Microbial production of antimicrobial compounds from … delen ervan te kopiëren voor persoonlijk...

Faculteit Bio-ingenieurswetenschappen

Academiejaar 2015-2016

Microbial production of antimicrobial

compounds from biorefinery sidestreams

Kyrina Denis

Promotors: Prof. Dr. ir. Korneel Rabaey & Prof. Dr. ir. Ingmar Nopens

Tutors: ir. Pieter Candry & ir. Timothy Van Daele

Masterproef voorgedragen tot het behalen van de graad van

Master in de bio-ingenieurswetenschappen: Chemie en Bioprocestechnologie

De auteur en promotor gerren de toelating deze scriptie voor consultatie beschikbaar te stellen

en delen ervan te kopiëren voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkin-

gen van het auteursrecht, in het bijzonder met betrekkíng tot de verplichting uitdrukkelijk de

bron te vermelden bij het aa,nhalen van resultaten uit deze scriptie.

The author and promoter 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 specifi.cally

the source must be extensively specified when using results from this thesis.

Ghent, June 2016

The promoters,

Raba*y Prof- dr. ir. Ingmar Nopens

The author,The tutors.

q - - : .- : i . - ' . - . . . - - - , t '

ir. Pieter Candry ir. Timothy Van Daeie Kyrina Denis

Acknowledgment

Via this acknowledgment I would like to thank everyone who helped me through this journey.

I can not believe how these last few years studying bio-engineering have crept by, ending with

this literal master piece.

At first I would like to thank my promoters Prof. dr. ir. Korneel Rabaey and Prof. dr. ir. Ingmar

Nopens. The meetings were always very useful, as they were the penultimate strategy to move

the thesis in a consistent direction. The additional literature, knowledge and support were

extremely helpful and much appreciated.

Next, I would like to thank my tutors, ir. Pieter Candry and ir. Timothy Van Daele. I think

the help I received was above all expectations, ranging from revising my work, explaining

procedures, helping me determine the direction of the thesis, practical help and even doing

experiments when I was not able to. Therefore, I can not express how grateful I am for never

feeling I had to do it completely on my own.

Another important person, which had a major impact on the course of this thesis and deserves

a special place in this acknowledgment is dr. Jan Arends. Thank you, not only for teaching

me a lot of the practical things used in this thesis but also for your continuous presence

throughout.

Further, I would also like to thank the entire CMET team and my fellow students. More

specifically Tim Lacoere and Greet Van de Velde for analyzing a lot of my samples this year.

Lastly I would like to thank my close friends, family and in-laws, more specifically:

My boyfriend Yoram ♥, with whom I’ve been with for almost 6 years. Gradually I am

forgetting how life was before meeting you.

My parents, who have cared and supported me my whole life, and keep supporting me even

though I already moved out.

My older brother Yoshi and little brother Ymre. Since growing up, I now realize how lucky

I am to have the support system of two people who have been with me for as long as I can

remember.

i

Abstract

Fossil fuel depletion has resulted in a search for more sustainable solutions. One possibility

is the usage of biomass obtained from industrial or agricultural waste streams. This can be

converted into so-called platform chemicals using bio-processes, from which they can be fur-

ther valorized into an array of chemicals used for fuels, solvents, etc. Short-chain fatty acids

(SCFAs) are one of those platform chemicals and are produced during anaerobic digestion.

When methanogenesis is suppressed and the right environmental conditions are maintained,

SCFAs can be coupled to other substrates present in or added to the fermentation broth

such as ethanol, producing medium chain fatty acids (MCFAs, e.g. caproate) in a process

called “chain elongation”. MCFAs are interesting compounds which can be used as fossil

fuels, antimicrobials, etc. However, MCFAs are toxic compounds for the chain elongating

microorganisms as well, thus continuous extraction might be necessary to produce economic

amounts. An electrochemical cell which can be used for extraction has proven to be a valuable

option for boosting MCFA production.

In order to gain fundamental knowledge about the process described above, i.e. fermentation,

chain elongation and extraction, modeling can be a powerful tool. A lot has been done in

literature on modeling the fermentation and extraction part, but nothing on chain elongation.

Therefore, in this thesis a mathematical model was built for chain elongation, based upon

stoichiometry derived by Spirito et al. (2014) and bacterial growth models.

The model consisted of several parameters which were not readily available from literature.

Consequently, experiments were designed and conducted that would allow parameter estima-

tion. As model simulations showed that initial conditions influence the subsequent growth

rate, producing growth curves in a 96-well plate appeared to be an attractive possibility. Af-

ter conducting the experiments however, it appeared that the acquired data deviated tremen-

dously from what would be expected. Part of the problem was thought to be that the

stoichiometry derived by Spirito et al. (2014) did not encompass thermodynamic limitations.

Next to that, side reactions seemed to occur as well, resulting in larger substrate consumption

than expected. This study thus demonstrates the need for a model which takes thermody-

namic limitations and side reactions into account, even during parameter estimation.

iii

Samenvatting

De uitputting van fossiele brandstoffen hebben een zoektocht naar alternatieven in gang

gestoken. Een mogelijkheid is het gebruik van biomassa, vergaard als afvalstroom van de

landbouw of industriele processen. Biomassa kan omgezet worden met behulp van biopro-

cessen tot de zogenaamde platformchemicalien. Deze hebben de mogelijkheid om nog verder

omgevormd te worden tot een reeks van nieuwe moleculen, gebruikt als brandstoffen, solven-

ten, etc. Korte keten vetzuren (KKVZ) zijn een van de mogelijke platform chemicalien en

worden geproduceerd tijdens anaerobe vergisting. Onder de juiste omstandigheden en indien

methanogenese onderdrukt wordt, kunnen KKVZ gekoppeld worden aan andere gereduceerde

moleculen aanwezig in de vergister. Dit proces noemt men ketenverlenging en produceert

middellange keten vetzuren zoals caproaat, die gebruikt kunnen worden als brandstof, an-

tibiotica, etc. Echter zijn deze ook toxisch voor de ketenverlengende microorganismen, dus

continue extractie van deze moleculen kan noodzakelijk zijn om een voldoende hoge productie

te garanderen, onder andere mogelijk via een electrochemische cel.

Modelleren kan een waardevol hulpmiddel vormen om meer kennis te vergaren over het com-

plete proces. In de literatuur was reeds veel informatie te vinden omtrent het modelleren

van fermentatie en extractie, maar niets over ketenverlenging. Daarom werd in deze thesis

de focus gelegd op het bouwen van een mathematisch model voor ketenverlenging. Dit was

gebaseerd op massabalansen voor de stoichiometrie vergaard van Spirito et al. (2014) en bac-

teriele groei modellen.

Dit model bevatte verschillende parameters die momenteel niet in literatuur beschikbaar

bleken voor ketenverlenging. Daarom werden experimenten ontworpen en uitgevoerd om ook

hier meer informatie over te vergaren. Uit modelsimulaties bleek het gebruik van groeicurven

in 96-well platen een mogelijke manier om veel van deze parameters tegelijkertijd te kunnen

schatten. Indien echter de zo verworven data geanalyseerd werd bleek deze ver af te liggen van

wat verwacht werd. Verondersteld werd dat een deel van het probleem te wijten was aan de

stoichiometrie van Spirito et al. (2014) die geen rekening houdt met thermodynamische limi-

taties. Verder leken ook nevenreacties plaats te vinden. Deze thesis toont daarmee aan dat,

ook gedurende parameterschatting, rekening gehouden moet worden met thermodynamische

aspecten en nevenreacties.

v

Contents

Acknowledgment i

Abstract iii

Nederlandse samenvatting v

Contents ix

Abbreviations xi

Symbols xiii

1 Literature Review 1

1.1 Biorefineries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Carboxylate platform . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.2 Fermentation and anaerobic digestion . . . . . . . . . . . . . . . . . . 3

1.1.3 Inhibition of methanogenesis . . . . . . . . . . . . . . . . . . . . . . . 4

1.1.4 Chain elongation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.1.5 Fatty acid toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.1.6 Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.2 Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.2.2 Bacterial growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.2.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.2.2.2 Bacterial growth models . . . . . . . . . . . . . . . . . . . . . 12

1.2.2.3 Parameter estimation . . . . . . . . . . . . . . . . . . . . . . 15

1.2.3 ADM1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1.3 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

vii

2 Materials and Methods 21

2.1 Mixed Culture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.1.1 Mixed culture at HRT 7d . . . . . . . . . . . . . . . . . . . . . . . . . 21


2.2 Pure Culture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.3 Chain elongation model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.4 Parameter Estimation of µmax, K S and K I . . . . . . . . . . . . . . . . . . . 26

2.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.4.2 Initial experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.4.3 Control experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.4.4 General experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.5 Parameter estimation of growth yield . . . . . . . . . . . . . . . . . . . . . . . 29

2.6 pH change of medium in function of added protons . . . . . . . . . . . . . . . 29

2.7 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.7.1 FA Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.7.2 EtOH Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.7.3 Solids Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.7.4 Headspace Gas Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.8 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.8.1 Growth rate determination . . . . . . . . . . . . . . . . . . . . . . . . 30

2.8.2 Model simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3 Results 33

3.1 Reactor data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33



3.2 The effect of proton addition on pH change of medium . . . . . . . . . . . . . 39

3.3 Method development of growth curves . . . . . . . . . . . . . . . . . . . . . . 41

3.4 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.4.1 High SEtOH experiment . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.4.2 High SBut experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.4.3 Ratio-experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.5 Growth yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4 Discussion 55

4.1 Reactor performance and stability . . . . . . . . . . . . . . . . . . . . . . . . 55

4.2 Model simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

4.3 Quality control of pure culture kinetics . . . . . . . . . . . . . . . . . . . . . . 58

4.3.1 Comparison of final FA concentrations BTs and WP . . . . . . . . . . 58

viii

4.3.2 Comparison between actual and expected product formation . . . . . 58

4.3.3 Does substrate consumption lead to growth? . . . . . . . . . . . . . . 66

4.3.4 Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4.4 Parameter estimation: a critical review . . . . . . . . . . . . . . . . . . . . . . 67

5 Conclusions and perspectives 69

Bibliography 71

A ADM1 77

B Stock solutions 81

B.1 Trace element solution SL-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

B.2 Selenite-tungstate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

B.3 Seven vitamin solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

C Additional figures 83

C.1 From literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

C.2 Layout of WPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

C.3 Initial experiments growth curves . . . . . . . . . . . . . . . . . . . . . . . . . 87

C.4 Control experiment growth curves . . . . . . . . . . . . . . . . . . . . . . . . 89

C.5 Model simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

C.6 Substrate consumption versus product formation . . . . . . . . . . . . . . . . 113

C.7 Substrate consumption versus growth . . . . . . . . . . . . . . . . . . . . . . 114

C.8 Parameter estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

ix

Abbreviations

Ac Acetate

ADM1 Anaerobic Digestion Model 1

AEM Anion Exchange Membrane

BT Balch tube

But Butyrate

Cap Caproate

CH4 Chemical formula for methane

CSTR Continuous flow stirred-tank reactor

COD Chemical Oxygen Demand

EtOH Ethanol

FA Fatty Acid

FCM Flow Cytometry

FID Flame ionization detector

GC Gas Chromatograph

HRT Hydraulic Retention Time

iBut iso-Butyrate

IC Ion Chromatography

iVal iso-Valerate

MCFA Medium-Chain Fatty Acid

ME Membrane Electrolysis

Prop Propionate

OD Optical Density

SCFA Short-Chain Fatty Acid

TCC Total Cell Count cells L−1

TSS Total Suspended Solids g cells L−1

Val Valerate

VSS Volatile Suspended Solids g cells L−1

WP Well plate

xi

Symbols

∆G01R Standard Gibbs energy change of the reaction at pH 7 kJ mol−1

∆G1R Actual Gibbs energy change of the reaction kJ mol−1

∆GCAT Gibbs energy of the catabolic reaction kJ (e–mol)-1

1YM

GX

Gibbs energy needed to make 1 C-mol of biomass kJ (C-mol X)-1

γD Degree of reduction of the donor

γX Degree of reduction of the biomass

λ Lag-phase h

µ Growth rate h-1

µmax Maximal growth rate h-1

νi,j Biochemical rate coefficient of component i for process j

ρj Rate of process j kg COD m-3 d-1

kLa Dynamic gasliquid transfer coefficient d-1

A Maximal growth value

a Moles of EtOH consumed when linked to Ac consumption

b Moles of But produced when linked to Cap production

C Amount of Carbon atoms in the substrate

D Dilution rate h-1

Dcrit Critical dilution rate h-1

e− Electron

xiii

F Flow rate m3 h-1

KH Henry’s law equilibrium constant M bar-1

KI Inhibition constant g L-1

KS Substrate affinity parameter g L-1

m Maintenace coefficient h-1

PCO2,gas CO2 gas phase partial pressure bar

PH2,act Actual H2 partial pressure atm

PH2,eq H2 partial pressure in equilibrium for certain SEtOH and SAc atm

PH2 H2 partial pressure atm

pHLL Lower limit of the pH where either 50% or 0% of the microorganisms are inhibited

pHUL Upper limit of the pH where either 50% or 0% of the microorganisms are inhibited

QS Maximal substrate uptake rate h-1

qS Substrate uptake rate h-1

qin Incoming flow rate m3 d-1

qout Outgoing flow rate m3 d-1

R Gasconstant 8.3144598 J (mol K)-1

S Substrate concentration g L-1

S∗ Threshold substrate concentration g L-1

Sin Incoming substrate concentration g L-1

SCO2,liq Liquid CO2 concentration M

Sin,i Incoming concentration of component i kg COD m-3 or kmole Nm-3

Sliq,i Reactor concentration of a component i kg COD m-3 or kmole Nm-3

T Temperature K

V Volume tank m3

Vliq Liquid volume m3

X Biomass concentration g L-1

xiv

Y Growth yield g biomass (g substrate)-1

YM Maximal yield when only growth occurs g biomass (g substrate)-1

xv

CHAPTER 1Literature Review

1.1 Biorefineries

Fossil fuels are a non-renewable source, so the availability of these is decreasing over time.

Apart from that, their utilization is linked to environmental pollution and their price is not

stable. These concerns have resulted into a search for more sustainable solutions, for example

the use of biomass in the so-called “biorefineries”. These offer an alternative to fossil resources

with a lower environmental impact for production of energy carriers and chemicals (Cherubini

and Jungmeier, 2010). Similar to oil refineries, the value of each stream must be maximized

so as to create additional fuels or chemicals, while nutrients and water are recycled (Agler

et al., 2011).

1.1.1 Carboxylate platform

Biorefineries are divided into platforms, based on the targeted or central chemical or an im-

portant intermediate. Two very well known platforms exist for biorefineries. First, the sugar

platform consists of biomass being converted into five or six-carbon monosaccharides, which

can be further processed into fuels. Second, syngas platform processes convert biomass into

syngas, which can then be further processed e.g. by catalysis into fuels (Agler et al., 2011).

However, a third platform exists as well, which is called the “carboxylate platform”, with

carboxylates being the dissociated form of fatty acids (FAs). In this thesis however, the dis-

sociated name will be used to refer to both the dissociated and undissociated form of the acid.

In the carboxylate platform biomass, often derived as industrial and agricultural waste, is con-

verted into Short-Chain Fatty Acids (SCFAs, which are C2 to C5 carboxylic acids acetate

(Ac), propionate (Prop), iso-butyrate (iBut), butyrate (But), iso-valerate (iVal) and valerate

(Val)). The SCFAs are made by subsequently hydrolyzing and fermenting the feedstock using

2 1.1 BIOREFINERIES

undefined and anaerobic mixed cultures. The use of mixed cultures is preferable, since they

are able to handle the complexity and variability of these waste streams (Agler et al., 2011).

Apart from that, mixed cultures are more resilient, which means the need for sterilization

and antibiotic additions can be circumvented, facilitating the operation of the bioprocesses

in a (semi)-continuous mode for many years. Lastly, when working with an open mixed cul-

ture, it is also possible for the culture to be enriched with uncultured isolates which can have

advanced functions (Spirito et al., 2014). The SCFAs made from the primary fermentation

are oftentimes further modified via secondary fermentation steps, in separate bioprocesses

or using separate pure-culture biochemical, electrochemical or thermochemical steps (Agler

et al., 2011).

Since SCFAs are a key intermediate for the production of methane, anaerobic digestion is

included in the carboxylate platform (Agler et al., 2011). Anaerobic digestion is known

as an important technique to treat organic waste streams due to the production of biogas,

which has a high calorific value and can be used as a renewable energy source (Appels et al.,

2008). However, it is still considered as a low value product, resulting in economically un-

desirable production without government incentive. Therefore, it may be useful to suppress

the methane-producing bacteria in order to achieve products with a higher monetary value

(Spirito et al., 2014).

In what follows, the production of caproate (Cap), a high value product targeted by many

studies on carboxylate platform processes, will be described. Cap, also known as hexanoic

acid or caproic acid, can for instance be used as an antimicrobial compound (Vasudevan et al.,

2014). Since more and more bacteria are becoming resistant to known antibiotics, a search

for new antibiotics is of crucial importance for the future of mankind (Burow et al., 2014).

