Bioresource Technology 102 (2011) 948–959

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Bioresource Technology

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Modelling mono-digestion of grass silage in a 2-stage CSTR anaerobic digesterusing ADM1

T. Thamsiriroj a,b, J.D. Murphy a,b,⇑a Department of Civil and Environmental Engineering, University College Cork, Cork, Irelandb Environmental Research Institute, University College Cork, Cork, Ireland

a r t i c l e i n f o

Article history:Received 21 July 2010Received in revised form 9 September 2010Accepted 14 September 2010Available online 19 September 2010

Keywords:Grass silageMono-digestionModellingADM1

0960-8524/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.biortech.2010.09.051

⇑ Corresponding author at: Environmental ResearchCork, Cork, Ireland. Tel.: +353 21 4902286; fax: +353

E-mail address: jerry.murphy@ucc.ie (J.D. Murphy

a b s t r a c t

This paper examines 174 days of experimental data and modelling of mono-digestion of grass silage in atwo stage wet process with recirculation of liquor; the two vessels have an effective volume of 312 Leach. The organic loading rate is initiated at 0.5 kg VS m�3 d�1 (first 74 days) and subsequently increasedto 1 kg VS m�3 d�1. The experimental data was used to generate a mathematical model (ADM1) whichwas calibrated over the first 74 days of operation. Good accuracy with experimental data was foundfor the subsequent 100 days. Results of the model would suggest starting the process without recircula-tion and thus building up the solids content of the liquor. As the level of VFA increases, recirculationshould be employed to control VFA. Recirculation also controls solids content and pH. Methane produc-tion was estimated at 88% of maximum theoretical production.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Research and development of clean and sustainable fuels isessential in the world we live in. Furthermore production of thesefuels needs to be optimised, both economically and sustainably.Biogas/biomethane has a significant role to play. Traditionally pro-duced as an end product of an anaerobic waste treatment process,in recent years biogas is increasingly produced from crops grownspecifically for energy production, particularly in Germany andAustria (Walla and Schneeberger, 2005; Hopfner-Sixt et al.,2005). Grass is a significant source of biogas in these countries.The scientific literature on grass biogas is concerned with agron-omy (i.e. different grass species, intensity of grassland, harvestingperiods, and conservation methods) (Prochnow et al., 2005) andtechnology (reactor configurations, organic loading rate (OLR)and hydraulic retention time (HRT)) (Nizami and Murphy, 2010).The high solids content of grass allow it to be used in a wet process(Continuously Stirred Tank Reactor (CSTR)) or a dry batch process(Chynoweth et al., 2001; Nizami et al., 2009). This paper considersa wet system with two digesters in series with recycle of liquorfrom the second vessel to the first. A key issue for such a systemis the tendency for grass to float. This floating layer can becomean indigestible scum layer causing a decrease in biogas yield(Thamsiriroj and Murphy, 2010). Conversely, this tendency to floatleads to extended solid retention time (SRT) causing an increase in

ll rights reserved.

Institute, University College21 4901932.

).

biogas production. It is difficult to precisely assess accurate biogasyield in these conditions. In optimising the two stage wet digestionprocess the proportion of the recycled liquor must be assessed. Thelevel of recycle influences the HRT and the biogas yield. This paperdescribes the effect of HRT on biogas yield. Physical experimenta-tion as employed here is time consuming. Thus mathematicalmodelling may be used to supplement the experimental datathrough simulation of different scenarios based on a preliminaryset of experimental data (Gali et al., 2009).

Initially model development considered organic matter as awhole and did not account for the composition of the feedstock.Newer model approaches consider complex feed compositions(carbohydrate, protein, volatile fatty acids (VFA) and other organ-ics) generating more accurate results (Lyberatos and Skiadas,1999). Under the IWA Task Group for Mathematical Modelling ofAnaerobic Digestion Processes, Batstone et al. (2002) developed acommon platform known as ADM1 combining a number of bio-chemical and physiochemical processes to simulate the behaviourof various components in the anaerobic digesters. The ADM1 mod-el is structured to allow for disintegration and hydrolysis, acido-genesis, acetogenesis and methanogenesis steps (Fig. 1); it alsoincludes the transformation of products from liquid to gas phase.Different forms of differential equations may be applied dependingon the reactor configuration. Examples of feedstock and digesterconfigurations modelled include:

� Batch system: – mono-digestion and co-digestion of agro-wastes(Gali et al., 2009).

Composite particulate material and inactive bacterial biomass

Carbohydrates Proteins Fats

Monosaccharides Amino acids Long chain fatty acids

Propionate, Valerate, Butyrate

Acetate H2

CH4 & CO2

Inerts Disintegration

Hydrolysis

Acidogenesis

Acetogenesis

Methanogenesis

Dead microbes

Fig. 1. The anaerobic model as structured in ADM1 model.

Table 1Characteristics of grass silage in the study (Thamsiriroj and Murphy, 2010).

pH 4.3Ammonia (% of total N) 9Protein (% DS) 9.5ME (MJ kg�1 DS) 10DMD or D-value (% DS or D-value) 64Silage intake or Palatability (g kg�1 W0.75) 89Lactic acid (%DS) 4.3Lactic acid (% total acids) 7.3VFA (%DS) 1.6PAL (meq kg�1 DS) 821NDF (%DM) 59Soluble sugars (% DS) 5FME (MJ kg�1 DS) 8.2FME/ME ratio 0.81Oil (%DS) 3.3C (%DS) 43.04H (%DS) 5.82N (%DS) 1.61DS (%) 30.66VS (%) 92.46COD equivalent (gCOD g�1 VS) 1.40

T. Thamsiriroj, J.D. Murphy / Bioresource Technology 102 (2011) 948–959 949

� Single-stage system: – mono-digestion of grass (Wichern et al.,2009); co-digestion of olive mill wastewater and olive mill solidwaste (Boubaker and Ridha, 2008); co-digestion of manure andenergy crops (Lübken et al., 2007).� 2-stage system: – sewage sludge (thermophilic CSTR followed by

mesophilic CSTR) (Blumensaat and Keller, 2005); traditionalChinese medicine wastewater (CSTR followed by UASB) (Chenet al., 2009).

The model proposed here is a 2-stage CSTR system digestinggrass silage at an OLR of 0.5 kg VS m�3 d�1 for 74 days followedby 1.0 kg VS m�3 d�1 for 100 days. The observed biogas yield (i.e.L biogas kg�1 VS) for the latter 100 days are significantly higherthan the former 74 days, and at the upper bound of theoretical bio-gas potential of grass silage. A hypothesis is made that the SRT inthe latter 100 days is significantly greater than the HRT due tofloating grass accumulation. The mathematical model is designedto simulate the digester system, determine the actual SRT and esti-mate the actual biogas yield at an OLR of 1.0 kg VS m�3 d�1. Themodel is also used to simulate the effect of recycled liquor on thebiogas yield and to simulate the change in solids content of the di-gester in the long term operation.

