Embed Size (px)
Citation preview
ALGINATE BASED ENCAPSULATION
OF MICROBIAL GRANULES AS A
PROTECTIVE MEANS TO REDUCE
STRESS DURING ANAEROBIC
DIGESTION
Karlien Springael Student number: 01405489
Promotor(s): Prof. dr. ir. Nico Boon, Dr. ir. Jo De Vrieze
Tutor: Eng. Cindy Ka Y Law
Master’s Dissertation submitted to Ghent University in partial fulfilment of the requirements for the
degree of Master of Science in Bioscience Engineering: Cell and Gene Biotechnology
Academiejaar: 2018 - 2019
De auteur en de promotoren geven de toelating deze scriptie voor consultatie beschikbaar te stellen en delen van de scriptie te kopiëren voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkingen van het auteursrecht, in het bijzonder met betrekking tot de verplichting de bron uitdrukkelijk te vermelden bij het aanhalen van resultaten uit deze scriptie.” “The author and the promotors give the permission to use this thesis for consultation and to copy parts of it for personal use. Every other use is subject to copyright laws, more specifically the source must be extensively specified when using the results from this thesis.”
Ghent, 7th June, 2018
The promotors, The tutor, The author,
Prof. dr. ir. Nico Boon Eng. Cindy Ka Y Law Karlien Springael
Dr. ir. Jo De Vrieze
ACKNOWLEDGEMENT - DANKWOORD
Met pijn in het hart neem ik na 5 jaar afscheid van het prachtige Boerekot, waar ik onvergetelijke
momenten heb beleefd en vrienden voor het leven heb gemaakt. Het laatste jaar, hét thesisjaar, was
er eentje met veel ups, maar ook wel met een paar stevige downs. Overgestroomde reactoren, kapotte
gastellers, gaslekken, … ze zijn allemaal de revue gepasseerd. Toch was het een jaar waarin ik enorm
veel heb bijgeleerd en dat ik bijgevolg met veel trots kan afsluiten.
Eerst en vooral wil ik Jo De Vrieze bedanken voor al de tijd die hij, ondanks zijn drukke agenda, voor
mij heeft vrijgemaakt. Zijn vindingrijkheid, positieve ingesteldheid en kritische kijk op de zaken hebben
er mee voor gezorgd dat ik deze thesis uiteindelijk mooi kan afronden. Ook Cindy wil ik in het bijzonder
bedanken omdat ik altijd op haar kon rekenen en omdat ze altijd met veel enthousiasme voor me klaar
stond. Ook wil ik mijn promotor Prof. dr. Ir. Nico Boon bedanken die mij de kans heeft gegeven om
mijn thesisonderzoek hier bij CMET uit te voeren.
Bedankt ook aan het hele CMET-team voor het creëren van een amicale werksfeer in een leuke
omgeving. Als ik vragen had, stond er altijd wel iemand klaar in het labo om me met de glimlach verder
te helpen. Wie ik in dit dankwoord ook zeker niet mag vergeten, zijn de mensen die in de K32 stonden
en er steeds weer voor zorgden dat ik elke dag met plezier naar het labo kwam.
Tot slot wil ik mijn mama en papa danken omdat ze er steeds weer stonden om me door de moeilijkere
periodes te helpen. Als ik in de vijf jaar al eens een dipje had, zorgden zij er telkens voor dat ik de moed
er inhield en doorbeet. Ook mijn beste vriendinnen en mijn vriend moet ik danken voor hun
vriendschap en geduld. En om af te sluiten wil ook nog Dominique danken voor haar zeer
gewaardeerde Engelse taaltips en -adviezen.
En zoals Madeleine Ferron zo mooi zei:
‘Pour réussir il ne suffit pas de continuer, il faut toujours se dépasser’
CONTENT LITERATURE STUDY ........................................................................................................................... 1
1 Anaerobic digestion ......................................................................................................................... 1
1.1 Introduction ............................................................................................................................. 1
1.2 Anaerobic digestion process ................................................................................................... 1
1.3 Microbial population ............................................................................................................... 2
1.3.1 Hydrolytic-acidogenic bacteria ........................................................................................ 2
1.3.2 Acetogenic bacteria ......................................................................................................... 3
1.3.3 Methanogenic archaea .................................................................................................... 3
1.4 Different types of waste streams ............................................................................................ 4
1.4.1 Industrial waste streams ................................................................................................. 4
1.4.2 Manure ............................................................................................................................ 5
1.4.3 Energy crops and agricultural waste ............................................................................... 5
1.4.4 Municipal waste .............................................................................................................. 5
1.5 Control of anaerobic digestion ................................................................................................ 5
1.5.1 Effect of pH ...................................................................................................................... 5
1.5.2 Effect of temperature ...................................................................................................... 6
1.5.3 Effect of organic loading rate .......................................................................................... 6
1.5.4 Essential growth factors .................................................................................................. 6
1.6 Inhibitors of the anaerobic digestion process ......................................................................... 7
1.6.1 Sulfate and sulfide ........................................................................................................... 7
1.6.2 Long chain fatty acids ...................................................................................................... 8
1.6.3 Ammonium and ammonia ............................................................................................... 8
1.6.4 Salt ................................................................................................................................... 9
1.6.5 Trace elements ................................................................................................................ 9
2 Anaerobic granulation technology ................................................................................................ 10
2.1 Introduction ........................................................................................................................... 10
2.2 Anaerobic granulation reactor technologies ......................................................................... 10
2.2.1 Continuous stirred tank reactor (CSTR) ......................................................................... 10
2.2.2 Upflow anaerobic sludge blanket reactor (UASB) ......................................................... 11
2.2.3 Expanded granular sludge bed reactor (EGSB) .............................................................. 12
2.2.4 Internal circulation reactor (IC) ..................................................................................... 12
2.3 Anaerobic granulation theories ............................................................................................. 13
2.3.1 Structural models .......................................................................................................... 13
2.3.2 Thermodynamic models ................................................................................................ 16
2.3.3 Proton translocation dehydration theory ..................................................................... 17
2.4 Parameters influencing anaerobic granulation ..................................................................... 18
2.4.1 Reactor temperature ..................................................................................................... 18
2.4.2 Reactor pH ..................................................................................................................... 18
2.4.3 Characteristics of seed sludge ....................................................................................... 18
2.4.4 Upflow velocity and hydraulic retention time............................................................... 19
2.4.5 Organic loading rate ...................................................................................................... 19
2.4.6 Wastewater composition/characteristics of substrate ................................................. 19
2.4.7 Addition of natural and synthetic polymers .................................................................. 20
2.4.8 Addition of cations ........................................................................................................ 20
MATERIAL AND METHODS .......................................................................................................... 21
1 Experimental approach ................................................................................................................. 21
2 Experimental set-up and operation .............................................................................................. 22
2.1 Reactor set-up ....................................................................................................................... 22
2.2 Feedstock............................................................................................................................... 23
2.2.1 Start-up .......................................................................................................................... 23
2.2.2 Experiment 1 ................................................................................................................. 23
2.2.3 Experiment 2 ................................................................................................................. 23
2.2.4 Experiment 3 ................................................................................................................. 23
2.3 Sludge inoculum .................................................................................................................... 24
2.3.1 Start-up .......................................................................................................................... 25
2.3.2 Experiment 1 ................................................................................................................. 25
2.3.3 Experiment 2 ................................................................................................................. 25
2.3.4 Experiment 3 ................................................................................................................. 25
2.4 Volumetric methane production and methane yield ............................................................ 26
3 Analytical techniques .................................................................................................................... 26
3.1 Total Kjeldahl nitrogen .......................................................................................................... 26
3.2 total ammonia nitrogen ........................................................................................................ 27
3.3 Total suspended solids and volatile suspended solids .......................................................... 27
3.4 Total solids and volatile solids ............................................................................................... 28
3.5 pH .......................................................................................................................................... 28
3.6 Chemical oxygen demand ..................................................................................................... 29
3.7 Biogas composition ............................................................................................................... 29
3.8 Volatile fatty acids ................................................................................................................. 29
3.9 Cations ................................................................................................................................... 30
3.10 Anions .................................................................................................................................... 30
4 Biochemical methane potential (BMP) test .................................................................................. 30
5 Batch tests ..................................................................................................................................... 31
5.1 Shear stress batch test .......................................................................................................... 31
5.2 Potassium and phosphate batch test .................................................................................... 32
RESULTS ............................................................................................................................................. 33
1 Characterization of inoculum ........................................................................................................ 33
2 Characterization of molasse .......................................................................................................... 33
3 Start-up .......................................................................................................................................... 34
4 Reactor experiments ..................................................................................................................... 36
4.1 Disintegration of the alginate matrix .................................................................................... 36
4.1 pH .......................................................................................................................................... 38
4.2 Volatile fatty acids ................................................................................................................. 39
4.3 Volumetric methane production ........................................................................................... 40
4.4 Methane yield ....................................................................................................................... 42
4.5 Cations ................................................................................................................................... 43
5 Biochemical methane potential (BMP) test .................................................................................. 44
6 Batch test ....................................................................................................................................... 45
6.1 Shear stress batch test .......................................................................................................... 45
6.2 Potassium and phosphate batch test .................................................................................... 45
DISCUSSION ...................................................................................................................................... 47
1 Disintegration of the alginate matrix ............................................................................................ 47
1.1 Disintegration due to degradation by the microbial biomass ............................................... 48
1.2 High concentrations of Na+ cause swelling and consequently disintegration ...................... 48
1.3 Shear stress accelerates the disintegration as a result of microbial degradation ................ 49
2 Characteristics of the encapsulated sludge ................................................................................... 50
2.1 pH of the encapsulated sludge .............................................................................................. 50
2.2 Methane production of the encapsulated sludge ................................................................. 51
2.3 Elevated Ca2+ levels in the reactor containing the encapsulated sludge ............................... 52
CONCLUSION AND FUTURE PERSPECTIVES ............................................................................. 55
BIBLIOGRAPHY ................................................................................................................................. 57
APPENDIX 1: ANION AND CATION COMPOSITION OF SYNTHETIC MEDIUM 1 AND 2 .................... 67
APPENDIX 2: CALCULATIONS OF COD OF ALGINATE ....................................................................... 69
APPENDIX 3: CATION CONCENTRATIONS OF THE THREE EXPERIMENTS ......................................... 71
APPENDIX 4: REPLICATES SHEAR STRESS BATCH TEST ...................................................................... 73
APPENDIX 5: REPLICATES POTASSIUM AND PHOSPHATE BATCH TEST ............................................ 79
APPENDIX 6: COMPARISON BETWEEN SHEAR STRESS BATCH TEST AND POTASSIUM PHOSPHATE
BATCH TEST ........................................................................................................................................... 83
TABLE OF ABBREVIATIONS
AD Anaerobic digestion
BMP Biomethane potential
CMC Carboxymethylcellulose
COD Carbon oxygen demand
CSTR Continuous stirred tank reactor
DLVO Derjaguin-Landau-Verwey-Overbeek
DS Degree of substitution
ECP Extracellular polymers
EGSB Expanded granular sludge bed
FA Free ammonia
FID Flame ionization detector
G Guluronate
HRT Hydraulic retention time
IC Internal circulation
IC Ion chromatograph
LCFA Long chain fatty acids
M Mannuronate
MSW Municipal solid waste
OHPA Obligatory H2-producing acetogenic bacteria
OLR Organic loading rate
Rpm Rounds per minute
SAB Syntrophic acetogenic bacteria
SAOB Syntrophic acetate oxidizing bacteria
SEM-EDS Scanning electron microscopy - energy dispersive microscopy
SMA Specific methanogenic activity
SRB Sulphate reducing bacteria
SRT Solid retention time
STP Standard temperature and pressure
TAN Total ammonia nitrogen
TKN Total Kjeldahl nitrogen
TS Total solids
TSS Total suspended solids
UASB Upflow anaerobic sludge blanket
VFA Volatile fatty acids
VS Volatile solids
VSS Volatile suspended solids
WWTP Wastewater treatment plant
ABSTRACT Emerging bio-refineries produce organic compounds from renewable raw materials, such as sugar beet
and energy crops, associated with the production of enormous quantities of toxic wastewaters.
However, the treatment of these wastewater via anaerobic digestion (AD) is complex. Anaerobic
digestion, especially methanogenesis, is very sensitive to different types of stress, such as salt stress,
fluctuations in pH, temperature and organic loading rate, and too high concentrations of, for example,
ammonium, sulfate and trace elements. When granular AD systems experience stress, the microbial
granules start to disintegrate, associated with the wash-out of these granules. Therefore, a new
approach of ‘granule engineering’ is applied in this thesis, in which the microbial granules are
encapsulated in an alginate matrix. If the microbial granules start to disintegrate, due to stress, the
matrix keeps the biomass close together, ensuring good settling properties and, consequently,
preventing wash-out of the granules. In this way, a robust and stress-tolerant AD process is created. In
this study, the stability of the alginate matrix to encapsulate granular sludge for the resistance towards
stress was investigated.
Two UASB reactors were run under steady state while different experiments were conducted. One
reactor contained alginate encapsulated granular sludge, while the other reactor, containing natural
granular sludge, served as a control. In the first experiment, molasse was used as influent, which
contained high concentrations of PO43-. After 10 days, the entire alginate matrix was broken down. In
the second experiment, a synthetic medium with low concentrations of PO43- and without carbon
source was used as influent. In the reactor that contained the encapsulated granular sludge, biogas
was produced, which indicated that the microbial biomass was able to degrade the alginate matrix. In
the last experiment, in which the same synthetic medium with carbon-source was used, four different
methods of encapsulation were tested, to slow down or to prevent the disintegration of the matrix.
The different methods consisted of the encapsulation with 1.3% and 1.8% alginate, the encapsulation
with a 1.3% alginate matrix mixed with glucose and the encapsulation with 0.5% alginate mixed with
carboxymethylcellulose. However, none of the methods slowed down or prevented the disintegration
of the alginate matrix.
From the aforementioned experiments, the overall performance of the encapsulated granular sludge
was also tested in terms of pH, VFA concentrations and biogas production. At the start of each
experiment, a pH drop was observed in the reactor containing the encapsulated granular sludge,
accompanied by an increase in VFA concentration. This may be explained by the longer lag-phase of
the methanogenic archaea compared to the hydrolytic, acidogenic and acetogenic bacteria. The lag-
phase also affected the methane production of the reactor containing the encapsulated sludge, which,
therefore, lags behind the control reactor. After the biomass was adapted, similar amounts of methane
production were observed among both reactors.
At the same time, the stability of the alginate matrix was tested in terms of shear stress and high PO43-
and K+ concentrations. From these batch experiments, it can be concluded that shear stress accelerates
the disintegration of the alginate matrix as a result of microbial degradation, but does not have an
effect on the stability of the alginate matrix alone. In addition, PO43- anions and K+ cations also had no
effect, but Na+, originating from Na2HPO4, did have a great influence on the disintegration of the
alginate matrix. High concentrations of Na+ (> 100 mg/L) first cause swelling of the matrix and
consequently disintegration.
In summary, encapsulation of anaerobic granular sludge by an alginate matrix as a protective means
to reduce stress, didn’t prevent the production of biogas. However, during the AD process, the matrix
wasn’t stable enough. Therefore, future research must establish how the stability of the alginate matrix
can be increased or must consider other encapsulation matrices.
SAMENVATTING In de nieuwste bio-raffinaderijen worden organische verbindingen aangemaakt uit hernieuwbare
grondstoffen, zoals suikerbieten en energiegewassen, waarbij enorme hoeveelheden afvalwaters
worden geproduceerd. De behandeling van dit afvalwater via anaerobe vergisting (AD - anaerobic
digestion) is echter complex. Anaerobe vergisting, met name methanogenese, is bijzonder gevoelig
voor een hele rist stressoren, waaronder zoutstress, schommelingen qua pH, temperatuur en
organische belasting, evenals te hoge concentraties van bijvoorbeeld ammonium, sulfaat en
sporenelementen. Wanneer de AD van granulair slib stress ondervindt, kunnen de microbiële granules
desintegreren, met als gevolg de uitspoeling van die granules. Daarom wordt een nieuwe benadering
van 'granule engineering' toegepast, waarbij de microbiële granules worden ingekapseld in een
alginaatmatrix. Wanneer als gevolg van stress de microbiële granules gaan desintegreren, weet de
matrix de biomassa bij elkaar te houden. Dat staat dan weer garant voor goede
bezinkingseigenschappen dat de uitspoeling van de granules voorkomt. Op deze manier wordt een
robuust en tolerant AD-proces gecreëerd. In deze studie werd de stabiliteit van de alginaatmatrix voor
het inkapselen van granulair slib in het kader van de stressbestendigheid onderzocht.
Er werden verschillende experimenten uitgevoerd in twee steady-state UASB-reactoren. In de ene
reactor zat ingekapseld granulair slib terwijl de andere, de controlereactor, natuurlijk granulair slib
bevatte. In het eerste experiment werd als influent molasse met hoge concentraties PO43- gebruikt. Na
10 dagen was de hele alginaatmatrix afgebroken. In het tweede experiment werd als influent een
synthetisch medium met lage PO43--concentraties zonder koolstofbron gebruikt. In de reactor met het
ingekapselde granulair slib werd biogas geproduceerd; wat aantoonde dat de microbiële biomassa in
staat was om de alginaatmatrix af te breken en dus als koolstofbron te gebruiken. In het laatste
experiment, waarbij hetzelfde synthetische medium met koolstofbron werd gebruikt, werden vier
verschillende inkapselingsmethoden getest om de desintegratie van de matrix te vertragen of te
verhinderen. De verschillende methoden bestonden uit de inkapseling met 1,3% en 1,8% alginaat, de
inkapseling met een 1,3% alginaatmatrix gemengd met glucose en de inkapseling met 0,5% alginaat
gemengd met carboxymethylcellulose. Geen van deze methoden vertraagde of verhinderde echter de
desintegratie van de alginaatmatrix.
Tijdens bovengenoemde experimenten werden tevens de algemene prestaties van het ingekapselde
granulair slib getest wat betreft pH, VVZ-concentraties en biogasproductie. Bij aanvang van elk
experiment werd in de reactor met het ingekapselde granulair slib een daling van de pH-waarde
waargenomen, die gepaard ging met een toename van de VVZ-concentratie. Dit kan worden verklaard
door de langere lag-fase van de methanogene archaea in vergelijking met de hydrolytische, acidogene
en acetogene bacteriën. De lag-fase had ook een impact op de methaanproductie van de reactor met
het ingekapselde slib, dat bijgevolg achter liep op de controlereactor. Nadat de biomassa zich had
aangepast, kon worden vastgesteld dat in beide reactoren vergelijkbare hoeveelheden methaan
werden geproduceerd.
Tegelijkertijd werd de stabiliteit van de alginaatmatrix getest op schuifspanning en hoge PO43- en K+
concentraties. Uit deze reeks experimenten kan worden geconcludeerd dat schuifspanning de
desintegratie van de alginaatmatrix versnelt als gevolg van microbiële afbraak, maar geen impact heeft
op de stabiliteit van de alginaatmatrix op zich. Daarnaast hadden PO43- anionen en K+ kationen ook
geen invloed, maar Na+, afkomstig van Na2HPO4, had wel een grote impact op de desintegratie van de
alginaatmatrix. Hoge Na+ concentraties (> 100 mg/L) leiden eerst tot het zwellen van de matrix en
vervolgens tot desintegratie.
Samengevat, kan worden gesteld dat wanneer granulair slib als bescherming tegen stress in een
alginaatmatrix wordt ingekapseld, de biogasproductie niet wordt gehinderd. Wel werd aangetoond
dat de alginaatmatrix tijdens het AD-proces niet voldoende stabiel was. Bijgevolg moet verder
onderzoek aantonen hoe de stabiliteit van de alginaatmatrix naar een hoger niveau kan worden getild
of moeten andere inkapselingsmatrices worden overwogen.
INTRODUCTION Concern for climate and the environment has become an integral part of our current society as the
numerous marches and youth strikes in Europe and around the globe have shown. The most recent
Intergovernmental Panel on Climate Change (IPCC) report ‘Global Warming of 1.5°C’ highlights the
urgency to reduce greenhouse gas emissions if the increase in global temperature is to be limited to
1.5°C. To meet this demand, it is imperative to reduce our dependency on fossil fuels and to cut down
on the amount of greenhouse gas emission society generates. Replacing fossil fuel energy with
renewable energy or bioenergy has become more urgent than ever. Many processes can provide
bioenergy while simultaneously ensuring that pollution control objectives are attained. One important
process in that particular context is anaerobic digestion (AD). Anaerobic digestion is an efficient and
environmentally sustainable technology that has three main advantages. Firstly, AD uses sludge
produced during the treatment of municipal wastewater, thereby reducing the amount of sludge that
needs to be disposed of. Secondly, AD is a sustainable way to bioprocess industrial wastewaters
generated by, for example, the food-processing industry and breweries, and the agricultural
wastewaters from intensive confinement farming and convert them into a valuable product. Finally,
AD provides bioenergy in the form of biogas, which is a mixture of mainly CH4 and CO2, without
releasing any gases into the atmosphere, thereby reducing overall emissions. Although methane is a
low-value product, biogas is catalytically converted to syngas (H2, CO), which can be used to produce
liquid fuel through conventional chemical manufacturing processes. In addition, methane can be
converted to other useful products such as methanol for use in the production of biodiesel. However,
AD also comes with a number of disadvantages attached. The significant capital investment that is
required and the considerable operational costs mean that it is unlikely to be viable as a single source
of renewable energy and should be regarded as part of an integrated system. Furthermore, anaerobic
digestion plants generate traffic. To minimize the impact on the environment caused by that traffic
and the nuisance for the neighborhood, the location of these plants should be chosen carefully. Finally,
AD requires pre-treatment of the feedstock and post-treatment of the effluent and biogas. Despite
these disadvantages, AD still has an important role to play in the fight against climate change and in
our efforts to create a better world for all (Angenent et al., 2004; Appels et al., 2008a; Dinsdale et al.,
2007; Jossen et al., 2019; Monnet, 2003).
1
LITERATURE STUDY
1 ANAEROBIC DIGESTION
1.1 INTRODUCTION Anaerobic digestion (AD) is a process in which the degradation of organic substances by
microorganisms under anaerobic conditions takes place, which eventually leads to the production of
microbial biomass and biogas. The AD has several advantages e.g., low sludge production, low energy
consumption, no aeration requirements, and the biogas produced can be used as a source of
renewable energy (Chen et al., 2008; McHugh et al., 2003). Due to these advantages, this process is
already successfully being used in the treatment of agricultural, industrial and municipal waste. Up to
10% of organic waste in several European countries, is treated by means of AD (Li et al., 2011). The
microbial population involved can be divided into different groups, each with its own specific function.
These groups differ widely from each other in terms of physiology, nutritional needs, growth kinetics
and sensitivity towards environmental conditions. Minor fluctuations in operational parameters, e.g.,
temperature and pH, can unbalance the microbial population and can lead to process failure. This poor
operational stability still prevents AD from being widely used without any complications (Chen et al.,
2008).
1.2 ANAEROBIC DIGESTION PROCESS Anaerobic digestion includes four stages (Figure 1). The first stage is called hydrolysis in which insoluble
organic material and macromolecules, e.g., polysaccharides, proteins, nucleic acids and lipids are
degraded into smaller soluble organic components (Appels et al., 2008a). This stage occurs due to
extracellular hydrolytic enzymes that are produced and excreted by the hydrolytic bacterial population
(Parkin & Owen, 1986). Because hydrolysis plays a major role during AD, this step may become the
rate limiting step (Molino et al., 2013). The substances produced are further split during acidogenesis
or the fermentative stage. In this second stage, volatile fatty acids (VFA), alcohols and organic acids
are formed by acidogenic or fermentative bacteria along with ammonia (NH3), H2, CO2, H2S and other
by-products. The third stage is acetogenesis, and is carried out by acetogenic bacteria. During this
stage, acetic acid, as well as H2 and CO2 are mainly produced. However, the partial pressure of H2 in
the mixture mainly determines the equilibrium state of this conversion (Appels et al., 2008a; Hattori,
2008). If for example the partial pressure of H2 gas exceeds a certain threshold1, the production of
methane (CH4) is inhibited, and the concentration of VFA will increase. Finally, in the last stage, CH4 is
produced during so-called methanogenesis. The formation of CH4 can be carried out by two groups of
methanogenic archaea. The first group of methanogens, the acetoclastic methanogens, cleave acetate
to generate CO2 and CH4, while the second group, the hydrogenotrophic methanogens, produce CH4
from the reduction of CO2 using H2 gas as electron donor (Parkin & Owen, 1986).
