Bioresource Technology 102 (2011) 10819–10828

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Bioresource Technology

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Model-based predictions of anaerobic digestion of agricultural substratesfor biogas production

Haidong Zhou a, Daniel Löffler b, Martin Kranert b,⇑a School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, Chinab Institute for Sanitary Engineering, Water Quality and Solid Waste Management, University of Stuttgart, 70569, Germany

a r t i c l e i n f o a b s t r a c t

Article history:Received 11 July 2011Received in revised form 2 September 2011Accepted 4 September 2011Available online 8 September 2011

Keywords:SimulationADM1Organic dry matter contentFresh matterOrganic loading rate

0960-8524/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.biortech.2011.09.014

⇑ Corresponding author. Tel.: +49 711 685 65500; fE-mail address: martin.kranert@iswa.uni-stuttgart

A modified Anaerobic Digestion Model No. 1 (ADM1), calibrated on a laboratory digester with a feedingmix of 30% weight of cow manure and 70% weight of corn silage, was implemented, showing its perfor-mances of simulation as a decision-making and planning-supporting tool for the anaerobic digestion ofagricultural substrates. The virtual fermenter obtained was used to conduct simulations with differentfeeding compositions and loading rates of cow manure, corn silage, grass silage and rape oil. All simula-tions were started at the same initial state which was represented by a steady state with an organic load-ing rate of 2.5 kg ODM/(m3

digester � d). The effects of the different feeding combinations on biogascomposition and biogas yield were predicted reasonably, and partly verified with the available literaturedata. Results demonstrated that the simulations could be helpful for taking decisions on agricultural bio-gas plant operation or experimental set-ups, if used advisedly.

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1. Introduction process understanding and development of appropriate solutions

Nowadays anaerobic digestion processes have been extensivelyused for not only the sewage sludge treatment but also the biogasproduction from organic substrates such as olive pulp (Gavalaet al., 2006), biowaste and foodwaste (Nayono, 2009), and agricul-tural wastes (Galí et al., 2009; Page et al., 2008; Wichern et al.,2009). Due to an increasing concern on the energy from renewableresources the processes have become more popular especially inthe aspect of biogas generation. It was estimated that up to1200 million tons of biodegradable wastes were generated inEuropean Community annually, 90% of which were from the agri-cultural substrates, especially animal wastes, and the rest werethe organic wastes coming from municipal and industrial wastes(Viotti et al., 2004). The biggest potential for the biogas technology,therefore, lies in the agricultural field. The energy production bymeans of anaerobic digestion of such waste fractions could annu-ally reach 200 billion kWh. The rising number of agriculturalbiogas plants worldwide, particularly in Germany (Agency ofRenewable Resources (FNR), 2010), gives evidences for the increas-ing interest in anaerobic digestion of agricultural substrates forbiogas production. Much technological development has takenplace in the biogas sector in the last decades; nevertheless thereis still potential for the technical improvement and issues of thedirection of development to be discussed. Regarding research,

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ax: +49 711 685 65460..de (M. Kranert).

for process handling, process simulation of the complex anaerobicdigestion processes can be a useful tool.

Anaerobic Digestion Model No. 1 (ADM1), a generic anaerobicdigestion model, developed by International Water Association’s(IWA) Task Group in 2002 (Batstone et al., 2002), has been widelyapplied and well accepted. The ADM1 is considered as a structuredmodel, where the physical, chemical, and biological processes areincluded in a biochemical kinetic matrix. It comprises disintegra-tion, hydrolysis, acidogenesis, acetogenesis and methanogenesissteps. In total, 19 processes, 24 components, and 56 stoichiometricand kinetic parameters are assumed for biological processesconcerned, and also, additional processes and parameters aredetermined for physico-chemical processes.