2. Methods

2.1. Physical experiment

2.1.1. Characteristics of grass silageBale grass silage (table 1) used for the experiment was obtained

from the Irish Agricultural Institute Teagasc. The herbage was har-vested on June 2nd (1st cut, early mature) from a homogenousperennial ryegrass dominant plot. The herbage was field wiltedfor 24 h before being baled and stored for around 5 weeks to allowensilage to take place. The silage was then taken from the large

bales and wrapped in a number of small 25 kg bags which wereopened when required and macerated prior to insertion to the di-gester (Thamsiriroj and Murphy, 2010).

2.1.2. Digester configurationThe 2-stage CSTR operated at an average temperature of 37 �C.

The two digester vessels each contained 312 L of fermentation vol-ume and 160 L of gas headspace. Liquor was transferred betweenthe two vessels through a straight connection pipe at a low level.A data log system which included for sensors for measuring tem-perature, pH, biogas flow rate, rotation and control of agitating mo-tor was wired to the PLC control. Grass silage was fed on a daily

950 T. Thamsiriroj, J.D. Murphy / Bioresource Technology 102 (2011) 948–959

basis to the digester vessel 1 through the feed hopper. The connec-tion pipe allowed a transfer of the substrate from vessel 1 to vessel2. The liquor in vessel 2 can be either pumped back to the feed hop-per as recycled liquor or removed as digestate. The digester vesselswere equipped with a mechanical stirring system, which consistedof a vertical shaft with horizontal mixer blades. Mixer blades wereof an airfoil shape twisted along the longitudinal axis, such that theblade movement can create a downward resultant force pushingthe floating substrate into the digesting liquor. Each digester vesselsat on a hot water bulk equipped with an internal core heating ele-ment. The heating coil was controlled by PLC to gradually bring thedigester to meet the target temperature. The layout of the 2-stageCSTR digester in the study can be found in Thamsiriroj and Murphy(2010).

2.1.3. Digester operationThe experiment period lasted 174 days (Table 2). Grass silage

was fed to the digester with an OLR of 0.5 kg VS m�3 d�1

(HRT = 221 days) for the first 74 days. Subsequently, the OLR wasincreased to 1.0 kg VS m�3 d�1 for 100 days. Of the latter 100 days,50 were operated with a HRT of 80 days, reduced to 60 days for thelast 50 days. The OLR remained at 1.0 kg VS m�3 d�1 for these100 days. HRT was adjusted through the recycling rate of liquorwithout addition of new water throughout the experimental peri-od. The experimental scheme is outlined in Table 2. Emphasis wasplaced on recirculation of liquor and as a result the rate of mixingwas kept constant throughout the experimental period to remove avariable and allow consistency in the experimental results.

2.1.4. Analytical methodsDry solids (DS) and volatile solids (VS) contents were measured

using methods detailed in APHA (1998). Total volatile fatty acids(total VFA) were determined by titration by the method as de-scribed by Ripley et al. (1986). A complete grass silage analysisbased on feeding value for dairy cattle was conducted by theAgri-Food and Biosciences Institute (AFBI), Belfast, UK. An ultimateanalysis (C–H–N content) of the grass silage was conducted by theDepartment of Chemistry, University College Cork, Ireland. Biogascomposition (in particular the methane content) was analysed bythe portable biogas analyser model PGD3-IR, supplied by StatusScientific Controls Ltd.

2.2. Mathematical model

2.2.1. Description of ADM1 modelThe ADM1 model simulates the behaviour of elements in the

anaerobic digester based on a number of biochemical and phys-ico-chemical processes.

The biochemical processes involve three overall biochemicalsteps namely: acidogenesis; acetogenesis; and methanogenesis.Two additional partially non-biological steps (disintegration andhydrolysis) are included (Fig. 1). The model assumes that the com-posite particulate feed input to the digester is homogeneous incomposition. Initially this composite particulate material is disin-tegrated to carbohydrate, protein and lipid. These three macromol-

Table 2Experimental and modelling schemes.

Physical experimentOLR = 0.5 kg VS m�3 d�1 OLR = 1.0 kg VS m�3 d�1

Day 1–74 (74 days) Day 75–124 (50 days) Day 125–174 (50 days)HRT = 221 days HRT = 80 days HRT = 60 days

Mathematical modelCalibration period Validation period

ecules are hydrolysed to their constituent monomers: thebiochemical steps now take place. Dead microbes in the digesterare also considered as part of the particulate material to be disin-tegrated. The disintegration and hydrolysis steps are assumed inthe ADM1 model to be of the first order function empirically rep-resenting the cumulative effect of a multi-step process. The bio-chemical steps are described by three expressions which includefor uptake, growth and decay. The uptake and growth processesare specified by the Monod-type kinetic function with inhibitionterms added. The decay process describes the transformation ofdead microbes to particulate material using the first orderfunction.

The physico-chemical processes are non-biologically mediatedand commonly occur in anaerobic digestion. Two physico-chemicalprocesses are included in the ADM1 model: liquid–liquid processes(i.e. ion association/dissociation) and liquid–gas processes (i.e. li-quid–gas transfer). The pH of the fermenting liquor in the digesteris a result of the liquid–liquid processes. Methane, carbon dioxideand hydrogen are transformed from liquid to gaseous forms (orvice versa) by the liquid–gas processes (Batstone et al., 2002).

2.2.2. Model implementationModel implementation is dependent on the digester configura-

tion and on mass conservation. In the original ADM1 differentialequation (DE) system, model implementation of a single-stageCSTR digester requires simultaneously solving of 32 differentialequations in liquid phase and 3 differential equations in gas phase(35 equations in total). Of the 32 equations in liquid phase thereare 24 equations based on biochemical processes; one each for cat-ion and anion states; and 6 for acid–base pairs. A schematic of thesingle-stage CSTR can be found in Batstone et al. (2002). A 2-stageCSTR system may be modelled as two single-stage CSTR digestersoperated in series with the fermenting liquid recirculated fromthe first to the other vessel. Therefore, the number of equations re-quired for the model of a 2-stage CSTR digester doubles the num-ber required for a single-stage CSTR. A total of 70 equations is thusrequired. Details of state variables, processes and related parame-ters are outlined in Table 3.