Secondly, Cap can be further processed into fuels. As mentioned earlier, there is a need for

renewable and sustainable alternatives to fossil fuels. Solar energy and wind energy can be

very useful, however there is still a need for liquid fuels as well. At the moment, the most

widely used biofuel is ethanol (EtOH) (Vasudevan et al., 2014). However, since the distilla-

tion of EtOH is energy-intensive (between 20 and 25% of the energetic value of the product is

used during distillation for corn and cellulosic EtOH) (Agler et al., 2012), alternatives which

are more easily separated can be very useful. Converting the EtOH into Cap can therefore

be beneficial, since Cap is more energy dense and can be turned into a fuel as well, as long

as it is more easily extracted (Vasudevan et al., 2014). Next to that, Cap can also be used as

an additive to animal feed, feedstock for esterification into food product (Vasudevan et al.,

2014) or corrosion inhibitor (Spirito et al., 2014).

CHAPTER 1 LITERATURE REVIEW 3

1.1.2 Fermentation and anaerobic digestion

Figure 1.1: Overview of the subsequent steps in the anaerobic digestion process

(Appels et al., 2008)

As was mentioned earlier, SCFAs are produced as an intermediate during anaerobic digestion,

making it useful to describe this process first. As can be seen in Figure 1.1, anaerobic

digestion begins with hydrolyzing the substrate. The undissolved compounds (proteins, fats

or polysaccharides) are split into monomers using water and the enzyme hydrolase from the

anaerobic bacteria. In the subsequent “acidogenic phase” these monomers are converted to

SCFAs, alcohols, H2 and CO2 (Deublein and Steinhauser, 2008).

The products resulting from the acidogenic phase, can now be used as a substrate for other

bacteria in the “acetogenic phase”. The homoacetogenic bacteria can reduce the CO2 with H2,

forming Ac. Next to that, Ac can also be formed by oxidation of the FAs, which simultaneously

produces H2 (Deublein and Steinhauser, 2008).

The last step of the anaerobic digestion process consists of the methanogenesis. This is

carried out by methanogens, a diverse group of strictly anaerobic organisms belonging to the

Euryarchaeota. Since the methanogens are not able to digest all the substrates, a classification

is made of three kinds of substrate in Table 1.1, which are linked to a type of methanogen

as well. However, this classification is not very strict, as e.g. CO2 can be converted by most

methanogens and not just by the hydrogenotrophic methanogens (Deublein and Steinhauser,

2008; Liu and Whitman, 2008).

4 1.1 BIOREFINERIES

Table 1.1: Classification of the different kinds of substrate used for methanogen-

esis (Deublein and Steinhauser, 2008; Liu and Whitman, 2008)

Category Substrates Type of methanogens

CO2 type CO2, HCOO– , CO Hydrogenotrophic

Methyl typeCH3OH, CH3NH3, (CH3)2NH +

2 ,

(CH3)3NH+, CH3SH, (CH3)2SMethylotrophic

Acetate type CH3COO– Acetoclastic

1.1.3 Inhibition of methanogenesis

As was mentioned earlier, CH4 is economically less interesting. Inhibiting the last step

(methanogenesis) may consequently be desirable (Spirito et al., 2014).

Inhibition can be achieved in multiple ways. One solution is adding CH4 analogs, for instance

iodoform (CHI3), bromoform (CHBr3) or the coenzyme M analog, 2-bromoethanesulfonic acid

(abbreviated to BES) (Thanakoses et al., 2003). This last solution has been applied quite

extensively in research, however it is too expensive to do so on larger scale. If no cheaper

chemical can be developed, an alternative method needs to be pursued. Two other methods

exist at the moment. A first solution is performing a periodic heat shock to enrich spore-

forming bacteria, whilst killing methane producing bacteria. This method can be valuable if

enough waste heat is provided, which will not always be the case. A second solution exists as

well, which consists of maintaining a low pH. Especially in combination with a high concen-

tration of carboxylic acids this can achieve good results, owing to the inhibiting undissociated

form of these compounds, which will be discussed in section 1.1.5 (Agler et al., 2011).

When looking at Figure 1.1, one can notice that inhibiting the methanogens leaves the fer-

mentation broth with mainly the following substrates: SCFAs, CO2 and H2. SCFAs are

useful chemicals by themselves. However, they have a relatively high oxygen-to-carbon ratio,

making them less energy dense (thus unable to use as fuel) and almost completely miscible

with water, so trying to extract them can become very costly. Therefore it may be useful to

add a second fermentation step, in order to turn these into Medium-Chain Fatty Acids (MC-

FAs, defined as the C6-C12 carboxylic acids) which are more hydrophobic thus more easily

extracted. This fermentation step, in which SCFAs are coupled to a reduced compound such

as EtOH, is called chain elongation (Grootscholten et al., 2013a).


1.1.4 Chain elongation

Depending on the conditions of the fermentation broth, the β-oxidation pathway (which is

used as the main pathway to break down FAs in order to use it as a carbon and energy source)

can be reversed in order to undergo reduction reactions. It is this reverse β oxidation pathway

that is considered the route for chain elongation (Spirito et al., 2014; Chen et al., 2014). The

pathway is depicted in Figure 1.2.

Figure 1.2: Overview of the reverse-β oxidation pathway in Clostridium kluyveri

(Seedorf et al., 2008)

There are two environmental conditions that need to be present in order for this pathway to

proceed. First, there needs to be an energy-rich reduced compound, that will provide energy

(in terms of ATP), reducing equivalents (NADH) and the acetyl-CoA that will proceed into

the reverse β-oxidation cycle. In Figure 1.2 this is achieved by EtOH. Other compound that

meet the requirements are lactate, methanol, Prop, amino acids in peptides, D-galactitol,

pyruvate and sugars, such as glucose, fructose, and sucrose (Spirito et al., 2014; Angenent

et al., 2016).

The second environmental condition that should be met is the presence of a high H2 partial

pressure (PH2). This reduced environment will prevent the oxidation and thus the conversion

of the SCFAs and MCFAs (Spirito et al., 2014). However, it can not become too high either,

6 1.1 BIOREFINERIES

because H2 is also produced during chain elongation, thus limiting the reaction thermody-

namically (Angenent et al., 2016).

The pathway depicted in Figure 1.2 can use other carboxylates than Ac as well, in which the

carbon chain from the carboxylate will be elongated with 2 C-atoms at a time. So Ac [C2]

will be elongated to But [C4], But [C4] can be elongated to Cap [C6], propionate [C3] can be

elongated to valerate [C5], etc (Spirito et al., 2014).

From Figure 1.2, one can see that from the six EtOH molecules that enter the pathway, one

EtOH molecule is transformed into Ac and the other five are coupled to five carboxylates.

The production of Ac from EtOH (∆G=10.5 kJ mol−1) produces one ATP via substrate-level

phosphorylation, which, when coupled to ADP phosphorylation, is a highly endergonic pro-

cess (∆G=32 kJ mol−1). To make the pathway thermodynamically feasible, this process must

thus be coupled to the exergonic reverse β-oxidation cycle (∆G = −38.6 kJ mol−1). This

results in a Gibbs energy change of about −37.0 kJ mol−1 for But and Cap formation at tem-

peratures ranging from 25 to 55 ◦C and a pH of 7. The Gibbs energy change of the chain

elongation which couples Ac and lactate has been calculated to be about −60 kJ mol−1 for

the same conditions. Another possibility is to use H2 as the electron donor. However, us-

ing H2 directly is thermodynamically unfeasible. It is only possible to do this indirectly by

converting the H2 into EtOH, using homoacetogenesis (producing Ac from H2 and CO2) and

then reducing this Ac into EtOH. This sequence however is very slow in microbiomes (i.e.

open microbial communities), but can be successful with some pure cultures (Seedorf et al.,

2008; Spirito et al., 2014).

1.1.5 Fatty acid toxicity

As was mentioned earlier, the MCFAs (for instance Cap) can be used as an antimicrobial

compound (Vasudevan et al., 2014). Organic acids have already a long history of being used

as preservative of perishable food. However, the mechanism in which they are able to inhibit

the microorganisms is not yet completely understood, although different theories exist (Ricke,

2003).

A first explanation is known as the uncoupling theory. This states that the undissociated

form of the organic acids can easily penetrate the lipid membrane of the bacteria. Once

inside, the organic acid will dissociate into the proton and anion due to the neutral pH of

the intracellular fluid (mostly ranging between 6.5 and 7.5). As protons are released, a drop

of the internal pH of the cells would be expected. However this is prevented by the use of

the plasma membrane ATPase. These are a group of enzymes able to pump the protons out

of the cell by hydrolyzing ATP molecules, which now cannot be used for bacterial growth.

If this is still not sufficient, new ATP needs to be formed, but at high concentrations it is

possible that the proton pumping capacity is exhausted. This will result in a depletion of


ATP and consequently in a drop of the internal pH, ultimately resulting in a degradation of

acid-sensitive proteins and DNA. Since the dissociated form of the organic acid is lipophobic,

it is only the undissociated form that is able to penetrate the membrane. As the amount

of the undissociated form depends on the pH of the broth, it is only logical that toxicity is

mostly a problem at lower pH (Palmqvist and Hahn-Hagerdal, 2000; Roos and Boron, 1981;

Ricke, 2003).

A second explanation is called “anion accumulation”. Like before, it is possible for the undis-

sociated form of the organic acid to diffuse through the lipid membrane. Once inside the

organic acid dissociates and gets captured. This leads to a combined effect of intracellular pH

decrease and enzyme inhibition or interference by the anions (Palmqvist and Hahn-Hagerdal,

2000).

Next to the previously described theories, other less direct antimicrobial actions were found

for organic acids as well. These are for instance the interference with nutrient transport,

damaging the cytoplasmatic membrane and ultimately causing leaks, disrupting the outer

membrane permeability and affecting the synthesis of macromolecules. However, it appears

that some microorganisms are more resilient to the FAs, because they are themselves able to

decrease the internal pH (Ricke, 2003). Further, it should be mentioned that not all the FAs

have the same effect. It was found that for C2-C6 FAs inhibition only started at a certain

threshold concentration, and that at this threshold concentration the inhibition was the same

except for Ac, which was found to be lower. This could probably be explained by the fact

that Ac is a byproduct from the sugar and amino acid fermentation (Pratt et al., 2012).

As is clear from the previous discussion, the pH from the fermentation broth plays an impor-

tant role in the inhibition of the microorganisms, as it determines the amount of undissoci-

ated organic acid. In Table 1.2 pKa values of different FAs from the carboxylate platform are

shown. Using formula 1.1, better known as the Henderson-Hasselbalch equation, the fraction

of undissociated organic acid can be calculated as shown in Figure 1.3. Here it is interesting

to see that at pH lower than 5.5 the fraction of protonated FAs starts to increase rapidly.

Because of this reason, it may be useful to work at higher pH’s. However, the pH should not

be too high either, as a lower pH will inhibit the methanogens (Agler et al., 2011).

pH = pKa + log

([A−]

[HA]

)(1.1)

8 1.1 BIOREFINERIES

Table 1.2: pKa of different FAs (Jencks and Regenstein, 2010)

Fatty Acid pKa

Acetic acid 4.76

Propionic acid 4.88

Isobutyric acid 4.86

Butyric acid 4.82

Iso-valeric acid 4.78

Valeric acid 4.86

Iso-caproic acid 4.85

Caproic acid 4.88

Heptanoic acid 4.89

Octanoic acid 4.89

Figure 1.3: The fraction of the undissociated or protonated form of the organic

acids, calculated using formula 1.1 for acetic acid and octanoic acid, as these have

respectively the lowest and highest pKa given in Table 1.2.


1.1.6 Extraction

Next to applying a higher pH, toxicity of FAs can also be decreased by continuously extracting

these from the broth so toxicity levels will not be reached. Apart from that, extraction will

also be necessary to purify or perhaps further valorise the end product. However, separating

and purifying the fermentation broth has proven pricey in the past, accounting for the ma-

jority of production cost. To decrease the separation cost, a lot of attention went into this

matter, providing different techniques along the way (Singhania et al., 2013).

A first technique is liquid-liquid extraction. It is one of the oldest techniques, and consists

of extracting the desired product with a suitable solvent. The efficiency of this process will

largely depend on the characteristics of the compound targeted for extraction, its concentra-

tion and finally the solvent used. Some examples of possible solvents are alcohols, ketones,

ethers, aliphatic hydrocarbons and organophosphates. After a first extraction, the compound

can be resuspended in an aqueous solution by the use of a pH gradient. To further improve

separation it is possible to use membrane-based solvent extraction. Here the transfer of the

FAs between the immiscible liquids will occur at the pores of a membrane. Then the usually

volatile solvent can be recovered by the use of stripping or distillation. Pertraction is another

technique that is very similar to liquid-liquid extraction. Here, extraction and stripping will

be done by the use of a three-phase contacter with two liquid-liquid interfaces through a

liquid membrane. There are three types of membranes that can be used: supported liquid

membrane, emulsion liquid membrane and bulk liquid membrane. Drawback of the previously

described techniques is the use of solvents, which are usually hazardous and not 100% efficient

(Singhania et al., 2013; Spirito et al., 2014).

Some other techniques for separation that will not be discussed in further detail are crystal-

lization, ion exchange (which requires a lot of acid, base and water and leads to formation

of salts), adsorption (which has a very short lifetime), distillation, esterification and reactive

extraction (Spirito et al., 2014; Trad et al., 2015).

Lastly, solvent-free membrane processes are another approach and are able to be more effi-

cient, eco-friendly as well as more economic than previous techniques (Trad et al., 2015). For

membrane separation, a feed is needed containing at least two components from which one or

more components will cross a semi-permeable barrier (the membrane) faster than the other.

Here it is necessary to obtain a constant purity after extraction, without having an altered

driving force for separation. The membrane itself can be composed of inorganic, organic

or composite materials and separate the compounds via diffusion (in which a concentration

difference is the driving force) or sieving (for which the absolute pressure drop is the driving

force). Various membrane configurations are applied, e.g. hollow fibers, sheets or combined

into bundled hollow fiber, flat plate or spiral wound sheet membranes. Different membrane

processes exist, e.g. pervaporation, electrodialysis and membrane electrolysis (Spirito et al.,

2014). This last example will be discussed in further detail, as it is the most relevant for this

10 1.1 BIOREFINERIES

thesis.

The experimental setup for membrane electrolysis is depicted in Figure 1.4. The cathode

compartment contains the catholyte which exists of circulated fermentation broth, and the

anode compartment contains the anolyte which consists of a clean and low pH solution (An-

dersen et al., 2014). The reactions that take place are summarized in Table 1.3.

Figure 1.4: Schematic overview of the Membrane electrolysis setup (Saxena et al.,

2007)

Table 1.3: Reactions at the membrane electrolysis (Saxena et al., 2007)

Location Type of reaction Reaction

Cathode Reduction 2H2O + 2e– −−→←−− 2OH– + H2 ↑Anode Oxidation H2O −−→←−− 2H+ + 0.5O2 ↑ + 2e–

Using an external power source, electrons are able to flow from the anode to the cathode.

However, to obey the charge conservation principle, this flow needs to be compensated by a

flow from either an anion from the catholyte to the anolyte or a cation from the anolyte to the

catholyte. By applying an anion exchange membrane (AEM), only an anion flow can close

the electrical circuit, while retaining solids, microorganisms and uncharged molecules larger

than the pore size of the membrane. If the pH of the catholyte is sufficiently high, then the

FAs will be present in their anion form, so they will be able to move through the AEM. In

the acidic anolyte, carboxylates will be protonated, inhibiting migration back to the cathode

compartment. The anodic oxidation of H2O - as seen in Table 1.3 - replenishes protons in

the anolyte (Andersen et al., 2014). In a study by Xu et al. (2015) it was possible to reach

concentrations of the FAs slightly above the maximum water solubility, thus creating a new

separated phase, which is the ultimate goal of this technique.


1.2 Modeling

1.2.1 Introduction

There are different potential objectives to create a process model. One possible reason is to

gain fundamental knowledge. This can then be used to detect bottlenecks in the process or to

optimize the process parameters (Thour, 2014). This might also be possible for the process

described above (i.e. fermentation, chain elongation and extraction). It can be used to find

ideal pH, initial concentrations, HRT, extraction current, etc. in order to increase the Cap

production, which is the ultimate goal.

For the fermentation part, the Anaerobic Digestion Model No.1 (ADM1) will be described in

section 1.2.3. For chain elongation, no existing models were found in literature. Therefore, a

model was developed based on bacterial growth models, which are explained in section 1.2.2.2.

The model is described in section 2.3. For the modeling part, the extraction process is not

investigated, since it is currently more important to set up a reliable kinetic model. Moreover,

extraction models are well-developed and widely available in literature (Adler et al., 2011;

Gjelstad et al., 2007; Kolev et al., 2013; Lu et al., 2008)

1.2.2 Bacterial growth

1.2.2.1 Introduction

The growth of a microorganism is illustrated in a simplified way in Figure 1.5. An energy

carrier (ATP in this scheme) can be produced via the conversion of substrate S into pro-

duct P, also called the catabolic reaction. This energy carrier will then be consumed via an

anabolic reaction, in which a carbon source SX and a nitrogen source NX are transformed

into the numerous biomass components (X ). This scheme shows that substrate consumption

can be linked to biomass growth. This linkage can also be expressed using the biomass yield

(Y ). Uncoupled catabolic and anabolic processes exist as well, i.e. when the energy carrier is

consumed to provide energy for a non-growth-related maintenance process (m).

In the absence of light, the catabolic and anabolic reaction are redox reactions, schematically

shown in Figure 1.6. For the catabolism, where energy is produced, electrons will always flow

from a reduced e– -donor to an oxidized e– -acceptor. For the anabolism however, the flow of

electrons is dependent on the oxidation state of the biomass versus the oxidation state of the

nitrogen and carbon source. If electrons are produced when making biomass, an e– -acceptor

will be required which will usually be the same as for the catabolic reaction (as depicted

in Figure 1.6). Otherwise, if electrons are needed for this conversion, an e– -donor will be

required which is also usually the same as for the catabolic reaction. If the stoichiometry

12 1.2 MODELING

Figure 1.5: Simplified scheme for microbial growth (Kleerebezem and Van Loos-

drecht, 2010)

of catabolic and anabolic reaction are known, Y can be calculated by a measurement of

two rates of consumption or production of compounds participating in the metabolic system

(Kleerebezem and Van Loosdrecht, 2010).