1 Sterling Jr. et al.reported normal H2 gas concentrations in digester biogas ranging from 6 to 20 Pa. On the contrary, De Vrieze reports H2 concentration values ranging 0.1 Pa to 101 Pa.
2
Figure 1 - Subsequent steps in the anaerobic digestion process
1.3 MICROBIAL POPULATION As already mentioned in 1.2, the microbial population participating in the AD process consists of three
trophic groups, namely the hydrolytic-acidogenic bacteria, the acetogenic bacteria and the
methanogenic archaea. The composition of these three groups may differ depending on the type of
feedstock, on the one hand, and the process temperature, on the other hand. The operational
temperature varies between psychrophilic (<20°C), mesophilic (30-38°C) or thermophilic temperature
(50-60°C) (Ziganshin et al., 2013). The first group only depends on acetogens and methanogens in
terms of H2 scavenging, while the acetogenic bacteria and methanogenic archaea are strictly
dependent on each other. Acetogenic bacteria act as H2 donor, while methanogenic archaea act as H2
acceptor. Thus, process failure leading to inhibition of methanogenesis also affects acetogenic
bacteria. Therefore they are often referred to as the methanogenic association or consortium (De
Vrieze, 2019; Michihiko & Tomonori, 1982).
1.3.1 HYDROLYTIC-ACIDOGENIC BACTERIA The first step, which is also the rate-limiting step of AD, is hydrolysis. Microorganisms that are involved
in this step mainly belong to the Firmicutes, Bacteroidetes and Proteobacteria phyla (De Vrieze, 2014).
To increase the rate of hydrolysis and the overall efficiency of the AD process, knowledge of the
microbial ecology during this step is of great importance (Wang et al., 2010). It is known that similar
bacteria that are involved in hydrolysis are responsible for acidogenesis. However, energy is yielded,
and bacteria grow rather by the acidogenesis of monomers than the hydrolyzation of polymers, which
still results in hydrolysis remaining the rate-limiting step.
3
As already mentioned, during this second step, VFA are produced with a very high conversion rate.
Nevertheless, the maximum concentration of organic acids attained reaches a threshold of 20 – 30 g/L.
Higher concentrations can inhibit hydrolysis as well as acidogenesis. (De Vrieze, 2019).
1.3.2 ACETOGENIC BACTERIA Acetogenic bacteria are very diverse, although many of the bacteria belong to the class Clostridia.
These bacteria convert the intermediary products, formed during the acidogenesis step, into acetate,
H2 and CO2. If H2 accumulates, significant H2 pressure can occur, which results in the inhibition of
acetogenic bacteria and the loss of acetate production. However, methanogenic archaea use H2 in their
pathway, meaning that, in a properly functioning AD process, significant H2 pressure does not occur,
and the formation of CH4 and CO2 can proceed undisturbedly (Gerardi et al., 2008).
Acetogenic bacteria mainly consist of three groups, i.e., the homoacetogenic or syntrophic acetate
oxidizing bacteria (SAOB), the syntrophic acetogenic bacteria (SAB), also called obligatory H2-producing
acetogenic bacteria (OHPA) and sulfate reducing bacteria (SRB).
The first group, the SAOB, cooperate with the hydrogenotrophic archaea. These SAOB convert acetate
to CO2 with the production of H2. This reaction is endergonic (ΔG0 = 104,6 kJ/mol) and is extremely
unfavorable at standard conditions. However, if there exists a sink for H2, the reaction becomes
exergonic (ΔG0 = - 135,6 kJ/mol), and can, therefore, proceed if H2-consuming hydrogenotrophic
archaea eliminate H2 gas. In sum, these bacteria and archaea depend on one another since the bacteria
require H2 scavengers and the archaea require H2 suppliers. (Gerardi et al., 2008; Hattori, 2008).
The second group are SAB, which convert VFA into acetate and H2. Like SAOB, they need the
partnership of H2-scavenging hydrogenotrophic methanogens to maintain their metabolic activity.
Most of the SAB that oxidize propionate belong to the Syntrophobacterales order and the
Peptococcaceae family and the ones that oxidize butyrate mainly belong to the Syntrophomonadaceae
family (De Vrieze, 2014).
The last group are SRB. These bacteria, such as Desulfovibrio desulfuricans, use acetate, H2 and VFA as
electron donor and sulfate as electron acceptor to form sulfide, which can influence acetogenesis.
Under low acetate concentration, the SRB obtain H2 and acetate more easily than methane-forming
archaea, which causes competition between SRB and the methanogenic archaea (Chen et al., 2014;
Gerardi et al., 2008; Hilton & Archer, 1988).
1.3.3 METHANOGENIC ARCHAEA The final step of AD is carried out by the methanogenic archaea. These methane-forming
microorganisms are classified as Archaea, which possess several unique characteristics that are not
found in Eubacteria, such as a non-rigid cell wall, unique lipids in the cell membrane, specialized
coenzymes and a substrate degradation that produces CH4 as waste. These clusters of archaea can be
split into three groups by means of three different pathways. The first group covers the acetoclastic
methanogens, which cleave acetate directly to methane and CO2. Two genera of methanogens,
Methanosarcina and Methanosaeta, are known to operate this biochemical process. The second group
contains the hydrogenotrophic methanogens, which reduce CO2 to CH4 using H2 as electron donor. The
third, and last, group are the methylotrophs, which can use reduced one-carbon compounds such as
methanol or methane as carbon source (De Vrieze, 2014; Gerardi et al., 2008; Hattori, 2008).
4
1.4 DIFFERENT TYPES OF WASTE STREAMS Organic waste can occur as solid or liquid material and require a different treatment in AD plants.
Before the different waste streams are digested, they can undergo a pre-treatment step. This step
removes non-biodegradable materials, which take up unnecessary space, provides a uniform small
particle size feedstock for efficient digestions, protects the equipment of the wastewater treatment
plant (WWTP) against physical damage and removes materials, which may decrease the quality of the
digestate. Various types of pre-treatment exist, depending on the kind of waste stream and whether
the waste is solid or liquid. An example of a pre-treatment step of municipal solid waste (MSW) is the
use of a hammermill to reduce the size of the waste particles. Another way to pre-treat MSW is manual
sorting to remove large and unrelated materials (Monnet, 2003). Solid waste streams, such as crop
residues, lignocellulosic sources, and paper have a total solid content of more than 15% and are treated
via solid-state AD. Liquid waste streams, such as liquid wastes from industrial areas, animal manure
and sewage sludge contain less than 15% total solid content, and are treated via the so called liquid
AD (Brown et al., 2012).
1.4.1 INDUSTRIAL WASTE STREAMS Organic industrial waste can exist in a liquid or solid form, and both can be suitable for AD. Industrial
waste streams can be subdivided into waste streams of the food industry, the paper and pulp industry,
and textile industry (Chen et al., 2008). Waste from the food industry contains high-value organic
matter, which makes these waste streams suitable for AD (Rinzema et al., 1988). Food industry sludge
of a WWTP in Bahadurgarh in India, for example, contains about 360 g organic carbon/kg sludge (Garg
et al., 2012). However, these waste streams contain several inhibitors. Wastewater from processed
seafood, for example, has a very high salt content2. High levels of salt can cause an osmotic shock to
the bacterial cells, which causes dehydration, and consequently inhibits AD (Rinzema et al., 1988).
Waste streams of the paper and pulp industry contain high carbon oxygen demand (COD)
concentrations, which makes AD of these waste streams very favorable. However, sulfide is produced
during the Sulfite process3, which is a common inhibitor during AD of these waste streams. The removal
of sulfide can be achieved by sulfur bacteria, which convert sulfide ions to elemental sulfur as a pre-
treatment of the paper industry waste stream (Buisman et al., 1991). Next to sulfide, these waste
streams also contain long chain fatty acids (LCFA), which inhibit the acetoclastic methanogens. Finally,
the presence of halogenated compounds produced during the bleaching process are also possible
inhibitors of the AD process (Hanaki et al., 1981; Koster & Cramer, 1987).
Textile wastewaters have a high chemical complexity, because of the variety of fibers, dyes and process
aids. Components of textile wastewaters, such as dye, dyeing auxiliaries, and surfactants, are inhibitors
of methanogenesis, which makes it very difficult to treat these wastewaters (Chen et al., 2008;
Vandevivere et al., 1998).
2 Kilcast & Angus reports salt concentrations in processed seafood ranging from 1% up to even 30% 3The Sulfite process is a chemical process for the manufacturing of paper pulp.
5
1.4.2 MANURE Because of the high amounts of manure produced each year, treatment of this waste stream is one of
the most current applications of AD (Monnet, 2003). Manure has a high nitrogen content (Appels et
al., 2011; Ward et al., 2008). However, the ammonia concentration in animal waste is often too high,
due to the presence of ammonia in manure itself, and due to the conversion of proteins and urea to
ammonia during AD. This can lead to the inhibition of anaerobic digesters. Manure is therefore often
co-digested with other waste streams (Chen et al., 2008; Hashimoto, 1986; Zeeman et al., 1985).
The use of manure in AD has several advantages. One of them is the obtained odorless digestate, which
can be used as a fertilizer. This digestate can then be used on the land to enrich the soil without odor
nuisance (Monnet, 2003). An additional advantage of AD is the controlled release of CH4. The storage
of manure can lead to uncontrolled CH4 emissions, which contribute to global warming effects.
Controlled AD prevents this uncontrolled CH4 release (Appels et al., 2011; Moller et al., 2004a, 2004b).
1.4.3 ENERGY CROPS AND AGRICULTURAL WASTE Crop residues, such as unused stalk, straw, vegetable waste and specially grown energy crops e.g.,
maize, beet and wheat, can also be used to produce biogas. Both crop residues and energy crops have
a high lignocellulosic content. The AD process can degrade cellulose up to 80%, which makes the AD
process of green waste economically beneficial. However, lignin forms a problem, because of its high
non-degradable rigid structure (Appels et al., 2011; Ress et al., 1998). These waste streams can
therefore often only be processed after chemical or physical pre-treatment, which increases the
processing costs (Monnet, 2003).
1.4.4 MUNICIPAL WASTE Municipal waste contains about 60% of organic material, which makes this waste stream very suitable
for AD. However, municipal waste needs to be sorted first to obtain a clean biodegradable fraction.
Sometimes an additional pre-sorting step is applied, necessary for the removal of heavy metals, which
unfortunately increases the treatment costs of AD. Municipal waste has a high content of protein-
containing materials. Ammonia is produced during the degradation of these materials, which can have
an inhibitory effect on the process if the concentrations are too high. (Appels et al., 2011; Ward et al.,
2008).
1.5 CONTROL OF ANAEROBIC DIGESTION Control of the operational parameters of the AD process is essential. Several operational parameters,
e.g., pH, temperature, organic loading rate (OLR) and added nutrients need to be controlled during the
process to optimize the microbial activity to obtain the maximum production of biogas.
1.5.1 EFFECT OF PH The optimal pH range for acidogenesis differs from the pH range for methanogenesis. Acidogenic
bacteria are more or less insensitive to the pH within a range of 4.5 to 9, while methanogenic archaea
can operate only in a pH range of 6.8 to 7.2 (De Vrieze, 2019; Mudrack & Kunst, 1986; Rajeshwari et
al., 2000). The optimal pH range for both groups is situated between 6.8 and 7.5. During AD, acidogenic
bacteria produce VFA, which can reduce the pH of the process. This acidification is countered by the
activity of the methanogenic archaea that produce products, such as CO2, ammonia and bicarbonate.
6
These three components act as a buffer to neutralize the effect of VFA and consequently keep the pH
at a near to constant value (Appels et al., 2008a; Turovskiy IS, 2006).
1.5.2 EFFECT OF TEMPERATURE The operating temperature of AD varies from very low temperatures (15°C), which is called
psychrophilic digestion, to higher temperatures (70°C), which is called hyperthermophilic digestion (De
Vrieze, 2014; Gerardi et al., 2008; Lier, 1995). Most AD systems operate under mesophilic conditions
(30-38°C) or thermophilic conditions (50-60°C) (Buhr & Andrews, 1976). Thermophilic digestion shows
several advantages in comparison with mesophilic digestion, e.g. a more efficient destruction rate of
organic solids, a greater resistance to pathogenic organisms and a higher gas production. Nevertheless,
the use of thermophilic temperatures also suffers from several disadvantages, e.g. a lack of process
stability related to high propionate concentrations and higher sensitivity to environmental changes
(Kim et al., 2006; Kim et al., 2002).
Independent of the advantages of thermophilic conditions, the optimum digestion temperature
depends on the type of waste stream used during AD and on the type of digesters. In addition, the
operating temperature needs to be as constant as possible to sustain the microbial composition in the
digester and consequently to maintain a high biogas production rate (Monnet, 2003).
1.5.3 EFFECT OF ORGANIC LOADING RATE The organic loading rate (OLR) is defined as the amount of COD applied in the AD system, per liter, per
day. During the start-up of a reactor, the OLR need to be increased gradually. If the system is fed above
its sustainable OLR, slow-growing methanogenic archaea can’t convert acetate and H2 fast enough to
CH4 and CO2 anymore, which results in the accumulation of VFA, a decrease in pH and, consequently,
a decrease in methanogenic activity and, thus, in lower biogas production. In conclusion, monitoring
the parameters of an AD process, such as pH, biogas production and VFA composition is crucial to
obtain an efficient AD process with stable biogas production (Appels et al., 2008a; Chen et al., 2008;
De Vrieze, 2014; Monnet, 2003).
1.5.4 ESSENTIAL GROWTH FACTORS Essential growth factors can be divided into macronutrients and micronutrients or trace elements. All
these elements are essential in at least one metabolic pathway in AD. It is of utmost importance that
nutrient limitations should be avoided to maintain biogas production (De Vrieze, 2014; Hutnan et al.,
2013; Vintiloiu et al., 2012).
1.5.4.1 MACRONUTRIENTS
Macronutrients, such as carbon, nitrogen, phosphorus and sulfur play an important role in the growth
and metabolism of anaerobic microorganisms (Pobeheim et al., 2010). The C:N ratio, for example,
represents the ratio of the mass of carbon to the mass of nitrogen present in the feedstock.
Microorganisms consume carbohydrates 25-30 times faster than nitrogen. To fulfil this requirement,
and to obtain the maximum yield of biogas, sufficient carbohydrates in the feedstock are needed.
Waste streams that are high in nitrogen and low in carbohydrate can be combined with waste streams
that are carbohydrate-rich (Gashaw, 2014).
7
1.5.4.2 MICRONUTRIENTS
The elements B, Co, Cr, Cu, Fe, Mn, Mo, Ni, Se and W are the most important micronutrients or trace
elements needed for optimum growth during AD. Although these trace elements need to be present
in very low concentrations, the lack of these nutrients can have an adverse effect on microbial growth
and performance (De Vrieze, 2014; Feng et al., 2010; Rajeshwari et al., 2000).
The correct dosages of these trace elements can have a positive impact on the process, such as a better
stabilization of the digester, more degradation of organic matter, lower VFA concentrations and
consequently a higher biogas production (Yaw et al., 2016).
The most important microorganisms in the AD process are the methane forming archaea, because they
avoid accumulation of VFA. The methanogenic archaea have very high internal concentrations of Fe,
Ni and Co. Some waste streams don’t contain sufficient concentrations of these three trace elements
to meet the required quantities for the methanogens. Such waste streams have to be supplemented
with these trace elements as a pre-treatment. This unfortunately increases the operation costs. An
inexpensive solution to nutrient limitation is co-digestion with nutrient-rich substrates (Rajeshwari et
al., 2000; Yaw et al., 2016).
1.6 INHIBITORS OF THE ANAEROBIC DIGESTION PROCESS Several substances can inhibit the AD process. The reason why AD is so easily inhibited is because it is
a very vulnerable process with different groups of microorganisms who have their own optimal living
conditions. To obtain the most efficient process conditions, each group of microorganisms needs to
function as best as possible, and a well-balanced system needs to be maintained.
1.6.1 SULFATE AND SULFIDE Different waste streams from the paper industry, the sugar industry and edible oil refineries may
contain high levels of sulfate. During AD of these waste streams, SRB convert this sulfate into sulfide.
However, if the sulfate levels in the waste streams become too high, methane production can be
inhibited by the activity of SRB, and, thus, inhibition of the AD process may occur. This inhibition can
take place on two different levels (Colleran et al., 1995; De Vrieze, 2014).
The first inhibition is caused by substrate competition between the SRB, on the one hand, and the
methanogenic archaea and acetogenic bacteria, on the other hand. While reducing sulfate to sulfide,
the SRB use acetate and H2 as electron donor, which are also the substrates of SAOB and methanogenic
archaea. In addition, when the concentrations of H2 and acetate are low, SRB can use VFA as electron
acceptor. This shows the competition towards SAB, which also use VFA as substrate (Chen et al., 2014;
Hilton & Archer, 1988).
The second inhibition is due to the formation of sulfides, which are highly reactive, corrosive and toxic
to microorganisms, plants, animals and also humans (Colleran et al., 1995). Toxicity of sulfides present
in AD is pH dependent, since only the unionized hydrogen sulfide form can pass through the cell
membrane. Therefore, the extent to which sulfide is toxic or not, depends on the characteristics of the
sludge (Hulshoff Pol et al., 1998; Speece, 1983). Once in the cytoplasm of bacterial cells, sulfide
denatures the native proteins through formation of sulfide and disulfide, cross-linked between
polypeptide chains. Sulfide can also interfere with the assimilatory metabolism of sulfur and it may
also affect the intracellular pH (Chen et al., 2008; Hulshoff Pol et al., 1998; Siles et al., 2010). The
abovementioned consequences reduce the rate of methanogenesis and consequently biogas
production. Therefore, excessive levels of sulfate in waste streams need to be avoided, for example
8
through dilution of the waste stream or pre-treatment, such as bioaugmentation, air stripping and
chemical precipitation. However, pre-treatment increases the operational costs (Chen et al., 2014;
Chen et al., 2008; Zhang & Angelidaki, 2015).
1.6.2 LONG CHAIN FATTY ACIDS Long Chain Fatty Acids (LCFAs) are fatty acids with aliphatic tails of 13 to 21 carbons. The LCFA arise
from the hydrolyzation of lipids to glycerol and LCFA. The LCFAs are further converted to H2 and acetate
by SAB and finally to CH4 and CO2 by methanogens. Accumulation of LCFA can inhibit the activity of
syntrophic acetogens and methanogens by the adsorption of LCFA onto the microbial surface, which
limits the transport of nutrients into the cell (Hwu et al., 1998; Pereira et al., 2005). However, LCFA
inhibition is reversible, which can be explained by two hypotheses. The first hypothesis is the
phenotypic adaptation of the existing bacterial community to higher concentrations of LCFA after a lag
phase, which is called physiological acclimatization. The second hypothesis is called population
adaptation and explains the reversible inhibition by a shift towards the enrichment of specific and
better adapted LCFA-degraders. The research Palatsi et al. conducted into these two hypotheses in
2010 indicated that the observed adaptation process can be attributed to the physiological hypothesis
(Palatsi et al., 2010; Pereira et al., 2004).
1.6.3 AMMONIUM AND AMMONIA Ammonia is the end-product of AD of proteins, urea and nucleic acids, and is an essential nutrient for
the growth of microorganisms. However, if the ammonia concentration is too high, it will inhibit the
process. Total ammonia nitrogen (TAN) contains two forms of nitrogen; free ammonia (FA) or
unionized ammonia (NH3) and ionized ammonia or ammonium (NH4+). The FA is the active compound
in AD inhibition, because FA can permeate the cell membrane more rapidly than ammonium (Rajagopal
et al., 2013; Siles et al., 2010). Once in the cell of the methanogens, some FA can be converted to
ammonium, due to the difference between the extracellular and intracellular pH. This process requires
the absorption of protons (H+) using a K+ antiporter, which results in a proton imbalance, potassium
deficiency, a change in intracellular pH and an increase in maintenance energy requirements.
Ammonium can also inhibit the methane synthesizing enzyme directly, which results in less serious
consequences (Chen et al., 2014; Chen et al., 2008).
The concentration of ammonia increases as the temperature increases. This is because of two main
reasons. The first reason is that ammonia is produced during hydrolysis by the degradation of
nitrogenous organic materials and the higher the temperature, the higher the metabolic rate of the
microorganisms, and hence, the higher the hydrolysis rate. The second reason can be derived from the
following equation:
𝑁𝐻3 (𝐹𝐴) = 𝑇𝐴𝑁 ∗ (1 + 10−𝑝𝐻
10−(0,09018+
2729,92𝑇(𝐾)
)−1
From this, it is clear that an increase in temperature will lead to an increase in FA concentration. This
equation also shows the relationship between the FA and the pH (Rajagopal et al., 2013; Sung & Liu,
2003). The pH can also affect the ammonia concentration. In aqueous solutions, there is a chemical
balance between FA and ammonium. If the pH value increases, the amount of FA will also increase,
and the biogas production will decrease. For example, an increase in pH from 7 to 8 will actually lead
to an eight-fold increase in the FA concentration (Chen et al., 2014).
9
Acclimation is another factor that influences the degree of ammonia inhibition. Methanogens can
acclimate to high concentrations of ammonia, making them more tolerant towards ammonia stress
(Sung & Liu, 2003).
1.6.4 SALT Different waste streams, such as wastewater from food processing industries and chemical industries,
can contain salt concentrations, possibly inhibiting the AD process. Addition of salt is possible during
industrial processing for pH adjustment, which results in waste streams with a very high salt
concentration. However, if the salt concentration reaches a certain threshold, it can be toxic or
inhibitory to the activity of the microorganisms present in the AD. This inhibition can mostly be
attributed to cations, e.g., Na+, K+, Mg2+ and Ca2+, which are the most common cations in AD (Appels
et al., 2008b; Chen et al., 2008).
Sodium concentrations of more than 8800 mg Na+/L are strongly inhibitory to methanogenic archaea.
Excessive levels of Na+ lower the maximum specific growth rate and the yield of acetoclastic
methanogens, while increasing their specific decay rate. However, the presence of a Na+ concentration
of 350 mg Na+/L is beneficial for methanogenic archaea, because the formation of ATP and NADH
requires a low concentration of Na+ (Appels et al., 2008b; Rinzema et al., 1988).
Potassium shows optimal concentrations similar to Na+. If the concentration is below 400 mg K+/L, the
AD process is enhanced. However, if the concentration exceeds 5850 mg K+/L, 50% of the acetate
utilizing methanogens is inhibited. High concentrations of K+ can lead to a passive influx of K+ ions,
thereby neutralizing the membrane potential. Potassium also extracts metals that were bound to
exchangeable sites in the sludge. This subsequently leads to the removal of essential metals, such as
Cu, Zn, Ni, Mo and Co from the activated sludge, which, in the end, is responsible for the low activity
of the methanogenic population (Appels et al., 2008b; Chen & Cheng, 2007; Chen et al., 2008). The
performance of AD improves with increasing concentration of Ca2+ and reaches a maximum at the
concentration of 3 g Ca2+/L. Higher concentrations of more than 5–7 Ca2+ g/L induces an adverse impact
on the performance of AD (Ahn et al., 2006). Methanogens can also adapt to the increasing salt
concentrations. Continuous exposure of methanogenic archaea leads to a higher tolerance towards
higher Na+ levels (Feijoo et al., 1995).
1.6.5 TRACE ELEMENTS As already mentioned in 1.5.4.2, excessive concentrations of trace elements or micronutrients can
have an adverse impact on the growth and activity of the microbial community. However, the toxicity
of these trace elements towards the microbial community is independent of the total metal
concentration in the digester, but rather depends on the concentration of free metals in the sludge (A.
Lawrence & McCarty, 1965; Mueller & Steiner, 1992). It is known that active, inactive and dead biomass
can bind heavy metals, which consequently results in the accumulation of high levels of heavy metals
(Kuyucak & Volesky, 1988). Acidogens are less susceptible to high concentrations of trace elements.