2.2.3. Differential equations in the implementation of 2-stage CSTRdigester

Liquid phase equations: Differential equations in the liquid phasedeal with two types of substrate: soluble substrate (liquid form)and particulate substrate (solid form). The equations differ be-tween vessel 1 and 2. The equation sets for soluble substrate (bothvessels) used in this study are shown in Eq. (1) and the equationsets for particulate substrate in Eq. (2). Eq. (3) is proposed in themodel to allow a recirculation of fermenting liquid from vessel 2to vessel 1. Parameters tres,X (for vessel 1) and t2res,X (for vessel 2)are added to the equations for the particulate substrate (Eq. (2))to allow for an estimation of extra SRT above HRT.

Acid–base equations: Six ion state equations (which describe theacid–base reactions and determine the pH in the liquid phase) areformulated in Eq. (4) (Boubaker and Ridha, 2008). The average pHin the vessels can be determined by solving the charge balanceequation in Eq. (5) using the procedure described by Rosen andJeppsson (2006). However, the complexity of the model may be re-duced by decreasing the number of variables and associated equa-tions. Differentiation of Eq. (5) and rearranging the terms yields Eq.(6): SH

+ is now the only state variable in the acid–base processes.Eq. (4) and (5) are now superfluous. The total number of differen-tial equations to be solved simultaneously is thus reduced to 60equations (30 equations for each digester vessel: 26 equationsfrom Eq. (1) and Eq. (2), 1 equation from Eq. (6) and 3 equationsfrom Eq. (7)).

Table 3State variables, biochemical processes and related parameters.

State variables i in liquid phaseSoluble substrate Particulate and biomassSliq,1 Monosaccharides (Ssu, gCOD L�1) Xliq,13 Composites (Xc, gCOD L�1)Sliq,2 Amino acids (Saa, gCOD L�1) Xliq,14 Carbohydrates (XCH, gCOD L�1)Sliq,3 Long chain fatty acids (Sfa, gCOD

L�1)Xliq,15 Proteins (Xpr, gCOD L�1)

Sliq,4 Total valerate (Sva, gCOD L�1) Xliq,16 Lipids (Xli, gCOD L�1)Sliq,5 Total butyrate (Sbu, gCOD L�1) Xliq,17 Sugar degraders (Xsu, gCOD

L�1)Sliq,6 Total propionate (Spro, gCOD L�1) Xliq,18 amino acid degraders (Xaa,

gCOD L�1)Sliq,7 Total acetate (Sac, gCOD L�1) Xliq,19 LCFA degraders (Xfa, gCOD L�1)Sliq,8 Hydrogen (Sh2, gCOD L�1) Xliq,20 Valerate & butyrate degraders

(XC4 , gCOD L�1)Sliq,9 Methane (SCH4, gCOD L�1) Xliq,21 Propionate degraders (Xpro,

gCOD L�1)Sliq,10 Inorganic carbon (SIC, mole C

L�1)Xliq,22 Acetate degraders (Xac, gCODL�1)

Sliq,11 Inorganic nitrogen (SIN, mole NL�1)

Xliq,23 Hydrogen degraders (XH2 ,gCOD L�1)

Sliq,12 Soluble inerts (SI, gCOD L�1) Xliq,24 Particulate inerts (XI, gCODL�1)

Ion state variablesSliq,25 Cations (Scat

+, mole L�1)Sliq,26 Anions (San

�, mole L�1)Sliq,27 Hydrogen ion (SH

+, mole H+ L�1)

State variables in gas phaseSgas;H2

Hydrogen (gCOD L�1)Sgas;CH4

Methane (gCOD L�1)Sgas;CO2

Carbon dioxide (mole C L�1)

Process j in liquid phasej = 1 Disintegration j = 13 Decay of sugar degradersj = 2 Hydrolysis of carbohydrates j = 14 Decay of amino acid degradersj = 3 Hydrolysis of proteins j = 15 Decay of LCFA degradersj = 4 Hydrolysis of lipids j = 16 Decay of Valerate & butyrate

degradersj = 5 Uptake of sugars j = 17 Decay of propionate degradersj = 6 Uptake of amino acids j = 18 Decay of acetate degradersj = 7 Uptake of LCFA j = 19 Decay of hydrogen degradersj = 8 Uptake of valeratej = 9 Uptake of butyratej = 10 Uptake of propionatej = 11 Uptake of acetatej = 12 Uptake of hydrogen

ParametersLiquid phase Gas phaseSfeed,i Soluble substrate i in composite

feed (gCOD L�1)qT,i Specific mass transfer rate of gasi (gCOD L�1 d�1)

Xfeed,i Particulate/biomass i incomposite feed (gCOD L�1)

qgas Gas flow rate (L d�1)

ffeed Composite feed fraction in totalinput to vessel 1

Patm Atmospheric pressure (bar)

Sin,i Input of soluble substrate i tovessel 1 (gCOD L�1)

Pgas Total gas pressure in headspace(bar)

Xin,i Input of particulate/biomass i tovessel 1 (gCOD L�1)

pgas,i Partial pressure of gas i inheadspace (bar)

Sini,i Initial condition of solublesubstrate i (gCOD L�1)

kp Pipe resistance coefficient (Ld�1 bar-1)

Xini,i Initial condition of particulate/biomass i (gCOD L�1)

mi,j Stoichiometric coefficientsqi Kinetic rate equations (gCOD L�1

d�1)Vliq Liquid volume (L)qliq Liquid flow rate (L d�1)tres,X Extra SRT above HRT (d)

T. Thamsiriroj, J.D. Murphy / Bioresource Technology 102 (2011) 948–959 951

Vessel 1 :dSliq;i

dt ¼qliqV liqðSin;i � Sliq;iÞ þ

P

j¼1�19qjV i;j

Vessel 2 :dS2liq;i

dt ¼qliq

V2liqðSliq;i � S2liq;iÞ þ

P

j¼1�19q2jV2i;j

i ¼ 1; . . . ;12; i ¼ 25� 26:

ð1Þ

Vessel 1 :dXliq;i

dt ¼qliqXin;i

V liq� Xliq;i

tres;XþV liq=qliqþ

P

j¼1�19qjV i;j

Vessel 2 :dX2liq;i

dt ¼qliqXliq;i

V2liq� X2liq;i

t2res;XþV2liq=qliqþ

P

j¼1�19q2jV2i;j

i ¼ 13� 24:

ð2Þ

Sin;i¼ðSfeed;i� ffeedÞþðS2liq;i�ð1� ffeedÞÞ i¼1; . . . ;12; i¼25�26Xin;i¼ðXfeed;i� ffeedÞþðX2liq;i�ð1� ffeedÞÞ i¼13�24

ð3Þ

where S2, X2, q2, m2, V2, t2res,X are variables and parameters invessel 2.