Figure 1.6: Overview of the redox reactions for catabolism and anabolism in the

absence of light (Kleerebezem and Van Loosdrecht, 2010)

1.2.2.2 Bacterial growth models

The most popular model used to describe the relationship between substrate consumption and

microbial growth was proposed by Monod, given by equations 1.2 (Monod, 1943). It contains

three parameters (Y, µmax and K S) which are characteristic for a particular microorganism

at certain conditions (T, medium, etc.), and is able to describe experimental data with bio-


logically relevant parameters. In this equation µmax is the upper limit of the growthrate µ for

a specific organism growing on a specific substrate. In theory this limit can not be reached,

since this would require an infinite amount of substrate. However, in practice this will be met

if S � K S. If S equals K S, the growthrate is half of the maximum growthrate. When K S

for a substrate decreases, microorganisms can grow more easily on this substrate, even at low

concentrations, which explains why K S is better known as the substrate affinity parameter

(Panikov, 1991).

dX

dt= µmax

S

KS + SX

dS

dt= − 1

Y

dX

dt(1.2)

The previous equations can be modified for continuous flow stirred-tank reactors (CSTRs) as

well, giving equations 1.3. A comparison between a batch reactor and CSTR is depicted in

Figure 1.7.

dX

dt= (µ−D)X

µ = µmaxS

KS + SdS

dt= D(Sin − S)− 1

Y

dX

dt

D =F

V(1.3)

Figure 1.7: (a) Batch reactor (b) CSTR (Caccavale et al., 2011)

Here, Sin is the incoming substrate concentration, F is the pumping or flow rate, V is the

volume of the tank and D is the dilution rate, which is the inverse of the average residence

time for a certain particle in the reactor. From equation 1.3 it is found that, if there is no net

growth (dXdt and dSdt = 0), microorganisms will grow with the same rate as they are removed

from the medium, simplifying the equation to µ = D. Next to that, there also exists a critical

14 1.2 MODELING

dilution rate at which higher dilution rates will result in washout, given in equation 1.4. By

choosing D and S in wisely, it can be found that for a chemostat the microorganisms can grow

endlessly with a growthrate between 0 and µmax (Panikov, 1991).

Dcrit = µmaxSin

KS + Sin(1.4)

However, Monod’s equation does not always describe experimental data adequately, necessi-

tating the use of one of the following empirically derived formulas.

µ = µmax(1−KS) (1.5)

µ = µmaxSn

KS + Sn(1.6)

µ = µmaxS

KSX + S(1.7)

Here, the utilization of equation 1.5 should be avoided as it is purely empirical. Better

would be to use equation 1.6 (for which n is an exponent) or equation 1.7 since they have

a more fundamental meaning: Equation 1.6 is also known as the Moser equation, which has

an analogue in enzymology and in this way incorporates synergistic effects of the enzyme

performance across the metabolism. Equation 1.7 on the other hand is known as the Contois

equation, and via the extra X term it attributes for by-products having an auto-inhibitory

effect on growth (Panikov, 1991).

Next to this it may be necessary to account for other processes as well. A first effect is the

maintenance of cells (m), because growth and substrate usage will then be partly uncoupled.

This can be done using equations 1.8. From the first equation, it is assumed that substrate

uptake rate (qs) obeys the Michaelis-Menten kinetics, with Qs being the maximal substrate

uptake rate. Next to that the uptake rate is also linked to a mass balance, since the substrate

is consumed either for growth or maintenance. Depending on how µmax is defined, the first

equation can be rewritten into the second or third equation. Here YM is the maximal yield

when only growth occurs (zero maintenance) and S ∗ is a threshold substrate concentration,

for which below this concentration no growth can be achieved (Panikov, 1991).

qS = QSS

KS + S=

µ

Y M+m

µ = µmaxS

KS + S−mY M for µmax = Y MQS

µ = µmaxS − S∗

KS + Sfor µmax = Y M(QS −m)

S∗ =KSmY

M

µmax(1.8)

Another effect that is relevant for bacterial growth is the substrate inhibition. Several equa-

tions have been derived for this (Panikov, 1991; Kim et al., 2005).


µ = µmaxS

KS + S

KI

KI + S(1.9)

µ = µmaxS

KS + S + S2

KI

(1.10)

µ = µmaxS

KS + Sexp

(−SKI

)(1.11)

Equations 1.9, 1.10 and 1.11 are respectively called the Monod-Ierusalimsky equation, the

Haldane-Andrews equation and the Aiba-Edwards equation. Here they are shown for sub-

strate inhibition, but the first equation can be used for product inhibition as well. KI is the

inhibition parameter. The smaller its value, the more easily a microorganism is inhibited, as

the effects are noticeable at smaller concentrations.(Panikov, 1991; Kim et al., 2005).

Lastly it is also possible to model intermediate product formation, schematically depicted in

Figure 1.8. This is done using equations 1.12. Here the equations are written for a continuous

reactor, which can be simplified for batch reactors since then S1,in and D equal zero. S1 is the

substrate and S2 is an intermediate product. It is found that S2 is first produced and later

on consumed at two different rates, µ1(S1,S2)Y11

and µ1(S2)Y22

, which are both coupled to bacterial

growth via the first equation (Harvey et al., 2014).

Figure 1.8: Schematic representation for modeling an intermediate (Harvey et al.,

2014)

dX

dt= [µ1(S1, S2) + µ2(S2)−D]X

dS1dt

= (S1,in − S1)D −µ1(S1, S2)

Y11X

dS2dt

= −S2D +

[µ1(S1, S2)

Y21− µ2(S2)

Y22

]X (1.12)

1.2.2.3 Parameter estimation

During previous discussion, growth could be described by the Monod equation using three

parameters: Y, µmax and K S, which can be extended to also account for maintenance (m)

16 1.2 MODELING

and substrate or product inhibition (K I). Generally, thermodynamics yield no information

about the reaction rate of a certain process. However, Heijnen (1999) found a link between a

few of these kinetic parameters and the thermodynamics of the reaction (equations 1.13). For

the substrate affinity and inhibition parameters no such thermodynamic relationships were

found. This was due to the wide variability of parameter values found in literature, even for

the same microorganism.

µmax =[3(−∆GCAT)/γD − 4.5]

1/Y MGX

exp

(−69, 000

R

(1

T− 1

298

))1

Y MGX

= 200 + 18(6− C)1.8 + exp[((3.8− γD)2)0.16(3.6 + 0.4C)]

m =−4.5

∆GCATexp

(−69, 000

R

(1

T− 1

298

))Y =

−∆GCAT

1/YGX + γX/γD(−∆GCAT)(1.13)

∆GCAT is the Gibbs energy of the catabolic reaction, γD is the degree of reduction of the

donor, R is the universal gas constant, T is the temperature, 1YGX

is the Gibbs energy needed

to make 1 C-mol of biomass, for which the subscript M is used if its the maximal yield (i.e.

only growth occurs), C is the amount of carbon atoms in the substrate and γX is the degree

of reduction of the biomass.

Another possible solution is the use of experimental data (e.g. growth curves) to estimate the

different kinetic parameters. This more generic methodology also allows to estimate substrate

affinity and inhibition parameters.

A growth curve is produced when the optical density (OD) at 600 nm, which correlates to

the bacterial concentration, is measured over time and plotted. For batch experiments the

growth rate (µ(t)) can be determined by the slope of the growth curve at each point. The

maximal growth rate for an individual sample however will go through the inflection point of

the growth curve, giving µ (Koch, 1970; Perni et al., 2005).

The Monod equation for biomass looks very similar to the Michaelis-Menten equation, given

in equation 1.14. However the Michaelis-Menten equation is based upon theoretical principles,

while the Monod equation is empirically derived. The Michaelis-Menten equation is plotted in

Figure 1.9. A similar plot can thus be made for the Monod equation, where µ for every sample

is plotted against the initial substrate concentration. As for Figure 1.9, a determination of

µmax and KS can be found. This can be done via graphical techniques (Lineweaver-Burk plot,

Langmuir-Hanes plot, Eadie-Hofstee or the direct linear plot), software, statistical analysis

or non linear parameter estimation (Geueke and Kohler, 2010; Panikov, 1991).

v = vmaxS

KS + S(1.14)


Figure 1.9: The reaction rate versus the substrate concentration of an enzyme

following the Michaelis-Menten kinetics (Geueke and Kohler, 2010)

For product inhibition, when µ is plotted against the initial concentration, the graph will look

like 1.10. This plot can in an analogue way be used to determine µmax and KI.

Figure 1.10: µ plotted against initial substrate concentration when product inhi-

bition proceeds.

1.2.3 ADM1

Anaerobic processes are often modeled based on the work done by Batstone et al. (2002).

Here a CSTR with a single input and output stream and constant liquid volume is used. In

an anaerobic digester two main type of reactions take place. On the one hand, there are the

biochemical reactions, which are mediated by intra or extra cellular enzymes. On the other

hand, also physico-chemical reactions take place, such as ion association or dissociation, pre-

cipitation or gas transfer.

The biochemical reactions have already been discussed in section 1.1.2. At first the complex

18 1.2 MODELING

particulate substrate is converted into monomers (carbohydrates, amino acids and lipids).

This can be done by processes such as (hydro)lysis, non-enzymatic decay, phase separation,

and physical breakdown, which in the work of Batstone et al. (2002) are all assumed to be first

order kinetic reactions. The amino acids and monosacharides are then converted to SCFAs,

H2 and CO2 by acidogenic bacteria. Next, the acetogenic bacteria further convert the SCFAs

into Ac and H2. The Ac can then be used by acetoclastic methanogens and H2 is used by

hydrogenotrophic methanogens. All of these intracellular biochemical reactions are modeled

using Monod kinetics. Biomass decay is assumed to be first order, for which the dead biomass

is maintained in the reactor as complex particulate substrate. Inhibition also plays a role:

extreme pH inhibits all the microorganisms and is modeled using two empirical equations.

Equation 1.15 is used when both high and low pH inhibition occur, and pHUL and pHLL are

upper and lower limits where the group of organisms is 50% inhibited, respectively. Equation

1.16 on the other hand is used when only low pH inhibition occurs, and here pHUL and pHLL

are upper and lower limits where no microorganisms are inhibited. H2 inhibits acetogenic

bacteria and free ammonia inhibits acetoclastic methanogens, which can be modeled using

non-competitive inhibition terms, such as the Monod-Ierusalimsky equation. Next to that,

there is a secondary Monod term for inorganic nitrogen (ammonia and ammonium), which

will ensure no growth can occur in nitrogen limited environments. Lastly, an extra Monod

term is added for competitive uptake of But and Val by the single group that utilises these

two organic acids. The overview of the biochemical reactions is listed in a Gujer matrix,

which can be found in Figure A.1 and Figure A.2.

I =1 + 2× 100.5(pHLL−pHUL)

1 + 10(pH−pHUL) + 10(pHLL−pH)(1.15)

I = exp

(−3

(pH− pHUL

pHUL − pHLL

)2)

(1.16)

For the physico-chemical reactions, precipitation was not modeled. Gas transfer of CO2, H2,

CH4 and water vapour, and ion association or dissociation of CO2, FAs, NH3 and H2O in

function of the pH are implemented in the model (Batstone et al., 2002).

For each component in the liquid stream, a mass balance can be made given by equation

1.17. Here Sliq,i and Sin,i are the concentrations of a component in the reactor and in the

incoming stream, qin and qout are respectively the incoming and outgoing flow rate, Vliq is

liquid volume and∑

j=1−19 ρjνi,j is the sum of the kinetic rates for process j multiplied by

its rate coefficient νi,j , summarized in Figure A.1 and Figure A.2 (Batstone et al., 2002).

dSliq,idt

=qinSin,iVliq

− qoutSliq,iVliq

+∑

j=1−19

ρjνi,j (1.17)

For the components which are able to diffuse to the gas phase, an additional term needs to

be added. As an example, the transfer of CO2 is shown in equation 1.18. Here ρ10,T is the


additional rate term dependent on the temperature, kLa is the dynamic gas-liquid transfer

coefficient, KH,CO2 is the Henry’s law equilibrium constant, PCO2,gas is the CO2 gas phase

partial pressure and SCO2,liq is the liquid CO2 concentration (Batstone et al., 2002).

ρ10,T = kLaCO2(SCO2,liq −KH,CO2PCO2,gas) (1.18)

To account for the ion association or dissociation, the acidic and basic form of the chemical

are put together in one formula (e.g. SCO2 and SHCO −3

become SIC which equals S10). An

additional charge balance will then complete the set of equations. To calculate the individual

concentrations, the acid-base equilibrium constant is used (Batstone et al., 2002).

For the gas phase similar equations such as equation 1.17 can be made. However, there is

only one production rate term, which is the gas transfer to the liquid phase. Next to that,

no input stream is provided and the output will be either equalized to the total transfer rate

or calculated from headspace pressure and restricted flow through an orifice, which need to

be adjusted for the water vapour pressure at the reactor temperature (Batstone et al., 2002).

1.3 Research objectives

The process described above consists of three subprocesses: fermentation, chain elongation

and extraction using an electrochemical cell. In literature, there is a lot of information about

modeling of fermentation and extraction (Adler et al., 2011; Gjelstad et al., 2007; Kolev et al.,

2013; Lu et al., 2008; Batstone et al., 2002), but nothing about modeling chain elongation.

In order to fully model the entire process, this part needs to be modeled as well. For this

reason, the objectives of this thesis are as followed:

� Maintain a mixed culture able in chain elongation in order to get more feeling with the

process (possible pH, retention time, etc.) and to create inoculum for experiments

� Create a mathematical model for chain elongation using literature

� Design and conduct experiments for parameter estimation

� Do a quality control on acquired data

� Estimate the parameters using acquired data

CHAPTER 2Materials and Methods

2.1 Mixed Culture

2.1.1 Mixed culture at HRT 7d

A semi-continuous batch reactor system was installed in a temperature controlled room of

34 ◦C in order to create inoculum for further kinetic tests and get more feeling with the pro-

cess. The reactor had a volume of 900 mL, was mixed by a magnetic stirrer and fed manually

by extracting 300 mL of effluent, of which about 15 mL was extracted before settling and

the rest after settling to retain biomass. The reactor was then replenished by 300 mL of M9

medium with additional Ac and EtOH, for which the composition is given in Table 2.1. This

feeding process was done three times per week, resulting in a hydraulic retention time (HRT)

of 7 days. The pH of the fermentation broth was kept above 6.95 using a Prominent pH

controller, a pH probe, and a pump for dosing a 2 M NaOH solution. Gas was collected in an

acid gas trap.

Table 2.1: Composition of M9 medium

Chemical Concentration

Na2HPO4 · 2 H2O 7.5 g L−1

KH2PO4 3 g L−1

NaCl 0.5 g L−1

NH4Cl 0.5 g L−1

MgSO4 · 7 H2O 0.1 g L−1

Acetate 5 g L−1

Ethanol 2 mL L−1

22 2.1 MIXED CULTURE

Figure 2.1: Experimental setup of the semi-continuous batch reactor. The reactor

itself is located on the left, the Prominent pH controller in the back and the gas

tube with acidic solution on the right. The bottle in the middle contains 2 M

NaOH solution and is used for pH correction

Due to the reactor’s initial poor performance a batch test was carried out. The reactor was

sampled at day 6 of operation, centrifuged and resuspended in bottles containing either M9

medium or modified DSM52 medium. The latter is suitable for a well-known chain elongater:

C. kluyveri and its composition is given in Table 2.2. The headspace was flushed with N2

and the bottles were incubated on a shaker for a few days at 34 ◦C. At the end, VSS and FA

concentrations were measured. The outcome of the batch test resulted in a change of medium

composition for the reactor to modified DSM52 medium at day 22. In contrast to the normal

DSM52 medium, the modified medium was nor sterile nor anaerobic since a mixed culture is

generally expected to be more robust as was the case in previous research when working with

a mixture enriched for chain elongation (Debeuckelaere, 2015; Candry, 2015).

At day 32 of running the reactor, methane production became visible. Since methanogens are

inhibited by FAs, at day 43 the EtOH concentration in the feed was doubled in order to have

a higher production of FAs and thus more inhibition of the methanogens. This however was

not sufficient, so at day 50 the pH was lowered as well to 6. However, at day 78 the reactor

was shut down since the reactor behaviour was unstable.

CHAPTER 2 MATERIALS AND METHODS 23

Table 2.2: Composition of modified DSM52 medium for mixed culture


K2HPO4 0.31 g L−1

KH2PO4 0.23 g L−1

NH4Cl 0.25 g L−1

MgSO4 · 7 H2O 0.2 g L−1

Trace element solution SL-10 (see B.1) 1 mL L−1

Selenite-tungstate (see B.2) 1 mL L−1

Yeast extract 1 g L−1

NaHCO3 2.5 g L−1

Seven vitamin solution (see B.3) 1 mL L−1

K-acetate 8.172 g L−1

Ethanol 10 mL L−1


At a later time point, a new reactor set up was started. To tackle issues, which will be de-

scribed in section 3.1.1, another approach was taken for this reactor system. In the reactor,

pH was controlled at 6.5 and instead of manual feeding, the feeding process was more con-

tinuous with the use of pumps. A time-based controller was used to control the two pumps.

