Worden, R. M. (1998). Mass-transfer properties of microbubbles. 1.
Experimental studies. Biotechnology Progress, 14(1):31–38.
Buckel, W. and Thauer, R. K. (2013). Energy conservation via electron bifurcating ferredoxin
reduction and proton/Na+ translocating ferredoxin oxidation. Biochimica et Biophysica
Acta (BBA) - Bioenergetics, 1827(2):94–113.
Chatterjee, S., Grethlein, A. J., Worden, R. M., and Jain, M. K. (1996). Evaluation of support
matrices for an immobilized cell gas lift reactor for fermentation of coal derived synthesis
gas. Journal of Fermentation and Bioengineering, 81(2):158–162.
Chen, J., Gomez, J., Höffner, K., Barton, P., and Henson, M. (2015). Metabolic modeling of
synthesis gas fermentation in bubble column reactors. Biotechnology for Biofuels, 8(1):89.
Cherubini, F. (2010). The biorefinery concept : Using biomass instead of oil for producing
energy and chemicals. Energy Conversion and Management, 51:1412–1421.
Clark, J. E. and Ljungdahl, L. G. (1984).
Purification and properties of 5,10methylenetetrahydrofolate reductase, an iron-sulfur flavoprotein from Clostridium formicoaceticum. The Journal of biological chemistry, 259(17):10845–9.
Cotter, J. L., Chinn, M. S., and Grunden, A. M. (2009a). Ethanol and acetate production by
Clostridium ljungdahlii and Clostridium autoethanogenum using resting cells. Bioprocess
and Biosystems Engineering, 32(3):369–380.
Cotter, J. L., Chinn, M. S., and Grunden, A. M. (2009b). Influence of process parameters
on growth of Clostridium ljungdahlii and Clostridium autoethanogenum on synthesis gas.
Enzyme and Microbial Technology, 44(5):281–288.
Daniell, J., Köpke, M., and Simpson, S. D. (2012). Commercial biomass syngas fermentation.
Energies, 5(12):5372–5417.
De Heyder, B., Vanrolleghem, P., Van Langenhove, H., and Verstraete, W. (1997). Kinetic characterization of mass transfer limited biodegradation of a low water soluble gas in
batch experiments -Necessity for multiresponse fitting. Biotechnology and Bioengineering,
55(3):511–519.
Debevere, J., Devlieghere, F., Jacxsens, L., Uyttendaele, M., and Vermeulen, A. (2011).
Levensmiddelenmicrobiologie en -conservering. Brugge : Die Keure.
Devarapalli, M. and Atiyeh, H. K. (2015). A review of conversion processes for bioethanol
production with a focus on syngas fermentation. Biofuel Research Journal, 2(3):268–280.
Bibliography
70
Drake, H. L. and Daniel, S. L. (2004). Physiology of the thermophilic acetogen Moorella
thermoacetica. Research in microbiology, 155(6):422–36.
Drake, H. L., Gößner, A. S., and Daniel, S. L. (2008). Old Acetogens , New Light Old Acetogens , New Light. Annals of the New York Academy of Sciences, 1125(January):100–128.
Drzyzga, O., Revelles, O., Durante-Rodrı́guez, G., Dı́az, E., Garcı́a, J. L., and Prieto, A.
(2015). New challenges for syngas fermentation: towards production of biopolymers. Journal of Chemical Technology & Biotechnology, 90(10):1735–1751.
EPA (2015). Overview of Greenhouse Gases.
European Union (2009). Directive of the European Parliament and of the Council of 23
April 2009 on the Directives, promotion of the use of energy from renewable sources and
amending and subsequently repealing 2001/77/EC and 2003/30/EC.
Fontaine, F. E., Peterson, W. H., McCoy, E., Johnson, M. J., and Ritter, G. J. (1942). A New
Type of Glucose Fermentation by Clostridium thermoaceticum. Journal of Bacteriology,
43(6):701–715.