For each vessel :dsi

dt¼ Rj¼A1�A6qjv i;j

i ¼ ac�;pro�;bu�; va�;hco�3 ;nhþ4 and

A1� A6 ¼ acid-base process for va; bu; pro; ac; IC; IN ð4Þ

Scatþ þ Snhþ4þ SHþ � ShCO�3 �

Sac�

64� Spr�

112� Sbu�

160� Sva�

208� SOH� � San� ¼ 0

where SOH� ¼kw

SHþ; pH¼�log10½SHþ �

ð5Þ

dSHþ

dt¼ A

B

where

A ¼ dSan�

dtþ Ka;IN

ðKa;IN þ SHþ ÞdSIN

dtþ Ka;CO2

ðKa;CO2 þ SHþ ÞdSIC

dt

þ 164

Ka;ac

ðKa;ac þ SHþ ÞdSac

dtþ 1

112Ka;pro

ðKa;pro þ SHþ ÞdSpro

dt

þ 1160

Ka;bu

ðKa;bu þ SHþ ÞdSbu

dtþ 1

208Ka;va

ðKa;va þ SHþ ÞdSva

dt� dSIN

dt� dScatþ

dt

B ¼ 1þ Ka;INSIN

ðKa;IN þ SHþ Þ2 þ

Ka;CO2 SIC

ðKa;CO2 þ SHþ Þ2

þ 164

Ka;acSac

ðKa;ac þ SHþ Þ2 þ

1112

Ka;proSpro

ðKa;pro þ SHþ Þ2 þ

1160

Ka;buSbu

ðKa;bu þ SHþ Þ2

þ 1208

Ka;vaSva

ðKa;va þ SHþ Þ2 þ

Kw

SHþð6Þ

Gas phase equations: the transformation of biogas from liquid togaseous form is considered in Eq. (7). Gasses considered are meth-ane, carbon dioxide and hydrogen. The biogas yield is calculatedusing Eq. (8). The equation includes for vapour pressure and assumesan overpressure in the headspace (Rosen and Jeppsson, 2006).

dSgas;i

dt¼ �

qgas

VgasSgas;i þ

V liq

VgasqT;i i ¼ CH4;CO2;H2 ð7Þ

qgas ¼ kpðPgas � PatmÞPgas

Patm

wherePgas ¼ pgas;H2þ pgas;CH4

þ pgas;CO2þ pgas;H2O ð8Þ

2.2.4. Calibration and validation of the modelTo solve the differential equations, the model requires a set of

initial conditions which contains an accurate value of state vari-ables. This could be problematic as there are in total 60 state vari-ables (30 state variables for each vessel) in the 2-stage CSTRdigester (Table 3). Therefore, the model needs to be calibrated toestimate the initial value of some unobserved variables by fittingwith the experimental data. The experimental data from the day

Table 4Model input and initial conditions.

Grass silage input to model Remarks

Sfeed,su 16.352 gCOD L�1 5% soluble sugar �30.66%DS /180g mole�1 � 192gCOD mole�1 � 1000g L�1

Sfeed,aa -Sfeed,fa -Sfeed,va 0.50 gCOD L�1 5%valerate (assumed) � 1.6%total VFA � 30.66%DS /102g mole�1 � 208gCOD mole�1 � 1000g L�1

Sfeed,bu 0.892 gCOD L�1 10%butyrate (assumed) � 1.6%total VFA � 30.66%DS /88g mole�1 � 160gCOD mole�1 � 1000g L�1

Sfeed,pro 0.371 gCOD L�1 5%propionate (assumed) � 1.6%total VFA � 30.66%DS /74g mole�1 � 112gCOD mole�1 � 1000g L�1

Sfeed,ac 4.186 gCOD L�1 80%acetate (assumed) � 1.6%total VFA � 30.66%DS /60g mole�1 � 64gCOD mole�1 � 1000g L�1

Sfeed,h2 -Sfeed,ch4 -Sfeed,IC -Sfeed,IN -Sfeed,I -Xfeed,c 370.682 gCOD L�1 (100% -5%soluble sugar -1.6%VFA) � 30.66%DS � 92.46%VS � 1.4gCOD g�1VS � 1000g L�1

Xfeed,ch -Xfeed,pr -Xfeed,li -Xfeed,su 0.01 gCOD L�1 Assumed to be minimumXfeed,aa 0.01 gCOD L�1 Assumed to be minimumXfeed,fa 0.01 gCOD L�1 Assumed to be minimumXfeed,c4 0.01 gCOD L�1 Assumed to be minimumXfeed,pro 0.01 gCOD L�1 Assumed to be minimumXfeed,ac 0.01 gCOD L�1 Assumed to be minimumXfeed,h2 0.01 gCOD L�1 Assumed to be minimumXfeed,I -Sfeed,cat

+ -Sfeed,an

� -

Characteristics of inoculum (initial conditions) RemarksVessel 1 Vessel 2 Sample calculations for vessel 1DS = 1.4%, VS = 64.79%,

VFA = 0.09 g L�1DS = 0.75%, VS = 67.49%,VFA = 0.078 g L�1

Sini,su - S2ini,su -Sini,aa - S2ini,aa -Sini,fa - S2ini,fa -Sini,va 0.0092 gCOD L�1 S2ini,va 0.008 gCOD L�1 5%valerate � 0.09g L�1 total VFA /102g mole�1 � 208gCOD mole�1

Sini,bu 0.0164 gCOD L�1 S2ini,bu 0.0142 gCOD L�1 10%butyrate � 0.09g L�1 total VFA /88g mole�1 � 160gCOD mole�1

Sini,pro 0.0068 gCOD L�1 S2ini,pro 0.0059 gCOD L�1 5%propionate � 0.09g L�1 total VFA /74g mole�1 � 112gCOD mole�1

Sini,ac 0.0768 gCOD L�1 S2ini,ac 0.0666 gCOD L�1 80%butyrate � 0.09g L�1 total VFA /60g mole�1 � 64gCOD mole�1

Sini,h2 - S2ini,h2 -Sini,ch4 0.01 gCOD L�1 S2ini,ch4 0.01 gCOD L�1 Curve fittingSini,IC 0.045 mole C L�1 S2ini,IC 0.033 mole C L�1 Curve fittingSini,IN 0.005 mole N L�1 S2ini,IN 0.005 mole N L�1 Curve fittingSini,I - S2ini,I -Xini,c 8.889 gCOD L�1 X2ini,c 4.961 gCOD L�1 1.4%DS � (64.79%VS � 70%grass) � 1.4gCOD g�1VS � 1000g L�1