The ADM1 basically focused on sewage sludge while other sub-strates like agro wastes or municipal solid wastes were not studiedin the same way (Galí et al., 2009). A growing number of publica-tions have, however, reported the application of the model in theareas of agro wastes and energy crops which have differentcharacteristics and higher contents of solids, especially in therecent years. Such publications could be found for energy crops(Wolfsberger, 2008), for agro-wastes (Galí et al., 2009), forco-digestion of organic waste with sewage sludge (Derbal et al.,2009), for olive pulp (Gavala et al., 2006), for cattle manure and en-ergy crops (Lübken et al., 2007), for different organic materials,manure and corn silage (Rojas et al., 2011), for manure and organicwastes (Schön, 2009), and for grass silage (Wichern et al., 2009;Koch et al., 2010). All these showed the increasing trend of applica-tions and also some modifications of the ADM1. For example, Galí

10820 H. Zhou et al. / Bioresource Technology 102 (2011) 10819–10828

et al.(2009) developed a modified version of ADM1 for its applica-tion in the anaerobic digestion of agro-wastes including apple,pear, orange, rape, sunflower, pig manure and glycerol wastes.The anaerobic biodegradability of agro-wastes was used to charac-terize the substrates and considered as the basis for feeding themodel. After the validation of the model with the mono-substrateand co-substrate cases in batch and continuous reactors, the modelpredicts correctly the degradation of agro-wastes. Besides, Kochet al. (2010) implemented the ADM1 to simulate anaerobicdigestion of grass silage. The model was modified with a separatecompound of inert decay products and integrated with a solid-influenced hydrolysis function reflecting nitrogen incorporationand release. The extended model was calibrated by using themodified Nash–Sutcliffe coefficient to evaluate simulation quality.Simulation results from the extended ADM1 showed good agree-ments with measurements.

Thus the attempt in the following is to show the performance ofsimulations with a modified ADM1 as a decision-making and plan-ning-supporting tool for the anaerobic digestion of agriculturalsubstrates. A calibrated virtual digester was set up and fed withdifferent combinations and organic loading rates of four types ofagricultural substrates. The substrates were composed of cornsilage (CS), cow manure (CM), grass silage (GS), and rapeseed oil(RO). The first two were chosen as the most commonly usedsubstrates in Germany.

2. Methods

2.1. The model

In this work, a modified ADM1 model was implemented as a dif-ferential and algebraic equation (DAE) system with 29 dynamicstate variables (substrates and reaction intermediates, as well asgaseous products) and seven algebraic variables, which representthe ionized states contributing to the charge balance. Therefore,the DAE system consists of 36 nonlinear equations, 29 of whichare differential. These equations have a nonlinear part which de-scribes the chemical transformations in the system and is expressedthrough the various kinetic mechanisms, and a linear part describ-ing the hydraulic behavior of the reactor (Cimatoribus, 2009). Theimplementation of the ADM1 using Matlab� software as a platformwas described in detail by Cimatoribus (2009) and checked for con-sistency with the benchmark version from Lund University (Rosenand Jeppsson, 2006; Rosen et al., 2006). A following calibrationand validation procedure was applied before simulations. The cal-culations in the ADM1 were based on the units of kg COD (chemicaloxygen demand) per m3 and kmol/m3 (Batstone et al., 2002), andtherefore the input fractioning was done in the mentioned units.

2.2. Characterization of substrates from agricultural residues

A detailed mathematical characterization of the selected inflowsubstrates is required as the characterization has a strong influenceon the gas composition (Wichern et al., 2009) and the general re-sults of the anaerobic digestion processes simulated with theADM1. For the estimation of the input characterization the re-quired values actually derived from measured data represent thebest case. The lowest accuracy is achieved by plausibility estima-tions or assumptions. However, getting all the values from mea-sured data cannot be realized in many cases. COD measurementsare commonly used for the analyzes of wastewaters, but difficultfor substrates like manures or energy crops (Lübken et al., 2007).In addition, literature (as exemplarily listed above) also providedsome data used for simulation purposes for energy crops and agrowastes. Thus the data utilized were a combination of some mea-sured data and values from literature. A procedure to derive the

needed values in terms of COD from common measurements,therefore, is necessary. Several ways of substrate characterizationwere described by Huete et al (2005), Wichern et al (2007), Lübkenet al.(2007), Kleerebezem and Van Loodsrecht (2006), Koch et al.(2010), Rojas et al.(2011), and Wichern et al. (2009). The procedureused to derive the substrate characteristics was similar to that sug-gested by Lübken et al.(2007) and Wichern et al. (2009), based onthe measured data of Weender analyses which were carried outaccording to ‘VDLUFA-methods, Handbook III’ (Naumann and Bass-ler, 1976) with near-infrared spectroscopy involved. The dry mat-ter content (DM) and the organic dry matter content (ODM) wereobtained according to German standards. Table 1 gives an over-view of the basic data and its origin.