Formenti, L. R., Nørregaard, A., Bolic, A., Hernandez, D. Q., Hagemann, T., Heins, A.-L.,
Larsson, H., Mears, L., Mauricio-Iglesias, M., Krühne, U., and Gernaey, K. V. (2014). Challenges in industrial fermentation technology research. Biotechnology Journal, 9(6):727–738.
Gaddy, J. L., Arora, D. K., Ko, C.-W., Phillips, J. R., Basu, R., Wilkstrom, C. V., and
Clasuen, E. C. (2003). Methods for increasing the production of ethanol from microbial
fermentation.
Ganigué, R., Volcke, E. I. P., Puig, S., Balaguer, M. D., Colprim, J., and Sin, G. (2010).
Systematic model development for partial nitrification of landfill leachate in a SBR. Water
Science and Technology, 61(9):2199–2210.
Garcia-Ochoa, F. and Gomez, E. (2009). Bioreactor scale-up and oxygen transfer rate in
microbial processes: An overview. Biotechnology Advances, 27(2):153–176.
Genthner, B. R. S. and Bryant, M. P. (1987). Additional characteristics of one-carboncompound utilization by Eubacterium limosum and Acetobacterium woodii . Applied and
Environmental Microbiology, 53(3):471–476.
Gottwald, M. and Gottschalk, G. (1985). The internal pH of Clostridium acetobutylicum and
its effect on the shift from acid to solvent formation. Archives of Microbiology, 143(1):42–46.
Grethlein, A. J., Worden, R. M., Jain, M. K., and Datta, R. (1991). Evidence for production
of n-butanol from carbon monoxide by Butyribacterium methylotrophicum. Journal of
Fermentation and Bioengineering, 72(1):58–60.
Griffin, D. W., Schultz, M. A., Irving, E., and Road, P. (2012). Fuel and Chemical Products
from Biomass Syngas : A Comparison of Gas Fermentation to Thermochemical Conversion
Routes. Environmental progress & sustainable energy, 31(2):219–224.
Bibliography
71
Haus, S., Jabbari, S., Millat, T., Janssen, H., Fischer, R.-J., Bahl, H., King, J. R., and
Wolkenhauer, O. (2011). A systems biology approach to investigate the effect of pH-induced
gene regulation on solvent production by Clostridium acetobutylicum in continuous culture.
BMC systems biology, 5(1):10.
Heiskanen, H., Virkajärvi, I., and Viikari, L. (2007). The effect of syngas composition on
the growth and product formation of Butyribacterium methylotrophicum. Enzyme and
Microbial Technology, 41(3):362–367.
Hu, J., Yu, F., and Lu, Y. (2012). Application of Fischer–Tropsch Synthesis in Biomass to
Liquid Conversion. Catalysts, 2:303–326.
Hurst, K. M. and Lewis, R. S. (2010). Carbon monoxide partial pressure effects on the
metabolic process of syngas fermentation. Biochemical Engineering Journal, 48(2):159–165.
INEOS Bio (2012). Advanced bioethanol production and renewable energy generation from
ligno-cellulosic materials, biomass waste and residues.
Islam Mozumder, M. S., Garcia-Gonzalez, L., Wever, H. D., and Volcke, E. I. (2015). Poly(3hydroxybutyrate) (PHB) production from CO2: Model development and process optimization. Biochemical Engineering Journal, 98:107–116.
James A. Zahn, J. S. (2012). Ethanologenic Clostridium species, Clostridium coskatii .
James L., G. E. C. C. (1992). Clostridium ljungdahlii, an anaerobic ethanol and acetate
producing microorganism.
Jim Lane (2014). On the Mend: Why INEOS Bio isn’t producing ethanol in Florida.
Karnholz, A., Küsel, K., Gößner, A., Schramm, A., and Drake, H. L. (2002). Tolerance
and metabolic response of acetogenic bacteria toward oxygen. Applied and Environmental
Microbiology, 68(2):1005–1009.