Influent and effluent were pumped simultaneously 5 times a day, resulting in an HRT of 4

days. Medium composition was similar to that in Table 2.2, but K-Ac was 4.28 g L−1 and

EtOH 10.3 mL L−1. The medium without vitamins, trace elements and NaHCO3 was first

autoclaved, followed by sterile addition of these components. The pH of the influent was

corrected between 6.5 and 7. The reactor underwent three phases. During phase 1, no BES

was added. However, as CH4 was being produced, at day 28 a new phase was initiated in

which 25 mM BES was added to the mixture to inhibit the methanogens. At day 100, phase

3 started in which BES addition was halted and MgSO4 was replaced by 0.3 g L−1 L-cysteine.

2.2 Pure Culture

Clostridium kluyveri (DSM 555) was grown in an anaerobic DSM52 medium. The composition

of this medium is given in Table 2.3. To make the medium sterile and anaerobic, the following

procedure was followed based on the considerations of Plugge (2005). First the salts, resazurin

and K-Ac were dissolved and boiled. The bottle was then cooled in ice under N2 atmosphere.

When the solution was cooled down, EtOH was added and the mixture was distributed over

Balch tubes (BTs) and/or penicillin flasks under N2 atmosphere. The tubes or flasks were

24 2.3 CHAIN ELONGATION MODEL

closed using rubber stoppers and aluminum crimp seals. The headspace was flushed with N2

using a gas exchange device and the mixture was autoclaved. A first filter-sterilized stock

solution under N2 atmosphere containing 30 mL 1 M NaHCO3, 1 mL trace elements SL-10 (see

Table B.1), 1 mL Se-WO4 (see Table B.2) and 1 mL 7 vitamin solution (see Table B.3) were

added to reach a final concentration of 2.5 g NaHCO3 L−1. A second filter-sterilized stock

solution of 5 g L−1 Cysteine-HCl.H2O and 5 g L−1 Na2S · 9 H2O in DSM52 salt solution under

N2 atmosphere was added to reach concentrations of 0.25 g L−1 of both compounds in the

medium.

Table 2.3: Composition of modified DSM52 medium for Clostridium kluyveri


K2HPO4 0.31 g L−1

KH2PO4 0.23 g L−1

NH4Cl 0.25 g L−1

MgSO4 · 7 H2O 0.2 g L−1

Trace element solution SL-10 (see B.1) 1 mL L−1

Selenite-tungstate (see B.2) 1 mL L−1

Yeast extract 1 g L−1

Resazurin 0.5 mg L−1

NaHCO3 2.5 g L−1

Seven vitamin solution (see B.3) 1 mL L−1

L-Cysteine-HCl.H2O 0.25 g L−1

Na2S · 9 H2O 0.25 g L−1

K-acetate 10 g L−1

Ethanol 20 mL L−1

2.3 Chain elongation model

Figure 1.2 shows the overview of the chain elongation from Ac to But. An analogous scheme

can be followed, but instead of coupling to Ac, EtOH can now be coupled to But forming Cap.

When written into chemical formulas following equations were found for the chain elongating

reaction (Seedorf et al., 2008; Spirito et al., 2014):

6 EtOH + 4 Ac– r1−−→ 5 But– + H+ + 2 H2 + 4 H2O

6 EtOH + 5 But–r2−−→ 1 Ac– + 5 Cap– + H+ + 2 H2 + 4 H2O

Assuming that i) the first reaction would take place at rate r1 and the second reaction at

rate r2; ii) the reactions only take place in one direction and; iii) water concentration remain


constant, following mass balances were derived:

dSEtOH

dt= −6r1 − 6r2

dSAc

dt= −4r1 + r2

dSBut

dt= 5r1 − 5r2

dSCap

dt= 5r2

dSH2

dt= 2r1 + 2r2

dSH+

dt= r1 + r2 (2.1)

As But is first produced, but later on consumed, it is possible to model this as an intermediate.

Therefore the last equation from equations 1.12 can be used. If the dilution rate is zero (which

is the case for batch reactors), then the equation for the intermediate product will simplify to

equation 2.2. Here the growthrates µ1 and µ2 will be subject to Monod substrate limitation

(µ1 will be limited for EtOH and Ac and µ2 for EtOH and But) and non-competitive inhibition

parameters from the FAs and EtOH.

dSBut

dt=

[µ1Y21− µ2Y22

]X (2.2)

When comparing the mass balance equation to the modeling of the intermediate equation for

But, it is possible to notice that they are quite similar. Therefore, the rates r1 and r2 can be

rewritten into equation 2.3 and equation 2.4 respectively.

r1 =µ1X

5Y21(2.3)

r2 =µ2X

5Y22(2.4)

These can then be put into equations 2.1, giving following equations.

dSEtOH

dt=−6µ1X

5Y21− 6µ2X

5Y22dSAc

dt=−4µ1X

5Y21+µ2X

5Y22dSBut

dt=

µ1X

Y21− µ2X

Y22dSCap

dt=

µ2X

Y22dSH2

dt=

2µ1X

5Y21+

2µ2X

5Y22dSH+

dt=

µ1X

5Y21+µ2X

5Y22(2.5)

26 2.4 PARAMETER ESTIMATION OF µMAX, K S AND K I

The yields provided in equations 2.5 are written in terms of But. However, it is possible to

define two new yields, YEtOH,1 and YEtOH,2, which will differ a factor 56 from previous defined

yields. This is done, since not But but EtOH is provided as a substrate, which results in

equations 2.5. Together with the first equation of equations 1.12 which simplifies in a batch

reactor since D = 0, these are the final equations to model chain elongation separately. It

is thus possible to add this to the ADM1 model (described in section 1.2.3) and extraction

model, to model the entire process.

dSEtOH

dt=

−µ1XYEtOH,1

− µ2X

YEtOH,2

dSAc

dt=

−2µ1X

3YEtOH,1+

µ2X

6YEtOH,2

dSBut

dt=

5µ1X

6YEtOH,1− 5µ2X

6YEtOH,2

dSCap

dt=

5µ2X

6YEtOH,2

dSH2

dt=

µ1X

3YEtOH,1+

µ2X

3YEtOH,2

dSH+

dt=

µ1X

6YEtOH,1+

µ2X

6YEtOH,2

2.4 Parameter Estimation of µmax, K S and K I

2.4.1 Introduction

Parameter estimation using growth curves was explained in section 1.2.2.3. For chain elonga-

tion however µ is dependent on multiple parameters instead of purely a maximal growth rate

and substrate affinity or product inhibition parameter explained previously. This is illustrated

in equation 2.6. When on the other hand all the initial concentrations of the compounds re-

main constant except for one, the growth rate measured will in a similar way depend on the

initial concentration of this varying compound. Depending on whether high or low initial

concentration for this compound are used, it is thus possible to estimate either its substrate

affinity or inhibition parameter. An overview of the different experiments needed is given in

Table 2.4. The layout of the WP for the different experiments is shown in Figure C.2.

dX

dt= µX

µ =

(µmax,1

SAc

KAc + SAc+ µmax,2

SBut

KBut + SBut

)SEtOH

KEtOH + SEtOH

Ki,EtOH

Ki,EtOH + SEtOH

Ki,Ac

Ki,Ac + SAc

Ki,But

Ki,But + SBut

Ki,Cap

Ki,Cap + SCap

(2.6)


Table 2.4: Overview of the different experiments needed for estimation of µmax,

KS and KI

Parameter Experiment Target concentration

KEtOH Different ratios Ac and EtOH + other experiments

KAc Low SAc 0-15 g L−1

KBut Low SBut 0-5 g L−1

Ki,EtOH High SEtOH 10-100 mL L−1

Ki,Ac High SAc 30-100 g L−1

Ki,But High SBut 15-40 g L−1

Ki,Cap High SCap 5-10 g L−1

µmax,1 Combination different experiments

µmax,2 Combination different experiments

2.4.2 Initial experiments

For initial experiments, growth curves were produced based on the technique used by Geir-

naert et al. (2014) with some adaptations. For the pure culture, sterile and anaerobic DSM52

medium was prepared in BTs and, if necessary, corrected with stock solutions of the substrates

until the desired substrate concentration was reached. Next, the tubes were inoculated with

1:10 inoculum:medium of a culture which was first grown in a 37 ◦C incubator. A transpar-

ent and flat-bottomed 96-WP was then prepared in an anaerobic workstation (GP-Campus,

Jacomex, TCPS NV, Rotselaar, Belgium) under a N2:CO2 (90:10, v/v) atmosphere. Each

well, apart from the wells at the outer rim, was inoculated with 200 µL coming from the

BTs. In this thesis each experiment, regardless of what was tested, was executed in triplicate

except if mentioned otherwise. The lid of the 96-WP was then sealed using commercially

available petroleum jelly. The 96-WP was removed from the anaerobic chamber, and put into

a plate reader (Tecan Infinite M200 PRO, Grodig, Austria), where growth was monitored by

measuring the OD600 nm every half hour at 37 ◦C. The residual liquid in the BTs was used to

compare results.

For the mixed culture, an analogue approach was used. Substrate-free medium was prepared

in BTs, which were flushed and autoclaved. By sterile additions of EtOH and/or Ac-stock

solutions, the desired concentrations were achieved in the tubes. For the microorganisms,

the reactor was sampled and divided over falcon tubes, centrifuged (Sorvall RC6 plus) and

resuspended in C-free medium. This solution was then used to inoculate the prepared BTs

1:10 inoculum:medium, subsequently used for preparation of the 96 WP.

28 2.4 PARAMETER ESTIMATION OF µMAX, K S AND K I

2.4.3 Control experiments

Because of experimental issues, an alternative approach was tested and compared to the

earlier described method. In this alternative approach, the WP, for which the layout is shown

in Figure 2.2, was put in a Tecan Sunrise plate reader inside the anaerobic chamber at 37 ◦C

to maintain anaerobic conditions. To prevent excessive evaporation, the outer rim of the WP

was filled with 300 µL of sterile medium. This alternative approach was compared with the

initial approach with an identical plate layout for C. kluyveri. As another control, a third

sealed plate was also put in an incubator at 37 ◦C.

Figure 2.2: Layout of the 96-WP for the control experiment. Wells contained

DSM52 medium with 10 mL L−1 EtOH and 3 g L−1 Ac or But.

2.4.4 General experiments

The actual growth curve experiments were conducted using C. kluyveri. Experiments were

designed as such, that the desired substrate concentrations were obtained after inoculation.

This was done by changing the medium composition of DSM52 and stock solutions which

were added to the medium, summarized in Table 2.5. After inoculation, the BTs were used

to prepare the 96-WP inside the anaerobic chamber. The layout of the WP for the different

experiments are shown in Figure C.2. The outer rim was filled with 300 µL of sterile medium

to prevent excessive evaporation, after which the plate was put in a Tecan Sunrise plate reader

inside the anaerobic chamber.

Table 2.5: Stock solutions prepared for growth curve experiments

What was investigated? Substrate in DSM52 Substrate in stock solutions

Ratio None Different concentrations of EtOH and Ac

K S and K I of Ac/But EtOH Different concentations of Ac/But

K I of Cap EtOH and Ac Different concentrations of Cap

K S and K I of EtOH Ac Different concentrations of EtOH


2.5 Parameter estimation of growth yield

Penicillin bottles were prepared, which contained 10 mL L−1 of EtOH and 3 g L−1 Ac or But.

These bottles were inoculated (1:10 inoculum:medium) and sampled. Samples were analyzed

on EtOH concentration and biomass concentration using VSS. The bottles were incubated at

37 ◦C and at a later time point analyzed again. YETOH,1 and YETOH,2 could be determined

using equation 2.7 for the Ac and But bottles respectively.

YEtOH =VSSend −VSSbegin

SEtOH,begin − SEtOH,end(2.7)

2.6 pH change of medium in function of added protons

As chain elongating reactions produce H+, it was checked how the pH of the medium changed

if protons were added. 1 L of Ac-free medium was divided over 3 equal volumes of 330 mL.

Here, either no K-Ac, 1.372 g of K-Ac or 2.734 g of K-Ac was added. Each Ac concentration

was further divided over three times 100 mL, in order to conduct the experiment in triplicate.

A stock solution of exactly 1 M HCl was made, and pH was checked after every addition of

0.1 mL HCl solution. At a later time point, 1 L of Ac-free medium was prepared, which was

divided over 3 times 100 mL and 3 times 200 mL. Again pH was checked after every addition

of either 0.1 mL HCl stock solution for 100 mL medium or 0.2 mL HCl stock solution for 200

mL medium.

2.7 Analysis

2.7.1 FA Analysis

FA Analysis was performed in accordance with Andersen et al. (2014). C2-C8 fatty acids

(including isoforms C4-C6) were measured by gas chromatography (GC-2014, Shimadzur,

The Netherlands) with DB-FFAP 123-3232 column (30 m x 0.32 mm x 0.25 µm; Agilent,

Belgium) and a flame ionization detector (FID). Liquid samples were conditioned with sulfuric

acid and sodium chloride and 2-methyl hexanoic acid as internal standard for quantification

of further extraction with diethyl ether. Prepared sample (1 µL) was injected at 200 ◦C with

a split ratio of 60 and a purge flow of 3 mL min−1. The oven temperature increased by

6 ◦C min−1 from 110 ◦C to 165 ◦C where it was kept for 2 minutes. FID had a temperature of

220 ◦C. The carrier gas was nitrogen at a flow rate of 2.49 mL min−1.

30 2.8 SOFTWARE

2.7.2 EtOH Analysis

The samples containing EtOH were diluted first to obtain EtOH concentrations below 1 g L−1

prior to the measurements. The analysis was performed using Ion Chromatography (IC)

(Dionex DX 500).

2.7.3 Solids Analysis

Total Suspended Solids (TSS) and Volatile Suspended Solids (VSS) Analysis were performed

following Standard Methods 2540D and E (APHA, 1992).

2.7.4 Headspace Gas Analysis

The gas phase composition was analyzed using a Compact Gas Chromatograph (GC) (Global

Analyser Solutions, Breda, The Netherlands), equipped with a Molsieve 5�A pre-column and

Porabond column (CH4, O2, H2 and N2) and a Rt-Q-bond pre-column and column (CO2,

N2O and H2S). Concentrations of gases were determined by means of a thermal conductivity

detector.

2.8 Software

2.8.1 Growth rate determination

The growth rates were determined using the gcFit function in the “grofit” package by Kahm

and Kschischo (2015). The package was called upon in RStudio (RStudio Team, 2015). The

calculated growth curves were produced with the parameters estimated with a spline fit,

whilst assuming logistic growth via equation 2.8. Here A is a the maximal growth value, µ

the maximal slope thus growth rate and λ the lag-phase. This was done, as the spline fit

always gave estimated parameters, whilst other fits only gave parameters if the fit was good

enough.

y(t) =A

1 + exp(4µA (λ− t) + 2)(2.8)

2.8.2 Model simulations

Model simulations were done using the “Model” instance in the biointense model environment

(Van Daele et al., 2015), which provides the ability to construct a model consisting of algebraic

equations, ordinary differential equations or a combination of both. The package was called

upon in IPython notebook (Perez and Granger, 2007). Literature values were used for the


parameter values, which are listed in Table 2.6, and initial concentrations of standard DSM52

medium for C.kluyveri were used as reference.

Table 2.6: Parameter values

Parameter Value Reference

KEtOH 60 µM (Kalyuzhnyi, 1997)

KAc 600 µM (Labib et al., 1993)

KBut 5 µM (Labib et al., 1993)

Ki,EtOH 0.3 M (Angenent et al., 2016)

Ki,Ac 1.08 M (Eilersen et al., 1995; Pratt et al., 2012)

Ki,But 0.172 M (Eilersen et al., 1995; Pratt et al., 2012)

Ki,Cap 0.108 M (Eilersen et al., 1995)

YEtOH,1 0.034 g cells (g EtOH)−1 (Rittmann and McCarty, 2001)

YEtOH,2 0.034 g cells (g EtOH)−1 (Rittmann and McCarty, 2001)

µmax,1 0.044 h−1 (Heijnen, 1999; Spirito et al., 2014)

µmax,2 0.044 h−1 (Heijnen, 1999; Spirito et al., 2014)

CHAPTER 3Results

3.1 Reactor data


To determine the stability (required to do additional parameter estimation tests) and perfor-

mance of the reactor, the evolution of the FA concentrations, the VSS concentration, and the

gas production were monitored.

Figure 3.1 shows the concentrations of the FAs in the reactor. During a period of 78 days (11

times the HRT), no stable operation was achieved . Initially, But and Cap concentrations were

very low. A batch test (Figure 3.2) was performed to see if DSM52 medium would give better

results. Since the batch test showed higher VSS, But, and Cap concentrations, the DSM52

medium was used from day 22 onwards. This change resulted in a boost of the (MC)FA

production, as illustrated in Figure 3.1. The maximum total FA concentration following the

change in medium composition was 10.76 g L−1, with a Cap concentration of 6.02 g L−1.

To counteract methanogenesis, the EtOH concentration was increased in the influent at day

43, which appeared to boost the FA production. As methanogenesis still proceeded how-

ever, at day 50 the pH was lowered to 6 as well. However, this resulted in a lower reactor

productivity, since the FA concentrations were reduced (and Ac showed an increase).

34 3.1 REACTOR DATA

0

2

4

6

8

10

12

0 15 30 45 60 75

Co

nce

ntr

atio

n F

A [

g L

-1]

Time [d]

Cap

But

Ac

22 50

Figure 3.1: Concentration of FAs for the old reactor. At day 22 the reactor

medium was changed from M9 to DSM52 medium, at day 43 SEtOH of influent

was doubled and at day 50 the pH was of reactor was lowered.