Important for the characterization of the substrates is thefractioning into proteins, lipids and carbohydrates given by the‘Weender analyses’. As explained further by Kaiser (2007) nitro-gen-free extracts (NfE) were calculated by Eq. (1)

NfE ¼ ODMDM � CP� CL� CF ð1Þ

where NfE are the nitrogen-free extracts from a substrate in driedform (% DM), ODMDM is the organic dry matter content (ODM) ofa substrate in dried form (g ODM/100 g DM), CP is the crude proteincontent of a substrate in dried form (% DM), CL is the crude lipidcontent of a substrate in dried form (% DM), and CF is the crude fibercontent of a substrate in dried form (% DM).

Table 2 gives an overview of the variables used in ADM1, andtheir values which were further adopted in this article. The deriva-tion of their values for the ADM1 from the above given data waspresented in the following.

SIN was estimated as the free ammonia concentration whichcould be calculated with Eq. (2) according to the literature(Wendland, 2008)

SIN ¼NH4 � N

1þ 10pKa�pH ð2Þ

where SIN is the concentration of free ammonia (kmol/m3), NH4ANis the concentration of ammonia (kmol/m3); pKa is the dissociationconstant for ammonia ion, dependent on temperature.

A conversion of components in substrates to values in COD isnecessary as the ADM1 calculations are COD-based. The parame-ters cfch (1.18 kg COD/kg DM), cfpr (1.53 kg COD/kg DM), cfli

(2.86 kg COD/kg DM), and cfi (1.38 kg COD/kg DM) as arrangedby Cimatoribus (2009) from Huete et al. (2005) were adopted asthe conversion factors of the fractions of carbohydrates, proteins,lipids and inerts, respectively.

Eqs. (3)–(7) led to a rough estimation of the theoretically calcu-lated total COD (CODtotal-th) including the inert fraction

Xi-th ¼ cfi � ðDM� ODMFMÞ � 10 ð3Þ

Xli-th ¼ cfli � CL � DM � 10 ð4Þ

Xpr-th ¼ cfpr � CP � DM � 10 ð5Þ

Xch-th ¼ cfch � ðNfEþ CFÞ � DM � 10 ð6Þ

CODtotal-th ¼ Xch-th þ Xpr-th þ Xli-th þ Xi-th ð7Þ

where Xi, Xli, Xpr, and Xch represent the concentrations of particulateinerts, particulate lipids, particulate proteins and particulate carbo-hydrates, respectively (kg COD/m3), CODtotal is the total COD (kgCOD/m3), and the appendix ‘-th’ indicates the theoretically and pre-liminarily calculated values of the particulate fractions.

The values for soluble components: monosaccharides (Ssu), ami-no acids (Saa), total long chain fatty acids (LCFA) (Sfa), total acetate(Sac) and inerts (Si) were calculated by multiplication of the CODto-

tal-th with the percentages suggested by Wichern et al. (2007).

Table 1Basic data for substrate characterization.

Item Unit Cow manure Corn silage Grass silage Rapeseed oil

DM g DM/100 g FM 9.3a 30.1a 40.0d 98.6e

ODMDM g ODM/100 g DM 81.7a 96.4a 89.8b 100.0f

ODMFM g ODM/100 g FM 7.6b 29.0b 35.9b 98.6b

Weender analysesCrude ash (CA) % DM 3.6a

Crude protein (CP) % DM 12.2c 6.9a 13.2d

Crude lipids (CL) % DM 4.3c 3.1a 3.7d 100.0f

Crude fiber (CF) % DM 17.8c 24.1a 29.3d

Nitrogen-free extracts (NfE) % DM 47.6b 62.3b 43.6d

pH – 7.4c 3.9c 4.6c

NH4AN mg NH3AN/L 2289c 630c 10.85c

Where FM means ‘fresh matter’ (not dried).a Source: measured.b Source: calculated.c Source: Wichern et al. (2007).d Source: Fritz (2008).e Source: Lübken et al. (2007).f Source: assumption.

Table 2Variables used and substrate characterization for the modified ADM1.