Kim, B., Bellows, P., Datta, R., and Zeikus, J. (1984). Control of Carbon and Electron Flow
in Clostridium acetobutylicum Fermentations: Utilization of. Applied and Environmental
Microbiology, 48(4):764–770.
Kim, D. and Chang, I. S. (2009). Electricity generation from synthesis gas by microbial
processes: CO fermentation and microbial fuel cell technology. Bioresource Technology,
100(19):4527–4530.
Kim, Y. K., Park, S. E., Lee, H., and Yun, J. Y. (2014). Enhancement of bioethanol production in syngas fermentation with Clostridium ljungdahlii using nanoparticles. Bioresource
Technology, 159:446–450.
Klasson, K., Ackerson, C., Clausen, E., and Gaddy, J. (1992a). Biological conversion of
synthesis gas into fuels. International Journal of Hydrogen Energy, 17(4):281–288.
Klasson, K., Ackerson, M., Clausen, E., and Gaddy, J. (1991). Bioreactor design for synthesis
gas fermentations. Fuel, 70(5):605–614.
Klasson, K. T., Ackerson, M. D., Clausen, E. C., and Gaddy, J. L. (1992b). Bioconversion of
synthesis gas into liquid or gaseous fuels. Enzyme and Microbial Technology, 14(8):602–608.
Bibliography
72
Köpke, M., Held, C., Hujer, S., Liesegang, H., Wiezer, A., Wollherr, A., Ehrenreich, A., Liebl,
W., Gottschalk, G., and Dürre, P. (2010). Clostridium ljungdahlii represents a microbial
production platform based on syngas. Proceedings of the National Academy of Sciences of
the United States of America, 107(29):13087–92.
Kopke, M., Mihalcea, C., Liew, F., Tizard, J. H., Ali, M. S., Conolly, J. J., Al-Sinawi, B., and
Simpson, S. D. (2011). 2,3-Butanediol Production by Acetogenic Bacteria, an Alternative
Route to Chemical Synthesis, Using Industrial Waste Gas. Applied and Environmental
Microbiology, 77(15):5467–5475.
Kumar, A., Jones, D. D., and Hanna, M. A. (2009). Thermochemical Biomass Gasification:
A Review of the Current Status of the Technology. Energies, pages 556–581.
Kundiyana, D. K., Huhnke, R. L., and Wilkins, M. R. (2010). Syngas fermentation in a
100-L pilot scale fermentor: Design and process considerations. Journal of Bioscience and
Bioengineering, 109(5):492–498.
Kundiyana, D. K., Wilkins, M. R., Maddipati, P., and Huhnke, R. L. (2011). Effect of temperature, pH and buffer presence on ethanol production from synthesis gas by ”Clostridium
ragsdalei ”. Bioresource technology, 102(10):5794–9.
LanzaTech Inc. (2015). LanzaTech summary overview.
LanzaTech Inc. (2016). Chemicals.
Latif, H., Zeidan, A. a., Nielsen, A. T., and Zengler, K. (2014). Trash to treasure: Production of biofuels and commodity chemicals via syngas fermenting microorganisms. Current
Opinion in Biotechnology, 27:79–87.
Lee, S. Y., Park, J. H., Jang, S. H., Nielsen, L. K., Kim, J., and Jung, K. S. (2008). Fermentative butanol production by Clostridia. Biotechnology and bioengineering, 101(2):209–28.
Leigh, J. A., Mayer, F., and Wolfe, R. S. (1981). Acetogenium kivui, a new thermophilic
hydrogen-oxidizing acetogenic bacterium. Archives of Microbiology, 129(4):275–280.
Lennartsson, P. R., Erlandsson, P., and Taherzadeh, M. J. (2014). Integration of the first and
second generation bioethanol processes and the importance of by-products. Bioresource
Technology, 165:3–8.
Lewis, R. S., Us, U. T., Tanner, R. S., and Us, O. K. (2010). Isolation and characterization
of novel Clostridial species.