Hence, the decrease in biogas production and the accumulation of VFA in the reactor can indicate the
presence of toxic levels of heavy metals. However, heavy metals may affect the production of acetate
and butyrate in different ways. In 1993, Lin showed that an increase in concentration of mixed metals
induced an increase in the production of butyrate, but a decrease in the production of acetate and vice
versa (Lin, 1993). Sulfides, derived from the conversion of sulfate to sulfide by SRB, can precipitate
with heavy metals, leading to a reduced effect of toxic heavy metals. The precipitation of heavy metals
10
with sulfides may occur over a wide pH range. At neutral pH, many heavy metals also precipitate as
hydroxide. Chen et al. evaluated the toxicity of heavy metals during AD and reported different
inhibiting concentrations, ranging from 70 to 400 mg/L for Cu, 200 to 600 mg/L for Zn and 10 to 2000
mg/L for Ni (A. Lawrence & McCarty, 1965; Lin & Chen, 1999; Mueller & Steiner, 1992; Tiwari et al.,
2006).
2 ANAEROBIC GRANULATION TECHNOLOGY
2.1 INTRODUCTION The anaerobic granular sludge bed technology has become more and more popular in industrial
wastewater treatment, because it has several benefits in comparison with systems that use non-
granular dispersed sludge. Non-granular sludge has a loose structure, and can only be partially
separated from the liquid fraction after it has settled, while granular sludge has a clear and visible
granular shape, which can be separated completely from the liquid fraction. Several advantages of
granular sludge over non-granular sludge will be listed. First, granular sludge settles much faster than
non-granular sludge, which makes granular sludge less susceptible to the wash out of biomass during
start-up. Another advantage is that methanogens in granular sludge are more tolerant to oxygen. This
group of archaea is surrounded by facultative anaerobic bacteria that utilize incoming oxygen before
it can reach the core of the granules, which predominantly consist of methanogens. In addition,
granular sludge is less sensitive to substrate inhibition, compared with non-granular sludge. Finally,
substrate conversion to intermediates and the transfer of intermediates for further degradation is
enhanced in granular sludge, because of the clustering of various bacterial groups in a small area
(Baloch, 2011).
2.2 ANAEROBIC GRANULATION REACTOR TECHNOLOGIES As already mentioned in the introduction of AD, anaerobic treatment shows many advantages over
aerobic processes, e.g., low levels of excess sludge production, less space requirements, no
requirement for aeration and the production of biogas. Because of these advantages, a tremendous
increase in AD of waste was experienced in the last decades, which made the development of
anaerobic reactor technologies indispensable. Upflow anaerobic sludge blanket (UASB) reactor designs
and expanded granular sludge bed (EGSB) reactor designs represent the main proportion of anaerobic
reactor technologies, especially for the treatment of liquid waste streams. For more solid waste
streams, other reactor technologies, such as continuous stirred tank reactor (CSTR) and internal
circulation (IC), can be the choice of preference.
2.2.1 CONTINUOUS STIRRED TANK REACTOR (CSTR) A CSTR consists of one big vessel with a mixer inside that mechanically agitates the reactor to keep the
active anaerobic sludge in suspension. Due to this mixing, it is assumed that there exists no
concentration gradient in the vessel. The feedstock is entering the reactor at the top of the vessel with
the same rate as the outgoing substrate effluent that is leaving the reactor at the bottom of the vessel
(Figure 2). However, in some cases, the feedstock enters at the bottom of the vessel, while the
outgoing effluent leaves the reactor at the top of the vessel. In these two systems, the retention time
11
of the anaerobic biomass, i.e. solid retention time (SRT), equals the hydraulic retention time (HRT)
(Cunningham et al., 2010; Kaparaju et al., 2008; Karim et al., 2005). This type of reactor can bear
organic loading rates ranging from 3 to maximum 10 kg COD/m3/d and is typically used for solid rich
waste streams, such as manure and energy crops (De Vrieze, 2014; Pycke et al., 2011; Sundberg et al.,
2013).
2.2.2 UPFLOW ANAEROBIC SLUDGE BLANKET REACTOR (UASB) A UASB reactor consists of one single vessel, and is typically used for low to medium strength
wastewaters. The inlet of the wastewater is situated at the bottom, which makes the upward flow
(about 1.0 m/h upflow velocity) through an anaerobic granular sludge bed possible. The granules are
formed due to the natural aggregation of the microbial community in flocs and granules, on the one
hand, and the combination of the upflow mode with shear, on the other hand. The maintenance of
this sludge bed is possibly through the accumulation of incoming suspended solids and bacterial
growth on these solids. When the wastewater passes this bed, it comes in close contact with the
granular microbial community, which enables degradation of organic matter. These granules have
good settling properties, which avoids the wash-out of biomass. The treated effluent leaves through
an outlet at the top of the reactor, as does the produced biogas. This produced biogas causes hydraulic
turbulence in the sludge bed, which provides an adequate mixing within the system and, therefore,
eliminates mechanical mixing. This mixing provides a better contact between biomass and organics in
the ingoing wastewater (De Vrieze, 2019; McHugh et al., 2003). Another advantage of this reactor
technology is the application of a higher OLR (up to 20 kg COD/m3/d) in comparison with aerobic
systems. Hence less reactor volume and space are required, which reduces the operational costs
(Frankin, 2001; Seghezzo et al., 1998). The SRT is much higher than the HRT of the wastewater, which
gives the biomass sufficient time to grow. Finally, the reactor consists of a three-phase separator, also
called gas-liquid-solids separator, which separates the three phases occurring in the reactor (Fout!
Verwijzingsbron niet gevonden., left) (Bal AS, 2001; De Vrieze, 2019; McHugh et al., 2003).
Figure 2 – Schematic overview of a CSTR
12
2.2.3 EXPANDED GRANULAR SLUDGE BED REACTOR (EGSB) Although UASB set-ups are frequently used in industry, this reactor type still struggles with some
problems. First of all, internal mixing was not optimal in a pilot-scale UASB reactor operating at
temperatures ranging from 4 to 20°C (Man et al., 1986). This leads to the creation of dead zones and,
thus, to a reduction in the treatment efficiency. Nevertheless, the use of higher upflow velocities (6 –
15 m/h) in EGSB reactors has solved this problem. These velocities can be obtained either by effluent
recirculation and/or tall narrow reactor design. The increased upflow liquid velocity expands the
granular sludge bed, eliminating dead zones, and increases hydraulic mixing, providing a better
biomass-substrate contact. Compared to UASB reactors, higher OLR up to 30 kg COD/m3/d can be
applied in EGSB systems. Consequently, biogas production is higher. This also improves hydraulic
mixing, which again enhances reactor performance and stability. The EGSB reactor is particularly
suitable for the treatment of low-strength wastewaters containing low levels of COD. Low substrate
levels lead to a lower biogas production rate and, consequently, to a lower mixing intensity. However,
the increased upflow liquid velocities compensate for this lower mixing intensity, making digestion of
low-strength wastewaters possible, due to the improved biomass-substrate contact. A drawback of
this reactor technology is that the granules tend to be washed out of the sludge bed (McHugh et al.,
2003; Seghezzo et al., 1998) (Fout! Verwijzingsbron niet gevonden., right).
2.2.4 INTERNAL CIRCULATION REACTOR The internal circulation (IC) reactor is developed by the Dutch company Paques, and has evolved from
the UASB and the EGSB reactors. The IC reactor consists of two inter-connected UASB compartments
on top of each other. First, the industrial wastewater enters at the bottom of the reactor, and is mixed
with the granular anaerobic biomass in the mixing section. Just above the mixing section, organic
components are converted into methane in the first expanded sludge bed. The produced biogas is
separated from the effluent in the lower phase separator and is, together with water, collected via the
riser pipe in the gas/liquid separator on top of the reactor. Biogas leaves the system and water returns
through the downer pipe into the mixing section where it is mixed with the incoming influent. This is
Figure 3 – Schematic overview of a UASB reactor (left) and an EGSB reactor (right)
13
where the name ‘internal circulation reactor’ comes from. In the upper compartment of the reactor,
the effluent is polished a second time in the second expanded sludge bed and biogas is collected in the
upper phase separator. The effluent leaves the system and biogas and water are again separated at
the top of the reactor (Driessen, 2016; PAQUES, n.d.). Due to the internal circulation, incoming influent
is diluted resulting in the potential application of high OLR, up to 30 kg COD/ m3/d. The production of
biogas occurring in two phase separators permits the use of a very high upflow, ranging from 20 to
even 30 m/h. Any biomass lost from the first compartment of the reactor is retained in the upper
section. This facilitates sludge retention within the system and, accordingly, facilitates the use of high
OLR. On the other hand, the effluent in the upper phase separator has a low OLR, which aids the very
efficient separation of biogas, biomass granules and treated effluent (De Vrieze, 2019; McHugh et al.,
2003) (Figure 4).
2.3 ANAEROBIC GRANULATION THEORIES
2.3.1 STRUCTURAL MODELS During anaerobic granulation, both biological and microbiological factors are involved. To understand
the microbiological characteristics of UASB granules and the interactions between different bacteria
and archaea, some structural models for anaerobic granulation were developed (Liu et al., 2003).
2.3.1.1 INERT NUCLEI MODEL
The inert nuclei model for anaerobic granulation was initially proposed in 1980 by Lettinga et al. This
process is initiated by microorganisms that attach themselves to the particle surface of inert
microparticles with a lower specific gravity than the gravity of the biomass present in the reactor. In
this way, the initial biofilm is formed and embryonic granules are created which can further develop
through the continued growth of the attached micro-organisms under given operation conditions
Figure 4 – Schematic overview of an IC reactor
14
(Figure 5). Addition of inert matter is effective in the initiation of the formation of anaerobic granules
(Lettinga et al., 1980; Liu et al., 2003; Yoda et al., 1989).
2.3.1.2 SELECTION PRESSURE MODEL
The selection pressure theory explains the effects of liquid upflow velocity on anaerobic granulation.
If the liquid upflow velocity is high (high selection pressure), light and dispersed sludge tends to be
washed out, while heavier components can remain in the reactor. If a low liquid upflow velocity is
applied (low selection pressure), growth will take place, mainly as dispersed biomass, which gives rise
to the formation of a bulking type of sludge. This model suggests that the formation of microbial
aggregation may be an effective protection strategy against these high selection pressures in terms of
upflow velocity (Hulshoff Pol et al., 2004; Liu & Tay, 2002; Liu et al., 2003).
2.3.1.3 MULTI-VALENCE POSITIVE ION-BONDING MODEL
At normal pH values, microbial surfaces are negatively charged. If two surfaces are either both
positively charged or negatively charged, there exists a free energy barrier between them, which acts
as a repulsive force. By introducing multi-valence positive ions, such as Al3+, Ca2+, Fe2+ and Mg2+ the
electrostatic repulsive force between different bacteria is reduced. Another advantage of introducing
positively charged ions is the formation of multi-valent bridges between negatively charged groups on
cell surfaces, which stimulates aggregations of microbial cells (Figure 6). The rate of sludge granulation
during the start-up of a UASB reactor is enhanced by Ca2+ concentrations in the waste stream ranging
from 80 – 150 mg/L (Alibhai & Forster, 1986). However, another study reports optimal Ca2+
concentrations ranging from 150 – 300 mg/L (Yu et al., 2001). The difference in optimum ranges
indicates that the actual effect of Ca2+ on granule formation still needs to be understood. Higher
concentrations (< 600 mg/L) of Ca2+ may be detrimental to the granules because of the formation of
CaCO3, which can precipitate and may block the intragranular pores, leading to severe mass transfer
limitations (Liu et al., 2003; Mahoney et al., 1987; Tiwari et al., 2006; Yu et al., 2001).
Figure 5 – Inert nuclei model
Figure 6 – Multi-valence positive ion-bonding model
15
The multi-valent positive ions may promote sludge granulation by bonding with extracellular polymers
(ECP). There exists a high affinity between Ca2+ and ECP, which implies that the initial structure of the
microbial community can form through a ECP – Ca2+ – ECP bridge or cell – Ca2+ – cell linkage (Liu et al.,
2002, 2003).
2.3.1.4 CAPETOWN’S MODEL
It is assumed that Methanobacterium strain AZ produces ECP. The Methanobacterium strain AZ is an
archaeon that utilizes H2 as its sole energy source and can produce all its amino acids, except for the
essential amino acid, cysteine. Under high H2 partial pressure and limited concentrations of cysteine,
several amino acids would be over-secreted. The over-secretion induces ECP formation.
Methanobacterium strain AZ and other bacteria and archaea get stuck in, leading to granulation
initiation. In the Capetown’s model, the overproduction of ECPs is considered a key step for initiating
anaerobic granulation (Liu et al., 2002, 2003).
2.3.1.5 SPAGHETTI MODEL
The first step in the spaghetti model is the formation of precursors. These precursors can consist of
very small aggregates of Methanosaeta, originated by the turbulence generated by the gas production,
or they can consist of the attachment of Methanosaeta to finely dispersed mater. Next, additional
filamentous Methanosaeta will attach to these precursors, which can form a three-dimensional
network through a branched growth process. Other micro-organisms, such as Methanosarcina, can
easily be entrapped in this network, forming a denser aggregate due to microbial growth, whereby the
granules are more spherically shaped, due to the hydraulic shear stress of the upflowing liquid and
biogas. In this model, the formation of structured aggregates is a crucial step in the overall granulation
process (Liu et al., 2003; Tay et al., 2000).
2.3.1.6 SYNTROPHIC MICROCOLONY MODEL
Many different species are involved in biodegradation of organic waste, which makes anaerobic
digestion a very complex process. To make a process as energy-efficient as possible, these species live
in a close synergistic relationship where different products, such as H2 gas and other intermediates,
can be easily transported from cell to cell. Hirsch suggested in 1984 that this close coexistence
eventually leads to the formation of stable microcolonies or consortia, i.e., initial granules (Hirsch,
1984).
2.3.1.7 MULTI-LAYER MODEL
In 1990, MacLeod was the first to create a multi-layer model to explain the formation of granules (Guiot
et al., 1992; MacLeod et al., 1990). According to this model, a granule is made up of 3 different layers.
The first, and innermost, layer consists of methanogenic archaea producing biogas, and this layer is
necessary for the development of the granule. On this nucleus, a second layer is attached, which
contains H2-producing acetogenic bacteria. In a next step, hydrolytic-acidogenic bacteria adhere to this
small aggregate forming the outermost and third layer of the granule. Unlike the model of MacLeod,
Rocheleau et al. showed in 1999 that the center of a UASB granule didn’t contain any living archaea or
bacteria. This can be explained by the accumulation of metabolically inactive, decaying biomass and
inorganic materials in the center of the granule (Figure 7). Other research conducted into the microbial
structure of anaerobic granules also showed a multi-layer model with in the center a granule nucleus
16
that didn’t contain any trace of life. Therefore, the model of Rocheleau is more generally accepted
than the model of MacLeod (Hulshoff Pol et al., 2004; Liu et al., 2003; Rocheleau et al., 1999).
2.3.2 THERMODYNAMIC MODELS When two bacteria approach each other, they both experience physico-chemical interactions in terms
of repulsive electrostatic force, attractive van der Waals force, and repulsive hydration interaction
(Parsegian & Rand, 1991). These interactions not only occur between two cell walls, but can also occur
between a cell wall and a microparticle surface. To understand the present physico-chemical
interactions, some thermodynamic models have been developed (Liu et al., 2002).
2.3.2.1 SECONDARY MINIMUM ADHESION MODEL
The secondary minimum adhesion model is explained with the help of the Derjaguin-Landau-Verwey-
Overbeek (DLVO) free energy curve (Figure 8). The DLVO theory describes the force between two
charged surfaces over a distance of more than 1 nm through a liquid medium. This force combines the
effect of Van der Waals interactions and electrostatic repulsions. The initial adhesion between two cell
walls or between a cell wall and a microparticle surface takes place in the secondary minimum of the
DLVO curve. Because of the small Gibbs energy (∆G < 0) of the secondary minimum and because of the
separation distance and a remaining thin water film between the two adhering surfaces, the adhesion
in this minimum is reversible. However, if a micro-organism can reach the primary minimum (∆G <<<
0), short-range interaction forces can be applied and, subsequently, irreversible adhesion occurs. After
irreversible adhesion, colonization starts. The cells start to divide and start producing ECP. According
to the spaghetti model, micro-organisms are trapped in this biofilm structure, and granules are formed
(Costerton et al., 1990; Hulshoff Pol et al., 2004; Liu et al., 2003).
Figure 7 – Multi-layer model of Rocheleau
Figure 8 – General DLVO curve illustrating the main features of this interaction
17
2.3.2.2 LOCAL DEHYDRATION AND HYDROPHOBIC INTERACTION MODEL
Under normal culture pH conditions, the cell walls of micro-organisms are hydrated. The thin water
film between two bacteria, prevents them from approaching one another, because of the strong
repulsive hydration interactions. Local dehydration of surfaces that are a short distance apart is a pre-
requisite for bacteria adhesion, because two bacteria will only adhere irreversibly, if both bacterial
surfaces are strongly hydrophobic. This indicates that hydrophobicity and hydrophilicity of microbial
surfaces are important factors in irreversible adhesion (Rouxhet & Mozes, 1990a; Wilschut & Hoekstra,
1984). Because of their hydrophilic surface character, most hydrolytic-acidogenic bacteria are situated
on the outer layer of granules, while methanogens and acetogens, because of their hydrophobic
surface character, are situated in the inner layers of the granules. This explains why hydrolytic-
acidogenic bacteria are most often located in the outer layer of anaerobic granules (Daffonchio et al.,
1995; Liu et al., 2003).
2.3.2.3 SURFACE TENSION MODEL
In general, hydrophilic microorganisms that have a high surface tension tend to form aggregates in
bulk solutions with a low surface tension, while hydrophobic microorganisms that have a low surface
tension tend to form aggregates in bulk solution with a high surface tension. Microorganisms in UASB
reactors may grow in loose association, in multi-layered granules or in mixed conglomerates,
depending on the liquid surface tension present in the reactor (Grootaerd et al., 1997; Thaveesri et al.,
1995). As already mentioned in 2.3.2.2, most hydrolytic and acidogenic bacteria are hydrophilic, while
most of the acetogens and methanogens appear to be hydrophobic. Granulation at low surface
tensions gives rise to granules with hydrolytic and acidogenic bacteria around a acidogenic-
methanogenic association. In contrast to granules formed in high surface tension solution, this type of
granule is less susceptible to adhesion to gas bubbles and subsequent wash-out, and ensures a more
stable reactor performance (Hulshoff Pol et al., 2004; Liu et al., 2002, 2003; Tay et al., 2000).
2.3.3 PROTON TRANSLOCATION DEHYDRATION THEORY The proton translocation dehydration theory was proposed based on the proton translocation activity
on bacterial membrane surfaces. This granulation hypothesis consists of four steps. The first step is the
dehydration of bacterial surfaces. Bacterial surfaces are negatively charged, and are therefore
surrounded by water molecules in aqueous solutions. However, if wastewater is fed into a reactor that
first contained tap water and sludge, the hydrolytic and acidogenic bacteria start to degrade complex
organic compounds, coupled with the activation of electron transport. This electron transport
consequently activates proton pumps on the membranes of these bacteria, which can cause surface
protonation. The existing proton gradient across the surface membranes can break hydrogen bonds
between negatively charged groups on the cell wall and water molecules, and can also partly neutralize
the negatively charged surface of the bacteria, inducing dehydration of the bacterial surfaces. The
second step is called the embryonic granule formation. In this step, proton pumps of acetogens and
methanogens are also activated, causing their membrane surfaces to become dehydrated. Hydrolytic
bacteria, acidogens, acetogens and methanogens may adhere to each other due to external hydraulic
forces and weakened hydration repulsion. This leads to the formation of embryonic granules whose
adhesion is further strengthened by the continued dehydration of the bacterial and archaeal surfaces.
Next, embryonic granules will grow into maturated granules in the third step, which is called granule
maturation. The microorganisms within the embryonic granule continue growing, while other
dispersed bacteria and archaea in the medium can adhere to the granule. The microbial biomass in the
18
granules is efficiently organized, depending on the orientation of intermediate metabolite
transference. The last step is post-maturation. Due to the activity of proton pumps in mature granules,
the bacterial surfaces remain hydrophobic, which is principally responsible for maintaining the mature
granules. In addition, ECP is produced in the mature granules which protects granules against shear
stress and attachment to gas bubbles (Hulshoff Pol et al., 2004; Liu et al., 2002, 2003).
2.4 PARAMETERS INFLUENCING ANAEROBIC GRANULATION A UASB system can be initiated using existing granules, which can be assessed in terms of VSS/TSS
content, specific methanogenic activity (SMA) and the ability to settle. However, granular sludge is
expensive (220 – 300 €/ton) and the availability is limited. Unfortunately, formation of anaerobic
granules requires 2 to 8 months, so enhanced and fast production of anaerobic granules is essential
and strongly recommended. Therefore, knowledge of the major factors influencing the granulation
process is of great importance.
2.4.1 REACTOR TEMPERATURE AD is operated under mesophilic conditions (30-38°C) or thermophilic conditions (50-60°C). However,
temperature can have a great impact on the performance of anaerobic granules. Generally, UASB
reactors filled with granular sludge operate under mesophilic conditions with an optimum
temperature of 35°C. Careful temperature control at mesophilic conditions is still necessary, because
mesophilic granules are sensitive to sudden temperature changes, leading to disintegration of the
granules. In addition, the higher the temperature above 38°C, the higher the chances of sludge wash-
out and the higher the risk of decreasing COD removal efficiency. Temperatures lower than 30°C can
inhibit growth of methanogens (Fang & Lau, 1996; Lepisto & Rintala, 1999; Tiwari et al., 2006).
2.4.2 REACTOR PH As already mentioned in section 1.5.1, acidogens are more or less insensitive to the pH within a broad
range (4.5 – 9), while methanogens prefer a more neutral environment. The optimal pH for AD ranges
between 6.8 and 7.5. However, the strength of anaerobic granules decreases with an increase in pH
ranging from 8.5 – 11 or a decrease in pH ranging from 5 – 3. By contrast, the strength of the granules
remains unchanged when the pH ranges from 5.5 to 8. In conclusion, the stability of the granule
structure is enhanced in a slightly acid environment, which can be explained by the proton
translocation dehydration theory (Liu et al., 2003; Tay et al., 2000).
2.4.3 CHARACTERISTICS OF SEED SLUDGE In theory, any medium can be used as seed sludge to start a UASB reactor, as long as the seed sludge
contains the proper microbial community for AD. However, the quality of the applied seed sludge
mainly depends on two factors: the chance of being washed out and the composition of the seed
sludge. Two types of sludge wash-out were distinguished. The first type is sludge bed erosion wash-
out, which is the selective wash-out based on the difference in ability to settle. The second type is
sludge bed expansion. When using lighter, more diluted sewage sludge in the treatment of medium
strength wastewater, wash-out occurs due to the expansion of the sludge bed as a result of increased
hydraulic and gas loading rate. The latter can be avoided when choosing heavier and more
concentrated sewage sludge (De Zeeuw, 1988). Research on the composition of sludge used for
19
granulation showed that granulation with syntrophic and methanogenic enriched consortia as
precursor proceeded more rapidly than when using acidogenic flocs as precursor. In other words, some
microbial species would enhance and speed up the granulation process, while other species are less
competent in forming aggregates. In conclusion, manipulation of the composition of the seed sludge
can be beneficial for UASB start-up (El-Mamouni et al., 1997; Hulshoff Pol et al., 1983; Liu et al., 2003).
2.4.4 UPFLOW VELOCITY AND HYDRAULIC RETENTION TIME High upflow liquid velocity (1m/h) in a UASB reactor is associated with a short HRT, which is favorable
for the granulation-process. The effect of a high upflow liquid velocity can be explained by the selection
pressure theory. In fact, granulation enhances the settling velocity of the granules, which reduces
wash-out of granular sludge. However, the effect of reducing the specific wash-out rate of smaller
particles is not assured (Hulshoff Pol et al., 1988). A high liquid upflow velocity also has a significant
positive effect on the mean granule size. Increasing granule size promotes increasing settling velocity,
although some authors have reported that settling velocity seems to be independent from the
diameter of the particles (Arcand et al., 1994; Beeftink & van den Heuvel, 1988). In conclusion, a short
HRT combined with a high upflow velocity ensures that microorganisms unable to form granules are
washed out and, accordingly, that sludge granulation is promoted (Hulshoff Pol et al., 1988).