Xini,ch - X2ini,ch -Xini,pr - X2ini,pr -Xini,li - X2ini,li -Xini,su 0.42 gCOD L�1 X2ini,su 0.42 gCOD L�1 Curve fittingXini,aa 1.18 gCOD L�1 X2ini,aa 1.18 gCOD L�1 Curve fittingXini,fa 0.24 gCOD L�1 X2ini,fa 0.24 gCOD L�1 Curve fittingXini,c4 0.30 gCOD L�1 X2ini,c4 0.30 gCOD L�1 Curve fittingXini,pro 0.27 gCOD L�1 X2ini,pro 0.15 gCOD L�1 Curve fittingXini,ac 0.70 gCOD L�1 X2ini,ac 0.40 gCOD L�1 Curve fittingXini,h2 0.31 gCOD L�1 X2ini,h2 0.31 gCOD L�1 Curve fittingXini,I 6.595 gCOD L�1 X2ini,I 3.391 gCOD L�1 1.4%DS � ((92.46 -64.79)%VS +(64.79%VS � 30%residue)) � 1gCOD g�1VS (assumed) � 1000g L�1

Sini,cat+ 0.01 mole L�1 S2ini,cat

+ 0.01 mole L�1 Curve fittingSini,an

- 0.6 mole L�1 S2ini,an- 0.64 mole L�1 Curve fitting

Sini,H+ 2.239e-7 mole H+ L�1 S2ini,H

+ 6.918e-8mole H+L�1 pH = 6.65

952 T. Thamsiriroj, J.D. Murphy / Bioresource Technology 102 (2011) 948–959

1–74 with an OLR of 0.5 kg VS m�3 d�1 is used to calibrate themodel. The model is then validated using the experimental datafrom the day 75–174 with an OLR of 1.0 kg VS m�3 d�1 (Table 2).The model was coded by the authors using Matlab/Simulink withthe ODE15s solver to solve the differential equations.

3. Results and discussions

3.1. Model calibration

3.1.1. Model input of grass silage and initial conditionsThe measured characteristics of grass silage (Table 1) are used

to generate the components of the input silage (Sfeed,i and Xfeed,i)

as outlined in Table 4. The input grass silage is considered a com-posite material (Xc), which will be subsequently hydrolysed to car-bohydrates (Xch), proteins (Xpr), lipids (Xli) and particulate inerts(XI). The initial conditions of inoculum are estimated from the sol-ids content and total VFA in the vessels; this may not be very accu-rate. However, after simulating for 74 days (the calibration period)with daily feeding of grass silage, the components in the vessels areclosely related to the modelled values.

3.1.2. Stoichiometric and kinetic parametersThe full list of stoichiometric and kinetic parameters with de-

fault values used in the original ADM1 model is given by Batstoneet al. (2002) and Rosen and Jeppsson (2006). Some of these

T. Thamsiriroj, J.D. Murphy / Bioresource Technology 102 (2011) 948–959 953

parameters are adjusted in this study as outlined in Table 5. Thestoichiometric parameters are adjusted by the measured charac-teristics of grass silage (Table 1). The fractions of carbohydrates(fch_xc), proteins (fpr_xc) and lipids (fli_xc) in the composite material(Xc) are summed equal to the VS content of grass silage. As sug-gested by Rosen et al. (2006), the nitrogen and carbon contentsof disintegrated components (carbohydrates, proteins, lipids andinerts) are summed equal to the nitrogen and carbon contents ofthe composite material. Some kinetic parameters are also adjustedin the model by values from the literature and from curve fitting(Table 5). The disintegration rate (kdis) is adjusted by fitting thesimulation results to the observed experimental data (curve fit-ting); while the other parameters are obtained from the literature.The kdis value differs between the two vessels. The disintegration ofgrass substrate in vessel 1 appears to be more straight forwardthan the process in vessel 2. This is shown by a higher kdis for vessel1 (kdis = 0.05 d�1 as compared to 0.02 d�1 for vessel 2). The easilysoluble material is solubilised in the vessel 1 while the fermentingliquor transferred to vessel 2 contains a higher proportion of sub-strate which is slow to disintegrate.

3.1.3. Calibration resultsThe model calibrating results are compared with the experi-

mental data as outlined in Fig. 2. It is expected that the simulationresults merge with the observed data by the end of the calibrationperiod. The results here at the end of 74 days are such that to con-firm calibration; the model and the components in the digester areready for use as initial conditions in the validation period. Withinthe calibration period problems arose associated with commission-ing of the digesters as described previously by the authors

Table 5Stoichiometric and kinetic parameters.

Stoichiometric parametersDescription

fch_xc Carbohydrates fraction in grassfpr_xc Proteins fraction in grassfli_xc Lipids fraction in grassfxI_xc Particulate inerts fraction in grassfsI_xc Soluble inerts fraction in grassNxc Nitrogen content in grass (mole N g�1 COD)NI Nitrogen content in inerts substrate of grass (mole N g�1 COD)Cxc Carbon content in grass (mole C g�1 COD)

Stoichiometric parametersDefault Vessel

1&2Remarks

fch_xc 0.20 0.797 1 � fpr_xc �fli_xc �fxI_xc �fsI_xc

fpr_xc 0.20 0.095 From Table 1fli_xc 0.25 0.033 From Table 1fxI_xc 0.25 0.075 (100 -% VS)/100fsI_xc 0.10 0.0Nxc 0.002 0.00089 1.61% N (Table 1)/92.46% VS/1.4 gCOD g�1 VS/14 gN moNI 0.002 0.003 (Nxc –(Naa � fpr_xc))/fxI_xc; Naa is nitrogen content in aminCxc 0.03 0.0308 (Cch � fch_xc) + (Cpr � fpr_xc) + (Cli � fli_xc) + (CxI � fxI_xc); Cc

For default, Cch = 0.0313, Cpr = 0.03, Cli = 0.022, and CxI =

Kinetic parametersDefault Vessel 1

kdis 0.50 0.05km;C4

20 13.7Ks;C4

0.20 0.357km,pro 13 5.5Ks,pro 0.10 0.392km,ac 8 7.1Ks;H2

7e-6 3e-5

(Thamsiriroj and Murphy, 2010). Between day 20 and 23 therewere problems both with temperature control and a gas leak.The data in this period should be neglected. Due to the low solidscontent in both digesters (about 1.4% in vessel 1 and 0.8% in vessel2 at initial stage) and the low OLR (0.5 kg VSm�3 d�1), the effect ofsolids accumulated in the vessels (causing SRT to exceed HRT) isconsidered low. The model in this calibration period is thus simu-lated by assuming SRT equal to HRT for both vessels (trex,X =t2res,X = 0). Methane yield is estimated by taking the average ofthe final 15 days (142.3 L d�1). This may be compared with145.3 L d�1 from the simulation (2% error) (Fig. 2). The methaneproduction is better reported as 455 L CH4 kg�1 VSadded

(142.3 L d�1/(0.5 kg VS m�3 d�1 � 312 L effective volume � 2reactors)).