Liou, J. S. C., Balkwill, D. L., Drake, G. R., and Tanner, R. S. (2005). Clostridium carboxidivorans sp. nov., a solvent-producing clostridium isolated from an agricultural settling lagoon, and reclassification of the acetogen Clostridium scatologenes strain SL1 as
Clostridium drakei sp. nov. International Journal of Systematic and Evolutionary Microbiology, 55(5):2085–2091.
Ljungdhal, L. G. (1986). The Autotrophic Pathway of Acetate Synthesis in Acetogenic Bacteria. Annual Review of Microbiology, 40(1):415–450.
Bibliography
73
Maddipati, P., Atiyeh, H. K., Bellmer, D. D., and Huhnke, R. L. (2011). Ethanol production
from syngas by Clostridium strain P11 using corn steep liquor as a nutrient replacement
to yeast extract. Bioresource Technology, 102(11):6494–6501.
Maddox, I. S., Steiner, E., Hirsch, S., Wessner, S., Gutierrez, N. a., Gapes, J. R., and Schuster,
K. C. (2000). The cause of ”acid-crash” and ”acidogenic fermentations” during the batch
acetone-butanol-ethanol (ABE-) fermentation process. Journal of molecular microbiology
and biotechnology, 2(1):95–100.
Menon, S. and Ragsdale, S. W. (1996). Evidence That Carbon Monoxide Is an Obligatory
Intermediate in Anaerobic Acetyl-CoA Synthesis. Biochemistry, 35(37):12119–12125.
Michael T. Madigan, John M.Martinko, David A. Stahl, D. P. C. (2012). Biology of microorganisms. Benjamin Cummings;, 13 edition.
Millat, T., Janssen, H., Bahl, H., Fischer, R.-j., and Wolkenhauer, O. (2011). The pH-induced
Metabolic Shift from Acidogenesis to Solventogenesis in Clostridium acetobutylicum – From
Experiments to Models. Beilstein-institut, pages 33–54.
Mohammadi, M. (2014). Effect of Organic Substrate on Promoting Solventogenesis in
Ethanologenic Acetogene Clostridium Ljungdahlii ATCC 55383. International Journal of
Engineering, 27(2 (B)):185–194.
Mohammadi, M., Mohamed, A. R., Najafpour, G. D., Younesi, H., and Uzir, M. H. (2014).
Kinetic studies on fermentative production of biofuel from synthesis gas using Clostridium
ljungdahlii . The Scientific World Journal, 2014(1).
Mohammadi, M., Najafpour, G. D., Younesi, H., Lahijani, P., Uzir, M. H., and Mohamed,
A. R. (2011). Bioconversion of synthesis gas to second generation biofuels: A review.
Renewable and Sustainable Energy Reviews, 15(9):4255–4273.
Mohammadi, M., Younesi, H., Najafpour, G., and Mohamed, A. R. (2012). Sustainable
ethanol fermentation from synthesis gas by Clostridium ljungdahlii in a continuous stirred
tank bioreactor. Journal of Chemical Technology and Biotechnology, 87(6):837–843.
Morrin, S., Lettieri, P., Chapman, C., and Mazzei, L. (2012). Two stage fluid bed-plasma
gasification process for solid waste valorisation: Technical review and preliminary thermodynamic modelling of sulphur emissions. Waste Management, 32(4):676–684.
Mozumder, M. S. I., Goormachtigh, L., Garcia-Gonzalez, L., De Wever, H., and Volcke, E. I.
(2014). Modeling pure culture heterotrophic production of polyhydroxybutyrate (PHB).
Bioresource Technology, 155:272–280.
Müller, V. (2003). Energy Conservation in Acetogenic. Applied and Environmental Microbiology, 69(11):6345–6353.
Munasinghe, P. C. and Khanal, S. K. (2010). Syngas fermentation to biofuel: Evaluation of
carbon monoxide mass transfer coefficient (kLa) in different reactor configurations. Biotechnology Progress, 26(6):1616–1621.