2.4.5 ORGANIC LOADING RATE The OLR is one of the most important operating parameters in the anaerobic granulation process. It
has been demonstrated that a gradual increase of the OLR during start-up enhances the granulation
process. However, different OLR values need be considered to maximize the granulation velocity, but
still maintain process stability (De Zeeuw, 1988; Fang & Chui, 1993; Hulshoff Pol, 1989). An OLR that is
too high can have several disadvantages for the granulation process. Firstly, it may inhibit
methanogenic activity, causing an accumulation of VFA and, thus, a decrease in pH. As already
mentioned in 2.4.2, a pH that is too low, weakens the strength of the granules, causing them to
disintegrate. The second disadvantage is the increased biogas production rate. This can cause serious
hydrodynamic turbulence, which may be the cause of the seed sludge to wash out from the reactor. A
third and last disadvantage of an OLR that is too high is the increased growth rate. According to the
Monod kinetic, OLR is proportional to the growth rate and high growth rates reduce the strength of
the three-dimensional structure of the microbial community (Liu et al., 2003; Morvai et al., 1992;
Quarmby & Forster, 1995).
2.4.6 WASTEWATER COMPOSITION/CHARACTERISTICS OF SUBSTRATE The composition of the waste stream fed into the reactor can influence the formation, composition
and structure of anaerobic granules. Substrate can be distinguished as high-energy and low-energy
feed, depending on the free energy of oxidation of organics. Rather simple carbohydrate sources are
categorized as high-energy carbohydrate feed, and can promote the growth of hydrolytic and
acidogenic bacteria and the production of ECP. However, the more complex the substrate composition,
the wider the diversity of methanogenic sub-populations. During research on the principles of start-up
and operations of anaerobic systems, Hickey et al. found in 1991 that wastewater containing 10%
sucrose and 90% VFA mixture created granular and flocculent sludge that could not be effectively
separated. By contrast, the use of acidified wastewater gave rise to the formation of a rather
voluminous type of sludge (R. F. Hickey et al., 1991). From this, it can be concluded that the formation
20
and microstructure of the granules depend on the characteristics and, thus, on the complexity of the
applied substrate (Chen & Lun, 1993; Liu et al., 2003; Wu, 1991).
2.4.7 ADDITION OF NATURAL AND SYNTHETIC POLYMERS According to some structural granulation models, the development of granules from non-granular
sludge is initiated by the microbial attachment on nuclei or biocarriers. The enhancement of the
granulation process can happen in two ways. The first way is the use of synthetic or natural polymers
to imitate the function of ECP, which promotes microbial agglomeration. Similarly, other polymers can
be used to facilitate the granulation process in a UASB start-up (Wang et al., 2005). El-Mamouni et al.
demonstrated in 1998 that the granulation rate in a chitosan containing reactor was 2.5 times higher
than in a reactor to which no polymers were added, while the SMA remained unchanged. However,
synthetic polymers have several disadvantages, such as inhibitory effect on substrate transfer to
granules, they are less safe for the environment and are not biodegradable. Therefore, research also
focuses on natural polymers, such as bio-flocculants (El-Mamouni et al., 1998; Wang et al., 2005). A
second way to enhance granulation is to use synthetic or natural polymers as inert nuclei or biocarrier.
The addition of inert particles, such as zeolite, improved the formation of granules. Also adding water-
absorbing polymers seemed to promote the granulation rate. It can be concluded that the presence of
synthetic or natural polymers can assist anaerobic granulation (Hulshoff Pol, 1989; Imai, 1997; Liu et
al., 2003).
2.4.8 ADDITION OF CATIONS To a certain extent, the addition of cations, such as Ca2+, Mg2+, Fe2+ and Al3+, can have a positive effect
on the formation of granules. As already mentioned in section 2.3.1.3, adding Ca2+ in concentrations
ranging from 80 – 150 mg/L or 150 – 300 mg/L can stimulate granule formation in several ways. The
first way can be explained by the secondary minimum adhesion model. The DLVO curve shows that
when two negatively charged bacterial cells approach each other, both particles will experience
electrical repulsion, preventing one particle adhering to another. Calcium will bind to the negatively
charged surfaces, thereby neutralizing the charges on the bacterial surfaces. This will lead to a decrease
in electrical repulsion and the adhesion between two cells will be facilitated (Rouxhet & Mozes, 1990b).
Another possible way to enhance anaerobic granulation is the binding between Ca2+ and ECP. As
already mentioned in 2.3.1.3, according to the multi-valence positive ion-bonding model, ECP can bind
to Ca2+ and form a ECP – Ca2+ – ECP bridge, which can serve as the initial structure of growing granules
(Forster & Lewin, 1972; Rudd et al., 1984). The last way of improving the granulation rate ties in with
the proton translocation dehydration theory. Calcium can aid the breakdown of hydrogen bonds
between bacterial cells and, consequently, the neutralization of negatively charged surfaces of the
microorganisms. In other words, Ca2+ can help in inducing dehydration of bacterial cell surfaces (Tay
et al., 2000). Important to raise is that high Ca2+ concentrations of over 600 mg/L may be detrimental,
because of the precipitation of CaCO3 in the granules themselves, which may block intragranular pores,
leading to severe mass transfer limitation and higher ash content in granules and, consequently, to a
lower SMA (Langerak et al., 2000; Yu et al., 2001). If the precipitation occurs outside of the granules,
CaCO3 can provide inert support for bacterial attachment. This leads to granules containing high
amounts of ash accompanied by a relatively high SMA (Kettunen & Rintala, 1998; Liu et al., 2003; Tiwari
et al., 2006).
21
MATERIAL AND METHODS
1 EXPERIMENTAL APPROACH Three different experiments were performed. During each experiment, two UASB reactors were
operated under steady state. One reactor (reactor A) was inoculated with alginate (1.3%) encapsulated
anaerobic granular sludge, while the other reactor (control reactor) was inoculated with non-
encapsulated anaerobic granular sludge. A different type of influent was used for every experiment to
test the effect of the applied influent on the alginate matrix. During the first experiment (1), molasse
was used as influent. In the second experiment (2), a synthetic medium, without C-source (synthetic
medium 1) was composed and fed to the reactor, while in the last experiment a synthetic medium with
C- source (synthetic medium 2) was applied as feed for the reactors. During the last experiment,
different variations regarding the encapsulation matrix were evaluated. These variations could be
divided into four parts. The first variation was the encapsulation with 1.3% alginate (3a). The second
variation was the encapsulation with 1.3% alginate, mixed with glucose (3b). The third variation
contained the encapsulation with a concentration of 1.8% alginate, instead of 1.3% (3c), and the last
variation contained an encapsulation matrix, made of 0.5% alginate and carboxymethylcellulose
(CMC)(3d). The main difference between molasse and the synthetic medium was the amount of PO43-
. Table 1 gives an overview of the three conducted experiments.
Before the start of the experiments, both reactors were run for 54 days to let the microbial biomass
adapt to the environment. During this start-up period, reactor A was inoculated with 1.3% alginate
encapsulated granular sludge, while the control reactor was inoculated with non-encapsulated
granular sludge and the influent that was used, was molasse.
Table 1 - Overview of the start-up and the three different experiments
Encapsulation matrix Type of influent Duration (days)
Start-up
1.3% alginate Molasse 54
Experiment 1
1.3% alginate Molasse 15
Experiment 2
1.3% alginate Synthetic medium 1 12
Experiment 3
a 1.3% alginate Synthetic medium 2 9 b 1.3% alginate + D-glucose Synthetic medium 2 8 c 1.8% alginate Synthetic medium 2 11 d Carboxymethylcellulose (CMC) + alginate Synthetic medium 2 15
22
2 EXPERIMENTAL SET-UP AND OPERATION
2.1 REACTOR SET-UP During the entire experiment, two identical UASB
set-ups were used with a volume of 2L. Both
reactors were inoculated with anaerobic granular
sludge at a concentration of 447.93 g sludge/2L
reactor, obtained from the Alpro manufactory in
Wevelgem. The sludge of one of the two reactors
(reactor A) was encapsulated in an alginate matrix.
At the bottom of each reactor a marble was placed,
to restrict the loss of sludge. Both reactors were
attached with two clamps to a tripod and were
placed in a spill box. The influent from the influent
vessel was pumped into each reactor at the bottom,
through PVC tubing, with the help of a peristaltic
pump (ProMinent®, flow rate reactor A 22.7
mL/min, control reactor 20.0 mL/min). A timer was
used to control the amount of influent entering the
reactors to ensure this was the same in both
reactors. The effluent, leaving the system at the top
of both reactors, ended up, via PVC tubing, in the
effluent vessels standing in the spill box. The loop
system at the top of each reactor prevented oxygen
from entering the reactors via the effluent tubing.
The recirculation of the sludge through both
reactors was accomplished using a recirculation pump, Leroy Somer Varmeca, that pumped at a
constantly flowrate to obtain the desired upflow velocity of 1m/h. Each reactor was sealed with the
help of a rubber plug that was pierced with a plastic pipette. One end of the pipette came out inside
of the reactor and the other end of the pipette was connected to PVC tubing, outside of the reactor.
Through this tubing, gas could flow out of the reactors and could enter the gas counter. After the
amount of gas was measured, gas was led, via tubing, to an exhaust system. Gas samples were
collected using a syringe with a needle. Between the rubber plug and the gas counter, a serum flask
was connected to the tubing to protect the gas counter from potential incoming liquid. The gas counter
existed of two parts. The first part was a U-shaped tube, connected with the reactors, filled with
mineral oil. However, there was still a headspace of approximately 13 mL left for the gas to enter the
tube. When the gas eventually entered the tube, the mineral oil was being pushed upwards by the gas,
until it reached the eye of a detector, which was the second part of the gas counter. The gas counter
gave a click, and simultaneously released the incoming gas via a tubing to the exhaust system. Knowing
the amount of clicks and knowing the volume of the headspace in the U-shaped tube, the amount of
biogas produced could be calculated. However, only in experiment 2 the gas counters were used to
calculate the amount of biogas produced. Due to gas leaks and failure of the gas counters, the amount
of biogas produced in experiment 1 and 3 were calculated theoretically with the help of the COD
measurements. It should also be noted that the gas counter was an in-house system, which is therefore
not commercially available. Figure 9 gives a picture of the experimental set-up of the control reactor.
Figure 9 - Experimental set-up of the UASB
reactor
23
2.2 FEEDSTOCK During the start-up, both reactors were fed with molasses for 54 days, after which they were shut
down for 45 days. After 45 days, both reactors were again fed with molasse for a period of 12 days, to
get them working again. After these 12 days, the first experiment took place in which both reactors
were fed with molasse during 15 days. In the second experiment, the molasses influent from
experiment 1 was replaced by a synthetic medium without glucose, which was fed to the reactors for
12 days. In the last experiment, both reactors were fed with a synthetic medium with glucose. During
each experiment, every Monday, Wednesday and Friday, fresh influent was made and fed to the
reactors. At the start of each experiment, the microbial biomass of both reactors was mixed to ensure
the same microbial community in both reactors. After mixing, half of the sludge was added to the
control reactor, while the other half was used again to make fresh encapsulated alginate beads, which
were, thereafter, added to reactor A.
2.2.1 START-UP During the start-up, both UASB reactors were fed with molasse, obtained from AVEBE in the
Netherlands, which contained 453.48 g COD/kg molasse. The applied OLR was 5 g COD/L/d. To obtain
this OLR, 22.05 g molasses was diluted in 1 L of water4. However, at the initiation of the experiments,
the sludge needed to adapt to the novel feedstock5 and the environment. Therefore, the OLR may not
be too high, which made an OLR of 1 g COD/L/d suitable at the start. Every week, the OLR was increased
with 1 g COD/L/d, until after 5 weeks the desired OLR of 5 g COD/L/d was reached. The OLR was
increased by the adjustment of the amount of incoming influent, which was increased from 200 mL/d
to 1000 mL/d in steps of 200 mL/week.
2.2.2 EXPERIMENT 1 During the first experiment, the same molasse was used as feedstock as during the start-up. By diluting
the molasse 45 times, an OLR of 5 g COD/L/d could be applied immediately.
2.2.3 EXPERIMENT 2 In the second experiment, a synthetic medium was made, which contained a buffer solution, a
macronutrient solution and a trace element solution. No C-source was added to the synthetic medium.
The reason to change from molasse to this medium, was to obtain a lower amount of PO43- in the
reactor. The exact composition of the synthetic medium can be found in Table 2, under the name of
‘Synthetic medium 1’. The anion and cation composition are calculated theoretically and can be found
in Appendix 1 (Table 12).
2.2.4 EXPERIMENT 3 In the last experiment, the same synthetic medium as in 2.2.3 was used, but this time with the addition
of a C-source, in particular D-glucose. Because the sludge was already adapted to the environment
during the start-up, there was no need to slightly increase the OLR, and immediately an OLR of 2.5 g
COD/L/d was applied. To obtain 2.5 g COD/L/d, the influent vessel should contain 2.35 D-glucose/L.
The reason why 2.5 g COD/L/d was applied and not 5 g COD/L/d was the following. Glucose is the
4 Diluting 22.05 g molasse in 1L water corresponds to a dilution factor of 45 5 The sludge came from the Alpro manufactory in Wevelgem, which was stored in a cold room of 4°C
24
easiest carbon source for microorganisms to degrade. It would be degraded very fast and possible
acidification could occur, because the acetogenic bacteria couldn’t degrade the produced VFA fast
enough with accumulation of VFA as result. Adding 2.5 g COD/L/d instead of 5 g COD/L/d reduces this
risk. The composition of this medium is listed in Table 2 under the name ‘Synthetic medium 2’.
Table 2 - Composition of synthetic medium 1 (without C-source) and synthetic medium 2 (with C-
source)
Synthetic medium 1 Synthetic medium 2
Carbon source (g/L)
D-glucose 0 2.35
Buffer (g/L)
KH2PO4 0.07 0.07 K2HPO4 0.09 0.09 NaHCO3 0.84 0.84 KHCO3 1.00 1.00 K2HPO4.3H2O 0.11 0.11
Macronutrients (g/L)
CaCl2.2H2O 0.2 0.2 MgCl2.6H2O 0.1 0.1 Fe2(SO4)3 0.1 0.1 NH4Cl 0.5 0.5
Trace elements solution (μg/L)
NiSO4.6H2O 500 500 MnCl2.4 H2O 500 500 FeSO4.7H2O 500 500 ZnSO4.7H2O 100 100 H3BO3 100 100 Na2MoO4.2H2O 50 50 CoCl2.6H2O 50 50 CuSO4.5H2O 5 5
(Aiyuk & Verstraete, 2004; Vrieze et al., 2013)
2.3 SLUDGE INOCULUM Both reactors were inoculated with anaerobic granular sludge derived from the Alpro manufactory.
The volatile suspended solids (VSS) content of the sludge was 44.65 g VSS/kg sludge, while the desired
amount of VSS in the reactors was 10 g VSS/L. Therefore, to each reactor, 447.93 g sludge was added.
However, at the beginning of each experiment, the sludge of reactor A was encapsulated in an alginate
matrix.
25
2.3.1 START-UP During the start-up, the sludge of reactor A was encapsulated in a 1.3% (w/v) alginate matrix. First,
73.52 g of CaCl2.2H2O was added to 1 L of distilled water to make a 0.5 M CaCl2 buffer (7.3% (w/v) CaCl2
solution). Next, 447.93 g of sludge was added to 552.07 mL tap water to obtain 20 g VSS/L6. To this
solution, 15 g of sodium alginate was added to obtain a final concentration of 1.5% sodium alginate or
1.3% alginate. After mixing with a magnetic stirrer, the alginate beads were formed with the help of a
micropipette of 1 mL and dripped in the CaCl2 buffer7. Reactor A had a volume of 2 L, thus adding a
suspension of 1 L alginate beads with a content of 20 g VSS/L and 1 L of tap water to a reactor volume
of 2 L, made a final reactor content of 10 g VSS/L.
2.3.2 EXPERIMENT 1 In the first experiment, the encapsulation of the sludge was done in the exact same way as in section
2.3.1.
2.3.3 EXPERIMENT 2 In the second experiment, the encapsulation of the sludge was done in the exact same way as in section
2.3.1.
2.3.4 EXPERIMENT 3 In the third and last experiment, four different variations on the encapsulation of the granules were
tested. It should be noted that at the start of experiment 3, each reactor contained only 368 g sludge,
in contrast to the 447.93 g sludge at the beginning of experiment 1. In the first part of experiment 3,
the encapsulation of the sludge was conducted in the same way as in 2.3.1. The second part of the
experiment contained the first variation. Alginate beads were prepared in the same way as during the
start-up. However, before dripping the sodium alginate-sludge solution into the CaCl2 buffer, 5 g/L of
D-glucose was added to this solution. The hypothesis of the addition of glucose was to give the
microorganisms an initial and easy to degrade C-source to avoid the degradation of the alginate matrix
by the biomass. After a certain moment, channels are made in the beads, which can deliver glucose
from the influent to the granules, which eliminates the need of degrading the alginate matrix. Before
the encapsulation started, 87 g fresh granular sludge was added to each reactor to obtain 447.93 g
sludge per reactor again. In the third part of the experiment, again alginate encapsulated granular
sludge was applied, but this time with an alginate concentration of 1.8% instead of 1.3%. Due to some
sludge losses during the production of the alginate beads of the second part, each reactor contained
415 g of sludge instead of 447.93 g sludge. In the last part, an article of Youngsukkasem et al., 2012
was called upon to create an encapsulation matrix with a concentration of 0.5% alginate and 2.6% of
CMC. First the sludge was suspended in a 2.6% (w/v) CaCl2 solution, containing 2.6% CMC, with a
volume ratio of digesting sludge to CaCl2 and CMC solution of about 1:18. The degree of substitution
(DS) of the CMC was 0.9 and was added to increase the viscosity. This solution was further dropped
into a 0.6% sodium alginate (i.e. 0.5% alginate) solution containing 0.1% (v/v) Tween 20 to improve the
permeability of the encapsulation matrix. The resulting solution was stirred. After stirring, the beads
6 Alginate beads with a VSS content of 20 g VSS/L ensures a more concentrated biomass per bead. 7 Alginate is a polymer, which can crosslink with divalent cations, such as Ca2+, to form a gel. 8 448 g sludge and 52 mL water were added to 500 mL of CaCl2 and CMC solution. This will give an end concentration in the reactor of 10 g VSS/L
26
were washed with distilled water and allowed to harden in a 1.3% (w/v) CaCl2 solution for 20 minutes.
Before the encapsulation of the last part of experiment 3 started, 58 g of fresh granular sludge was
added to each reactor to obtain 447.93 g sludge per reactor again.
2.4 VOLUMETRIC METHANE PRODUCTION AND METHANE YIELD The volumetric methane production in mL CH4/L/d and the methane yield in % were calculated for
each experiment. In the second experiment, the amount of methane gas produced was determined
with the help of the gas counters. In experiment 1 and 3 the amount of methane gas produced was
calculated theoretically, without the use of gas counters, by multiplying the consumed COD with
350mL CH4 per gram of COD9. The methane yield was calculated for experiment 1 and 3 by dividing the
consumed COD per day by the actual added COD per day, not the theoretical added COD. No methane
yields were calculated for experiment 2. This can be explained as follows. Both influent vessels didn’t
contain any COD, because no carbon source was added to the synthetic medium. Therefore, the added
COD per day equals zero. If the methane yield is calculated, one need to divide by the added COD,
which is zero and division by zero is undefined.
3 ANALYTICAL TECHNIQUES
3.1 TOTAL KJELDAHL NITROGEN The total Kjeldahl nitrogen (TKN) was analyzed according to Standard methods (4500-Norg B; APHA,
1992). The total organic nitrogen was determined as the difference between TKN and the total
ammonium nitrogen (TAN). Following equation is used to calculate the total organic nitrogen present
in the sample:
𝑂𝑟𝑔𝑁 (𝑚𝑔 𝑁/𝐿) = 𝑇𝐾𝑁 − 𝑇𝐴𝑁
First, sample preparation for the TKN analysis need to be done. One mL (whether or not diluted)
sample was added to 19 mL of distilled water and placed in a Kjeldahl tube. This was done in triplicate.
The dilution was necessary to obtain a concentration between 9 and 250 mg NH4+-N/L in the tube.
Next to the samples, also three blanks, filled with 20 mL of distilled water, were analyzed. Subsequently
10 mL of sulfuric acid and one Kjeldahl tablet was added to each tube. A Kjeldahl tablet contained 5 g
K2SO4, 0.50 g CuSO4.5H2O/tablet. After preparation of the samples, the tubes were preheated in the
destruction block (150°C) to convert all the nitrogen to ammonia. Next, the temperature was increased
up to 400°C and samples were digested for 2 hours at this temperature. After 2 hours, samples were
cooled down and distillation could take place in the distillation apparatus (Gerhardt, Vadopest 30S,
Königswinter, Germany). The samples were placed in the left side of the machine and a recipient with
20 mL of a boric acid indicator with a pH of 5.3, was placed in the right side of the machine. Ammonia
was distilled, condensed and further dissolved in the boric acid solution with indicator. After
distillation, the samples were titrated with HCl with the help of an automatic titration unit
9 The consumption of 1 g COD yields 350 mL CH4
27
(Metrohm,719S Titero, Herisau, Switzerland) to determine the total amount of NH3 present in the
sample. The following equation was used to determine mg TKN-N per liter of sample:
𝑇𝐾𝑁 (𝑚𝑔 𝑁/𝐿) =(𝐴 − 𝐵) ∗ 𝑇 ∗ 14.001 ∗ 1000 ∗ 𝑓
𝑉𝑠𝑎𝑚𝑝𝑙𝑒
A: volume HCI titrated for the sample (mL)
B: volume HCI titrated for the blank (mL)
F: dilution factor
T: titer of the HCI solution (0.02 N or adapted)
Vsample: volume of the sample in mL (mL)
3.2 TOTAL AMMONIA NITROGEN The total ammonia nitrogen (TAN) was measured to determine, together with the TKN, the total
amount of organic nitrogen. Three sample replicates and three blanks were prepared. One mL of
sample was added to 9 mL of distilled water and placed in a Kjeldahl tube. The three blanks contained
20 mL of distilled water. Samples were diluted to obtain a concentration between 5 and 300 mg NH4+-
N/L in the tube. Subsequently, 0.4 g of MgO was added to each sample. After the sample preparation,
samples were placed one by one in the left side of the distillation apparatus (Gerhardt, Vadopest 30S,
Königswinter, Germany) and a recipient with 20 mL of a boric acid indicator with a pH of 5.3, was
placed in the right side of the machine. After distillation, the samples were titrated with HCl with the
help of an automatic titration unit (Metrohm,719S Titero, Herisau, Switzerland) to determine the total
amount of NH3 present in the sample. The following equation was used to determine mg TAN-N per
liter of sample:
𝑇𝐴𝑁 (𝑚𝑔 𝑁/𝐿) =(𝐴 − 𝐵) ∗ 𝑇 ∗ 14.001 ∗ 1000 ∗ 𝑓
𝑉𝑠𝑎𝑚𝑝𝑙𝑒
A: volume HCI titrated for the sample (mL)
B: volume HCI titrated for the blank (mL)
F: dilution factor
T: titer of the HCI solution (0.02 N or adapted)
Vsample: volume of the sample in mL (mL)
3.3 TOTAL SUSPENDED SOLIDS AND VOLATILE SUSPENDED SOLIDS Solids analyses were performed by centrifugation of a known sample volume and by weighing the
difference between the pellet before and after drying at 105°C (TSS) and incinerating at 550°C (VSS).
The total suspended solids (TSS) are the solid residues (both organic and inorganic fraction) left in a
vessel after separation from the aqueous phase by means of centrifugation. The volatile suspended
solids (VSS) represent the amount of organic material, present in the sample. First, a crucible was
placed in the oven (Memmert, Schwabach, Germany) at 103-105°C for 1 hour. Next, the crucible was
cooled down in the desiccator, after which the initial dried crucible was weighed (A). The sample was
28
placed in a falcon tube, which was centrifugated during 10 minutes at 7830 g. The supernatant was
poured away, and the pellet was washed with distilled water, after which it was again centrifugated.