3.2. Model validation

3.2.1. Simulation cases in the model validationThe initial conditions in the model validation are the final con-

ditions of the model calibration. The validation of the model com-pares the experimental data from day 75 to174 with the model forthe calibration period. Two sub-experiments (Table 2) are in-cluded: the first 50 days at a HRT of 80 days and the second50 days at a HRT of 60 days. The experimental results show a sig-nificantly higher methane yield in the validation period as com-pared to the calibration period (506 L CH4 kg�1 VSadded comparedto 455 L CH4 kg�1 VSadded). The maximum obtainable yield fromgrass silage is estimated to be only 500 L CH4 kg�1 VSadded (Table 6).The observed methane yield exceeding the maximum obtainableyield is verified by performing the mass balance of input VS, output

Kinetic parametersDescription

kdis Disintegration rate (d�1)km;C4

Max. uptake rate of valerate and butyrate (d�1)Ks;C4

Half saturation coefficient of valerate and butyrate uptake (gCOD L�1)

km,pro Max. uptake rate of propionate (d�1)Ks,pro Half saturation coefficient of propionate uptake (gCOD L�1)

km,ac Max. uptake rate of acetate (d�1)Ks;H2

Half saturation coefficient of hydrogen uptake (gCOD L�1)

le�1

o acids (default = 0.007)h, Cpr, Cli and CxI are carbon content in carbohydrates, proteins, lipids and inerts.0.03.

Vessel 2 Remarks

0.02 Values determined by calibration (curve fitting)13.7 From Lübken et al. (2007)0.357 From Lübken et al. (2007)5.5 From Lübken et al. (2007)0.392 From Lübken et al. (2007)7.1 From Lübken et al. (2007)3e-5 From Lübken et al. (2007)

0 10 20 30 40 50 60 70 800

100

200

300

400

500

600Overall

Biog

as y

ield

(L d

-1)

Days

0 10 20 30 40 50 60 70 800

100

200

300

400Overall

Met

hane

yie

ld (L

d-1)

Days

0 10 20 30 40 50 60 70 800

20

40

60

80

100Overall

CH

4 in b

ioga

s (%

)

Days

Fig. 2. Results of the calibration period: overall biogas yield, methane yield and methane content in biogas (vessel 1 + 2). o experimental data, — simulating results.

Table 6Methane potential from grass silage used in the study (Thamsiriroj and Murphy,2010).

Silage sample on a dry basis:C = 43.035%, H = 5.82%, VS = 92.46%, Ash = 7.54% (Table 1)Sheng’s formula (Sheng and Azevedo, 2005):Energy content of biomass (MJ kg�1

DS) = �1.3675 + 0.3137C + 0.7009H + 0.0318O*=�1.3675 + 0.3137(43.035)+0.7009(5.82)+0.0318(100–43.035–5.82–7.54)=17.6 MJ kg�1 DS=19.03 MJ kg�1 VSEnergy content of methane gas = 37.78 MJ m�3

Methane potential from grass silage = 19.03/37.78 = 0.50 m3 kg�1 VSwhere: O* = 100-C–H-ash

954 T. Thamsiriroj, J.D. Murphy / Bioresource Technology 102 (2011) 948–959

VS and the change of VS content in the two vessels. For example,during the last 50 days of the model validation period the totalVS input was 38.95 kg (30.43 kg from grass and 8.52 kg from recy-cled liquor), total VS removed as digestate (bled from the system)was 3.02 kg, VS increase in vessel 1 was 1.37 kg and VS increase invessel 2 was 2.77 kg. The total VS destroyed was thus 31.79 kg (VSin – VS out – VS increase in vessels). This equated to 104.5% of VSdestruction as compared to total VS input from grass. Therefore, itis likely that the accumulated solids in the digester contribute to

the increasing yield. The accumulation of solids tends to occur invessel 1 where the grass silage is fed to the digester. It is postulatedthat homogeneous mixing and transferring of fermenting liquor isunlikely to take place as grass substrate tends to float on the toplayer while the saturated substrate is only allowed to transfer be-tween the two vessels from a low level. This results in SRT exceed-ing HRT in vessel 1 (tres,X > 0). In vessel 2, the fermenting liquortransferred from vessel 1 is more saturated and free from accumu-lated floating solids. Therefore, the SRT is approximately equal toHRT (t2res,X = 0). To analyse this, the model is simulated undertwo conditions: with and without extra SRT in vessel 1. The meth-ane yields in both conditions are compared with the experimentaldata. The results are shown in Fig. 3a.

3.2.2. Determination of the actual SRTFig. 3a shows the biogas and methane yields as the total of the

two vessels. The yields simulated without extra SRT (tres,X = -t2res,X = 0) are at the lower boundary of results of the observeddata. An additional simulation is performed considering SRT is15 days greater than HRT in vessel 1 (tres,X = 15 days, t2res,X = 0).The yields are improved and closer to the observed data. The aver-age methane yields from the experiment are calculated from the fi-nal 15 days of each 50 day operation (at HRT = 80 days and

T. Thamsiriroj, J.D. Murphy / Bioresource Technology 102 (2011) 948–959 955

60 days, respectively), to be 289 and 316 L CH4 d�1 (463 and 506 LCH4 kg�1 VSadded). These experimental results compare very well tothe average yields of 289 and 318 L CH4 d�1 from the simulationmodel. The model is thus validated with the SRT greater than theHRT by 15 days in vessel 1. Thus for example at a HRT of 80 days,the HRT of vessel 1 is 40 days and the SRT of vessel 1 is 55 days; theSRT is equal to the HRT in vessel 2.

3.2.3. Estimated methane yieldAs the experiment explores the effect of SRT exceeding HRT, the

observed methane yields (L CH4 kg�1 VS) are thus higher than theexpected values. The model simulation is used to estimate theyields omitting such an effect; these yields would reduce to 421and 441 L CH4 kg�1 VSadded at a HRT of 80 and 60 days, respectively,(84% and 88% volatiles destruction). The methane yield at a HRT of80 days is about 5% lower than the yield at a HRT of 60 days eventhough the substrate has 20 days longer retention time. The con-cept of higher yields at lower HRT’s is anti-intuitive. The 2-stageCSTR digester is a complex system which involves a number ofparameters. Another trend is the maturing of the system andincreasing dry solids content of the digesters. Computer simulationis required to optimise system operation.