Munasinghe, P. C. and Khanal, S. K. (2011). Biomass-derived syngas fermentation into
biofuels. Biofuels, 101(13):79–98.
Bibliography
74
Nagarajan, H., Sahin, M., Nogales, J., Latif, H., Lovley, D. R., Ebrahim, A., and Zengler, K.
(2013). Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of Clostridium ljungdahlii . Microbial Cell Factories, 12(1):118.
Naik, S. N., Goud, V. V., Rout, P. K., and Dalai, A. K. (2010). Production of first and second
generation biofuels : A comprehensive review. Renewable and Sustainable Energy Reviews,
14:578–597.
Ndaba, B., Chiyanzu, I., and Marx, S. (2015). n -Butanol derived from biochemical and
chemical routes : A review. Biotechnology Reports, 8:1–9.
Nopens, I. (2014). Modelleren en simuleren van biosystemen.
Oakley, S. D. (2012). Carbon capture in fermentation.
Ragsdale, S. W. (2004). Life with Carbon Monoxide. Critical Reviews in Biochemistry and
Molecular Biology, 39(3):165–195.
Ragsdale, S. W. (2008). Enzymology of the Wood-Ljungdahl Pathway of Acetogenesis. Annals
of the New York Academy of Sciences, 1125(1):129–136.
Ragsdale, S. W. and Ljungdahl, L. G. (1984). Hydrogenase from Acetobacterium woodii .
Archives of Microbiology, 139(4):361–365.
Ragsdale, S. W. and Pierce, E. (2008). Acetogenesis and the Wood–Ljungdahl pathway of CO2 fixation. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics,
1784(12):1873–1898.
Ramió-Pujol, S., Ganigué, R., Bañeras, L., and Colprim, J. (2015). Incubation at 25 ◦ C
prevents acid crash and enhances alcohol production in Clostridium carboxidivorans P7.
Bioresource Technology, 192:296–303.
Rao, G. and Mutharasan, R. (1987). Altered electron flow in continuous cultures of Clostridium acetobutylicum induced by viologen dyes. Applied and Environmental Microbiology,
53(6):1232–1235.
REN 21 (2015). Renewables 2015 Global Status Report. Technical report, Renewable Energy
Policy Network for the 21st Century.
Richter, H., Martin, M. E., and Angenent, L. T. (2013). A two-stage continuous fermentation
system for conversion of syngas into ethanol. Energies, 6(8):3987–4000.
Sakai, S., Nakashimada, Y., Inokuma, K., Kita, M., Okada, H., and Nishio, N. (2005). Acetate
and ethanol production from H2 and CO2 by Moorella sp. using a repeated batch culture.
Journal of bioscience and bioengineering, 99(3):252–258.
Sander, R. (2015). Compilation of Henry’s law constants (version 4.0) for water as solvent.
Atmospheric Chemistry and Physics, 15(8):4399–4981.
Savage, M. D., Wu, Z., Daniel, S. L., Lundie, L. L., and Drake, H. L. (1987). Carbon monoxide
dependent chemolithotrophic growth of Clostridiurn thermoautotrophicum. Appl. Environ.
Microbiol., 53(8):1902 – 1906.
Bibliography
75
Saxena, J. and Tanner, R. S. (2011). Effect of trace metals on ethanol production from
synthesis gas by the ethanologenic acetogen, Clostridium ragsdalei. Journal of Industrial
Microbiology and Biotechnology, 38:513–521.
Schuchmann, K. and Müller, V. (2014). Autotrophy at the thermodynamic limit of life:
a model for energy conservation in acetogenic bacteria. Nature reviews. Microbiology,
12(12):809–821.
Serveis Tècnics de Recerca (2013). Determinació del contingut en C, H, N en mostra per
Anàlisi Elemental. Technical report, Universitat de Girona.