0

0.5

1

1.5

2

Medium M9 Medium DSM52

VS

S [

g c

ells

L

-1]

(a) VSS

0

2

4

6

8

10

12

Medium M9 Medium DSM52

FA

co

nce

ntr

atio

n [

g L

-1]

Cap

Val

But

Prop

Ac

(b) FAs

Figure 3.2: Comparison of M9 medium with DSM52 medium

CHAPTER 3 RESULTS 35

The TSS and VSS concentrations (Figure 3.3) fluctuated largely, with the latter ranging from

0.11 g VSS L−1 to 2.39 g VSS L−1. Part of the problem was the changes made in medium com-

position and pH of the reactor. However, the reactor still did not stabilize over a period of 4

times HRT after day 50, when no more changes were made. Next to that, peak concentrations

of VSS and longer chain FA seemed to appear at the same time.

0

0.5

1

1.5

2

2.5

3

3.5

0 15 30 45 60 75

So

lid

co

nce

ntr

atio

n [

g L

-1]

Time [days]

VSS

TSS

Figure 3.3: Concentration of VSS and TSS for the old reactor.

Two gases, produced in the reactor, can be used as indicators of the reactor performance, i.e.

H2 and CH4 (Figure 3.4). More specific, H2 indicates that chain elongation is occurring, while

CH4 production counteracts the process of interest. At first, no CH4 production was visible,

but this changed at day 32. As was explained in section 2.1, different approaches were used

to limit the CH4 production (i.e. by increasing EtOH concentration to boost FA production

and lowering the pH), which resulted in a drop of CH4 production, and a short peak in H2

production. However, no stable reactor conditions were found for the gas production as well,

resulting in the termination of this reactor at day 78.

36 3.1 REACTOR DATA

0

50

100

150

200

250

300

0 15 30 45 60 75Gas

pro

du

ctio

n [

mL

d-1

]

Time [d]

H₂

CH₄

22 50

Figure 3.4: Gas composition of the headspace for the old reactor. At day 22 the

reactor medium was changed from M9 to DSM52 medium, at day 43 SEtOH of

influent was doubled and at day 50 the pH was of reactor was lowered.


To circumvent the problems occurring with the old reactor, a new reactor was set up (as was

described in section 2.1). Reactor stability and performance were evaluated using the same

performance indicators. The evolution of the FA concentration in function of time for this new

reactor is depicted in Figure 3.5. Compared to previous reactor, the FA concentrations were

more stable, especially after day 55. Next to that, mostly But was formed instead of Cap,

which was different compared to the previous reactor. The total FA concentrations ranged

between 2.1 and 8.6 g L−1, with an average value of (5.4± 0.6) g L−1 (n=23) at steady-state.

0

1

2

3

4

5

6

7

8

9

0 20 40 60 80 100

Co

nce

ntr

atio

n F

A [

g L

-1]

Time [d]

Oct

Cap

Val

iVal

But

Prop

Ac

28 100

Figure 3.5: Concentration of FAs for the new reactor. At day 28 BES was added

and at day 100 this addition was stopped.


The VSS and TSS concentrations of the new reactor are shown in Figure 3.6. The two

concentrations followed a similar trend, as was the case for the previous reactor. The VSS

concentration had an average value of (0.4± 0.1) g L−1 (n=23). Compared to concentrations

from the old reactor, which fluctuated between 0.11 and 2.39 g L−1, the concentrations were

more stable.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 20 40 60 80 100

Co

nce

ntr

atio

n s

oli

ds

[g L

-1]

Time [d]

VSS

TSS

Figure 3.6: Concentration of VSS and TSS from the new reactor.

As a last performance indicator, the production of different gases is shown in Figure 3.7. After

a few days already, CH4 production was visible. As the former solutions to counteract CH4

production (i.e. add more EtOH and lowering of the pH) caused other problems, a different

solution was considered here. To mitigate the methanogens, BES was added to the medium at

day 28. This addition had an immediate impact, as the CH4 production stopped completely

at day 31. However, BES was still added afterwards, to ensure that all the methanogens were

depleted. At day 100, BES addition was stopped after which methanogenesis did not start

again. H2 production was quite variable. Sometimes, very high concentrations were detected

(up to 57% (v/v)). Peak productions however did not seem to correspond to significantly

higher FA concentrations, nor to higher biomass concentration. The gas production seemed

to be the least stable of the variables discussed, but in general the reactor conditions were

quite steady. Overall, the pH change of the medium acts consistently on the amount of pro-

tons added, irrespective of the volume or S)Ac.

38 3.1 REACTOR DATA

0

50

100

150

200

250

0 20 40 60 80 100

Gas

pro

du

ctio

n [

mL

d-1

]

Time [d]

CO₂

H₂

N₂

O₂

CH₄

28 100

Figure 3.7: Gas production for the new reactor. At day 28 BES was added and

at day 100 this addition was stopped.


3.2 The effect of proton addition on pH change of medium

As H+ is produced during chain elongation, it is investigated how the pH of the medium

changes in function of amount of H+ provided. If the results are consistent at different FA

concentrations, time points, and volumes, measuring the pH difference might be coupled to

production of longer chain FA in a later stage. This way, measuring the pH difference becomes

a quick and easy method in determining (MC)FA concentrations.

Figure 3.8a shows the results for different concentrations of Ac in the medium. Here all three

conditions are within the standard deviation of each other, indicating that Ac does not have

an influence on pH change of the medium. Figure 3.8b shows the same for different points

in time. As the results are similar, this indicates that the experiment is reproducible. Figure

3.8c shows the pH drop in function of the amount of H+ added in mol equivalent, which is

the amount of mol HCl divided by a correction factor (1 for 100 mL and 2 for 200 mL) to take

the unequal initial volumes into account. No significant differences can be observed between

the different volumes after this correction takes place either.

5.8

6.3

6.8

7.3

7.8

0 0.0005 0.001 0.0015 0.002 0.0025

pH

amount of H+ added [mol]

0 g L⁻¹ Ac

2.5 g L⁻¹ Ac

5 g L⁻¹ Ac

(a) pH change of medium containing different SAc in function of added protons.

40 3.2 THE EFFECT OF PROTON ADDITION ON PH CHANGE OF MEDIUM

5.8

6.3

6.8

7.3

7.8

0.0E+00 5.0E-04 1.0E-03 1.5E-03 2.0E-03 2.5E-03

pH

amount of H+ added [mol]

16-Feb

02-Mar

(b) pH change of medium in function of added protons at different time points.

5.8

6.3

6.8

7.3

7.8

0 0.0005 0.001 0.0015 0.002 0.0025

pH

amount of H+ added [mol equivalent]

100 mL

200 mL

(c) pH change of different volumes of medium in function of the added proton equivalents

(i.e. the amount of protons corrected for the differences in volumes).

Figure 3.8: pH change in function of added protons, for different variables.


3.3 Method development of growth curves

Parameter estimation was attempted using the growth curve technique. During the initial

experiments, the growth curves were evaluated on different aspects. It was verified whether

growth could be observed and be related to chain elongation. Next, deviations among tripli-

cates were investigated and outcomes of BTs and WP were compared.

Figure 3.9 shows data from the first growth experiment conducted for C. kluyveri as de-

scribed in section 2.4.2. The titles correspond to the calculated concentrations of EtOH and

Ac respectively. These differ from the real concentrations which were measured from the

BTs. The layout of the upper 4 rows of plots is as such, that identical proportions of EtOH

and Ac (1:0,2:1,4:1 and 8:1) are plotted horizontally next to each other, and identical EtOH

concentrations (0.075 M, 0.15 M, 0.3 M and 0.6 M) are plotted vertically next to each other.

In nearly all the plots from Figure 3.9, triplicates showed a similar pattern. Further, all the

growth curves showed an initial increase in OD at around 2 hours. This increase was possibly

not caused by growth, considering that the uninoculated wells experienced this increase as

well. As there was no second increase visible in all three replicates, microorganisms were

probably unable to grow under present conditions.

When the same experiment was performed with the mixed community from the old reactor

(Figure C.3), all the inoculated growth curves showed an increase in OD. As the uninocu-

lated growth curves remained nearly flat, this implied that growth could be observed in all the

wells. In Figure 3.10 the FA concentrations at the end of the experiment were compared. The

BTs had a higher SCap and SBut , and lower SAc. When linking this to the chain elongation

reaction, it was concluded that the microorganisms in the BT had a higher productivity. In

the WP little to no But and Cap were detected, indicating that growth taking place during

the experiment was not caused by chain elongating organisms.

42 3.3 METHOD DEVELOPMENT OF GROWTH CURVES

EtOH 0.075 MAc 0 M

0.1

20.1

60.2

0

EtOH 0.15 MAc 0 M

EtOH 0.3 MAc 0 M

EtOH 0.6 MAc 0 M

EtOH 0.075 MAc 0.0375 M

0.12

0.16

0.20

EtOH 0.15 MAc 0.075 M

EtOH 0.3 MAc 0.15 M

EtOH 0.6 MAc 0.3 M

EtOH 0.075 MAc 0.019 M

0.12

0.16

0.2

0

EtOH 0.15 MAc 0.0375 M

EtOH 0.3 MAc 0.075 M

EtOH 0.6 MAc 0.15 M

EtOH 0.075 MAc 0.009 M

0.1

20.

16

0.20

EtOH 0.15 MAc 0.019 M

EtOH 0.3 MAc 0.0375 M


EtOH 0 MAc 0.0375 M

0 20 40 60

0.12

0.16

0.20

EtOH 0 MAc 0.075 M

0 20 40 60

C−free inoc

0 20 40 60

C−free uninoc

0 20 40 60

Time [h]

OD

600

nm

[−

]

Figure 3.9: The optical density (OD600 nm) measured over time for C. kluyveri

with Tecan Infinite M200 Pro outside the anaerobic chamber for different EtOH

and Ac concentrations.

WP BT WP BT WP BT WP BT WP BT WP BT WP BT WP BT WP BT WP BT0.00

0.04

0.08

0.12

EtOH 0.075 M EtOH 0.15 M EtOH 0.3 M EtOH 0.6 M EtOH 0.075 M EtOH 0.15 M EtOH 0.3 M EtOH 0.6 M C−free inoc C−free uninocAc 0 M Ac 0 M Ac 0 M Ac 0 M Ac 0.0375 M Ac 0.075 M Ac 0.15 M Ac 0.3 M


0.04

0.08

0.12

EtOH 0.075 M EtOH 0.15 M EtOH 0.3 M EtOH 0.6 M EtOH 0.075 M EtOH 0.15 M EtOH 0.3 M EtOH 0.6 M EtOH 0 M EtOH 0 MAc 0.019 M Ac 0.0375 M Ac 0.075 M Ac 0.15 M Ac 0.009 M Ac 0.019 M Ac 0.0375 M Ac 0.075 M Ac 0.0375 M Ac 0.075 M

Ac But Cap

Con

centr

atio

n o

f FA

[m

ol L

−1 ]

Figure 3.10: FA concentrations at the end of the experiment for the mixed com-

munity. WP indicates the results as was measured in the 96-well plate, and BT

indicates the result as was measured in the Balch tubes. The concentrations shown

below, are the calculated initial concentrations which were added.

43


The experiment was also repeated for a pure culture of only C. kluyveri, as previously no

growth could be observed. The results can be found in Figure C.4. Here, the variability

between the different triplicates was higher than in the previous experiment and apart from

a decrease in OD at the start, the OD of the uninoculated wells remained nearly constant in

time. Some other wells showed a rise in OD, which therefore implied that growth was taking

place in these wells. However, when final FA concentrations of the 96-WP were compared to

the FA concentrations in the BT (depicted in Figure 3.11) very different concentrations for

the two methods were found as well. In most cases the BTs contained more Cap, more But

and less Ac, as was the case for the mixed community. This then implied that microorganisms

were more active in the BT than in the WP. However, for the wells containing only Ac the

opposite was true, although there seemed to be even more problems occurring here in regards

to mass balances (i.e. 0.075 M Ac and 0 M EtOH should normally not result in 0.1 M Cao).


0.10

0.20

0.30

EtOH 0.075 M EtOH 0.15 M EtOH 0.3 M EtOH 0.6 M EtOH 0.075 M EtOH 0.15 M EtOH 0.3 M EtOH 0.6 M C−free inoc C−free uninocAc 0 M Ac 0 M Ac 0 M Ac 0 M Ac 0.0375 M Ac 0.075 M Ac 0.15 M Ac 0.3 M


0.10

0.20

0.30

EtOH 0.075 M EtOH 0.15 M EtOH 0.3 M EtOH 0.6 M EtOH 0.075 M EtOH 0.15 M EtOH 0.3 M EtOH 0.6 M EtOH 0 M EtOH 0 MAc 0.019 M Ac 0.0375 M Ac 0.075 M Ac 0.15 M Ac 0.009 M Ac 0.019 M Ac 0.0375 M Ac 0.075 M Ac 0.0375 M Ac 0.075 M

Ac But Cap

Con

centr

atio

n o

f FA

[m

ol L

−1 ]

Figure 3.11: FA concentrations at the end of the experiment for C. kluyveri, for

which OD was measured using Tecan Infinite M200 Pro outside the anaerobic

chamber. WP indicates the results as was measured in the 96-well plate, and BT

indicates the result as was measured in the Balch tube. The concentrations shown

below, are the calculated initial concentrations which were added.

45


Since the current approach had a low replicability, a new approach (i.e. measuring growth

curves inside the anaerobic chamber, as described in section 2.4.3) was compared to the one

taken in the first experiments. Figure 3.12 shows the growth curves obtained for the different

methods for C. kluyveri. Only the experiment inside the anaerobic chamber delivered results

consistent with the positive controls. The increase of the black and orange lines appeared to

be comparable to the increase observed for the inoculated wells which contained But. For the

growth curves measured from the WP stored in the incubator, an increase in OD was noticed

as well. However, as the uninoculated wells produced the same pattern, this could not be

linked to growth. For the growth curves produced with a Tecan infinite M200 PRO outside

the anaerobic chamber, no significant increase in OD was found. Lastly, none of the green

curves had a significant increase compared to the uninoculated counterpart. The penicillin

bottle from which the WP were filled producing these green lines also contained a higher VSS

concentration at the start ((0.52± 0.07) g L−1)n=3 than at the end ((0.40± 0.03) g L−1)n=3.

This implied that the microorganisms were already dead (e.g. due to an oxygen shock) thus

unable to produce growth curves.

Inoculated AcAnaerobic chamber

0.2

0.4

0.6

0.8

1.0

Anaerobic chamberInoculated But

Anaerobic chamberUninoculated Ac

Anaerobic chamberUninoculated But

IncubatorInoculated Ac

0.2

0.4

0.6

0.8

1.0

IncubatorInoculated But

IncubatorUninoculated Ac

IncubatorUninoculated But

M200 PROInoculated Ac

0.2

0.4

0.6

0.8

1.0

0 10 30 50

M200 PROInoculated But

0 10 30 50 0 10 30 50

M200 PROUninoculated Ac

M200 PROUninoculated But

0 10 30 50

Time [h]

OD

620

nm

[−

]

Figure 3.12: The optical density (OD620 nm) measured for different methods in

time for C. kluyveri. The colors represent growth curves which were produced

from the same penicillin bottle.


To further compare the methods, final FA concentrations were measured for the different

methods as well (Figure 3.13). For the WP stored in the incubator, large variations in final

concentrations of the different compounds were found. The WP stored and measured in the

Tecan Infinite M200 PRO plate reader on the other hand showed very little variability in

composition among the replicates. However, not much activity seemed to have taken place

either, as there was only little Cap production. Most comparable were the final concentra-

tion in the penicillin bottle and the WP measured inside the anaerobic chamber. However,

statistical analysis to determine whether the two methods were the same was not possible as

the data did not fulfill the criteria for a paired t-test (i.e. normality of the data and equal

variances for the datasets), and there was a lack of repetition to do a one-sample sign test.

M200 PRO Incubator An chamber Bottle M200 PRO Incubator An chamber Bottle0.00

0.02

0.04

0.06

0.08

0.10

0.12

Inoculated Ac Inoculated ButAc But Cap

FA

con

centr

atio

n [

mol

L−

1 ]

Figure 3.13: FA concentrations for the different methods at the end of the exper-

iment for C. kluyveri.

A similar comparison was performed on the mixed culture, given in Figure C.5 and Figure

C.6. For both methods the inoculated wells had a relative increase in OD620 nm compared

to their uninoculated counterpart, which was quite similar for the two methods. When com-

paring the substrate, this relative increase was also approximately the same. The final FA

concentrations for the three methods, depicted in Figure C.6, appeared to be quite similar

as well. However, there was no significant amount of Cap formed in either of the methods.

48 3.4 DATA COLLECTION

This again seemed to contradict the statement that growth occurred. As it appeared that

growth and Cap formation were not linked, it was possible that growth occurred via different

reactions. This seems plausible, as in a mixed culture there are different species, making

the possible reactions less straightforward. Therefore, for the actual experiments used for

parameter estimation only the pure culture C. kluyveri was used.

3.4 Data collection

Table 2.4 provides an overview of the different experiments conducted for estimation of µmax,

KS and KI of the different compounds. However, only the high SEtOH, high SBut and ratio-

experiment will be discussed due to failure of the other experiments and a lack of time to

redo them. Data was reviewed on the same points as previously (i.e. comparable triplicates,

whether growth was observed through chain elongation, if BTs and WP had similar results).

Next to that, it was determined whether substrate consumption and product formation took

place following stoichiometry derived by Spirito et al. (2014).