0 10 20 30 400

200

400

600

800

1000

Bio

gas

yiel

d (L

d-1

)

D

Ov

0 10 20 30 400

100

200

300

400

500

Met

hane

yie

ld (

L d-1

)

D

Ov

0 10 20 30 400

20

40

60

80

100

CH

4 in b

ioga

s (%

)

D

Ov

Fig. 3a. Results of the validation period: overall biogas yield, methane yield and methantres,X = t2res,X = 0, — simulating results with tres,X = 15 days and t2res,X = 0.

3.2.4. Comparison of biogas yield between vessel 1 and 2In Fig. 3b the biogas and methane yields are compared for each

vessel. Obviously the majority of gas is generated in digester 1. Thesplit is of the order of 80:20 vessel 1:vessel 2. Of the total biogasproduced the production from vessel 2 increases from 18% to21% when the HRT reduces from 80 days to 60 days. Thus as recir-culation increases, the HRT decreases, and feedstock is transferredto vessel 2 faster which then produces more gas. The methane con-tent in biogas is a little higher in vessel2 than vessel1 (around 56%as compared to 52%). This may be explained as the majority of airbubbles associated with the new feed is released in vessel 1.

3.2.5. Other simulationsThe total VFA, DS and VS contents and pH are simulated in

Fig. 3c. The simulating results approximately fit with the experi-mental data except the total VFA in vessel 2 which the simulatingcurve develops too slowly. However, the observed total VFA tendsto stabilise at the end of experiment and is not a critical parameter;these levels will not cause inhibition at the OLR’s applied. The sim-ulated DS content of vessel 1 deviated from the experimental datawhen the simulation allowed for SRT exceeding HRT. On thecontrary, the simulating curve fits better to the DS content data

50 60 70 80 90 100ays

erall

50 60 70 80 90 100ays

erall

50 60 70 80 90 100ays

erall

e content in biogas (vessel 1 + 2); o experimental data, - * - simulating results with

0 20 40 60 80 1000

100

200

300

400

500

600

700Vessel 1

Bio

gas

yiel

d (L

d-1

)

Days0 20 40 60 80 100

0

100

200

300

400

500

600

700Vessel 2

Bio

gas

yiel

d (L

d-1

)

Days

0 20 40 60 80 1000

50

100

150

200

250

300

350

400Vessel 1

Met

hane

yie

ld (

L d-1

)

Days0 20 40 60 80 100

0

50

100

150

200

250

300

350

400Vessel 2

Met

hane

yie

ld (

L d-1

)

Days

0 20 40 60 80 1000

20

40

60

80

100Vessel 1

CH

4 in b

ioga

s (%

)

Days0 20 40 60 80 100

0

20

40

60

80

100Vessel 2

CH

4 in b

ioga

s (%

)

Days

Fig. 3b. Results of the validation period; biogas yield, methane yield and methane content in biogas; o experimental data, - * - simulating results with tres,X = t2res,X = 0, —simulating results with tres,X = 15 days and t2res,X = 0.

956 T. Thamsiriroj, J.D. Murphy / Bioresource Technology 102 (2011) 948–959

of vessel 2 in the simulation with SRT exceeding HRT. It is postu-lated however that the simulated DS content is more realistic thanthe observed data from vessel 1. Vessel 1 is inhomogeneouslymixed and representative samples are difficult to take due to float-ing grass. This is not the case for the samples taken from vessel 2which represent a more accurate DS content as the substrate ismore saturated and well mixed in the vessel.

3.3. Long term operation

3.3.1. Effect of recirculation on total VFA and DS contentFig. 4 shows the simulation of total VFA and DS content at var-

ious HRT in long term operation. The simulation is performed forthe total VFA and DS content for a 1000 day operation. The diges-ter fed with a constant OLR tends to approach the steady state

with a stabilised DS content. A high DS content in vessel 1 wouldbe obtained from the digester operated with a high HRT, while theDS content in vessel 2 would be relatively low. Although the DScontent tends to stabilise, the total VFA does not appear to stabi-lise. The simulation suggests a significant increase in long termoperation. An accumulation of total VFA in the absence of the buf-fering of bicarbonates could cause a decrease in pH and subse-quent failure of the anaerobic digestion process. As discussed byWichern et al. (2009), free ammonia (NH3) is a sensitive parameterin grass digestion which can lead to the inhibition of VFA uptake.At short retention times (high recirculation) the accumulation ofVFA is in vessel 1. For high retention times (no recirculation) theaccumulation is in vessel 2. Hence the recirculation of fermentingliquor can be managed to minimise the accumulation in eithervessel.

0 20 40 60 80 1000

50

100

150

200Vessel 1

Tot

al V

FA

(m

g L-1

)

Days0 20 40 60 80 100

0

50

100

150

200Vessel 2

Tot

al V

FA

(m

g L-1

)

Days

0 20 40 60 80 1000

1

2

3

4

5

6

7

8Vessel 1

DS

con

tent

(%

)

Days0 20 40 60 80 100

0

1

2

3

4

5

6

7

8Vessel 2

DS

con

tent

(%

)

Days

0 20 40 60 80 1000

20

40

60

80

100Vessel 1

VS

con

tent

(%

)

Days0 20 40 60 80 100

0

20

40

60

80

100Vessel 2

VS

con

tent

(%

)

Days

0 20 40 60 80 1005

5.5

6

6.5

7

7.5

8

8.5

9Vessel 1

pH

Days0 20 40 60 80 100

5

5.5

6

6.5

7

7.5

8

8.5

9Vessel 2

pH

Days

Fig. 3c. Results of the validation period; total VFA, DS and VS contents and pH; o experimental data, - * - simulating results with tres,X = t2res,X = 0, — simulating results withtres,X = 15 days and t2res,X = 0.

T. Thamsiriroj, J.D. Murphy / Bioresource Technology 102 (2011) 948–959 957

0 200 400 600 800 10000

500

1000

1500

2000

2500

3000

3500

4000Vessel 1

Tota

l VFA

(mg

L-1)

Days0 200 400 600 800 1000

0

500

1000

1500

2000

2500

3000Vessel 2

Tota

l VFA

(mg

L-1)

Days

0 200 400 600 800 10001

2

3

4

5

6Vessel 1

DS

cont

ent (

%)

Days0 200 400 600 800 1000

1

2

3

4

5

6Vessel 2

DS

cont

ent (

%)

Days

HRT = 284 days (no recirculation) 160

80 20

284(no recirculation)

160 80

HRT = 20 days

284

160

80

HRT = 20 days

(no recirculation) 20 80

160

HRT = 284 days (no recirculation)

Fig. 4. Total VFA and DS content at various HRT, OLR = 1.0 kg VS m�3 d�1, tres,X = t2res,X = 0, 1000 day operation.