Variable Abbr. Unit Cow manure (kg COD/m3) Corn silage (kg COD/m3) Grass silage (kg COD/m3) Rapeseed oil (kg COD/m3)

Particulate composites Xc kg COD/m3 10.041805 36.533875 93.440000Particulate carbohydrates Xch kg COD/m3 29.592107 209.869062 194.412208Particulate proteins Xpr kg COD/m3 7.074110 22.243599 48.470400Particulate lipids Xli kg COD/m3 5.873149 18.680662 25.396800 2819.960000Particulate inerts Xi kg COD/m3 46.503947 78.514119 136.656400Monosaccharides Ssu kg COD/m3 13.376491Amino acids Saa kg COD/m3 3.344123Total LCFA Sfa kg COD/m3 0.990851Total acetate Sac kg COD/m3 4.943802 12.040592Inorganic nitrogen SIN kmol/m3 0.165777 0.045000 0.077502Soluble inerts Si kg COD/m3 7.059815 9.507311 13.087600

H. Zhou et al. / Bioresource Technology 102 (2011) 10819–10828 10821

Not all of the materials were considered digestible by anaerobicdigestion. DLG- Futterwerttabellen (2003) gave detailed values fordigestibility and characteristics of different feed materials for ani-mals. As the anaerobic digestion processes showes similarities tocow’s digestion (Kaiser, 2007) these values were often adoptedfor the estimation of biogas production and methane yield. Never-theless Kaiser (2007) stated that the analogy to the ‘Futterwerttab-ellen’ (tables of feed values) might lead to an imprecise calculation.Therefore, it was assumed that some part of Xli-th, Xpr-th and Xch-th

was not digestible and added to Xi-th, and another part was consid-ered still having to go through the disintegration step which meantthat this part formed Xc of the inflow fraction. The transfer coeffi-cients assumed are shown in Table 3. Cow manure as a substratewhich already passed a digestion step in the cow’s rumen (Rojaset al., 2011) was considered to contain a high fraction of inertmaterial for the anaerobic digestion processes, and as well a highfraction of material which had already passed a disintegration step.Corn and grass silage were considered to contain less inert materialand rapeseed oil was regarded completely digestible. The assumedcoefficients for cow manure and corn silage led to satisfying resultsin the following calibration.

Table 3Transfer coefficients to Xi and Xc used in the modified ADM1.

Substrate Transfer coefficient

Fraction to Xi:Cinert (%) Fraction to Xc:CPcomposite (%)

Cow manure (CM) 30 10Corn silage (CS) 20 10Grass silage (GS) 20 20

With the coefficients in Table 3, the final particulate fractions ofthe input materials were calculated according to Eqs. (8)–(12)

Xli ¼ Xli-th � ð100� Cinert � CPcompositeÞ=100� Sfa ð8Þ

Xpr ¼ Xpr-th � ð100� Cinert � CPcompositeÞ=100� Saa ð9Þ

Xch ¼ Xch-th � ð100� Cinert � CPcompositeÞ=100� Ssu � Sac ð10Þ

Xc ¼ ðXch þ Xpr þ XliÞ � CPcomposite=100 ð11Þ

Xi ¼ Xi-th � Si þ ðXch þ Xpr þ XliÞ � Cinert=100 ð12Þ

where Xc is the concentration of particulate composites of a sub-strate (kg COD/m3); Cinert is the transfer coefficient for the fractionof a substrate to be added to Xi (%), and CPcomposite, the transfercoefficient to Xc (%).

2.3. Calibration and validation

The calibration of the ADM1 on a real digester was done to ad-just the model parameters with the goal of finally making thesimulation results as close as possible to the results of the realoperation. A laboratory-scale digester of a total volume of 39.3 Land a filled volume of 36.62 L was operated (Fig. 1). The digesterwas operated in a container with a constant ambient temperatureof 35 �C, and equipped with a continuously operating stirrer. Theinput was a mixture of 30% weight of cow manure and 70%weight of corn silage. The volume of produced gas was measuredwith a drum gas counter from Ritter Apparatebau GmbH and thencollected in a gas bag. The gas composition was measured before

valve

valve

Fig. 1. Schematic diagram of the laboratory-scale digester.