3.4.1 High SEtOH experiment

Figure 3.14 shows the resulting growth curves, produced for the high SEtOH experiment.

Data was corrected with the uninoculated control containing no EtOH, which gave nearly flat

growth curves with an average OD620 nm of 0.100± 0.003 (n=358). The correction ensured an

OD close to zero to allow a better fit to the growth curves by the Grofit-package, improving

the accuracy of estimations for µ, λ and A. From Figure 3.14 it was seen that triplicates were

quite similar for lower SEtOH, but became more dissimilar when higher concentrations were

provided. Next to that, the growth curves had a characteristic shape: the OD rose until a

maximum was reached and then started to decline. This corresponded to lag-time, growth

and decay respectively. However, this downward slope could not be predicted by the model

as decay was not added to the model. This was because it was not of importance during

parameter estimation and would already be added to the fermentation part of an overall

model. Apart from that, the corrected growth curves and calculated growth curves using

Grofit were quite similar. When the final FA concentrations were determined for WP and

BT, depicted in Figure C.7, the WP had a higher SCap and SBut and lower SAc than the BTs,

which indicated higher activity of the microorganisms in the WP.


EtOH 0.17 M

0.0

0.3

0.6 EtOH 0.21 M EtOH 0.26 M EtOH 0.35 M

EtOH 0.46 M

0.0

0.3


EtOH 0.92 M

0.0

0.3


EtOH 1.44 M

0.0

0.3


No EtOH inoc

0.0

0.3

0.6

0 20 40 60 80

No EtOH uninoc

0 20 40 60 80

DSM52 inoc

0 20 40 60 80

DSM52 uninoc

0 20 40 60 80

Time [h]

OD

620n

m [−

]

Figure 3.14: Corrected optical density (OD620 nm) measured in function of time

for varying EtOH concentrations. The title contains the measured initial EtOH

concentrations. Each initial concentration was done in triplicate, shown with the

red, blue and green solid lines. The dashed lines are the growth curves calculated

using Grofit.

Finally, using the stoichiometry derived by Spirito et al. (2014), expected behaviour could be

calculated for substrate consumption versus product formation. The Ac consumption (given

as the final Ac concentration subtracted for the initial Ac concentration) was plotted against

But and Cap production (in which initial concentrations of But and Cap are subtracted from

the final concentrations) in Figure 3.15. According to Spirito et al. (2014), 5 moles of But

were formed from 4 moles of Ac. However, if these 5 moles of But were further converted

into 5 moles of Cap, 1 additional mole of Ac was formed. Thus for 1 mol of But 45 moles of

Ac were needed, whilst for 1 mol of Cap only 35 moles of Ac were necessary. These expected

amounts were plotted on the graph using dotted lines. As usually both But and Cap were

formed, it was expected for the points to be between these two lines. For the BTs this seemed

to be the case, but for the WP the points were nearly always above this line. This would

indicate that in this case there is more product formed than substrate consumed.


●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●

●

●

Balch tube

0.00 0.02 0.04 0.06 0.08 0.10

0.00

0.02

0.04

0.06

0.08

0.10

0.12

●

●

●●

●●

●●

●

●

●

●

●

●

●

●

●●

●

●

96 well−plate

0.00 0.02 0.04 0.06 0.08 0.10Ac consumption [mol L−1]

But

and C

ap p

roduct

ion [

mol

L−

1 ]

Only But production Only Cap production

Figure 3.15: Product formation in function of substrate consumption for the high

SEtOH experiment. The dotted lines indicate the expected formation if either only

But is formed or only Cap formed.

3.4.2 High SBut experiment

The growth curves of the high SBut experiments (Figure 3.16) were analyzed on similar as-

pects. The OD620 nm values were again corrected to achieve a better model fit for the calculated

growth curves using Grofit. The correction was done against the C-free uninoculated control,

for which the growth curve remained nearly flat at an average OD of 0.113± 0.002 (n=268).

The chosen But concentrations, for the wells containing both But and EtOH, appear to have

been too high as only the lowest concentration led to significant growth. Next to that, it seems

that growth was still possible even when no EtOH was provided in the medium. As residual

EtOH from the penicillin bottle in which C kluyveri was grown could be transferred during

inoculation, this might have been enough for the reaction to proceed. When comparing BTs

and WP on final FA concentrations (Figure C.8), the latter contained usually less Cap and

But and more Ac. This would suggest that there was less chain elongating activity than in

the BTs.


But 0.09 M0.

00.2

0.4 But 0.14 M But 0.15 M But 0.18 M

But 0.23 M

0.0

0.2

0.4 But 0.23 M But 0.27 M But 0.28 M

But 0.29 M

0.0

0.2

0.4 But 0.29 M But 0.36 M But 0.37 M

Only But 0.026 M

0.0

0.2

0.4 Only But 0.048 M Only But 0.091 M Only But 0.16 M

Only EtOH inoc

0.0

0.2

0.4

0 30 60 90

Only EtOH uninoc

0 30 60 90

C−free inoc

0 30 60 90

C−free uninoc

0 30 60 90

Time [h]

OD

620n

m [−

]


for varying But concentrations. The title contains the measured initial But

concentrations. The EtOH concentrations were kept approximately constant at

10 mL L−1, except for the wells with only But in which no EtOH was provided

in the medium. Each initial concentration was done in triplicate, shown with the

red, blue and green solid lines. The dashed lines are the growth curves calculated

using Grofit.

Comparing But consumption against Cap formation, the behaviour was expected to be around

the dotted red line since according to Spirito et al. (2014) 5 moles of But produce 5 moles

of Cap. Again for the BTs the results were as expected. For the 96-WP however, the values

were always below this line, meaning more But was consumed than Cap produced. This could

therefore indicate that side reactions were occurring.


●

●

●

●

●

●

●

●

●●

●

●

●

●

● ●●

●●●

Balch tube

0.0 0.1 0.2 0.3 0.4

0.0

0.1

0.2

0.3

0.4

● ●

●

●

●

●

●

●

●●

●●

●●

●● ●

●●●

96−well plate

0.0 0.1 0.2 0.3 0.4But consumption [mol L−1]

Cap

pro

duct

ion [

mol

L−

1 ]

Expected behaviour from model

Figure 3.17: Product formation in function of substrate consumption for the high

SBut experiment. The dotted lines indicate the expected formation.

3.4.3 Ratio-experiment

For the last experiment, in which EtOH and Ac were provided in different ratios, the growth

curves are found in Figure 3.18. The OD620 nm values were corrected for uninoculated C-

free wells (not shown here) which was nearly constant with an average OD of 0.104± 0.002

(n=346). Due to the varying of two compounds at the same time, it was difficult to find a

trend in the plot.

When the BTs and WP were compared, shown in Figure C.9, a lot of wells were very com-

parable. However, when initial Ac concentrations were higher, differences between the two

methods became more visible. Mostly the WP contained more Ac and less Cap, indicating

that there was less activity, as was the case for the high SBut experiment.

In regards to the stoichiometry of the reaction, Figure C.29 shows the But and Cap formation

against Ac consumption . In contrast to the high SEtOH experiment, here both the BT and

WP follow the expected results, although the latter shows seems to deviate more often.


EtOH 0.07 MAc 0 M

0.0

0.4

0.8 EtOH 0.12 M

Ac 0 MEtOH 0.23 M

Ac 0 MEtOH 0.17 M

Ac 0 M


0.0

0.4

0.8 EtOH 0.07 M

Ac 0.02 MEtOH 0.24 M

Ac 0.07 MEtOH 0.35 M

Ac 0.1 M

EtOH 0.06 MAc 0.015 M

0.0

0.4

0.8 EtOH 0.12 M

Ac 0.029 MEtOH 0.23 MAc 0.045 M

EtOH 0.52 MAc 0.093 M


0.0

0.4

0.8

0 30 60 90

EtOH 0.11 MAc 0.016 M

0 30 60 90


0 30 60 90

EtOH 0.38 MAc 0.037 M

0 30 60 90

Time [h]

OD

620n

m [−

]


for varying EtOH and Ac concentrations. The title contains the measured initial

EtOH and Ac concentrations. Each initial concentration was done in triplicate,

shown with the red, blue and green solid lines. The dashed lines are the growth

curves calculated using Grofit.

54 3.5 GROWTH YIELD

3.5 Growth yield

To determine the growth yields YEtOH,1 and YEtOH,2, penicillin bottles were inoculated con-

taining EtOH and Ac or EtOH and But respectively. For the bottles containing EtOH and Ac,

one bottle (the green lines in Figure 3.12) appeared to have not grown. This was confirmed by

measuring the VSS concentration, which was higher at the start (0.52 g cells L−1) than at the

end (0.4 g cells L−1). As tests were done in triplicate, only two samples remained for which the

growth yield could be determined. This resulted in YEtOH,1 of (0.047± 0.008) g cells (g EtOH)−1,

which was determined as the average and standard deviation of the two values. No proper

error propagation was performed, as this value was rather used as an indicative value. When

the same was attempted for the YEtOH,2, the VSS concentration was always higher at the be-

ginning than at the end. Next to that, in two of the three bottles more EtOH was produced

than consumed. Because of these considerations, no YEtOH,2 could be determined.

CHAPTER 4Discussion

4.1 Reactor performance and stability

The reactor data was already discussed in section 3.1. The values gained here were used

to compare the reactors to each other and to literature values in regards to stability, final

concentrations of FAs and their ratios.

Stability was only reached for the reactor with an HRT of 4 days. In this reactor however

mostly But and only little Cap was produced. On average, after stability was reached, the

effluent contained (0.42± 0.37) g L−1 Cap (n=23). The reactor with an HRT of 7 days on the

other hand had a higher SCap but higher variability as well ((2.04± 2.01) g L−1)(n=37). The

maximal SCap here was 6.02 g L−1 and the total FA concentration never went above 11.4 g L−1.

Comparing this to literature values, the most similar setup was a (fed)-batch reactor by Stein-

busch et al. (2011). This reactor started with synthetic medium which contained 3 g L−1 of

Ac and 2.3 g L−1 of EtOH, less than applied in this thesis. Apart from addition of EtOH when

it became depleted, no additional substrate was added. This resulted in effluent containing

final Cap concentrations of 8.27 g L−1 at pH 7 and 0.12 g L−1 at pH 5.5. As SCap in this thesis

remained between these values as well, this was quite comparable but rather on the low side.

As it however was not in the objective to have a high SCap but rather stable conditions, lower

Cap concentrations were tolerated if stability was reached.

As this was not the case for the first reactor, an explanation was sought. The unstable nature

could for instance be caused by addition of high substrate concentrations. This resulted in

high activity which in turn entailed high (MC)FA-concentrations. It appeared that high FA-

concentrations (according to Figure 3.1 this seemed to be around the 8-10 g L−1 benchmark)

inhibited microorganisms. It then took several dilutions for the FA concentrations to become

low enough before inhibition was lifted and growth could re-start. This was checked, by plot-

ting the subsequent biomass production (in percentage of the biomass concentration) for the

respective Cap concentration in Figure 4.1. There seemed to be a somewhat downward trend

56 4.1 REACTOR PERFORMANCE AND STABILITY

between the two, although this was not significant. The figure on the other hand neither

proves nor disproves former statement, as other processes were happening concurrently.

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

0 1 2 3 4 5 6

−10

0−

500

5010

0

Cap concentration [g L−1]

Bio

mas

s pro

duct

ion [%

]

Figure 4.1: Subsequent biomass production (in percentage) in function of the

Cap concentration in the reactor.

According to Ge et al. (2015) undissociated Cap concentration at which complete inhibition

is achieved was found to be 0.87 g L−1 at 30 ◦C. Using equation 1.1 at pH 7 this would require

a total Cap concentration of 115.7 g L−1 which is a lot higher than the 6.02 g L−1 found in this

reactor. However, the value at which inhibition takes place is most likely temperature depen-

dent. From the explanation found by Palmqvist and Hahn-Hagerdal (2000) and Angenent

et al. (2016) it is expected that higher temperatures will decrease the concentration at which

full inhibition is experienced, as then membrane fluidity increases and thus FAs can more

easily penetrate the membrane. Apart from that, the presence of the other FAs will further

decrease this value, making it possible for inhibition to appear at such low concentrations.

In regards to relative But and Cap proportions, adding EtOH in excess should result in for-

mation of Cap as the main product (Barker et al., 1945). However, this was not noticeably

different when the SEtOH was doubled for the reactor with an HRT of 7 days. For the reactor

with an HRT of 4 days, the But/Cap ratio was a lot higher compared to that of the reactor at

HRT of 7 days. However it not only contained more EtOH in the influent but also less Ac, so

in relative terms even more EtOH compared to the first reactor thereby contradicting previous

statement. The higher But/Cap ratios could however not have been influenced by the higher

CHAPTER 4 DISCUSSION 57

EtOH/Ac concentration, but rather by the retention time. According to Grootscholten et al.

(2013b), lowering the HRT decreased Cap concentrations while the SBut remained approxi-

mately the same.

FA concentrations and ratios were further influenced by pH. This was noticed, as lowering

the pH resulted into higher SAc and lower SBut, SCap and total FA concentration. This was

comparable to what could be found in literature (Steinbusch et al., 2011) and could be ex-

plained by the higher fraction of undissociated FAs at lower pH. As it is the protonated form

resulting in inhibition, the activity decreases at higher pH, thus lowering the FA concentra-

tion (Palmqvist and Hahn-Hagerdal, 2000). Next to that, a lower pH in itself can reduce

activity, since part of the energy needs to be invested into maintaining a neutral internal pH

(Angenent et al., 2016).

4.2 Model simulations

Simulations on the chain elongation model were done for different parameters (listed in Table

2.6) and initial conditions. These simulations could then be used to determine if the model

behaviour acted normally and whether it was comparable to the obtained growth curves. The

parameters were literature values, as parameter estimation was not achieved due to lack of

data and issues that will be explained in section 4.3. The outcome of the simulations can be

found in the appendix (section C.5).

The model simulations acted as expected. Higher inhibition parameters (which indicate that

inhibition takes place only at higher concentrations) resulted in faster growth without altering

the final concentrations. Substrate affinity parameters influenced growth rates as well, but

depending on which compound was limiting, different steady-state outcomes were reached.

Different yields mostly influenced the steady-state concentrations of the biomass, but other

concentrations were also dependent on whether EtOH or Ac was the limiting substrate. The

same holds true for the maximal growth rates, however this mostly influenced how fast growth

proceeded.

When initial concentrations were varied, for instance SEtOH, higher concentrations resulted

in a higher biomass and Cap production, unless Ac was the limiting substrate. When SEtOH

became zero, But could no longer be transformed into Cap, resulting in steady-state But

concentrations larger than zero. Most interesting was that the model simulations did show

that higher concentrations led to slower growth due to the inhibition parameter. This effect

was most noticeable around the inhibition parameter as well (Figure C.11). The same holds

true for substrate affinity parameters, although not shown here. Consequently, this suggested

that growth curves, as described in section 1.2.2.3, was indeed a valid tool in determining

most model parameters.

Compared to actual obtained growth curves, steady-state conditions for biomass were reached

more quickly than modeled here, which would suggest µmax are higher than determined via

58 4.3 QUALITY CONTROL OF PURE CULTURE KINETICS

literature. Apart from that, final Cap concentrations were rather overestimated, with values

of more than 150 mM (17.4 g L−1). But on the other hand was underestimated, at concen-

trations lower than 0.5 mM (0.044 g L−1). This problem could be solved by having a lower

µmax,2 compared to µmax,1. In that case, But will quickly be formed but not as quickly fur-

ther elongated to Cap. When EtOH becomes depleted, this leaves higher But at steady-state.

However, this still would not explain why final SBut were also high during the high SEtOH ex-

periment, when EtOH was not the limiting substrate. Perhaps current inhibition phenomena

included in the model were not sufficient, and an extra factor should be added which would

allow complete inhibition, resolving into no growth instead of slower growth at very high FA

concentrations.

4.3 Quality control of pure culture kinetics

4.3.1 Comparison of final FA concentrations BTs and WP

As described in section 3.4, deviating results were found between the final FA concentrations

of the BTs and WP, which were experiment dependent. For instance in the high SEtOH exper-

iment, a higher activity was found for the WP, but for the high SBut and ratio-experiment the

opposite was true. In this section, possible explanations for this phenomenon are described.

There are various reasons why the activity of one method is different than the other. A clear

difference between the two methods is the volume. During growth in BTs, the microorgan-

isms flocculate and sink to the bottom. This could result in diffusion limitations, causing that

around the cells substrate is depleted and the products, which are toxic, are accumulating

and thereby hampering growth (Jeanson et al., 2015). As the volume is smaller for the WP,

this effect would take place to a lesser extent.

Another important difference between the methods was that the headspace of the WP was

not sealed. H2 produced during chain elongation could diffuse to the environment, preventing

its build-up in the headspace. Literature (Smith and Mccarty, 1989; Spirito et al., 2014; An-

genent et al., 2016) showed the need for a minimum PH2 in the headspace for chain elongation

to take place. Below this threshold, products can be degraded by the β-oxidation pathway.

This low PH2 in the headspace of the WP as opposed to the sealed BTs - where H2 could

build up - could potentially explain part of the discrepancy between the two experimental

conditions.

4.3.2 Comparison between actual and expected product formation

The stoichiometry derived by Spirito et al. (2014) was used to calculate the expected product

formation for the measured substrate consumption. When the actual product formation was

plotted on the same graph (described in section 3.4), this was as expected for the BTs but


not always for the WP, indicating that a difference exists between these two methods. Hence,

a hypothesis for these events is provided in this section.