The supernatant was again poured away, and the pellet was transferred to the crucible. After weighing
the filled crucible (B), the sample was evaporated by placing it overnight, in the oven at 103-105°C.
The crucible was cooled down to balance temperature and was again weighed (C). Subsequently, the
crucible was placed in the muffle oven (Nabertherm, GmbH, Lilienthal, Germany) at 550°C for at least
1.5 hours to analyze the VSS. The sample was again cooled down in the desiccator and was again
weighed (D). The following equations were used to determine the amount of TSS and VSS in the
sample:
𝑚𝑔 𝑇𝑜𝑡𝑎𝑙 𝑠𝑢𝑝𝑠𝑒𝑛𝑑𝑒𝑑 𝑠𝑜𝑙𝑖𝑑𝑠 (𝑇𝑆𝑆)
𝐿=
(𝐶 − 𝐴) ∗ 106
𝑠𝑎𝑚𝑝𝑙𝑒 𝑣𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿)
𝑚𝑔 𝑣𝑜𝑙𝑎𝑡𝑖𝑙𝑒 𝑠𝑢𝑝𝑠𝑒𝑛𝑑𝑒𝑑 𝑠𝑜𝑙𝑖𝑑𝑠 (𝑉𝑆𝑆)
𝐿=
(𝐶 − 𝐷) ∗ 106
𝑠𝑎𝑚𝑝𝑙𝑒 𝑣𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿)
3.4 TOTAL SOLIDS AND VOLATILE SOLIDS The total solids (TS) represent the total dry matter content present in the sample, both the organic as
the inorganic fraction, while the volatile solids (VS) only represent the organic fraction of the sample.
The same method that was used for the TSS and VSS measurement, was used for the TS and VS
measurement. However, there was no need for centrifugation of the samples, as both soluble and
suspended materials were included. The following equations were used to determine the amount of
TS and VS in the sample:
% 𝑡𝑜𝑡𝑎𝑙 𝑠𝑜𝑙𝑖𝑑𝑠 (𝑇𝑆) = (𝐶 − 𝐴) ∗ 100
𝐵 − 𝐴
% 𝑣𝑜𝑙𝑎𝑡𝑖𝑙𝑒 𝑠𝑜𝑙𝑖𝑑𝑠 (𝑉𝑆) = (𝐶 − 𝐷) ∗ 100
𝐵 − 𝐴
3.5 PH On Monday, Wednesday and Friday, when fresh medium was made, the pH of the effluent in the
reactors was also measured. This was done with the Consort C5010 pH probe. At the beginning of
every week, the pH probe was calibrated. The calibration was accomplished with the help of three
standard calibration buffers with a pH of respectively 4, 7 and 9.
29
3.6 CHEMICAL OXYGEN DEMAND The chemical oxygen demand (COD) is the amount of oxygen required to oxidize organic carbon
completely to CO2 by chemical means. The COD of the fresh made influent, the old influent and the
effluent in the vessel were analyzed with Nanocolor® kits (CODE; Macherey-Nagel). Depending on the
COD density in the concerned samples, Nanocolor tubes with different concentration ranges were
chosen (COD15000: 1-15 g O2/L, COD1500: 100-1500 mg/L, COD160: 15-160 mg/L, COD40: 2-40mg/L).
First, the samples were added to the Nanocolor tubes. Next, the Nanocolor tubes (with the addition of
the samples) were put in a destruction block with a temperature of 148°C, for 2 hours. After heating,
the tubes were shaken and were cooled for 30 minutes. The COD content was determined with the
help of the Nanocolor 500D spectrophotometer (Machery-Nagel, Nanocolor 500D, Düren, Germany).
3.7 BIOGAS COMPOSITION The composition of the biogas produced by the two reactors, i.e., CH4, CO2, H2 and H2S, was analyzed
with a Compact GC (Global Analyzer Solutions, Breda, The Netherlands), equipped with a Molsieve 5A
pre-column and Porabond column (CH4, O2, H2 and N2), and a Rt-Q-bond pre-column and Rt-QS-bond
column (CO2, N2O and H2S). Concentrations of gases were determined by means of a thermal
conductivity detector. The LOQ (Limit Of Quantification) for each gas is around 0.05% (500 ppm). The
analysis of the biogas compounds was carried out by three methods. Each method measured one of
the three compounds. The first method measured CH4, N2, O2 and H2, the second method measured
CO2 and N2O, and the last method was responsible for the amount of H2S. A gas sample of 10 mL was
taken with a syringe, and was placed on a rubber stopper to avoid mixing of biogas with the air. During
every method, approximately 2 mL of gas was injected in the Compact GC, after which the amount of
each compound could be read of the computer screen.
3.8 VOLATILE FATTY ACIDS The volatile fatty acids (VFA) (C2-C8) analysis was performed according to Andersen et al., 2014. The
C2-C8 fatty acids (including isoforms C4-C6) were measured by gas chromatography (GC-2014,
Shimadzu®, The Netherlands) with a DB-FFAP 123-3232 column (30m x 0.32 mm x 0.25 µm; Agilent,
Belgium) and a flame ionization detector (FID). Two mL of sample was conditioned with 0.500 mL 50
% H2SO4, 0.4 g of NaCl and 0.400 mL 2-methyl hexanoic acid as internal standard for further extraction
with diethyl ether. The prepared sample (1 µL) was injected at 280°C with a split ratio of 60 and a purge
flow of 3 mL/min. The oven temperature increased by 6°C/min from 110°C to 158°C and by 8°C/min
from 158°C to 175°C where it was kept for 1 minute. The FID had a temperature of 220°C. The carrier
gas was nitrogen gas at a flow rate of 2.49 mL/min. The detection limit of acetate was 30 mg/L, of
propionate 10 mg/L and of the other fatty acids 2 mg/L. The upper detection limit was 1000 mg/L.
30
3.9 CATIONS Na+, NH4
+, K+, Mg2+ and Ca2+ were determined on a 761 Compact Ion Chromatograph (IC) (Metrohm,
Switzerland), equipped with a conductivity detector. Ten mL of the samples were first centrifugated at
17 090 g for 10 minutes. The supernatant was filtered with a 0.20 μm filter. The filtered samples were
diluted 50x by adding 500 μL of the filtered samples to 9.5 mL of MQ water. The dilution was necessary,
because the detection limit of the IC ranged from 2 to 100 mg cation/L. The obtained samples were
placed in the autosampler of the IC. The IC contained a silica gel with carboxyl groups (stationary
phase), which retains the different cations during a determined period (retention time). Next, cations
are eluted from the column by the eluent (mobile phase). Cations are quantified based on conductivity
by means of a calibration curve.
3.10 ANIONS Cl-, NO3
-, PO43- and SO4
2- were determined on a 761 Compact Ion Chromatograph (IC) (Metrohm,
Switzerland), equipped with a conductivity detector. Ten mL of the samples were first centrifugated at
17 090 g for 10 minutes. The supernatant was filtered with a 0.45 μm filter. The filtered samples were
diluted 40x by adding 250 μL of the filtered samples to 9.750 mL of MQ water. The dilution was
necessary, because the detection limit of the IC ranged from 0.5 to 100 mg anion/L. The obtained
samples were placed in the autosampler of the IC. The IC contained a silica gel with carboxyl groups
(stationary phase), which retains the different cations during a determined period (retention time).
Next, cations are eluted from the column by the eluent (mobile phase). Cations are quantified based
on conductivity by means of a calibration curve.
4 BIOCHEMICAL METHANE POTENTIAL (BMP) TEST Biochemical methane potential (BMP) tests were performed in batch reactors, each with a working
volume of 80 mL and a headspace of 40 mL. The experiment was operated for 20 days under mesophilic
conditions (34°C). The inoculum consisted of anaerobic granular sludge. The test contained 6 serum
flasks. To three flasks, a specific amount of 1.3% alginate encapsulated anaerobic granular sludge was
added to obtain a final VS concentration of 10 g VSS/L. To the other remaining three flasks, a specific
amount of non-encapsulated anaerobic granular sludge was added, also to obtain a final concentration
of 10 g VSS/L. Next, molasse was added with a substrate to inoculum ratio of 0.5 g COD/g VSS. Finally,
tap water was added to acquire a total liquid volume of 80 mL in each bottle. After inoculum and
substrate addition, the serum flasks were sealed to avoid air intrusion, and connected to air-tight gas
columns by means of an air-tight needle. These gas columns were placed in a water bath containing a
solution of distilled water and HCl at pH < 4.3 to avoid CO2 in the biogas from dissolving. The serum
flasks were incubated in a linear shaking water bath (Grant, GLS Aqua 18 Plus, Shepreth, England) at
the chosen temperature. Volumetric biogas production was evaluated by means of water
displacement in the gas columns. Figure 10 shows a schematic overview of the BMP set-up. Biogas
production was measured on daily basis for 20 days, until the biogas production didn’t change anymore
for more than 3 days. Biogas volumes were reported at standard temperature (273 K) and pressure
(101 325 Pa) (STP) conditions. Biogas composition was evaluated at the end of the experiment with
the compact GC. The volumetric gas production of methane was expressed as the volume of methane
per liter per day (mL CH4/L/d).
31
5 BATCH TESTS
5.1 SHEAR STRESS BATCH TEST A batch test was performed to test whether the alginate matrix was resistant towards shear stress,
present in the reactor. This test also gave information about the degradation of the alginate matrix by
the microorganisms in the granules. The batch test contained 32 serum flasks. The first 18 serum flasks
contained 10 g VSS/L of 1.3% alginate encapsulated anaerobic granular sludge. The other 18 flasks
were filled with 1.3% alginate beads that didn’t contain any sludge, but only tap water. To imitate
reactor conditions in the flasks, each flask was filled with effluent of the reactors of experiment 1, with
molasse as influent. The ion concentration of the effluent can be found in Table 3. Nine of the 18 serum
flasks, containing the alginate encapsulated sludge, and nine of the 18 serum flasks, containing
aqueous alginate beads, were placed on a shaker (120 rpm) for 14 days to imitate the shear stress in
the reactor. Table 4 shows an overview of the set-up of the batch test. On day 0, 3, 7, 11 and 14, the
content of each of the serum flasks was placed individually on a grid of 1 cm2 to see whether the beads
decreased in size or not. After taking a photograph, the flasks were again filled with the same beads
and effluent as they contained before. Figure 11 shows the difference between the sludge containing
alginate beads (left) and the water containing alginate beads (right), placed on a mm grid with a total
surface of 0.25 cm2.
Table 3 – Cation concentrations in mg/L of the effluent that was added to the serum flasks of the
shear stress batch test
Na+ NH4+ K+ Ca2+ Mg2+ PO4
3-
Effluent (mg/L) 92 493 1927 1927 44 237
Figure 10 – Schematic overview of the BMP set-up
32
Table 4 - Overview of the set-up of the shear stress batch test
Shaking (+) Shaking (-)
Sludge (+) 9 flasks 9 flasks Sludge (-) 9 flasks 9 flasks
Figure 11 - Sludge containing alginate beads (left) and water containing alginate beads (right)
5.2 POTASSIUM AND PHOSPHATE BATCH TEST The second batch test was used to evaluate if the 1.3% alginate matrix was affected by different
concentrations of PO43- and K+. Eight different treatments were conducted, with 3 replicates of each
treatment. This provides a total of 24 serum flasks. Each serum flask was filled with 40 mL of synthetic
medium (section 2.2.3, without D-glucose) and a certain amount of K+ (in the form of KCl) and/or a
certain amount of PO43- (in the form of Na2HPO4). Table 5 gives an overview of the different
concentrations of K+ and PO43- of the different treatments, as well as the concentrations of Cl- and Na+.
The 24 serum flasks were placed on a shaker (120 rpm) to imitate reactor shear conditions, for 24 days.
On day 0, 2, 7, 10, 14 and 24 the content of each of the serum flasks were placed individually on a mm
grid with a total surface 1 cm2 to see whether the beads decreased in size or not. After taking a
photograph, the flasks were again filled with the same beads as they contained before.
Table 5 - Overview of the different potassium and phosphate treatments
K+ (g/L) Cl- (g/L) Na+ (mg/L) PO43- (mg/L)
Treatment 110 n.a.* 37.9 49 n.a.* Treatment 2 0.875 0.80 0 0 Treatment 3 1.75 1.60 0 0 Treatment 4 3.5 3.20 0 0 Treatment 5 0 0 98 202.5 Treatment 6 0 0 196 405 Treatment 7 0 0 392 810 Treatment 8 1.75 1.60 196 405
*n.a. = not available
10 Values for tap water derived from The open University
33
RESULTS
1 CHARACTERIZATION OF INOCULUM The TSS and VSS concentrations of the fresh granular sludge were measured in quadruplicate (Table
6). The TSS represent both the inorganic and organic fraction of the sludge, while the VSS only include
the organic material of the sludge. Both anion and cation measurements were also conducted on the
granular sludge in triplicate (Table 6).
Table 6 - Mean TSS and VSS values of the granular sludge inoculum in g/kg and their associated
standard deviations and mean cation and anion concentrations in mg/L of the granular sludge and
their associated standard deviations.
TSS
Sludge (g/kg) 51.6 ± 1.7
VSS
Sludge (g/kg) 44.7 ± 1.5
Cations
Na+ NH4+ K+ Ca2+ Mg2+
Sludge (mg/L) 103.6 ± 25.8 106.5 ± 26.4 1702.7 ± 85.6 19.9 ± 34.6 105.6 ± 4.1
Anions
Cl- PO43- SO4
2- NO3-
Sludge (mg/L) 145.2 ± 3.6 93.7 ± 9.1 254.4 ± 15.1 57.9 ± 0.5
2 CHARACTERIZATION OF MOLASSE The total organic ammonia nitrogen content was calculated by determining the TKN and the TAN in
triplicate. Measurements of the TS and VS of the molasse were analyzed in quadruplicate, while COD
measurements were done in duplicate. Anion, as well as cation triplicate measurements were also
conducted on the molasse (Table 7).
34
Table 7 - TAN, TKN, total organic nitrogen of molasse in g N/L, TS, VS and COD values of molasse and
their associated standard deviations in g/kg and the mean cation and anion measurements of the
molasse and their associated standard deviations in mg/L.
Molasse
TKN (g N/L) 179.2 ± 73.1
TAN (g N/L) 6.0 ± 2.7
Total organic nitrogen (g N/L) 173.2 ± 73.2
TS (g/kg) 549.7 ± 1.9
VS (g/kg) 340.4 ± 7.7
COD (g COD/kg) 453.5 ± 16.4
Cations
Na+ NH4+ K+ Ca2+ Mg2+
Molasse (mg/L) 2749 ± 55 1743 ± 39.99 762 ± 436 122 ± 6 4529 ± 228
Anions
Cl- PO43- SO4
2- NO3-
Molasse (mg/L) 8800 ± 650 10 750 ± 245 7770 ± 170 2247 ± 23
3 START-UP Due to technical problems with the gas counters and the COD measurements, no reliable results of
biogas production could be obtained. After the inoculation of the reactor, the alginate matrix in reactor
A was completely disintegrated after 30 days (Figure 12).
The initial pH in reactor A was 6.39 and in the
control reactor 6.83, which showed a difference of
0.44 between the initial pH of both reactors. After
24 days, both reactors reached constant pH values
(7.28 ± 0.09 in reactor A and 7.26 ± 0.09 in the
control reactor), with only a limited difference of
0.02 between both reactors (Figure 13).
The total VFA concentration refers to the sum of
the carbon concentrations of acetate, propionate,
butyrate, iso-butyrate, valerate, iso-valerate,
caproate and iso-caproate 11 . Throughout the
entire start-up, VFA concentrations in both
reactors remained very low, with total VFA
concentrations not exceeding 30 mg C/L. These
low VFA values are likely not causing any changes
in pH.
11 Due to the low pH created when extracting the samples, VFA are converted into the free acid form.
Therefore, sum of all VFA concentrations can only be made if all the values are converted to mg C/L.
A Day 0 A Day 30 Control Day 0
Figure 12 - Evolution of the disintegration of
the alginate matrix during the start-up
(alginate 1.3%) in reactor A (left) and the
granular sludge on day 1 in the control reactor
(right)
35
Figure 13 - Graphs of the start-up of the reactors (1.3% alginate, molasse, reactor A: encapsulated
granules, control reactor natural granules): course of the pH of reactor A and the control reactor (top),
course of total VFA concentration of reactor A and of the control reactor (bottom)
0
250
500
750
1000
1250
1500
1750
2000
2250
2500
0 5 10 15 20 25 30 35 40 45 50
Cat
ion
co
nce
ntr
atio
n(m
g/L)
Time (Days)
Figure 14 - Graph of the cation concentrations during the start-up: Na+ encapsulated granules (---), Na+
natural granules (···), NH4+ encapsulated granules (---), NH4
+ natural granules (···), K+ encapsulated
granules (---), K+ natural granules (···), Ca2+ encapsulated granules (---), Ca2+ natural granules (···), Mg2+
encapsulated granules (---), Mg2+ natural granules (···)
6,20
6,40
6,60
6,80
7,00
7,20
7,40
7,60
7,80
0 5 10 15 20 25 30 35 40 45 50
pH
Time (Days)
0
5
10
15
20
25
30
0 5 10 15 20 25 30 35 40 45 50
Tota
l VFA
co
nce
ntr
atio
n(m
g C
/L)
Time (Days)
36
The Na+, NH4+ and the Mg2+ concentrations in both reactors remained lower than 500 mg/L. On the
contrary, the K+ concentrations of both reactors were much higher, with concentrations above 1000
mg/L, but they were following a similar course in both reactors. The Ca2+ concentrations in the control
reactor remained constant during the entire start-up with an average value of 130 ± 26 mg/L. In
contrast, the initial Ca2+ concentration in reactor A was 12 times higher than the initial Ca2+
concentration in the control reactor. After 10 days, the Ca2+ concentration in reactor A started to
decrease until after 30 days, a constant average Ca2+ concentration of 94 ± 26 mg/L was reached (Figure
14).
4 REACTOR EXPERIMENTS
4.1 DISINTEGRATION OF THE ALGINATE MATRIX The decrease in the level of sludge in reactor A can be explained by the disintegration of the alginate
matrix. In other words, the more the sludge level decreased, the more the alginate matrix was
disintegrated (Figure 15). For each experiment, a gradually disintegration of the matrix was observed.
In experiment 1 (1), the entire matrix was broken down after 10 days, in experiment 2 (2) after 13 days,
in the third part of experiment 3 (3c) after 7 days and in the last part of experiment 3 (3d) after 15
days. In experiment 2 and in the last part of experiment 3, the encapsulated granules were floating.
After 3 to 4 days, the beads sunk again.
Experiment 1, 1.3% alginate
A Day 0 A Day 7 A Day 10 Control Day 0
37
Experiment 2, 1.3% alginate
A Day 0 A Day 3 A Day 6 A Day 10 A Day 13 Control Day 0
Experiment 3c, 1.8% alginate
A Day 0 A Day 4 A Day 7 Control Day 0
38
Experiment 3d, 0.5% alginate + CMC
A day 0 A Day 2 A Day 6 A Day 8 A Day 9 A Day 13 A Day 14 A Day 15 Control Day 0
Figure 15 – Evolution of the disintegration of the alginate matrix in reactor A (left) and the granular
sludge of the control reactor on day 0 (right) of experiment 1, experiment 2, experiment 3 part 3 and
4
4.1 PH The two red lines in Figure 16 represent the optimal pH range for AD. The initial pH values of the
reactors of experiment 1 (1) were both 7.36. Between day 1 and day 7, the pH in reactor A tended to
be on average 0.29 ± 0.1 lower than in the control reactor. From day 7, pH values of both reactor A
and the control reactor were constant with values of 7.19 ± 0.05 and 7.31 ± 0.08, respectively. In
experiment 2 (2), the pH of the control reactor was little above the optimal upper limit for AD with a
constant value of 7.58 ± 0.02. The pH values of reactor A were lower with values of approximately 6.82
± 0.07. During the first, the second and the third part of experiment 3 the pH values of both reactors
stayed under the optimal lower limit for AD. In the first part of experiment 3 (3a), the pH in reactor A
was on average 0.19 ± 0.1 lower than the pH in the control reactor. At the start of the second part of
experiment 3 (3b) a severe drop in pH in reactor A from 6.64 to 6.02 was observed, after which the pH
increased again to value of 6.63. The pH in the control reactor stayed constant with a value of 6.72 ±
0.13. During the third part of experiment 3 (3c), the pH of the control reactor stayed constant with pH
values of 6.63 ± 0.03. The pH in reactor A remained at 6.58 ± 0.07, except after 5 days, where the pH
value decreased to a value of 6.42, after which it increased again. In the last part of experiment 3 (3d),
the pH values of both reactors started low (6.43 in reactor A and 6.56 in the control reactor), but
increased over time. After 8 days, reactor A reached a value of 6.96 and the control reactor a value of
7.13. After 8 days the pH values decreased again to values of 6.59 in reactor A and 6.56 in the control
reactor at the end of the last experiment (Figure 16).
39
4.2 VOLATILE FATTY ACIDS The graph of Figure 17 plotted the total VFA concentration in mg C/L as a function of time. The total
VFA concentration refers to the sum of the carbon concentrations of acetate, propionate, butyrate,
iso-butyrate, valerate, iso-valerate, caproate and iso-caproate. The most abundant VFA throughout
the experiments were acetate, propionate and butyrate. In the first experiment (1), the total VFA
concentration in reactor A started at 24 mg C/L, and increased to a value of 332 mg C/L on day 3, which
explains the drop in pH on day 3. The VFA concentrations of the control reactor remained below the
detection limit during the entire experiment, except at the start of the experiment and on day 7.
However, on day 3 a drop in pH in the control reactor could be observed, but the accumulation of VFA
was not the cause. During entire experiment 2 (2), total VFA concentrations of the control reactor were
approaching zero (6 ± 1 mg C/L), which is consistent with the constant pH course of the control reactor.
The total VFA concentration of reactor A started at 37 mg C/L, after which the concentration decreased
to a value of approximately 10 mg C/L after 9 days. It should be noted that the only VFA concentration
above the detection limit was caproate. The VFA concentrations of the control reactor in the first part
of experiment 3 (3a) were below detection limit, except on day 2 of experiment 3a, where the total
VFA concentration reached a value of 15 mg C/L. Such low VFA concentrations aren’t likely to cause
pH variations. However, the pH value of the control reactor decreased after 2 days with a value of
more than one pH unit. On the other hand, after 2 days, a high total VFA concentration (264 mg C/L)
was reached in reactor A. This is in line with the pH drop after day 2 of experiment 3a in reactor A.
After day 2, the amount of VFA in reactor A gradually decreased again, while the pH gradually increased
again. In the second part of experiment 3 (3b), after 3 days, a very high total VFA concentration (535
mg C/L) was reached in reactor A. This corresponds with the large pH drop in reactor A (6.02) after 3
days. After 3 days, the total VFA concentration in reactor A reached approximately zero again (6 ± 1
mg C/L). During the entire part 2 of experiment 3, VFA concentrations in the control reactor remained
close to zero, which is in line with the constant pH values in the reactor. During the entire third part of
Figure 16 - The pH of reactor A (containing granules encapsulated in alginate) and in the control
reactor (containing natural granules) during the different experiments
6,00
6,20
6,40
6,60
6,80
7,00
7,20
7,40
7,60
7,80
8,00
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
pH
Time (Days)
1 2 3a 3b 3c 3d
40
experiment 3 (3c), the total VFA concentrations in the control reactor were below the detection limit.
From day 3 to day 5 of experiment 3c, the total VFA concentration in reactor A was on average 148 ±
7 mg C/L. After 5 days, the total VFA concentration started to decrease to reach values below the
detection limit towards the end of the experiment. This is in line with the pH increase in reactor A,
after 5 days. During the entire last part of experiment 3 (3d), the total VFA concentrations in both
reactor A and B, reached approximately zero with values of 5 ± 4 mg C/L and 4 ± 3 mg C/L, respectively,
except after 2 days of the experiment, were the total VFA concentration in reactor A reached a value
of 61 mg C/L (Figure 17).