958 T. Thamsiriroj, J.D. Murphy / Bioresource Technology 102 (2011) 948–959

3.3.2. Effect of recirculation on methane yieldResults of the model simulation suggest a variation of methane

yields with the amount of recirculation. A low rate of recirculation(large HRT) results in an increasing methane yield as it allows alonger time for the substrate to be digested. The methane yieldsalso depend on the initial solids content in the digester. High solidscontent in the digester would deliver high methane yield as the gasproduction is supplemented by the slowly degrading solids. It issuggested that ideal operation for such a 2-stage CSTR with recir-culation is as follows:

Initially feed without recirculation when the solids content inthe digester is relatively low. This leads to a long retention time.Subsequently initiate recirculation at a low level (reducing reten-tion time) increasing the DS content of the vessels until VFA startsto rise and pH to decrease. Recirculation should then be fixed at arate which generates a similar level of VFA in both vessels. Thisproposed operation should extend the life time of mono-digestionof grass silage, negating the need for new inoculum or co-digestion,while maintaining a high level of methane production.

4. Conclusions

Grass silage has a tendency to float to the surface in a wet diges-tion process. In a two stage process with a connection betweenvessels below the liquor level this leads to the SRT exceeding theHRT. It is estimated here for example that with a 80 day HRT(40 days in each vessel) that the SRT in the first vessel is 55 daysand 40 days in the second vessel. The model would suggest thatmethane production is at least 441 L CH4 kg�1 VSadded or about88% of the maximum theoretical value.

Acknowledgements

Research funding obtained from: Department of Agriculture,Fisheries and Food (DAFF) Research Stimulus Fund Project: ‘‘Green-Grass”. Higher Education Authority Programme for Research inThird Level Institutes Cycle 4 (HEA PRTLI Cycle 4). Padraig O’Kielyand Joe McEniry from Teagasc, Grange for supply of grass silage.Richard Kearney from Cork Institute of Technology for macerationof grass silage.

References

APHA, 1998. Standard methods for the examination of water and wastewater, 20thed. American Public Health Association, Washington, DC.

Batstone, D.J., Keller, J., Angelidaki, I., Kalyuzhnyi, S.V., Pavlostathis, S.G., Rozzi, A.,Sanders, W.T.M., Siegrist, H., Vavilin, V.A., 2002. Anaerobic Digestion Model No.1. IWA publishing, London, UK. p. 77.

Blumensaat, F., Keller, J., 2005. Modelling of two-stage anaerobic digestionusing the IWA Anaerobic Digestion Model No. 1 (ADM1). Wat. Res. 39,171–183.

Boubaker, F., Ridha, B.C., 2008. Modelling of the mesophilic anaerobic co-digestionof olive mill wastewater with olive mill solid waste using anaerobic digestionmodel No.1 (ADM1). Bioresour. Technol. 99, 6565–6577.

Chen, Z., Hu, D., Zhang, Z., Ren, N., Zhu, H., 2009. Modeling of two-phase anaerobicprocess treating traditional Chinese medicine wastewater with the IWAAnaerobic Digestion Model No. 1. Bioresour. Technol. 100, 4623–4631.

Chynoweth, D.P., Owens, J.M., Legrand, R., 2001. Renewable methane fromanaerobic digestion of biomass. Renewable Energy 22, 1–8.

Gali, A., Benabdallah, T., Astals, S., Mata-Alvarez, J., 2009. Modified version of ADM1model for agro-waste application. Bioresour. Technol. 100, 2783–2790.

Hopfner-Sixt, K., Amon, T., Amon, B., 2005. Anaerobic digestion of energy crops –State of the art of biogas technology. 14th European Biomass Conference, 17-21October 2005, Paris ETA Renewable Energies, Florence, Italy.

Lübken, M., Wichern, M., Schlattmann, M., Gronauer, A., Horn, H., 2007. Modellingthe energy balance of an anaerobic digester fed with cattle manure andrenewable energy crops. Wat. Res. 41, 4085–4096.

T. Thamsiriroj, J.D. Murphy / Bioresource Technology 102 (2011) 948–959 959

Lyberatos, G., Skiadas, I.V., 1999. Modelling of anaerobic digestion – a review. GlobalNest: Int. J. 1, 63–76.

Nizami, A.S., Korres, N.E., Murphy, J.D., 2009. Review of the integrated process forthe production of grass biomethane. Environ. Sci. Technol. 43, 8496–8508.

Nizami, A.S., Murphy, J.D., 2010. What type of digester configurations should beemployed to produce biomethane from grass silage? Renewable SustainableEnergy Rev. 14, 1558–1568.

Prochnow, A., Heiermann, M., Drenckhan, A., Schelle, H., 2005. Seasonal pattern ofbiomethanisation of grass from landscape management. AgriculturalEngineering International: the CIGR E journal. Manuscript EE 05 011. Vol. 7.December 2005.

Ripley, L.E., Boyle, W.C., Converse, J.C., 1986. Improved alkalimetric monitoring foranaerobic digestion of high strength wastes. J. WPCF. 58, 406–411.

Rosen, C., Jeppsson, U., 2006. Aspects on ADM1 implementation within the BSM2framework. Dept. of Industrial Electrical Engineering and Automation, LundUniversity.

Rosen, C., Vrecko, D., Gernaey, K.V., Pons, M.N., Jeppsson, U., 2006. ImplementingADM1 for plant-wide benchmark simulations in Matlab/Simulink. Wat. Sci.Technol. 54, 11–19.

Sheng, C., Azevedo, J.L.T., 2005. Estimating the higher heating value of biomass fuelsfrom basic analysis data. Biomass Bioenergy 28, 499–507.

Thamsiriroj, T., Murphy, J.D., 2010. The difficulties associated with mono-digestionof grass as exemplified by commissioning a pilot scale digester. Energy Fuels 24,4459–4469.

Walla, C., Schneeberger, W., 2005. Farm biogas plants in Austria – an economicanalysis. Jahrbuch der Österreichischen Gesellschaft für Agrarökonomie 13,107–120.

Wichern, M., Gehring, T., Fischer, K., Andrade, D., Lübken, M., Koch, K., Gronauer, A.,Horn, H., 2009. Monofermentation of grass silage under mesophilic conditions:Measurements and mathematical modeling with ADM1. Bioresour. Technol.100, 1675–1681.