For the high SEtOH experiment for instance, there was always more product (But and Cap)

formed than substrate (Ac) consumed in the WP, whilst this was not a problem for the BTs.

According to the mass balance, additional Ac had to be formed. As there were high amounts

of EtOH present in this experiment, the oxidation of EtOH would therefore be the most logical

choice to explain the addition Ac formation, given by following equation (Spirito et al., 2014):

EtOH + H2O −−→ Ac + H+ + 2H2

The shift to EtOH oxidation could then be explained by the low PH2 in the WP as opposed

to the BTs, where build-up was possible.

To confirm this hypothesis, thermodynamic calculations were done based on Kleerebezem and

Van Loosdrecht (2010). The Gibbs energy change is determined by equation 4.1. Here ∆G01R

is the standard Gibbs energy change of the reaction at pH 7, which equals 2.7 kJ mol−1 at

37 ◦C (Junicke et al., 2016) and ∆G1R is the actual Gibbs energy change for the reaction. If the

Gibbs energy change equals zero, the reaction is in thermodynamic equilibrium (Kleerebezem

and Van Loosdrecht, 2010). Using the initial SAc and SEtOH, the H2 partial pressure where

the reaction is in equilibrium can be calculated giving PH2,eq. If the actual PH2 (PH2,act) is

smaller than the PH2,eq, ∆G1R is smaller than zero, making the reaction thermodynamically

feasible. The larger the difference between the PH2,eq and PH2,act, the more negative ∆G1R

becomes and the more the reaction is inclined to proceed from a thermodynamic point of view.

Therefore, the PH2,eq is a measure of the thermodynamic driving force of the reaction. This

is because a higher calculated PH2,eq results in a larger potential difference between PH2,act

and PH2,eq which in turn makes ∆G1R more negative.

∆G1R = ∆G01

R +RTp2H2

SAc

SEtOH(4.1)

From the But and Cap production (which were calculated as the final concentration of But

and Cap minus the initial concentrations), the expected amount of Ac consumption was cal-

culated. The concentration of “produced Ac”, for instance via EtOH oxidation, could then be

determined as this was the difference between the expected Ac consumption and the actual

consumption. Based on the reaction stoichiometry, the additional EtOH consumed for Ac

production was equal to the produced Ac concentration. By dividing this by the initial EtOH

concentration, the fraction of EtOH used for oxidation could thus be determined.

In Figure 4.2 the maximal PH2,eq is plotted against the fraction of EtOH which according to

the data was consumed for Ac production. The term maximal is added, because PH2,eq will

even be smaller as there is a thermodynamic limitation for ATP production during EtOH

oxidation as well (∆G0 = 32 kJ mol−1) (Seedorf et al., 2008). The calculated values seem

quite realistic. There is no need for abnormally high PH2 for the reaction to become ther-

modynamically limited and the EtOH consumed to make up the gap between expected and


actual Ac consumption is less than 12.6%. Another important observation is that the process

is consistent, as all the values are above zero. Most interesting is the significant positive

correlation between these two values, summarized in Table 4.1. This indicates that when the

thermodynamic driving force is high, more EtOH appears to be converted to Ac.

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●

●●

0.00 0.02 0.04 0.06 0.08 0.10 0.12

0.10

0.15

0.20

0.25

0.30

Fraction of EtOH consumed for additional Ac production [−]

Max

imal

PH

2,eq

[at

m]

Maximal PH2,eq = 0.12 + 1.89 (Fraction of EtOH consumed)

R2 = 0.7923

Figure 4.2: Calculated maximal PH2,eq (which is an indicator for how well the

reaction would proceed from a thermodynamic point of view with the given initial

Ac and EtOH concentrations) in function of the fraction of EtOH consumed for

additional Ac production. Linear regression was performed, given by the dotted

line.

Table 4.1: PH2,eq in function of the expected EtOH consumption for Ac produc-

tion

Estimate Standard deviation error t value p(> |t|)

Intercept 0.119062 0.012045 9.885 3.23× 10−8

Slope 1.88789 0.24166 7.812 7.54× 10−7

However, the question remains whether it is possible to reach thermodynamic limitations in

the BTs as (in contrast to the WP in the anaerobic chamber, where gas composition is kept at

a 90:10 N2:CO2 atmosphere) accumulation of H2 in the headspace is feasible. Equation 4.1 is

Table 4.2: Calculated final SEtOH, SAc and PH2for which ∆G1

R becomes ap-

proximately zero. On the right the EtOH used for this reaction is determined in

percentage of the initial SEtOH and in mL L−1.

Initial SEtOH Initial SAc Final SEtOH Final SAc PH2 ∆G1R EtOH used for oxidation

[M] [M] [M] [M] [atm][kJ mol−1

][%]

[mL L−1

]0.053 0.168 0.050 0.171 0.321 6× 10−8 5.36 0.17

0.052 0.215 0.049 0.217 0.282 −3× 10−7 4.82 0.15

0.062 0.346 0.059 0.348 0.245 1× 10−6 3.52 0.13

0.057 0.464 0.055 0.466 0.204 −1× 10−6 3.16 0.11

0.056 0.526 0.054 0.528 0.190 −9× 10−7 3.00 0.10

0.061 0.700 0.060 0.701 0.173 −9× 10−7 2.49 0.09

0.063 0.839 0.061 0.841 0.160 −8× 10−7 2.26 0.08

0.061 0.932 0.059 0.933 0.150 −8× 10−7 2.18 0.08

0.070 0.917 0.069 0.919 0.162 −8× 10−7 2.04 0.08

0.063 0.259 0.060 0.261 0.284 −1× 10−6 4.01 0.15

0.061 1.218 0.060 1.219 0.131 −7× 10−7 1.91 0.07

0.061 1.438 0.060 1.439 0.121 −7× 10−7 1.75 0.06

0.057 1.374 0.056 1.376 0.119 −7× 10−7 1.85 0.06

0.056 1.583 0.055 1.584 0.110 −7× 10−7 1.74 0.06

0.055 1.527 0.054 1.528 0.112 −7× 10−7 1.78 0.06

0.058 1.764 0.057 1.765 0.106 −7× 10−7 1.63 0.05

0.058 0.268 0.055 0.270 0.268 −1× 10−6 4.11 0.14

0.063 0.294 0.061 0.297 0.268 −1× 10−6 3.75 0.14

61


used, to determine how much EtOH needs to be consumed to reach thermodynamic equilib-

rium. The results are given in Table 4.2. Expressed in percentages, between 1.63 and 5.36%

of the supplied EtOH needs to be oxidized to Ac for the reaction to reach thermodynamic

equilibrium. Translated in mL L−1, less than 0.17 mL L−1 is to be converted after which the

reaction becomes thermodynamically unfeasible. Using one of the controls, it is possible to

calculate EtOH consumption of the culture prior to inoculation, which was approximately

16 mL L−1. Comparing this value to the values obtained in Table 4.2, it is thus safe to say

that the amount necessary to reach thermodynamic equilibrium is realistic.

From the discussion above, it is clear that the reaction stoichiometry derived by Spirito et al.

(2014) did not encompass the thermodynamic limitations of the chain elongation reactions. In

the high SEtOH experiment this resulted in an underestimation of the Cap and But production

based on Ac consumptions for the WP. The issues regarding stoichiometry as determined by

Spirito et al. (2014) have been reviewed in a recent paper by Angenent et al. (2016). Accord-

ing to this paper, the stoichiometry of the EtOH and Ac fermentation is actually more flexible

than proposed by Spirito et al. (2014). Next to that, the EtOH/Ac ratio is very important as

well, as this determines the ratio in which But and Cap will be produced. The combination

of both ratios is also of importance, as this will determine the amount in which H2 is formed

and how (much) ATP is produced. Further, the concentration of EtOH, Ac and H2 influence

the thermodynamics of the reactions and thus its feasibility. Finally, it is possible for chain

elongation to use Cap as a substrate as well, producing caprylate (C8). Consequently, a new

stoichiometry was derived by Angenent et al. (2016) which takes all but the last one of this

considerations into account.

aEtOH+(10−a)(Ac– +H+) −−→ b(But+H+)+ 10−2b3 (Cap+H+)+ 6a+2b−40

3 H2+ 40−3a−2b3 H2O

The stoichiometry is fixed when parameters “a” (moles of EtOH consumed when linked to

Ac consumption) and “b” (moles of But produced when linked to Cap production) are deter-

mined. Parameter a has a value between 0 and 10 and depending on the value of a, parameter

b will be restricted to certain values. If a equals 5, then b equals 5 as well. For a equal to 6,

b has to be between 2 and 5. Lastly if a is larger than 6, b can vary between 0 and 5. During

this reaction, the ATP produced will equal 20−b6 .

This stoichiometry can now used to calculate Ac consumption using But and Cap production.

The deviation from the actual Ac consumption (in percentage) is used to compare the stoi-

chiometry derived by Spirito et al. (2014) to the one derived by Angenent et al. (2016), shown

in Figure 4.3. This shows that using the new stoichiometry, Ac consumption can be calculated

more accurately. Downsides however are that a and b are not fixed but experiment-dependent

and that they cannot be calculated beforehand. This can also result in a circular reasoning,


in which parameter a and b are estimated according to the experiment, which then results

into a better stoichiometric fit. It is thus important that the values for parameters a and

b can be linked to thermodynamic principles, which can be estimated before conducting the

experiment.

old new old new old new old new old new old new old new old new old new

020

4060

8010

0

old new old new old new old new old new old new old new old new

020

4060

8010

0

Ac

consu

mpti

on d

evia

tion

[%

]

Figure 4.3: Deviation in percentage from actual Ac consumption for Ac con-

sumption calculated using stoichiometry derived by Spirito et al. (2014) (old) and

Angenent et al. (2016) (new).

For the high SBut experiment in the WP there was more substrate consumed than necessary

for product formation using stoichiometry derived by Spirito et al. (2014). A possible reaction

is β-oxidation, described by the following equation:

But + 2H2O −−→ 2Ac + H+ + H2

The low PH2 in the WP might have allowed this reaction to take place instead of reverse

β-oxidation, cf. Smith and Mccarty (1989), Spirito et al. (2014) and Angenent et al. (2016).

Calculations were done to evaluate whether this was taking place. An overview is given in

Table 4.3. Cap production was calculated as the final concentration in the WP minus its ini-


tial value. According to the stoichiometry provided by Spirito et al. (2014), it was expected

for this to equal the But consumption. The discrepancy between the actual consumption

and consumption used for Cap production would then equal the But consumed for oxidation.

With this value, the Ac that should be produced because of oxidation could be calculated.

However, this value still deviates a lot from the measured Ac production, thereby suggesting

that But was consumed for processes other than chain elongation and β-oxidation, which were

not measured.

In conclusion, it was thought that H2 played an important role in the thermodynamics of

the pathway. Data suggested that PH2 was kept too low in the WP, resulting in additional

EtOH oxidation. This was opposed to the BT, for which according to calculations only a

small amount of EtOH oxidation already resulted in thermodynamically limitations for the

reaction. The switch to a new stoichiometry derived by Angenent et al. (2016), which is more

flexible, could lower the gap between the actual substrate consumption and the consumption

based on product formation. However, this was not sufficient, as other reactions seemed to

occur as well.

Table 4.3: Overview of calculations used to determine if β-oxidation was happen-

ing

Cap production But consumption But oxidation Ac produced from But Actual Ac produced

[mM] [mM] [mM] [mM] [g L−1] [g L−1]

9.04 84.77 75.73 151.45 9.09 0.00

7.79 130.54 122.75 245.50 14.74 0.00

18.72 144.54 125.82 251.64 15.11 0.00

36.49 175.55 139.07 278.13 16.70 0.00

24.81 212.58 187.77 375.53 22.55 0.00

105.67 147.73 42.06 84.12 5.05 4.54

80.64 136.75 56.12 112.23 6.74 16.71

37.70 198.76 161.06 322.13 19.34 18.22

13.79 268.20 254.41 508.81 30.55 0.00

9.32 284.81 275.49 550.98 33.09 0.00

20.51 360.48 339.97 679.94 40.83 0.00

26.81 347.83 321.01 642.03 38.55 0.00

65


4.3.3 Does substrate consumption lead to growth?

From the model described in section 2.3 it was expected that substrate consumption and

growth would go hand in hand. This seemed to be the case for the high SEtOH and ratio-

experiment. Here, plotting the growth in function of substrate consumption (Figure C.30 and

Figure C.31) revealed a significant positive correlation between both variables, as p-values of

the slope were very small (7.49× 10−10 and 2.65× 10−3 respectively). The values for the slope

were also very comparable. When the growth in function of But consumption is considered,

as shown in Figure 4.4, a very different trend was found. The points on the bottom right

indicated that a lot of substrate was consumed, which however did not result in growth. As

there was also more But consumed than Cap formed (cf. section 3.4.2), this again would

indicate side reactions were happening.

●

● ● ●●

●● ●

●●

●

●●

●

●

●

●

●

0.0 0.1 0.2 0.3 0.4

0.0

0.1

0.2

0.3

0.4

But consumption [mol L−1]

Gro

wth

[in

crea

se i

n O

D62

0 nm

]

Figure 4.4: Growth of the microorganisms, expressed as increase in OD620 nm in

function of But consumption in the WP for the high SBut experiment.

4.3.4 Yield

The yield determined in section 3.5 was compared to literature values, in order to see if the

indicative value for YEtOH,1 of (0.047± 0.008) g cells (g EtOH)−1 was realistic. Converting this

into g CODEtOH (g EtOH)−1 (assuming cell composition of C4.9H9.4O2.9N and Chemical Oxy-

gen Demand (COD) of EtOH equal to its theoretical value (1.05 g CODEtOH (g EtOH)−1))


this resulted into (0.06 ± 0.01) g CODEtOH. Typical yields for anaerobic microorganisms

used in wastewater treatment were found to be 0.04 g CODcells (g CODsubstrate)−1, which is

comparable to the value obtained (Rittmann and McCarty, 2001).

As for the second reaction, in which EtOH is coupled to But producing Cap, no growth yield

could be determined, as the VSS concentration was higher at the beginning than at the end.

However, the reactions are very similar, as is depicted in Figure C.1. Because of this, it is

quite probable that the EtOH yields are comparable.

4.4 Parameter estimation: a critical review

Although simulations confirm that growth curves could be used to do a parameter estimation

of the original model, reality proves to be more complex. A major issue is the problem in

regards to the thermodynamic shift, resulting in a non-fixed stoichiometry. As µ is dependent

on different compounds at the same time, not knowing beforehand in which amount the differ-

ent compounds will react with each other makes the behaviour of µ unpredictable. Although

the acquired graph, shown in Figure 4.5, looks promising, the interpretation of it is not as

straightforward. Hence, no parameter estimation could be done. Similar issues occur for the

high SBut experiment, as it is not known which compounds were produced and how they

influence µ or the reaction stoichiometry. Consequently, there is a need for a more advanced

modeling structure, able in handling these thermodynamic shifts and side reactions as well,

accompanied with more experimental data to model chain elongation and its parameters.

68 4.4 PARAMETER ESTIMATION: A CRITICAL REVIEW

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

0.0 0.5 1.0 1.5

0.0

100.0

150.0

200.

025

0.03

00.

035

0.040

Initial EtOH concentration [M]

µ [

h−

1 ]

Figure 4.5: The determined µ using the Grofit package in function of the initial

SEtOH.

CHAPTER 5Conclusions and perspectives

In this thesis, an attempt was made to model chain elongation, as this was not yet available in

literature. The chain elongation model was based upon the stoichiometry derived by Spirito

et al. (2014) and the bacterial growth models (Panikov, 1991; Harvey et al., 2014; Kim et al.,

2005). Model simulations seemed to acknowledge the growth curve methodology as a valid

tool for parameter estimation of the chain elongation model. However, the obtained data

showed deviating results, thus suggesting important flaws in the moment that need to be

tackled first.

One of these flaws was the deviation between the expected substrate consumption, determined

via stoichiometry and product formation data, and the actual substrate consumption. The

discrepancy was thought to be related to thermodynamic pathway shifts caused by the low

partial vapor pressure of H2. This resulted in additional acetate production through ethanol

oxidation which was not foreseen in the model. It was shown that using the stoichiometry

derived by Angenent et al. (2016), which takes more consideration upon these thermodynamic

principles, could lower the offset between expected and actual acetate consumption. The dis-

tinction however remained during another experiment, even when taking these considerations

into account. For this experiment, it was concluded that other side reactions could occur

as well. Because the growth curve methodology relied on a fixed stoichiometry in which the

influence of all the compounds is known, it was impossible to do a parameter estimation

with the acquired data. Hence, in future research more careful attention must go to the side

reactions and the corresponding thermodynamic considerations. In this respect, the stoichio-

metric model derived by Angenent et al. (2016) could help to minimize the offset between

measured and expected substrate consumption, but momentarily stoichiometry can only be

determined afterwards. Therefore, the influence of the initial conditions on the parameters

used in the stoichiometric model should be investigated in more detail using thermodynamical

principles.

70

Another aspect of this thesis was the reactor data obtained with a mixed community capable

in chain elongation, which proved to be quite unstable at first. Changing to lower Ac concen-

trations, HRT and a more continuous feeding regime however resolved the issues, although

then mostly But instead of Cap was produced. The more stable character seemed to be caused

by the lower fatty acid concentrations in general, though this needs further investigation.

In regards to the DSM52 medium, its pH change acted consistently if the same amount of

protons were added (provided a correction was made for the initial volume), irregardless on

the amount of Ac present. As H+ can also be an indication of chain elongating activity,

further research might be able to link pH change of the medium to the production of longer

chain fatty acids. This way, measuring the pH difference might become a quick and easy

method in determining chain elongating activity.