Shen, Y., Brown, R., and Wen, Z. (2014). Enhancing mass transfer and ethanol production
in syngas fermentation of Clostridium carboxidivorans P7 through a monolithic biofilm
reactor. Applied Energy, 136:68–76.
Sim, J. H., Kamaruddin, A. H., and Long, W. S. (2008). Biocatalytic conversion of CO to
acetic acid by Clostridium aceticum-Medium optimization using response surface methodology (RSM). Biochemical Engineering Journal, 40:337–347.
Sims, R. E. H., Mabee, W., Saddler, J. N., and Taylor, M. (2010). An overview of second
generation biofuel technologies. Bioresource Technology, 101(6):1570–1580.
Skidmore, B. (2010). Quantification of Assay Techniques, Reaction Kinetics and Pressure
Dependencies of the P11 Hydrogenase. PhD thesis, Brigham Young University.
Skidmore, B. E., Baker, R. a., Banjade, D. R., Bray, J. M., Tree, D. R., and Lewis, R. S.
(2013). Syngas fermentation to biofuels: Effects of hydrogen partial pressure on hydrogenase
efficiency. Biomass and Bioenergy, 55:156–162.
Soetaert, W. (2013). Industrial biotechnology.
Tanner, R. S., Miller, L. M., and Yang, D. (1993). Clostridium ljungdahlii sp. nov., an Acetogenic Species in Clostridial rRNA Homology Group I. International Journal of Systematic
Bacteriology, 43(2):232–236.
Tremblay, P.-l., Zhang, T., Dar, S. A., Leang, C., and Lovley, D. R. (2013). The Rnf Complex
of Clostridium ljungdahlii Is a Proton-Translocating Ferredoxin:NAD+ Oxidoreductase Essential for Autotrophic Growth. Microbial Biotechnology, 4(1):1–8.
USDA (2013). Biofuels Annual. Technical report, United States Department of Agriculture.
van Steen, E. and Claeys, M. (2008). Fischer-Tropsch Catalysts for the Biomass-to-Liquid
(BTL)-Process. Chemical Engineering & Technology, 31(5):655–666.
Wangt, G. and Wang, D. I. C. (1984). Elucidation of Growth Inhibition and Acetic Acid
Production by Clostridium thermoaceticum. Applied and Environmental Microbiology,
47(2):294–298.
Whitham, J. M., Tirado-Acevedo, O., Chinn, M. S., Pawlak, J. J., and Grunden, A. M.
(2015). Metabolic Response of Clostridium ljungdahlii to Oxygen Exposure. Applied and
Environmental Microbiology, 81(October):AEM.02491–15.
76
Bibliography
Winner, C. and Muller, V. (1989).
171(10):5473–5478.
Sodium Dependence of.
Journal of Bacteriology,
Woolcock, P. J. and Brown, R. C. (2013). A review of cleaning technologies for biomassderived syngas. Biomass and Bioenergy, 52:54–84.
Xie, B. T., Liu, Z. Y., Tian, L., Li, F. L., and Chen, X. H. (2015). Physiological response
of Clostridium ljungdahlii DSM 13528 of ethanol production under different fermentation
conditions. Bioresource Technology, 177:302–307.
Xu, D., Tree, D. R., and Lewis, R. S. (2011). The effects of syngas impurities on syngas
fermentation to liquid fuels. Biomass and Bioenergy, 35(7):2690–2696.
Younesi, H., Najafpour, G., and Mohamed, A. R. (2005). Ethanol and acetate production from
synthesis gas via fermentation processes using anaerobic bacterium, Clostridium ljungdahlii .
Biochemical Engineering Journal, 27(2):110–119.
Younesi, H., Najafpour, G., and Mohamed, A. R. (2006). Liquid fuel production from synthesis gas via fermentation process in a continuous tank bioreactor (CSTBR) using Clostridium
ljungdahlii . Iranian Journal of Biotechnology, 4(1):45–53.