4.3 VOLUMETRIC METHANE PRODUCTION The volumetric methane production of both reactors started to increase after the start of the
experiment 1 (1). After day 2, the control reactor produced on average 427 ± 18 mL CH4/L/d more than
reactor A. Reactor A produced on average 1033 ± 218 mL CH4/L/d during experiment 1. At a certain
moment, on day 10, the tube of the influent pump of the control reactor came outside of the feed,
which, thus, couldn’t pump feed into the reactor anymore. The control reactor, therefore, didn’t get
any feed for several hours. This explains the sudden decrease in methane production in the control
reactor from 1578 mL CH4/L/d on day 10 to 864 mL CH4/L/d on day 14. The average volumetric
methane production of the control reactor, when excluding the data from day 14, was 1264 ± 373 mL
CH4/L/d. At the start of experiment 2 (2) the control reactor produced very little methane (10 ± 3 mL
CH4/L/d). After 6 days, the control reactor didn’t produce methane anymore. On the other hand,
reactor A produced methane during 8 days, with the highest volumetric methane production after 5
days (76 mL CH4/L/d). After 7 days, the volumetric methane production in reactor A also reached a
value of zero and no methane was produced anymore. Throughout entire experiment 3, a lower
volumetric methane production in both reactors can be observed, compared to the volumetric
methane production in experiment 1. In the first part of experiment 3 (3a) the volumetric methane
1 2 3 3 3c 3d
0
50
100
150
200
250
300
350
400
450
500
550
600
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
Tota
l VFA
co
nce
ntr
atio
n(m
g C
/L)
Time (Days)
Figure 17 – Total VFA concentration in reactor A (containing granules encapsulated in alginate) and
in the control reactor (containing natural granules) during the different experiments
1 2 3a 3b 3c 3d
41
production of both reactors remained constant. The average volumetric methane production in
reactor A was 624 ± 35 mL CH4/L/d and in the control reactor 729 ± 118 mL CH4/L/d. In the beginning
of the second part of experiment 3 (3b), reactor A only produced 267 mL CH4/L/d, while the control
reactor produced 511 mL CH4/L/d. The low amount of methane in reactor A can be associated with the
drop in pH after 4 days. Towards the end, both reactors produced a similar amount of methane (383
mL CH4/L/d in reactor A and 299 mL CH4/L/d in the control reactor). During the third part of experiment
3 (3c), the volumetric methane production in the control reactor fluctuated between 450 and 600 mL
CH4/L/d, while the volumetric methane production in reactor A gradually decreased over time. At the
start, 652 mL CH4/L/d was produced, while after 10 days, only 273 mL CH4/L/d was produced in reactor
A. The volumetric biogas production values in the last part of experiment 3 (3d) were similar in both
reactors. Almost no difference can be seen between both reactors, while during most of the time of
the other experiments the methane production in reactor A lagged behind the methane production in
the control reactor. After 8 days, a drop in methane production can be observed in both reactors (636
to 196 mL CH4/L/d in reactor A, 634 to 311 mL CH4/L/d in the control reactor). The OLR of the influent
vessels of that day of reactor A and of the control reactor were 2.2 and 3 g COD/L, respectively. The
deviation between both values was probably due to an error in the weighing of glucose. The OLR should
have had a value of 5 g COD/L. Probably a too low amount of glucose was added to the influent, which
can explain the drop in both reactors (Figure 18).
0
200
400
600
800
1000
1200
1400
1600
1800
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
Vo
lum
etri
c m
eth
ane
pro
du
ctio
n
(mL
CH
4/L
/d)
Time (Days)
1 2 3a 3b 3c 3d
Figure 18 - Total volumetric methane production in reactor A (containing granules encapsulated in
alginate) and in the control reactor (containing natural granules) during the different experiments
42
4.4 METHANE YIELD When excluding the datapoint on day 14 of the control reactor in experiment 1 (1), the natural granules
allowed from day 4, an average methane yield of 20.6 ± 2.8% more than the encapsulated granules.
When calculating the methane yields of experiment 2 (2) one need to divide by the added COD, which
is zero and division by zero is undefined. Therefore no graphs could be made of the methane yield in
reactor A and in the control reactor of experiment 2. Instead the alginate degradation rate was
calculated for reactor A (Appendix 2). The total amount of consumed COD in reactor A, in the form of
alginate, was 0.80 g, which corresponds to 0.92 g alginate12. This means that only 6.96% of the initial
amount of alginate was converted to biogas. The total amount of produced CH4 in reactor A during the
entire experiment was 277 mL. After 7 days, the methane production stopped, and, thus, alginate was
not consumed any longer. Therefore, the alginate degradation rate is calculated within a timeframe of
168h and equals 5.48 mg/h or 2.74 mg/L/h. In the first part of experiment 3 (3a), during 7 days, the
natural granules achieved a 19.8 ± 11.7% higher methane yield than the encapsulated granules. After
7 days, the yields of both reactors were similar with a value of 75.8 ± 5.1% in reactor A and 80.1 ± 8.8%
in the control reactor. In the second part of experiment 3 (3b), after 4 days, the control reactor allowed
a methane yield that was 24.0% higher than in reactor A. On the other hand, after 7 days reactor A had
a 18.1% higher methane yield than the control reactor. During the entire third part of experiment 3
(3c), the methane yield in the control reactor remained constant with a value of 71.4 ± 3.1%. The
methane yield in reactor A (72.7 ± 2.5%) was during the first 7 days similar to the control reactor. After
7 days, the methane yield in reactor A decreased to 51.8%. In the last part of experiment 3 (3d), both
methane yields are following a similar course. In the beginning of 3d, the yield of both reactors was
rather low (18.59% in reactor A and 43.41% in the control reactor), but increased fast to even 97.9%
in reactor A and 97.8% in the control reactor after 10 days. However, after this peak, the methane yield
in both reactors decreased again to a yield of 52.8% in reactor A and 52.2% in the control reactor
towards the end of experiment 3d (Figure 19).
12 15 g of sodium alginate (C6H7NaO6) was added, which corresponds to a total amount of 13.25 g alginate (C6H7O6
-). The calculations for the COD of alginate can be found in appendix 2.
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
Met
han
e yi
eld
(%
)
Time (Days)
1 2 3a 3b 3c 3d
Figure 19 – Methane yield in reactor A (containing granules encapsulated in alginate) and in the
control reactor (containing natural granules) during the different experiments
43
4.5 CATIONS Because the sludge was encapsulated by dropping a mix of sodium alginate and sludge into a CaCl2
solution, a graph of Ca2+ concentrations was constructed. No big differences in other cation
concentrations were observed among both reactors. In Appendix 3 (Table 13), an overview of the
cation concentrations in reactor A and the control reactor during the different experiments can be
found. During entire experiment 1 (1) and 2 (2), Ca2+ concentrations in the control reactor were on
average 39 ± 13 mg/L. At the start of experiment 3 (3a), an increase of 83 mg/L was observed, after
which the Ca2+ concentration in the control reactor became constant again (107 ± 9 mg/L). No
datapoints of reactor A were available for the second part of experiment 3 (3b) and only the last
datapoint of the third part of experiment 3 (3c) of reactor A was available. In experiment 1, as well as
in experiment 2 and the first and last part of experiment 3, the Ca2+ concentrations first increased in
reactor A, after which they decreased again The maximum Ca2+ concentration of experiment 1 (1) was
140 mg/L, of experiment 2 (2) 383 mg/L, of the first part of experiment 3 (3a) 439 mg/L and of the last
part of experiment 3 (3d) 274 mg/L. No interesting trend can be seen in the Ca2+ concentrations
throughout the different experiments. However, during the disintegration of the alginate matrix, Ca2+
concentrations in reactor A first increased and after the alginate matrix had been disintegrated, Ca2+
concentrations decreased again to normal values (Figure 20).
Figure 20 – Calcium concentrations in reactor A (containing granules encapsulated in alginate) and
in the control reactor (containing natural granules) during the different experiments
0
50
100
150
200
250
300
350
400
450
500
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
Cal
ciu
m c
on
cen
trat
ion
(mg
/L)
Time (Days)
1 2 3a 3b 3c 3d
44
5 BIOCHEMICAL METHANE POTENTIAL (BMP) TEST The natural granules didn’t need a lot of time to adapt, and reached already after 3 days their maximal
amount of produced methane. On the contrary, the encapsulated granules needed more time (7days)
before reaching their maximal amount of produced methane (Figure 21). The natural granules
produced on average 29 ± 34 mL CH4/ g VS more than the encapsulated ones. In addition, the natural
granules reached on average a yield of 16.6 ± 19.6% more than the encapsulated granules (Table
8).Table 8 – Final methane production per g VS, final methane yield
Table 8 – Final methane production per g VS, final methane yield and final pH
Natural granules Encapsulated granules
Methane yield (mL CH4/g VS) 124 ± 34 95 ± 5 Methane yield (%) 70.7 ± 19.4 54.0 ± 2.9 pH 7.62 ± 0.03 7.04 ± 0.06
0
20
40
60
80
100
120
140
160
180
0 2 4 6 8 10 12 14 16 18 20
Met
han
e yi
eld
(mL
CH
4/g
VS)
Time (Days)
B1 Natural granulesB2 Natural granulesB3 Natural granulesA1 Encapsulated granulesA2 Encapsulated granulesA3 Encapsulated granules
0
10
20
30
40
50
60
70
80
90
100
0 2 4 6 8 10 12 14 16 18 20
Met
han
e yi
eld
(%
)
Time (Days)
B1 Natural granulesB2 Natural granulesB3 Natural granulesA1 Encapsulated granulesA2 Encapsulated granulesA3 Encapsulated granules
Figure 21 – Amount of produced methane (top) and methane yield (bottom) of granules
encapsulated in alginate and of control (natural granules)
45
6 BATCH TEST
6.1 SHEAR STRESS BATCH TEST An overview of the pictures of 1 of the 9 replicates of each situation A, B, C or D can be found in Table
9. Pictures of all the 32 serums flask, and, thus, pictures of each replicate, can be found in Appendix 4.
When comparing the evolution of C and D, the alginate matrix disintegrated faster in C than in D, which
indicates a contribution of the shear stress to the disintegration of the alginate. When comparing the
evolution of A and B, the same can be perceived. However, in these flasks, the contribution of the
degradation of the alginate matrix by the biomass also played an important role. Putting B and D,
where shear stress didn’t play a role, next to each other, it can be seen that in B, the matrix is already
completely disintegrated after 14 days, while after 14 days in D, there is still some alginate matrix left.
Table 9 - Evolution of granules over a period of 14 days
Timepoint Day 0 Day 3 Day 7 Day 11 Day 14
A (encapsulated granules with shaking)
B (encapsulated granules without shaking)
C (encapsulated water with shaking)
D (encapsulated water without shaking)
6.2 POTASSIUM AND PHOSPHATE BATCH TEST Each treatment was conducted in triplicate. The evolution of each treatment for only one replicate can
be found in Table 10, the other replicates can be found in Appendix 5. The beads of the treatments
with only K+, behaved in the same way as the beads in the control. Therefore, it can be concluded that
K+ had no effect on the stability of the alginate beads. However, the PO43- treatments did show an
effect on the stability of the beads. In addition, the higher the concentration of PO43-, the stronger the
effect. It should also be noted that the beads swelled in the 405 mg PO43-/L treatment as well as in the
810 mg PO43-/L. The last treatment is a combination of potassium and phosphate. After day 10 the
beads are completely disintegrated. Also here, a swelling can be observed (Table 10).
46
Table 10 - Evolution of the disintegration of beads over a period of 25 days
47
DISCUSSION
1 DISINTEGRATION OF THE ALGINATE MATRIX During the different experiments, the disintegration of the alginate matrix was observed. The
disintegration of these beads can take place in three possible different ways. The first is that the
microbial biomass uses alginate as a C-source. The second way is that high concentrations of Na+ in the
effluent can cause swelling and consequently disintegration of the matrix, and the last reason is that
the shear stress present in the reactor accelerates the disintegration of the beads as a result of
degradation by the microbial biomass. For a better understanding of these findings, the structure of
alginate should be considered more in-depth.
Alginate is a polysaccharide extracted from brown
algae. It is a linear copolymer containing blocks of
(1-4)-linked β-D-mannuronate (M) and α-L-
guluronate (G). The blocks are composed of
consecutive G residues, consecutive M residues
and alternating M and G residues. Figure 22 gives
the structure of alginate. Alginate is industrially
available in the form of sodium alginate. The Na+-
ions react with the negatively charged carboxyl-
groups of the guluronate and the mannuronate blocks. When divalent cations, such as Ca2+, are added
to sodium alginate, the Na+-ions are driven out by the Ca2+ - ions and cross-linking between the M and
G blocks occurs, resulting in a gel structure. The binding of the Ca2+-ions with the guluronate units,
form the so-called tight egg-box structure and, in contrast to mannuronate, serve mainly as the stable
structure within the gel. Figure 23 gives a schematic overview of sodium alginate (left) and calcium
alginate (middle), as well as a SEM-EDS of the tight egg-box structure of calcium alginate (right). On
the SEM-EDS of calcium alginate, the cross-linking is clearly visible (Ayarza et al., 2016; Ji-Sheng et al.,
2011; Yong & Mooney, 2012).
Figure 22- Structure of alginate
Figure 23 – Sodium alginate (left), Calcium alginate (middle), SEM-EDS of Calcium alginate (right)
48
1.1 DISINTEGRATION DUE TO DEGRADATION BY THE MICROBIAL BIOMASS The degradation of the alginate by the granular sludge is proven by experiment 2. No C-source was
present, but reactor A, containing the alginate encapsulated sludge, still did produce methane, which
indicates the use of alginate as C-source to produce methane. This is not surprising, many research is
already done on the use of macro- and microalgae as renewable substrates for AD. Brown algae, which
contain approximately 40% of alginate, have been highlighted as the most promising feedstock as a
renewable resource (Bohutskyi & Bouwer, 2012). Alginate is depolymerized through an alginate
degradation pathway mediated by alginate lyase. First, the alginate polymer is hydrolyzed into
oligomers by an endolytic lyase, which is called endotype lyase. Next, the oligomers are degraded into
unsaturated monomers through the action of an exolytic enzyme, known as exotype alginate lyase.
These monomers are eventually converted to pyruvate and glyceraldehyde-3-phosphate, which are
key intermediates of the glycolytic pathway (Kita et al., 2016).
The total added COD to reactor A in experiment 2 (2) was 11.52 g of which only 0.80 g was consumed.
After 7 days (168u), the whole matrix was disintegrated. From 11.52 g COD, 4.032 L CH4 can be
produced with an alginate degradation rate of 68 mg/h or 34 mg/L/h. However, only 277 mL CH4 was
produced with an alginate degradation rate of only 5.48 mg/h or 2.74 mg/L/h. The obtained volumetric
methane production and alginate degradation rate are lower than the theoretical obtained values.
Literature also reports higher values. Moen et al. studied in 1997 the alginate degradation during AD
of Laminaria hyperborean stipes. L. hyperborean is a specie of large brown algae, and contains
approximately 35% of alginate (Hanssen et al., 1987). Moen et al. reported a anaerobic sodium alginate
degradation rate of 230 mg/L/h, which corresponds to an alginate degradation rate of 203 mg/L/h.
This alginate degradation rate is approximately 70 times higher than 2.74 mg/L/h and 6 times higher
than the theoretical alginate degradation rate (34 mg/L/h). The difference in rate may be related to
the difference between sodium alginate and calcium alginate. Reactor A contained levels of calcium
alginate, while Moen et al. reported degradation rates of sodium alginate. The complexation with
calcium (Figure 23) limits the acces of alginate lyase to alginate, which is therefore degraded more
slowly than sodium alginate. However, because of the malfunctioning of the gas counters, which
created an underestimation of the total amount of biogas produced, this explanation is not conclusive
yet. Moen et al. also concluded that after 75h, minor amounts of alginate were degraded. In
experiment 2, no methane was produced after 168h, and, thus, no alginate was degraded anymore
after 168h. Moen & Østgaard also did research in 1997 on the aerobic degradation of calcium alginate
and sodium alginate. They reported an aerobic calcium alginate degradation rate of 160 – 200 mg /L/h,
which corresponds to an alginate degradation rate of 130-163 mg/L/h and an aerobic sodium alginate
degradation rate of 240 mg/L/h, which corresponds to an alginate degradation rate of 212 mg/L/h.
From these values, the difference between the lower degradation rate of Ca-alginate and the higher
degradation rate of Na-alginate is clearly visible.
1.2 HIGH CONCENTRATIONS OF NA+ CAUSE SWELLING AND
CONSEQUENTLY DISINTEGRATION
The effect of PO43- on the alginate matrix can be derived by comparing the treatments of the phosphate
potassium batch test with the different PO43- concentrations of Table 10 with the control treatment.
Phosphate concentrations lower than 202.5 mg PO43-/L do not have any effect on the disintegration of
49
the alginate matrix. However, PO43- concentrations above 405 mg PO4
3-/L, do play a role in the
disintegration of the alginate beads. From Table 10, it can also be seen that the alginate matrix first
swells, and subsequently disintegrates due to the presence of PO43-. The swelling is a result of the
uptake of water by the matrix. Bajpai & Sharma investigated in 2004 the swelling and degradation
behavior in a PO43- buffer (PBS) of alginate beads crosslinked with Ca2+. When preparing the alginate
beads, they dropped a solution of 4% sodium alginate into 2, 3 and 4% CaCl2 solutions. Next, the
alginate beads were placed in a 0.1 M PBS buffer. After analyzing the % of weight change, they
concluded that it wasn’t the PO43-, but the Na+, present in the PBS buffer, that induced the swelling.
They proposed the following theory: the Na+ ions from the PO43- buffer first undergo ion exchange with
the Ca2+ ions mainly bound to the M blocks, which destabilizes the structure and causes the beads to
swell due to the uptake of water. The released Ca2+ ions react with the dissolved PO43- to form
Ca3(PO4)2, which is insoluble in water and consequently precipitates. In a later stage, ion exchange also
occurs between the Na+ ions and the Ca2+ ions bound to the G blocks losing the stable egg-box
structure. Finally, the alginate beads disintegrate and eventually dissolve. The used PO43- buffer in the
potassium phosphate batch test was Na2HPO3, therefore the theory of Bajpai & Sharma may, thus, also
be applied to the PO43- treatments of Table 10. It should be noted that, when opening the serum flasks,
a small layer of precipitation could be observed, which was probably the precipitated Ca3(PO4)2.
However, the alginate beads treated with 202.5 mg PO43-/L almost didn’t swell or disintegrate. This is
probably because an insufficient amount of Na+ ions (98 mg Na+/L) was present to disintegrate the
beads. In conclusion, the alginate beads swell and disintegrate due to high concentrations of Na+ (>100
mg Na+/L, see Table 5) instead of PO43-.
From Table 10, it can be derived that K+ concentrations don’t affect the stability of the beads, which is
not in line with the theory of Bajpai & Sharma. If the same theory is followed, the K+ ions undergo ion
exchange with the Ca2+ ions and the released Ca2+ ions react with the Cl- ions. However, this is not the
case, because no swelling and no disintegration was observed. This is probably because of the relative
stronger affinity of Na+ over K+ to the carboxyl-groups (Vrbka et al., 2006).
1.3 SHEAR STRESS ACCELERATES THE DISINTEGRATION AS A RESULT OF
MICROBIAL DEGRADATION At the start of the shear stress batch test, the encapsulated water beads were placed in serum flasks
containing effluent of reactor A and the control reactor of experiment 1 (molasse as influent). The
cation concentrations of this effluent can be found in Table 3. When comparing the disintegration of C
(encapsulated water with shaking) and D (encapsulated water without shaking) of Table 9, it can be
clearly seen that the beads within the serum flasks placed on the shaker, disintegrated more rapidly
than the beads within those flasks not placed on the shaker. On the contrary, the water beads of the
control of the potassium phosphate batch test were added to serum flasks containing only tap water.
These serum flasks were also placed on a shaker. Nevertheless, the beads of this control almost didn’t
disintegrate. In this case, shear stress alone almost had no effect on the stability of the alginate matrix.
However, the strength and rigidity of alginate beads depend on the relative amount of G and M blocks
in the alginate polymer, on the Ca2+ concentration and on the sodium alginate concentration. Mancini
et al. did research on the mechanical properties of alginate gels. They concluded that the rigidity of
alginate depends on the amount of G and M-blocks. In other words, high guluronic ‘G’ alginates are
more rigid than high mannuronic ‘M’ alginates, because binding between Ca2+ and G blocks results in
50
a stable tight egg-box structure of alginate. Bajpai & Sharma found in 2004 that the lower the
percentage of CaCl2, the higher the water uptake, the bigger the swelling and, thus, the faster the
disintegration. Higher Ca2+ concentrations may thus result in a slower disintegration of the alginate
matrix. They also hypothesized that with an increase in alginate concentration, the number of
crosslinking points should increase, thus, resulting in delayed degradation. In addition, the increased
alginate density may also result in decreased mesh size within the gel beads, thus, making the ion-
exchange process slower (Bajpai & Sharma, 2004; Mancini et al., 1999). However, decreased mesh size
will also result in slower migration and consequently a slower uptake of COD and other nutrients by
the microbial biomass encapsulated in the alginate matrix. Summarized, depending on the type of
alginate and on how the beads are made, shear stress may or may not have an influence on the
disintegration of the alginate matrix.
Nevertheless, one can conclude from Table 9 that the shear stress, present in the reactor, did
accelerate the disintegration of the beads. The reason why the encapsulated water beads of the shear
stress batch test (both shaking and not shaking) did disintegrate and the encapsulated water beads of
the control of the potassium phosphate test didn’t disintegrate, can be explained by comparing three
different situations. The first situation refers to the control of the potassium phosphate batch test. The
second situation refers to the treatment of the potassium phosphate batch test with 405 mg PO43-/L.
And the last situation refers to the shear stress batch test with the encapsulated water beads (with
shaking). The properties of these three different situations can be found in Table 14 in Appendix 6 and
the swelling and the disintegration of these three situations in Table 15 in Appendix 6. It should be
noted that the beads of the shear stress batch test didn’t swell at all, while swelling was expected,
because the beads did disintegrate. However, Table 14 shows that the Na+ concentration in the tap
water and the effluent was much lower than in the PO43- treatment. Due to the low Na+ concentration
in situation 1, no swelling occurred. Therefore, no swelling occurred in situation 3 neither. A possible
explanation for the disintegration in situation 3, is that the effluent derived from the reactors still did
contain some small amounts of biomass. This biomass was probably responsible for the disintegration
of the alginate matrix, which was accelerated by the shear stress induced by the shaker. In conclusion,
the shear stress in the reactor accelerates the disintegration of the alginate matrix as a result of
microbial degradation, because the organic matter can come in close contact with the microbial
biomass, but doesn’t have an effect on the stability of the alginate matrix alone.
2 CHARACTERISTICS OF THE ENCAPSULATED SLUDGE
2.1 PH OF THE ENCAPSULATED SLUDGE During the entire three experiments, the pH in the reactor containing the encapsulated granules was
always lower than the pH in the reactor containing the natural granules. At the start of each
experiment, a pH drop was observed in reactor A, accompanied by an increase in VFA concentration.
Because the pH in the control reactor was during each experiment more or less constant, the observed
pH drop and VFA increase in reactor A may be a result of the encapsulation of the granular sludge. The
following hypothesis is proposed: when the sludge is encapsulated in an alginate matrix, the microbial
biomass need to adapt to the new growth conditions, this is the so-called lag phase. The hydrolytic,
acidogenic and acetogenic bacteria probably have a shorter lag phase than the methanogenic archaea.
Therefore, VFA are already produced, while the methanogens still need to adapt. This results in VFA
accumulation and a consequent decrease in pH. From the moment the methanogens are adapted, they
51
start to convert the VFA into biogas accompanied by a drop in VFA concentration and an increase in
pH. Akuzawa et al. showed that the accumulation of VFA in most situations reflects an imbalance
between acid producers (mostly bacteria) and consumers, and is usually associated with a drop in pH,
reinforcing the previous assumption.