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76

APPENDIX AADM1

77

Figure A.1: Biochemical rate coefficients (νi,j) and kinetic rate equations (ρj) for

soluble components (i = 1 12, j = 1 19) (Batstone et al., 2002)

78

Figure A.2: Biochemical rate coefficients (νi,j) and kinetic rate equations (ρj) for

soluble components (i = 13 24, j = 1 19) (Batstone et al., 2002)

79

APPENDIX BStock solutions

B.1 Trace element solution SL-10

Table B.1: Composition of trace element solution SL-10


HCl(25%;7.7 M) 10 mL L−1

FeCl2 ·H2O 1.5 mL L−1

ZnCl2 70 mg L−1

MnCl2 · 4 H2O 100 mg L−1

H3BO3 6 mg L−1

CoCl2 · 6 H2O 190 mg L−1

CuCl2 · 2 H2O 2 mg L−1

NiCl2 · 6 H2O 24 mg L−1

Na2MoO4 · 2 H2O 36 mg L−1

First dissolve FeCl2 in the HCl, then dilute in water, add and dissolve the other salts. Finally

make up to 1000 mL.

81

B.2 Selenite-tungstate

Table B.2: Composition of selenite-tungstate


NaOH 0.5 g L−1

Na2SeO3 · 5 H2O 3 mg L−1

Na2WO4 · 2 H2O 4 mg L−1

B.3 Seven vitamin solution

Table B.3: Composition of seven vitamin solution


VitaminB12 100 mg L−1

p-Aminobenzoic acid 80 mg L−1

D(+)-Biotin 20 mg L−1

Nicotinic acid 200 mg L−1

Calcium pantothenate 100 mg L−1

Pyridoxine hydrochloride 300 mg L−1

Thiamine-HCl · 2 H2O 200 mg L−1

82

APPENDIX CAdditional figures

C.1 From literature

Figure C.1: Metabolic pathways of (a) homoacetogenesis; (b) succinate formation;

and (c) reverse β-oxidation, with either EtOH or lactate as reduced compound,

coupling to either Ac or But producing But or Cap respectively (Spirito et al.,

2014).

84

C.2 Layout of WPs

(a) Ratio experiment

(b) Low SAc and SBut experiment

(c) High SAc experiment

85

(d) High SBut experiment

(e) High SCap experiment

(f) High SEtOH experiment

Figure C.2: Layout of the WPs for the different experiments. The concentrations

shown are the calculated concentrations.

86

C.3 Initial experiments growth curves

EtOH 0.075 MAc 0 M

0.0

0.2

0.4

0.6

EtOH 0.15 MAc 0 M

EtOH 0.3 MAc 0 M

EtOH 0.6 MAc 0 M

EtOH 0.075 MAc 0.0375 M

0.0

0.2

0.4

0.6

EtOH 0.15 MAc 0.075 M

EtOH 0.3 MAc 0.15 M

EtOH 0.6 MAc 0.3 M

EtOH 0.075 MAc 0.019 M

0.0

0.2

0.4

0.6

EtOH 0.15 MAc 0.0375 M


EtOH 0.6 MAc 0.15 M

EtOH 0.075 MAc 0.009 M

0.0

0.2

0.4

0.6

EtOH 0.15 MAc 0.019 M

EtOH 0.3 MAc 0.0375 M


EtOH 0 MAc 0.0375 M

0 20 40 60

0.0

0.2

0.4

0.6

EtOH 0 MAc 0.075 M

0 20 40 60

C−free inoc

0 20 40 60

C−free uninoc

0 20 40 60

Time [h]

OD

600

nm

[−

]

Figure C.3: The optical density (OD600nm) measured over time for the mixed

community with Tecan Infinite M200 Pro outside the anaerobic chamber for dif-

ferent EtOH and Ac concentrations. Each combination of concentrations was

done in triplicates producing three growth curves, plotted using red, blue and

green lines.

87

EtOH 0.075 M

Ac 0 M

0.0

0.4

EtOH 0.15 M

Ac 0 M

EtOH 0.3 M

Ac 0 M

EtOH 0.6 M

Ac 0 M

EtOH 0.075 M

Ac 0.0375 M

0.0

0.4

EtOH 0.15 M

Ac 0.075 M

EtOH 0.3 M

Ac 0.15 M

EtOH 0.6 M

Ac 0.3 M

EtOH 0.075 M

Ac 0.019 M

0.0

0.4

EtOH 0.15 M

Ac 0.0375 M

EtOH 0.3 M

Ac 0.075 M

EtOH 0.6 M

Ac 0.15 M

EtOH 0.075 M

Ac 0.009 M

0.0

0.4

EtOH 0.15 M

Ac 0.019 M

EtOH 0.3 M

Ac 0.0375 M

EtOH 0.6 M

Ac 0.075 M

EtOH 0 M

Ac 0.0375 M

0 20 40 60

0.0

0.4

EtOH 0 M

Ac 0.075 M

0 20 40 60

C−free inoc

0 20 40 60

C−free uninoc

0 20 40 60

Time [h]

OD

600nm

[−

]

Figure C.4: The optical density (OD600nm) measured over time for C. kluyveri

with Tecan Infinite M200 Pro outside the anaerobic chamber for different EtOH

and Ac concentrations.

88

C.4 Control experiment growth curves

Inoculated AcAnaerobic chamber

0.1

0.2

0.3

0.4

0.5

Anaerobic chamberInoculated But

Anaerobic chamberUninoculated Ac

Anaerobic chamberUninoculated But

IncubatorInoculated Ac

0.1

0.2

0.3

0.4

0.5

0 10 30 50

IncubatorInoculated But

0 10 30 50

IncubatorUninoculated Ac

0 10 30 50

IncubatorUninoculated But

0 10 30 50

Time [h]

OD

620n

m [−

]

Figure C.5: The optical density (OD620 nm) measured for different methods in

time for a mixed culture, sampled from the reactor. The colors represent growth

curves which were produced from the same penicillin bottle.

89

Incubator An chamber Bottle Incubator An chamber Bottle0.00

0.02

0.04

0.06

0.08

0.10

0.12

Inoculated Ac Inoculated But

Ac But Cap

FA

con

centr

atio

n [m

ol L

−1 ]

Figure C.6: FA concentrations for the different methods at the end of the exper-

iment for a mixed culture, sampled from the reactor.

90


0.02

0.04

0.06

0.08

0.10

0.12

0.14

EtOH 0.17 M EtOH 0.21 M EtOH 0.26 M EtOH 0.35 M EtOH 0.46 M EtOH 0.53 M EtOH 0.70 M EtOH 0.84 M EtOH 0.92 M EtOH 0.93 M


0.02

0.04

0.06

0.08

0.10

0.12

0.14

EtOH 1.22 M EtOH 1.37 M EtOH 1.44 M EtOH 1.53 M EtOH 1.58 M EtOH 1.76 M No EtOH inoc No EtOH uninoc DSM52 inoc DSM52 uninoc

Ac But Cap

Con

centr

atio

n o

f FA

[m

ol L

−1 ]

Figure C.7: Comparison of final FA concentrations of WP and BT for the high

SEtOH experiment. The title contains the measured initial EtOH concentrations.

91

WP BT WP BT WP BT WP BT WP BT WP BT WP BT WP BT WP BT WP BT

0.0

0.1

0.2

0.3

0.4

0.5

0.6

But 0.09 M But 0.14 M But 0.15 M But 0.18 M But 0.23 M But 0.23 M But 0.27 M But 0.29 M But 0.28 M But 0.29 M

WP BT WP BT WP BT WP BT WP BT WP BT WP BT WP BT WP BT WP BT

0.0

0.1

0.2

0.3

0.4

0.5

0.6

But 0.37 M But 0.36 M Only But 0.026 M Only But 0.048 M Only But 0.091 M Only But 0.16 M Only EtOH inoc Only EtOH uninoc C−free inoc C−free uninoc

Ac But Cap

Con

centr

atio

n o

f FA

[m

ol L

−1 ]

Figure C.8: Comparison of final FA concentrations of WP and BT for the high

SBut experiment. The title contains the measured initial But concentrations.

92

WP BT WP BT WP BT WP BT WP BT WP BT WP BT WP BT0.00

0.05

0.10

0.15

0.20

0.25

EtOH 0.07 M EtOH 0.12 M EtOH 0.23 M EtOH 0.17 M EtOH 0.09 M EtOH 0.07 M EtOH 0.24 M EtOH 0.35 MAc 0 M Ac 0 M Ac 0 M Ac 0 M Ac 0.03 M Ac 0.02 M Ac 0.07 M Ac 0.1 M

WP BT WP BT WP BT WP BT WP BT WP BT WP BT WP BT0.00

0.05

0.10

0.15

0.20

0.25

EtOH 0.06 M EtOH 0.12 M EtOH 0.23 M EtOH 0.52 M EtOH 0.09 M EtOH 0.11 M EtOH 0.25 M EtOH 0.38 MAc 0.015 M Ac 0.029 M Ac 0.045 M Ac 0.093 M Ac 0.01 M Ac 0.016 M Ac 0.03 M Ac 0.037 M

Ac But Cap

Con

centr

atio

n o

f FA

[m

ol L

−1 ]

Figure C.9: Comparison of final FA concentrations of WP and BT for the ratio-

experiment. The title contains the measured initial EtOH and Ac concentrations.

93

C.5 Model simulations

0.0

0.5

1.0Biomass

200

400

600

800EtOH

−50

0

50

100Ac

−0.5

0.0

0.5But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

100

200Cap

SEtOH,t=0 = 0.61 M SEtOH,t=0 = 0.62 M SEtOH,t=0 = 0.63 M SEtOH,t=0 = 0.64 M SEtOH,t=0 = 0.65 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.10: Model simulations of chain elongation for different (high) initial

SEtOH.

94

0.0

0.5

1.0

SEtOH,t=0 = 0.04 M SEtOH,t=0 = 0.07 M SEtOH,t=0 = 0.1 M SEtOH,t=0 = 0.13 M

0.0

0.5

1.0


0 10 20 30 40 50 60 70 80

Time [h]

0.0

0.5

1.0


Bio

mas

sco

ncen

trat

ion

[gL−

1]


SEtOH. The initial SEtOH are chosen to be either smaller, around or higher than

Ki,EtOH.

95

0.5

Biomass

0

200

400EtOH

−500

0

500

1000Ac

−1

0

1

2But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

SAc,t=0 = 0.01 M SAc,t=0 = 0.05 M SAc,t=0 = 0.1 M SAc,t=0 = 0.4 M SAc,t=0 = 0.8 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]


SAc.

96

0.2

0.4

0.6Biomass

100

200

300

400EtOH

0.0

0.2

Ac

−500

0

500

1000But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

SBut,t=0 = 0.01 M SBut,t=0 = 0.05 M SBut,t=0 = 0.1 M SBut,t=0 = 0.4 M SBut,t=0 = 0.8 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]


SBut.

97

0.5

Biomass

0

200

400EtOH

0

50

100Ac

0.0

0.5

But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

500

1000Cap

SCap,t=0 = 0.01 M SCap,t=0 = 0.05 M SCap,t=0 = 0.1 M SCap,t=0 = 0.4 M SCap,t=0 = 0.8 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]


SCap.

98

0

1

2Biomass

0

200

400EtOH

0

50

100Ac

0.0

0.5

But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

X = 0.1 M X = 0.2 M X = 0.4 M X = 0.8 M X = 1.5 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.15: Model simulations of chain elongation for different initial biomass

concentrations.

99

0.5

Biomass

0

200

400EtOH

0

50

100Ac

0.0

0.5

But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

Ki,EtOH = 0.1 M Ki,EtOH = 0.3 M Ki,EtOH = 0.8 M Ki,EtOH = 1.5 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.16: Model simulations of chain elongation for different Ki,EtOH.

100

0.5

Biomass

0

200

400EtOH

0

50

100Ac

0.0

0.5

But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

Ki,Ac = 0.25 M Ki,Ac = 0.5 M Ki,Ac = 1.08 M Ki,Ac = 2 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.17: Model simulations of chain elongation for different Ki,Ac.

101

0.5

Biomass

0

200

400EtOH

0

50

100Ac

0.0

0.5

But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

Ki,But = 0.025 M Ki,But = 0.05 M Ki,But = 0.172 M Ki,But = 0.5 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.18: Model simulations of chain elongation for different Ki,But.

102

0.5

Biomass

0

200

400EtOH

0

50

100Ac

0.0

0.5

But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

Ki,Cap = 0.025 M Ki,Cap = 0.05 M Ki,Cap = 0.10753 M Ki,Cap = 0.5 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.19: Model simulations of chain elongation for different Ki,Cap.

103

0.5

Biomass

0

200

400EtOH

0

50

100Ac

0.0

0.5

But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

KEtOH = 6e-05 M KEtOH = 0.0006 M KEtOH = 0.006 M KEtOH = 0.06 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.20: Model simulations of chain elongation for different KEtOH.

104

0.5

Biomass

0

200

400EtOH

0

50

100Ac

0.0

0.5

1.0

1.5But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

KAc = 6e-05 M KAc = 0.0006 M KAc = 0.006 M KAc = 0.06 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.21: Model simulations of chain elongation for different KAc.

105

0.5

Biomass

0

200

400EtOH

0

50

100Ac

0

20

40But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

KBut = 5e-06 M KBut = 5e-05 M KBut = 0.0005 M KBut = 0.005 M

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.22: Model simulations of chain elongation for different KBut.

106

0.5

Biomass

0

200

400EtOH

0

50

100Ac

0.0

0.5

But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

µmax,1&2 = 0.02 h−1 µmax,1&2 = 0.04 h−1 µmax,1&2 = 0.06 h−1 µmax,1&2 = 0.08 h−1

Con

cent

rati

on[m

Mor

gL−

1]

Figure C.23: Model simulations of chain elongation for different µmax,1 and µmax,2

simultaneously.

107

0.5

Biomass

0

200

400EtOH

0

50

100Ac

0

50

100But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

µmax,1 = 0.02 h−1 µmax,1 = 0.04 h−1 µmax,1 = 0.06 h−1 µmax,1 = 0.08 h−1

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.24: Model simulations of chain elongation for different KBut.

108

0.5

Biomass

0

200

400EtOH

0

50

100Ac

0

50

100But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

µmax,2 = 0.02 h−1 µmax,2 = 0.04 h−1 µmax,2 = 0.06 h−1 µmax,2 = 0.08 h−1

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.25: Model simulations of chain elongation for different µmax,2.

109

0.0

0.5

1.0

1.5Biomass

0

200

400EtOH

0

50

100Ac

0.0

0.5

But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

YEtOH,1&2 = 0.02 g cells (g EtOH)−1 YEtOH,1&2 = 0.04 g cells (g EtOH)−1 YEtOH,1&2 = 0.07 g cells (g EtOH)−1 YEtOH,1&2 = 0.09 g cells (g EtOH)−1

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.26: Model simulations of chain elongation for different YEtOH,1 and

YEtOH,2 simultaneously.

110

0.0

0.5

1.0

1.5Biomass

0

200

400EtOH

0

50

100Ac

0

20

40

60But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

YEtOH,1 = 0.02 g cells (g EtOH)−1 YEtOH,1 = 0.04 g cells (g EtOH)−1 YEtOH,1 = 0.07 g cells (g EtOH)−1 YEtOH,1 = 0.09 g cells (g EtOH)−1

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.27: Model simulations of chain elongation for different YEtOH,1.

111

0.0

0.5

1.0

1.5Biomass

0

200

400EtOH

0

50

100Ac

0

50

100But

0 10 20 30 40 50 60 70 80 90 100

Time [h]

0

50

100

150Cap

YEtOH,2 = 0.02 g cells (g EtOH)−1 YEtOH,2 = 0.04 g cells (g EtOH)−1 YEtOH,2 = 0.07 g cells (g EtOH)−1 YEtOH,2 = 0.09 g cells (g EtOH)−1

Con

cent

rati

on:E

tOH

and

FAs

[mM

],X

[gL−

1]

Figure C.28: Model simulations of chain elongation for different YEtOH,2.

112

C.6 Substrate consumption versus product formation

●●●

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−0.01 0.02 0.05 0.08

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−0.01 0.02 0.05 0.08Ac consumption [mol L−1]

But

and C

ap p

roduct

ion [

mol

L−

1 ]

Only But production Only Cap production

Figure C.29: Product formation in function of substrate consumption for the

ratio-experiment. The dotted lines indicate the expected formation if either only

But is formed or only Cap formed.

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C.7 Substrate consumption versus growth

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0.00 0.02 0.04 0.06

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0.7

Ac consumption [mol L−1]

Gro

wth

[in

crea

se i

n O

D62

0 nm

]

Growth = (8.3 ± 0.7) (Ac consumption)

R2 = 0.898

Figure C.30: Growth of the microorganisms, expressed as increase in OD620 nm

in function of Ac consumption in the WP for the high SEtOH experiment. Linear

regression was performed, given by the dotted line.

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0.00 0.02 0.04 0.06

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Ac consumption [mol L−1]

Gro

wth

[in

crea

se i

n O

D62

0 nm

]

Growth = (9 ± 2) (Ac consumption)R2 = 0.487

Figure C.31: Growth of the microorganisms, expressed as increase in OD620 nm in

function of Ac consumption in the WP for the ratio-experiment. Linear regression

was performed, given by the dotted line.

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C.8 Parameter estimation

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0.0 0.1 0.2 0.3

0.000

0.0

05

0.0

100.0

150.

020

Initial But concentration [M]

µ [

h−

1 ]

Figure C.32: The determined µ using the Grofit package in function of the initial

SBut.

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