When comparing the first part of experiment 3 (1.3% alginate, 3a) with the second part of experiment
3 (1.3% alginate + glucose, 3b) in reactor A, it can be seen that VFA in 3a accumulated after 2 days,
while the VFA in 3b accumulated after 4 days. In addition, the accumulated VFA concentrations in 3b
were twice as high as the concentrations of the accumulated VFA in 3a (Figure 16,Figure 17). Two
factors may play a role in the VFA accumulation in 3b. The first factor is the accumulation of VFA, due
to a longer lag phase of the methanogens. The second factor is the presence of the extra glucose in
the alginate encapsulated beads. During experiment 2 (2), no C- source was added, while during
experiment 3 (3) an OLR of 2.5 g COD/L/d was applied instead of 5 g COD/L/d as in experiment 1 (1).
This means, that in the beginning of 3b, the OLR existed of 2.5 g COD/L/d plus the present glucose in
the beads (5 g COD/L). This can result in an organic overload and a consequent accumulation of VFA.
In other words, the conversion of glucose to biogas by methanogens lagged behind the rapid
conversion of glucose to VFA by the acidogens and acetogens with VFA accumulation and a
consequently decrease in methane production as result. Experimental studies showed that higher
influent COD concentrations can lead to the formation of higher VFA concentrations (Buyukkamaci &
Filibeli, 2004). In addition, organic overloading initially results in the accumulation of acetate and H2,
which is in line with the relative high amount of acetate (577 mg/L) present in reactor A, after 4 days
in 3b (De Vrieze, 2014). Hickey & Switzenbaum reported that monitoring CO2 can provide information
on the level of stress being exerted on the CO2-reducing methanogenic population. The percentage of
CO2 after 4 days in reactor A in 3b was 45%, while in the control reactor a percentage of 30% CO2 was
observed, which also points to a possible organic overloading in reactor A (Hickey & Switzenbaum,
1988).
During experiment 1 (1), the pH of both reactors stayed between the optimal upper and lower limit.
From experiment 3 (3) onwards, the pH in both reactors fell below the optimal upper limit. This drop
in pH may be a result of the change from molasse to a synthetic medium with a lower buffering capacity
than molasse, due to lower PO43- concentrations. In a less well buffered system, the breakdown of the
buffering capacity, for example due to the production of VFA, happens faster than in a better buffered
system (Akuzawa et al., 2011). In experiment 2 (2), the pH in the control reactor didn’t fall below the
optimal lower limit, because no C-source was added, and, thus, no production of VFA occurred,
resulting in a stable and high pH.
2.2 METHANE PRODUCTION OF THE ENCAPSULATED SLUDGE Experiment 1 (1) can confirm the hypothesis made in 2.1, concerning the adaptation to the
encapsulation matrix. In experiment 1, the methane production in the reactor containing the
encapsulated beads is delayed, compared to the methane production in the other reactor. Reactor A
reached a methane production of 1305 mL CH4/L/d after 14 days, while the control reactor already
reached a volumetric methane production of 1361 after 4 days. This can be explained by the hypothesis
made in 2.1. The microbial biomass in reactor A, and especially the methanogens, first experience a
lag phase, as a consequence of the encapsulation, while the microbial biomass in the control reactor
52
immediately starts to produce biogas. Only when the methanogens are fully adapted to their
environment, a maximal amount of biogas can be produced.
When experiment 3 (3) started, a drop in volumetric methane production can be observed. Both
reactors in experiment (3) still produced biogas, but in much lower amount compared to experiment
1 (1), which can be attributed to the change from molasse to a synthetic medium. First of all, the buffer
capacity of the synthetic medium is lower, resulting in an overall lower pH (< 6.8, optimal lower limit).
When the HRT of the reactor is long enough for growth of methanogens, weakly acidic conditions do
not thoroughly inhibit methanogenic activity. However, the hydrogenotrophic methanogens are more
tolerant to acidic conditions than the acetoclastic methanogens which activity was inhibited (I. S. Kim
et al., 2004; Ye et al., 2013). Second, by changing the influent from molasse to the synthetic medium,
the OLR was also changed from 5 g COD/L/d to 2.5 g COD/L/d. Borja & Colmenarejo showed that an
increase in OLR, produced an increase in methane gas production per volume of the reactor. However,
an increase in OLR determined a progressive decrease in the removal efficiency. They found that the
best removal efficiencies were obtained at OLR values in the range of 1.0 – 4.1 g COD/L/d. At 8.1 g
COD/L/d, the removal efficiency dropped suddenly (Borja & Colmenarejo, 2005). From this, it can be
concluded that the decrease from 5 g COD/L/d to 2.5 g COD/L/d is co-responsible for the decrease in
methane production in experiment 3 (3).
2.3 ELEVATED CA2+ LEVELS IN THE REACTOR CONTAINING THE
ENCAPSULATED SLUDGE Important differences in Ca2+ concentrations among both reactors can be observed. The small increase
of 83 mg Ca2+/L in the control reactor between experiment 1 (1) and experiment 3 (3) was caused by
the change of molasse to synthetic medium, which contained a higher Ca2+ concentration than molasse
(3 mg Ca2+/L in molasse, 46 mg Ca2+/L in synthetic medium). However, Ca2+ concentrations in
experiment 3 were 108 ± 9 mg/L (> 46 mg/L), while Ca2+ concentrations in experiment 2 were only 35
± 4 mg/L (≈ 46 mg/L), although the same synthetic medium was used (Table 11). This may be attributed
to the inactivity of the microbial biomass in the control reactor during experiment 2 (2), compared to
the active biomass in experiment 3 (3). Because no C-source was added in 2, the microbial biomass in
the control reactor was not allowed to grow. In addition, it can be seen that no NH4+ was consumed,
indicating that they probably almost didn’t synthesize amino acids, DNA or RNA, and therefore didn’t
grow. It is also known that the growth of bacteria on media containing a marginal supply of glucose
results in abrupt cessation of growth when the substrate supply is exhausted (M. Lawrence &
Raymond, 1973). The higher levels of Ca2+ in 3 in the control reactor may thus be a consequence of
microbial growth. Bacteria and archaea get their energy from coupled transport. Dimroth reported in
1991 that H+ is the most common coupling ion for bioenergetic functions. Therefore, Cai & Lytton
proposed that it is likely that the bacterial and archaea exchangers utilize the H+ gradient, instead of
Na+, to extrude cytosolic Ca2+, which may, thus, elevate the Ca2+ levels in the effluent of the control
reactor (Cai & Lytton, 2004; Dimroth, 1991). This also may explain why the Na+ and K+ concentrations
in the control reactor didn’t differ among 2 and 3. However, it is not sure in which extent Ca2+/H+
exchangers may bring about changes in the Ca2+ concentrations in the effluent of the control reactor.
However, microorganisms also use Na+ or K+/H+ exchangers, but no differences in Na+ and K+
concentrations in the control reactor among 2 and 3 are observed (Booth, 1985).
53
Table 11 – Mean cation concentrations of effluent of experiment 2 and 3 and theoretical cation
concentrations of synthetic medium 1 and 2 in the control reactor
Control reactor Na+ NH4+ K+ Ca2+ Mg2+
Synthetic medium 1, 2 (mg/L) 230 169 450 46 12 Effluent Experiment 2 (mg/L) 254 ± 11 175 ± 11 454 ± 4 35 ± 4 40 ± 8 Effluent Experiment 3 (mg/L) 236 ± 17 74 ± 20 394 ± 44 108 ± 9 17 ± 2
At the start of each experiment, an increase in Ca2+ concentration in reactor A can be observed. After
about 6 days, the Ca2+ concentration start to decrease again. This increase and decrease in Ca2+
concentration can be explained by Figure 15 and by the hypothesis made in 1.2. In each experiment,
after about 6 days, the whole alginate matrix is degraded. This means that also the Ca2+ ions are
exempted from the alginate matrix and replaced by Na+, which increases the Ca2+ concentration in
reactor A. After about 10 days, the excess Ca2+ concentrations are removed with the effluent and
‘normal’ Ca2+ concentrations can be observed again. However, no change in Na+ concentrations are
observed in reactor A. This can be explained by the conclusion made in 1.1. The microbial degradation
of the alginate matrix occurs simultaneously with the substitution of Ca2+ by Na+. When the alginate
matrix is completely degraded by the microorganisms, no Na+ ions are trapped in the matrix anymore
(Figure 20).
54
55
CONCLUSION AND FUTURE PERSPECTIVES First, the encapsulation of granular sludge with an alginate matrix didn’t stop the AD process from
working. The amount of biogas produced by the encapsulated granular sludge was similar to the
natural granular sludge. However, the initial pH and VFA concentrations in the reactor containing the
encapsulated granular sludge were lower and higher, respectively, than in the control reactor, due to
a longer lag-phase of the methanogens, which also resulted in a maximal biogas production that was
reached less quickly than in the control reactor.
Second, the alginate matrix was used as carbon-source, and, consequently, degraded by the microbial
biomass. It was also observed that shear stress, occurring in the reactor, accelerates the degradation
of the alginate matrix by the microbial biomass. Therefore, addition of compounds slowing down the
degradation of the alginate matrix, such as formaldehyde, may be considered. Formaldehyde is widely
used in quite high concentrations to slow down microbial decomposition of brown seaweeds (Moen
et al., 1997b). However, this organic compound, which is very toxic to humans and a threat to the
environment, needs to be added in sufficiently low concentrations to avoid inhibition of the
methanogens. Literature also reports that the microbial biomass of the AD process is also capable of
degrading formaldehyde (Moen & Østgaard, 1997; Vidal et al., 1999). Therefore, research can be done
on different concentrations of formaldehyde, whether or not integrated in the alginate matrix, to find
the optimal concentration at which AD isn’t inhibited, and degradation of alginate by the microbial
biomass is slowed down. Other encapsulation matrices, such as chitosan, may also be tested. El-
Mamouni et al. reported in 1998 that chitosan enhanced the granulation process acting similarly to
the extracellular polymers (ECP). Turtakovsky et al. did research on the dechlorination of wastewaters
by chitosan encapsulated anaerobic species. No sign of bead disintegration was observed over the five-
week period of reactor operation. Therefore, chitosan may be a suitable candidate for the
encapsulation of the granular sludge (El-Mamouni et al., 1998; Turtakovsky et al., 1997).
Last, it was found that high concentrations of Na+ (> 100 mg/L) interfere with the cross-linked Ca2+,
resulting in swelling and consequently disintegrating of the matrix. Bajpai & Sharma found that the
lower the percentage of CaCl2, the higher the water uptake, the bigger the swelling and, thus, the
slower the disintegration. Therefore, higher percentages of CaCl2 may be tested to stabilize the
alginate matrix. Another way to increase the stability of the matrix is the use of Ba2+, instead of Ca2+
for the cross-linking of the matrix. Barium has a larger radius than Ca2+, and is supposed to fill a large
space between the alginate molecules with smaller voids. The exchange of the large Ba2+ ions with Na+
ions may be hindered resulting in a lower water uptake and higher stability, which was confirmed by a
study conducted by Bajpai & Sharma in 2004 (Bajpai & Sharma, 2004).
56
57
BIBLIOGRAPHY
Ahn, J., Hoan, T., Kim, S. D., & Hwang, S. (2006). The effect of calcium on the anaerobic digestion treating swine wastewater, 30, 33–38. https://doi.org/10.1016/j.bej.2006.01.014
Aiyuk, S., & Verstraete, W. (2004). Sedimentological evolution in an UASB treating SYNTHES , a new representative synthetic sewage , at low loading rates, 93, 269–278. https://doi.org/10.1016/j.biortech.2003.11.006
Akuzawa, M., Hori, T., Haruta, S., Ueno, Y., Ishii, M., & Igarashi, Y. (2011). Distinctive responses of metabolically active microbiota to acidification in a thermophilic anaerobic digester. Microbial Ecology, 61, 595–605.
Alibhai, K. R. K., & Forster, C. F. (1986). An examination of the granulation process in UASB reactors. Environmental Technology Letters, 1:12(7), 193–200.
Andersen, S., Hennebel, T., Gildemyn, S., Coma, M., Desloover, J., Berton, J., … Rabaey, K. (2014). Electrolytic membrane extraction enables fine chemical production from biorefinery sidestreams. Environ Sci Technol, 12(48), 7135–7142.
Angenent, L. T., Karim, K., Al-Dahhan, M. H., Wrenn, B. A., & Domíguez-Espinosa, R. (2004). Production of bioenergy and biochemicals from industrial and agricultural wastewater. Trends in Biotechnology, 22(9), 477–485. https://doi.org/10.1016/j.tibtech.2004.07.001
Appels, L., Baeyens, J., Degrève, J., & Dewil, R. (2008a). Principles and potential of the anaerobic digestion of waste-activated sludge. Progress in Energy and Combustion Science, 34(6), 755–781. https://doi.org/10.1016/j.pecs.2008.06.002
Appels, L., Baeyens, J., Degrève, J., & Dewil, R. (2008b). Principles and potential of the anaerobic digestion of waste-activated sludge. Progress in Energy and Combustion Science, 34(6), 755–781. https://doi.org/10.1016/J.PECS.2008.06.002
Appels, L., Lauwers, J., Degrve, J., Helsen, L., Lievens, B., Willems, K., … Dewil, R. (2011). Anaerobic digestion in global bio-energy production: Potential and research challenges. Renewable and Sustainable Energy Reviews, 15(9), 4295–4301. https://doi.org/10.1016/j.rser.2011.07.121
Arcand, Y., Desrochers, M., & Chavarieb, C. (1994). Impact of the reactor hydrodynamics and organic loading on the size and activity of anaerobic granules. The Chemical Engineering Journal, 56, 25–35.
Ayarza, J., Coello, Y., & Nakamatsu, J. (2016). SEM–EDS study of ionically cross-linked alginate and alginic acid bead formation, 1–10.
Bajpai, S. K., & Sharma, S. (2004). Investigation of swelling / degradation behaviour of alginate beads crosslinked with Ca 2 þ and Ba 2 þ ions, 59, 129–140. https://doi.org/10.1016/j.reactfunctpolym.2004.01.002
Bal AS, D. N. (2001). Upflow anaerobic sludge blanket reactor. Health (San Francisco), 11845345–11845345.
58
Baloch, M. I. (2011). Methanogenic granular sludge as a seed in an anaerobic baffled reactor, 25(1982), 171–180. https://doi.org/10.1111/j.1747-6593.2009.00206.x
Beeftink, H. H., & van den Heuvel, C. (1988). Physical properties of bacterial aggregates in a continuous flow reactor with biomass retention,. In G. Lettinga, A. J. B. Zehnder, J. T. C. Grotenhuis, & L. W. Hulshoff Pol (Eds.), Microbiology and Technology (pp. 162–169). Wageningen.
Bohutskyi, P., & Bouwer, E. (2012). Biogas Production from Algae and Cyanobacteria Through Anaerobic Digestion : A Review , Analysis , and Research Needs. https://doi.org/10.1007/978-1-4614-3348-4
Booth, I. A. N. R. (1985). Regulation of Cytoplasmic pH in Bacteria, 49(4), 359–378.
Borja, R., & Colmenarejo, M. F. (2005). Effect of organic loading rate on the stability , operational parameters and performance of a secondary upflow anaerobic sludge bed reactor treating piggery waste, 96, 335–344. https://doi.org/10.1016/j.biortech.2004.04.003
Brown, D., Shi, J., & Li, Y. (2012). Comparison of solid-state to liquid anaerobic digestion of lignocellulosic feedstocks for biogas production. Bioresource Technology, 124, 379–386. https://doi.org/10.1016/j.biortech.2012.08.051
Buhr, H. O., & Andrews, J. F. (1976). Review Paper: The Thermophilic Anaerobic Digestion Process. Water Research, 11, 129–143. https://doi.org/10.1016/0043-1354(77)90118-X
Buisman, C. J. N., Lettinga, G., Paasschens, C. W. M., & Habets, L. H. A. (1991). Biotechnological sulphide removal from effluents. In Water Sci. Technol. (24th ed., pp. 347–356).
Buyukkamaci, N., & Filibeli, A. (2004). Volatile fatty acid formation in an anaerobic hybrid reactor, 39(May 2003), 1491–1494. https://doi.org/10.1016/S0032-9592(03)00295-4
Cai, X., & Lytton, J. (2004). The Cation / Ca 2 1 Exchanger Superfamily : Phylogenetic Analysis and Structural Implications, 21(9). https://doi.org/10.1093/molbev/msh177
Chen, J. L., Ortiz, R., Steele, T. W. J., & Stuckey, D. C. (2014). Toxicants inhibiting anaerobic digestion: A review. Biotechnology Advances, 32(8), 1523–1534. https://doi.org/10.1016/j.biotechadv.2014.10.005
Chen, J., & Lun, S. Y. (1993). Study on mechanism of anaerobic sludge granulation in UASB reactors. Water Science and Technology, 28(7), 171–178.
Chen, Y., & Cheng, J. J. (2007). Effect of Potassium Inhibition on the Thermophilic Anaerobic Digestion of Swine Waste. Water Environment Research, 79(6), 667–674. https://doi.org/10.2175/106143007X156853
Chen, Y., Cheng, J. J., & Creamer, K. S. (2008). Inhibition of anaerobic digestion process: A review. Bioresource Technology, 99(10), 4044–4064. https://doi.org/10.1016/j.biortech.2007.01.057
Colleran, E., Finnegan, S., Lens, P., & van Leeuwenhoek, A. (1995). Journal of microbiology. Anaerobic treatment of sulphate-containing waste streams (Vol. 67).
Costerton, J., Marrie, T., & Cheng, K. (1990). Phenomen a of bacterial adhesion. Mechanisms and physiological significance. New York.
Cunningham, A. B., Lennox, J. E., & Rockford, J. R. (2010). Reactor Theory and Practice. Continuous Flow Stirred Tank Reactor (CFSTR). In The Biofilms Hypertextbook.
59
Daffonchio, D., Thavessri, J., & Verstraete, W. (1995). Contact angle measurement and cell hydrohpobicity of granular sludge from upflowanaerobic sludge bed reactors. Applied and Environmental Microbiology, 3676(61), 80.
De Vrieze, J. (2014). Methanosaeta vs. Methanosarcina in anaerobic digestion: the quest for enhanced biogas production.
De Vrieze, J. (2019). Chapter 1 : Anaerobic digestion Guest lecture by dr . ir . Jo De Vrieze.
De Zeeuw, W. J. (1988). Acclimatization of anaerobic sludge for UASB reactor start-up. Agricultural University of Wageningen.
Dimroth, P. (1991). Na+-coupled alternative to H+-coupled primary transport systems in bacteria. Bioessays, 13, 463–468.
Dinsdale, R., Bryne, K., & Tucker, D. (2007). The Anaerobic digestion of textile desizing wastewater. In Ecotextiles (pp. 163–167). Woodhead publishing.
Driessen, W. (2016). COMPACT COMBINED ANAEROBIC AND AEROBIC PROCESS FOR THE, (January 2000).
El-Mamouni, R., Leduc, R., & Guiot, S. R. (1997). Influence of the starting microbial nucleus type on the anaerobic granulation dynamics. Applied Microbiology and Biotechnology, (47), 189–194.
El-Mamouni, R., Leduc, R., & Guiot, S. R. (1998). Influence of synthetic and natural polymers on the anaerobic granulation process. Water Science and Technology, (38), 341–347.
Fang, H. H. P., & Chui, H. K. (1993). Maximum COD loading capacity in UASB reactors at 37 °C. Journal of Environmental Engineering, (119), 103–119.
Fang, H. H. P., & Lau, I. W. C. (1996). Start-up of thermophilic (55 °C) UASB reactors using different mesophilic seed sludges. Water Sci. Technol., (34), 445–452.
Feijoo, G., Soto, M., Méndez, R., & Lema, J. M. (1995). Sodium inhibition in the anaerobic digestion process: Antagonism and adaptation phenomena. Enzyme and Microbial Technology, 17(2), 180–188. https://doi.org/10.1016/0141-0229(94)00011-F
Feng, X. M., Karlsson, A., Svensson, B. H., & Bertilsson, S. (2010). Impact of trace element addition on biogas production from food industrial waste - linking process to microbial communities. FEMS Microbiology Ecology, 74(1), 226–240.
Forster, C. F., & Lewin, D. C. (1972). Polymer interaction at activated sludge surfaces. Effluent Water Treatment Journal, (12), 520–525.
Frankin, R. (2001). Full-scale experiences with anaerobic treatment of industrial wastewater.
Garg, V. K., Suthar, S., & Yadav, A. (2012). Bioresource Technology Management of food industry waste employing vermicomposting technology. Bioresource Technology, 126, 437–443. https://doi.org/10.1016/j.biortech.2011.11.116
Gashaw, A. (2014). Anaerobic Co-Digestion of Biodegradable Municipal Solid Waste with Human Excreta for Biogas Production: A Review. American Journal of Applied Chemistry, 2(4), 55. https://doi.org/10.11648/j.ajac.20140204.12
Gerardi, M., Baxter, R., Hastings, N., Law, A., & Glass, E. J. . (2008). The Microbiology of Anaerobic Digesters. Animal Genetics (Vol. 39). https://doi.org/10.1002/0471468967
60
Grootaerd, H., Liessens, B., & Verstraete, W. (1997). Effects of directly soluble and fibrous rapidly acidifying chemical oxygen demand and reactor liquid surface tension on granulation and sludge-bed stability in upflow anaerobic sludge-blanket reactors. Applied Microbiology and Biotechnology, 48(3), 304–310.
Guiot, S., Pauss, A., & Costerton, J. (1992). A structured model of the anaerobic granules consortium. Water Sci. Technol., 25, 1–10.
Hanaki, K., Mastsuo, T., & Nagase, M. (1981). Mechanism of inhibition caused by long chain fatty acids in anaerobic digestion process. In Biotechnol Bioeng (23rd ed., pp. 1591–1610).
Hanssen, J. F., Indergaard, M., Ostgaard, K., Arne, O., Pedersen, C. T. A., & Jensen, A. (1987). Anaerobic Digestion of Laminaria spp. and Ascophyllum nodosum and Application of End Products, 14, 1–13.
Hashimoto, A. G. (1986). Ammonia inhibition of methanogenesis from cattle waste. In Agricultural Wastes (pp. 241–261).
Hattori, S. (2008). Syntrophic Acetate-Oxidizing Microbes in Methanogenic Environments. Microbes and Environments, 23(2), 118–127. https://doi.org/10.1264/jsme2.23.118
Hickey, R. E., & Switzenbaum, M. S. (1988). The role of intermediate and product gases as regulator and indicator of anaerobic digestion. In A. Tilche & A. Rossi (Eds.), 5th Symp. on Anaerobic Digestion. Bologna.
Hickey, R. F., Wu, W.-M., Veiga, M. C., & Jones, R. (1991). Start-Up, Operation, Monitoring and Control of High-Rate Anaerobic Treatment Systems. Water Science and Technology, 24(8), 207–255.
Hilton, M. G., & Archer, D. B. (1988). Anaerobic digestion of a sulfate‐rich molasses wastewater: Inhibition of hydrogen sulfide production. Biotechnology and Bioengineering, 31(8), 885–888. https://doi.org/10.1002/bit.260310817
Hirsch, R. (1984). Microcolony formation and consortia. In S.-V. K. C. Marshall, ed. (Ed.), Microbial adhesion and aggregation (pp. 373–393). Berlin.
Hulshoff Pol, L. W. (1989). The phenomenon of granulation of anaerobic sludge. Agricultural University of Wageningen.
Hulshoff Pol, L. W., De Castro Lopes, S. I., Lettinga, G., & Lens, P. N. L. (2004). Anaerobic sludge granulation. Water Research, 38(6), 1376–1389. https://doi.org/10.1016/j.watres.2003.12.002
Hulshoff Pol, L. W., de Zeeuw, W. J., & Velzebber, C.T.M. Lettinga, G. (1983). Granulation in UASB-reactors. Water Sci. Technol., (8/9), 291–304.
Hulshoff Pol, L. W., Heijnekamp, K., & Lettinga, G. (1988). The selection pressure as a driving force behind the granulation of anaerobic sludge. Granular Anaerobic Sludge: Microbiology and Technology, 153–161.
Hulshoff Pol, L. W., Lens, P. N. L., Stams, A. J. M., & Lettinga, G. (1998). Anaerobic treatment of sulphate-rich wastewaters. Biodegradation, 9(182655), 213–224. https://doi.org/10.1023/A:1008307929134
Hutnan, M., Kolesarova, N., Bodik, I., & Czolderova, M. (2013). Long-term monodigestion of crude glycerol in a UASB reactor. Bioresource Technology, 130, 88–96.
Hwu, S. H., Tseng, S. K., Yuan, C. Y., Kulik, Z., & Lettinga, G. (1998). Biosorption of longchain fatty acids
61
in UASB treatment process. Water Research, 5(32), 1571–1579.
Imai, T. (1997). Advanced start up of UASB reactors by adding of water absorbing polymer. Water Science and Technology, (36), 399–406.
Ji-Sheng, Y., Xie, Y.-J., & He, W. (2011). Carbohydrate polymers, 84(1), 33–39.
Jossen, Q., Schobbens, Q., Vermeulen, P., van der Burg, L., & Worrall, L. (2019). FOSSIL FUEL SUBSIDIES : Hidden impediments on Belgian climate objectives, (February).
Kaparaju, P., Buendia, I., Ellegaard, L., & Angelidakia, I. (2008). Effects of mixing on methane production during thermophilic anaerobic digestion of manure: Lab-scale and pilot-scale studies. Bioresource Technology, 99(11), 4919–4928. https://doi.org/10.1016/j.biortech.2007.09.015
Karim, K., Hoffmann, R., Klasson, K. T., & Al-Dahhan, M. H. (2005). Anaerobic digestion of animal waste: Effect of mode of mixing. Water Research, 39(15), 3597–3606. https://doi.org/10.1016/j.watres.2005.06.019
Kettunen, R. H., & Rintala, J. A. (1998). Performance of an on-site UASB reactor treating leachate at a low temperature. Water Research, (32), 537–546.
Kilcast, D., & Angus, F. (2007). Reducing Salt in Foods: Practical Strategies.
Kim, I. S., Hwang, M. H., Jang, N. J., Hyun, S. H., & Lee, S. T. (2004). Effect of low pH on the activity of hydrogen utilizing methanogen in bio-hydrogen process, 29, 1133–1140. https://doi.org/10.1016/j.ijhydene.2003.08.017
Kim, J. K., Oh, B. R., Chun, Y. N., & Kim, S. W. (2006). Effects of temperature and hydraulic retention time on anaerobic digestion of food waste. Journal of Bioscience and Bioengineering, 102(4), 328–332. https://doi.org/10.1263/jbb.102.328
Kim, M., Ahn, Y., & Speece, R. E. (2002). Comparative process stability and efficiency of anaerobic digestion_2002.pdf, 36, 4369–4385.
Kita, A., Miura, T., Kawata, S., Yamaguchi, T., Okamura, Y., Aki, T., … Nakashimada, Y. (2016). Bacterial community structure and predicted alginate metabolic pathway in an alginate-degrading bacterial consortium, 121(3), 286–292. https://doi.org/10.1016/j.jbiosc.2015.06.014
Koster, I. W., & Cramer, A. (1987). Inhibition of methanogenesis from acetate in granular sludge by long-chain fatty acids. In Applied and Environmental Microbiology (53rd ed., pp. 403–409).
Kuyucak, N., & Volesky, B. (1988). Biosorbents for recovery of metals from industrial solutions. Biotechnology Letters, (10), 137–142.
Langerak, Ramaekers, H., Wiechers, J., Veeken, A., Hamelers, H., & Lettinga, G. (2000). Impact of location of CaCO3 precipitation on the development of intact anaerobic sludge. Water Research, (34), 437–446.
Lawrence, A., & McCarty, P. (1965). The role of sulfide in preventing heavy metal toxicity on anaerobic treatment. Journal of Water Pollution Control Federation, (37), 392–406.
Lawrence, M., & Raymond, D. (1973). Energy requirements of bacterial ion exchange, 60, 204–249.
Lepisto, R., & Rintala, J. (1999). Extreme thermophilic (70 °C), VFA-fed UASB reactor: performance, temperature response, load potential and comparison with 35 and 55 °C UASB reactors. Water Research, (33), 3162–3170.
62
Lettinga, G., van Velsen, A., Hobma, S., Zeeuw, & Klapwijk. (1980). Use of the upflow sludge blanket (USB) reactor concept for biological waste water treatment especially for anaerobic treatment. In Biotechnol Bioeng (pp. 699–734).
Li, Y., Park, S. Y., & Zhu, J. (2011). Solid-state anaerobic digestion for methane production from organic waste. Renewable and Sustainable Energy Reviews, 15(1), 821–826. https://doi.org/10.1016/j.rser.2010.07.042
Lier, J. B. Van. (1995). Thermophilic Anaerobic Wastewater Treatment ; Temperature Aspects and Process Stability. https://doi.org/10.1007/BF01941738
Lin, C. Y. (1993). Effect of heavy metal on acidogenesis in anaerobic digestion. Water Research, (27), 147–152.
Lin, C. Y., & Chen, C. C. (1999). Effect of heavy metals on the methanogenic UASB granule. Water Research, (33), 409–416.
Liu, Y., & Tay, J. (2002). The essential role of hydrodynamic shear force in the formation of biofilm and granular sludge, 36, 1653–1665.
Liu, Y., Xu, H. Lou, Show, K. Y., & Tay, J. H. (2002). Anaerobic granulation technology for wastewater treatment. World Journal of Microbiology and Biotechnology, 18(2), 99–113. https://doi.org/10.1023/A:1014459006210
Liu, Y., Xu, H. Lou, Yang, S. F., & Tay, J. H. (2003). Mechanisms and models for anaerobic granulation in upflow anaerobic sludge blanket reactor. Water Research, 37(3), 661–673. https://doi.org/10.1016/S0043-1354(02)00351-2
MacLeod, F. A., Guiot, S. R., & Costerton, J. W. (1990). Layered structure of bacterial aggregates produced in an upflow anaerobic sludge bed and filter reactor. Applied and Environmental Microbiology, 56(6), 1598–1607. https://doi.org/<p></p>
Mahoney, E. M., Varangu, L. K., Cairns, W. L., Kosaric, N., & Murray, R. G. E. (1987). The effect of calcium on microbial aggregation during uasb reactor start-up, 19, 249–260.
Man, A. W. A., Grin, P. C., Roersma, R. E., Grolle, K. C. F., & Lettinga, G. (1986). Anaerobic treatment of municipal wastewater at low temperatures. Amsterdam.
Mancini, M., Moresi, M., Rancini, R., Lellis, V. S. C. De, Viterbo, I.-, & Agroalimentari, T. (1999). Mechanical properties of alginate gels : empirical characterisation, 39, 369–378.
McHugh, S., O’Reilly, C., Mahony, T., Colleran, E., & O’Flaherty, V. (2003). Anaerobic granular sludge bioreactor technology. Reviews in Environmental Science and Biotechnology, 2(2–4), 225–245. https://doi.org/10.1023/B:RESB.0000040465.45300.97
Michihiko, N., & Tomonori, M. (1982). Interactions between amino‐acid‐degrading bacteria and methanogenic bacteria in anaerobic digestion.
Moen, E., Horn, S., & Østgaard, K. (1997a). Alginate degradation during anaerobic digestion of Laminaria hyperborea stipes, 157–166.
Moen, E., Horn, S., & Østgaard, K. (1997b). Biological degradation of Ascophyllum nodosum, 347–357.
Moen, E., & Østgaard, K. (1997). Aerobic digestion of Ca-alginate gels studied as a model system of seaweed tissue degradation, 261–267.
63
Molino, A., Nanna, F., Ding, Y., Bikson, B., & Braccio, G. (2013). Biomethane production by anaerobic digestion of organic waste. Fuel, 103, 1003–1009. https://doi.org/10.1016/J.FUEL.2012.07.070
Moller, H., Sommer, S., & Ahring, B. (2004a). Biological degradation and greenhouse gas emissions during pre-storage of liquid animal manure. In J. Environ Qual (pp. 27–36).
Moller, H., Sommer, S., & Ahring, B. (2004b). Methane productivity of manure, straw and solid fractions of manure. Biomass Bioenergy, 485(26).
Monnet, F. (2003). An Introduction to Anaerobic Digestion of Organic Wastes. Remade Scotland Report, (November), 1–48. https://doi.org/10.1007/978-3-319-24708-3_2
Morvai, L., Mihaltz, P., & Czako, L. (1992). The kinetic basis of a new start-up method to ensure the rapid granulation of anaerobic sludge. Water Sci. Technol., (25), 113–122.
Mudrack, K., & Kunst, S. (1986). Biology of sewage treatment and water pollution control.
Mueller, R., & Steiner, A. (1992). Inhibition of anaerobic digestion caused by heavy metals. Water Science and Technology, (26), 835–846.
Palatsi, J., Illa, J., Prenafeta-boldú, F. X., Laureni, M., Fernandez, B., Angelidaki, I., & Flotats, X. (2010). Long-chain fatty acids inhibition and adaptation process in anaerobic thermophilic digestion : Batch tests , microbial community structure and mathematical modelling. Bioresource Technology, 101(7), 2243–2251. https://doi.org/10.1016/j.biortech.2009.11.069
PAQUES. (n.d.). No Title. Retrieved November 24, 2018, from https://en.paques.nl/home
Parkin, G. F., & Owen, W. F. (1986). Fundamentals of Anaerobic Digestion of Wastewater Sludges. Journal of Environmental Engineering, 112(5), 867–920. https://doi.org/10.1061/(ASCE)0733-9372(1986)112:5(867)
Parsegian, V. A., & Rand, R. P. (1991). Forces governing lipid interaction and rearrangement. In Membrane fusion (pp. 65–85).
Pereira, M. A., Pires, O. C., Mota, M., & Alves, M. M. (2005). Anaerobic biodegradation of oleic and palmitic acids: evidence of mass transfer limitations caused by long chain fatty acid accumulation onto the anaerobic sludge. Biotechnol Bioeng, 92(1), 15–23.
Pereira, M. A., Sousa, D. Z., Mota, M., & Alves, M. M. (2004). Mineralization of LCFA associated with anaerobic sludge: kinetics, enhancement of methanogenic activity and effect of VFA. Biotechnology and Bioengineering, 88(4), 502–511.
Pobeheim, H., Munk, B., Johansson, J., & Guebitz, G. M. (2010). Bioresource Technology Influence of trace elements on methane formation from a synthetic model substrate for maize silage. Bioresource Technology, 101(2), 836–839. https://doi.org/10.1016/j.biortech.2009.08.076
Pycke, B. F. G., Etchebehere, C., Van de Caveye, P., Negron, A., Verstraete, W., & Boon, N. (2011). A time-course analysis of four full-scale anaerobic digesters in relation to the dynamics of change of their microbial communities. Water Sci. Technol., 63(4), 769–755.
Quarmby, J., & Forster, C. F. (1995). An examination of the structure of UASB granules. Water Research, (29), 2449–2454.
Rajagopal, R., Massé, D. I., & Singh, G. (2013). A critical review on inhibition of anaerobic digestion process by excess ammonia. Bioresource Technology, 143, 632–641. https://doi.org/10.1016/j.biortech.2013.06.030
64
Rajeshwari, K. V, Balakrishnan, M., Kansal, A., Lata, K., & Kishore, V. V. N. (2000). State-of-the-art of anaerobic digestion technology for industrial wastewater treatment, 4.
Ress, B., Calvert, P., Pettigrew, C., & Barlaz, M. (1998). Testing anaerobic biodegradability of polymers in a laboratory-scale simulated landfill. Environ Sci Technol, 821(32), 7.
Rinzema, A., van Lier, J., & Lettinga, G. (1988). Sodium inhibition of acetoclastic methanogens in granular sludge from a UASB reactor. Enzyme and Microbial Technology, 10(1), 24–32. https://doi.org/10.1016/0141-0229(88)90094-4
Rocheleau, S., Greer, C., Lawrence, J., Cantin, C., Laramee, L., & Guiot, S. (1999). Differentiation of Methanosaeta concilii and Methanosarcina barkeri in anaerobic mesophilic granular sludge by in situ hybridization and confocal scanning laser microscopy. Applied and Environmental Microbiology, (30), 199–207.
Rouxhet, P., & Mozes, N. (1990a). Physical chemistry of the interaction between attached microorganisms and their support. Water Sci. Technol., 22(1), 1–16.
Rouxhet, P., & Mozes, N. (1990b). Physical chemistry of the interaction between attached microorganisms and their support. Water Sci. Technol., 22(1), 16.
Rudd, T., Sterritt, R. M., & Lester, J. N. (1984). Complexation of heavy metals by extracellular polymers in the activated sludge process. Journal of Water Pollution Control Federation, (56), 1260–1268.
Seghezzo, L., Zeeman, G., Liel, J. B., van Hamelers, H. V. M., Lettinga, & Gatze. (1998). The Anaerobic Treatment of Sewage in UASB and EGSB Reactors.
Siles, J. A., Brekelmans, J., Martín, M. A., Chica, A. F., & Martín, A. (2010). Impact of ammonia and sulphate concentration on thermophilic anaerobic digestion. Bioresource Technology, 101(23), 9040–9048. https://doi.org/10.1016/j.biortech.2010.06.163
Speece, R. E. (1983). Environmental Science & Technology. Anaerobic biotechnology for industrial wastewater.
Sterling Jr., M. C., Lacey, R. ., Engler, C. R., & Ricke, S. C. (2000). Effects of ammonia nitrogen on H2 and CH4 production during anaerobic digestion or dairy cattle manure. Bioresource Technology, 77, 9–18.
Sundberg, C., Al-Soud, W. A., Larsson, M., Alm, E., Yekta, S. S., Svensson, B. H., … Karlsson, A. (2013). 454 Pyrosequencing Analyses of Bacterial and Archaeal Richness in 21 Full-Scale Biogas Digesters. FEMS Microbiology Ecology, 85(3), 612–626. https://doi.org/10.1111/1574-6941.12148
Sung, S., & Liu, T. (2003). Ammonia inhibition on thermophilic anaerobic digestion. Chemosphere, 53(1), 43–52. https://doi.org/10.1016/S0045-6535(03)00434-X
Tay, J.-H., Hai-Lou, X., & Khay-Chuan, T. (2000). Molecular mechanism of granulation I: H+ Translocation-dehydration theory. Journal of Environmental Engineering, 126(5). https://doi.org/10.1146/annurev.immunol.20.090501.112049
Thaveesri, J., Liessens, B., & Verstraete, W. (1995). ‘‘Granular sludge growth under different reactor liquid surface tensions in lab-scale upflow anaerobic sludge blanket reactors treating wastewater from sugarbeet processing. Applied Microbiology and Biotechnology, 43, 1122–1127.
The open University. (n.d.). Chemistry: essential concepts. Retrieved from https://www.open.edu/openlearn/ocw/mod/oucontent/view.php?printable=1&id=20880
65
Tiwari, M. K., Guha, S., & Tripathi, S. (2006). Influence of extrinsic factors on granulation in UASB reactor. Applied Microbiology and Biotechnology, (71), 145–154. https://doi.org/10.1007/s00253-006-0397-3
Turovskiy IS, M. P. (2006). Wastewater sludge processing. New York.
Turtakovsky, B., Petti, L., & Gulot, S. (1997). Microorganisms immobilized in chitosan crosslinked with lignosulphonate for purification of waste water, (19).
Vandevivere, P. C., Bianchi, R., & Verstraete, W. (1998). Treatment and reuse of wastewater from the textile wet-processing industry: review of emerging technologies. In J. Chem. Tech. Biotechnol. (72nd ed., pp. 289–302).
Vidal, G., Jiang, Z. P., Omil, F., & Thalasso, F. (1999). Continuous anaerobic treatment of wastewaters containing formaldehyde and urea, 70.
Vintiloiu, A., Lemmer, A., Oechsner, H., & Jungbluth, T. (2012). Mineral substances and macronutrients in the anaerobic conversion of biomass: An impact evaluation. Engineering in Life Sciences., 12(3), 287–294.
Vrbka, L., Jagoda-cwiklik, B., & Vácha, R. (2006). Quantification and rationalization of the higher affinity of sodium over potassium to protein surface, 21, 1–25.
Vrieze, J. De, Verstraete, W., & Boon, N. (2013). Repeated pulse feeding induces functional stability in anaerobic digestion. https://doi.org/10.1111/1751-7915.12025
Wang, H., Vuorela, M., Keränen, A. L., Lehtinen, T. M., Lensu, A., Lehtomäki, A., & Rintala, J. (2010). Development of microbial populations in the anaerobic hydrolysis of grass silage for methane production. FEMS Microbiology Ecology, 72(3), 496–506. https://doi.org/10.1111/j.1574-6941.2010.00850.x
Wang, J., Hu, Y., & Wu, C. (2005). Comparing the effect of bioflocculant with synthetic polymers on enhancing granulation in UASB reactors for low-strength wastewater treatment, 31(2), 177–182.
Ward, A., Hobbs, P., Holliman, P., & Jones, D. (2008). optimisation of the anaerobic digestion of agricultural resources. Bioresource Technology.
Wilschut, J., & Hoekstra, D. (1984). Membrane fusion: from liposome to biological membrane. Trend Biochem Sci, 479(9), 83.
Wu, W. M. (1991). Technological and microbiological aspects of anaerobic granules. Michigan State University.
Yaw, Y., Norli, I., Zuhairi, A., & Firdaus, M. (2016). Bioresource Technology Impacts of trace element supplementation on the performance of anaerobic digestion process : A critical review. Bioresource Technology, 209, 369–379. https://doi.org/10.1016/j.biortech.2016.03.028
Ye, J., Li, D., Sun, Y., Wang, G., Yuan, Z., Zhen, F., & Wang, Y. (2013). Improved biogas production from rice straw by co-digestion with kitchen waste and pig manure. Waste Manage., 33, 2653–2658.
Yoda, M., Kitagawa, M., & Miyaji, Y. (1989). Granular sludge formation in the anaerobic expanded micro-carrier process. In Water Science and Technology (pp. 109–120).
Yong, K., & Mooney, D. J. (2012). Progress in Polymer Science Alginate : Properties and biomedical applications. Progress in Polymer Science, 37(1), 106–126. https://doi.org/10.1016/j.progpolymsci.2011.06.003
66
Youngsukkasem, S., Rakshit, S. K., & Taherzadeh, M. J. (2012). Biogas production by encapsulated methane- producing bacteria, 7, 56–65.
Yu, H., Tay, J., & Fang, H. (2001). The roles of calcium in sludge granulation during UASB reactor start-up. Water Research, (35), 1052–1060.
Zeeman, G., Wiegant, W. M., Koster-Treffers, M. E., & Lettinga, G. (1985). The influence of the total ammonia concentration on the thermophilic digestion of cow manure. In Agricultural Wastes (14th ed., pp. 19–35).
Zhang, Y., & Angelidaki, I. (2015). Recovery of ammonia and sulfate from waste streams and bioenergy production via bipolar bioelectrodialysis. Water Research, 85, 177–184. https://doi.org/10.1016/j.watres.2015.08.032
Ziganshin, A. M., Liebetrau, J., Pröter, J., & Kleinsteuber, S. (2013). Microbial community structure and dynamics during anaerobic digestion of various agricultural waste materials. Applied Microbiology and Biotechnology, 97(11), 5161–5174. https://doi.org/10.1007/s00253-013-4867-0
67
APPENDIX 1: ANION AND CATION COMPOSITION
OF SYNTHETIC MEDIUM 1 AND 2 The anion and cation composition of the synthetic media are calculated theoretically and can be found
in Table 12.
Table 12 - Theoretically calculated anion and cation concentrations of synthetic medium 1 and 2
Cations
Na+ NH4+ K+ Ca2+ Mg2+
Synthetic medium (mg/L) 230 169 450 46 12
Anions
Cl- PO43- SO4
2- CO3-
Synthetic medium (mg/L) 487 144 413 1200
68
69
APPENDIX 2: CALCULATIONS OF COD OF ALGINATE In experiment 2, 15 g of sodium alginate (C6H7NaO6) was mixed with 448 g sludge and 552 g H2O. This
solution was dropped gradually in a CaCl2 solution, after which the beads were added to a reactor with
a volume of 2 L. This means that a total amount of 13.25 g alginate (C6H7O6-) was added to a reactor
volume of 2 L.
𝑀𝑤 (𝐶6𝐻7𝑂6_)
𝑀𝑤 (𝐶6𝐻7𝑁𝑎𝑂6)∗ 15 𝑔 𝐶6𝐻7𝑁𝑎𝑂6 =
175.12
198.11∗ 15 = 13.25 𝑔 𝐶6𝐻7𝑂6
_
The final alginate concentration in the reactor is thus 6.63 g/L. To calculate the COD of 6.63 g alginate/L,
following reaction is required:
𝐶6𝐻7𝑂6_ + 4.75 𝑂2
→ 6𝐶𝑂2 + 3.5𝐻2𝑂
The total number of moles of C6H7O6- is 0.038 mol/L, so the total number of moles of O2 is 4.75 * 0.038
= 0.18 mol/L. This amount of mol corresponds to a mass of oxygen of 0.18 mol * 32 g/mol = 5.76 g/L.
This is the total amount of oxygen that can be consumed by the reaction, which equals the COD. The
COD concentration in the reactor is thus 5.76 g/L and the total amount of COD added to the reactor is
2 L * 5.67 g/L = 11.52 g.
70
71
APPENDIX 3: CATION CONCENTRATIONS OF THE
THREE EXPERIMENTS
Different cation concentrations were observed in experiment 1, in which molasse was used as
feedstock compared to experiment 2 and 3, in which a synthetic medium was used as feedstock. The
reactors in experiment 1 contained much higher concentrations of NH4+, K+ and Mg2+ and lower
concentrations of Na+ and Ca2+.
Table 13 – Cation concentrations in reactor A (containing granules encapsulated in alginate) and in
the control reactor (containing natural granules) during the different experiments
Reactor A (mg/L) Control reactor (mg/L) Days Na+ NH4
+ K+ Ca2+ Mg2+ Na+ NH4+ K+ Ca2+ Mg2+
Experiment 1
0 160 483 1971 31 43 95 465 1896 62 38 7 94 458 1791 139 86 80 489 1623 29 40
14 84 389 1856 37 47 100 524 2260 33 52
Experiment 2
5 260 142 433 383 27 247 183 439 33 45 13 262 155 426 159 17 262 168 432 38 34
Experiment 3a
7 252 56 378 439 20 235 71 380 96 21
Experiment 3b
4 273 60 362 / 19 226 75 354 103 16
Experiment 3c
3 305 20 347 / 16 205 38 325 122 13 10 252 77 421 221 15 255 78 436 106 17
Experiment 3d
6 214 55 347 274 16 235 72 410 117 18 9 238 97 398 187 17 248 107 416 110 19
13 254 79 446 133 17 252 78 443 99 17
72
73
APPENDIX 4: REPLICATES SHEAR STRESS BATCH
TEST
74
75
76
77
78
79
APPENDIX 5: REPLICATES POTASSIUM AND
PHOSPHATE BATCH TEST
80
81
82
83
APPENDIX 6: COMPARISON BETWEEN SHEAR
STRESS BATCH TEST AND POTASSIUM PHOSPHATE
BATCH TEST The properties of three different situations can be found in Table 14 and the swelling and disintegration
of these three situations can be found in Table 15. Situation 1 refers to the control of the potassium
phosphate batch test. Situation 2 refers to the shear stress batch test with the encapsulated water
beads (with shaking) and the last situation refers to the shear stress batch test with the encapsulated
water beads (with shaking).
Table 14 – Properties of serum flasks filled with encapsulated water beads and tap water (situation
1), a solution of 405 mg PO43-/L (situation 2) or effluent (situation 3)
Situation Solution Na+ NH4+ K+ Ca2+ Mg2+ PO4
3- Disintegration Swelling
1 Tap water13 (mg/L) 49 n.a. n.a. 102 9 n.a. No No
2 405 mg PO4
3-
(mg/L) 196 / / / / 405 Yes Yes
3 Effluent (mg/L) 92 493 1927 42 44 237 Yes No
* n.a. = not available
13 (The open University, n.d.)
84
Table 15 - Comparison between the swelling and disintegrations of the control of potassium
phosphate batch test (situation 1), the treatment with 405 mg PO43- mg/L of the potassium
phosphate batch test (situation 2) and the shaken encapsulated water beads of the shear stress
batch test (situation 3)
Day 0 Day 2 Day 7 Day 10 Day 14
1 Tap
water
2 405 mg PO4
3- /L
Day 0 Day 3 Day 7 Day 11 Day 14
3 Effluent