10822 H. Zhou et al. / Bioresource Technology 102 (2011) 10819–10828

the daily feeding and occasionally at other times with a portablecomposition detector GA 90 from Ansyco GmbH. All quantitiesand flows of gas were converted to standard conditions(273.15 K and 1013.25 mbar) in terms of German Industry Norm‘DIN 1343’’ (German Institute for Standardization (DIN), 1990).The amount of water in the gaseous phase was eliminated; there-fore all gas volumes given were dry gas without vapor. The inputmaterial was stored in cooled barrels (at 4 �C) and weighted be-fore feeding. The pH of the outlet was measured daily beforefeeding. Other parameters such as NH4AN and volatile fatty acidswere only measured occasionally. The parameters and pH showeduncertainty due to the measuring procedure and sensors, andwere therefore not listed. These values were only used as feed-back about a proper range of the values during operation andsimulations.

To calibrate the model on the mentioned digester the only twoparameters, which had to be adjusted to different values fromthose given by Batstone et al. (2002) for mesophilic digesters, werethe monod maximum specific uptake rate for acetate (km,ac) andthe global first order decay rate (kdec). After a manual calibrationprocedure km,ac was set to 11 kg COD/(kg COD�d) and kdec to0.06 d�1 instead of the original values of 8 kg COD/(kg COD�d)and 0.02 d�1, respectively. The vector of the initial state of the pro-cesses as the starting point for the simulation was found manuallyand set to an applicable value. Besides, the values of the yield coef-ficients on the particulate composites (fproduct,substrate) of the differ-ent substrates suggested by Wichern et al. (2007) were used.

The result of the calibration for a data set of the biogas andmethane production in liters per day during 38-day operation isdemonstrated in Fig. 2. The good consistency of the measured data(single data points) and the simulated data (solid and dashed line)was obtained. To validate the calibration, the simulation was con-tinued over another time period. The simulated and the measureddata were also compared. It can be observed in Fig. 2 that simu-lated and measured data during the 20-day time period for valida-tion fit well together with reasonable accuracy.

The biogas production and gas composition were the twoparameters that the model was well calibrated on as the main datacollected constantly every day during the operation of the labora-tory-scale digester were from them. Data from other parametersrecorded sometimes were only used to assure to be in a reasonablerange with the calibration. No calibration on those parameters wasdone due to no coherent set of data available. Therefore only theresults from the calibrated parameters were shown and used forconclusions in the following simulations.

2.4. Initial starting state for the ‘simulation scenarios’

To start all the following simulations from the same stablestarting point of the processes a steady state at an organic loadingrate of 2.5 kg ODM/(m3

digester � d) was chosen. To arrive at this start-ing point the feeding rate for an organic loading rate of 2.5 kgODM/(m3

digester � d) with the same input composition of 30% weightof cow manure and 70% weight of corn silage was calculated.Departing from the fortieth day of the above calibration (Fig. 2)the simulation was then run with this feeding rate of 0.2836 kgcorn silage and 0.1215 kg cow manure for another 325 days (i.e.in total 1 year).

All the following simulations started from this steady state ofthe processes. This was considered reasonable for matters ofcomparability, and because the steady state was close to loadingrates and feeding compositions of the calibration.

2.5. Simulation scenarios

Emanating from the initial steady state a set of differentscenarios was simulated. In all scenarios the first 10 days of thesimulation were under the same conditions (substrates, composi-tion of the input mixture, and organic loading rate) as describedabove to reach the initial starting steady state. After the first10 days the different scenarios were applied and simulated overa time period of 355 days (i.e. in total 1 year). The composition of

Fig. 2. Calibration and validation of the modified ADM1 with the input mixture of 30% weight of cow manure and 70% weight of corn silage.

H. Zhou et al. / Bioresource Technology 102 (2011) 10819–10828 10823

the input mixture and the organic loading, i.e. the amount of input,were the two parameters that varied for the different scenarios,while none of the parameters was changed within any of thescenarios. All the scenarios and their specifications are listed inTable 4.

3. Results and discussion

The calibration of the model was realized only for the initialfeeding mix of cow manure and corn silage. The simulated scenar-ios covered different feeding compositions and additional sub-strates. Consequently it might be doubtful that the extension ofthe simulations to those scenarios outside of the calibration couldlead to reasonable results. As no measured data about those sce-narios were available and could only be obtained by intensiveexperiments it would be impossible to prove the contrary. The ba-sic aim was to demonstrate the possibilities of the utilization of theADM1 as a tool for taking decisions and qualitative conclusions.Therefore, a comparison between data from literature and the sim-ulated results for the single substrate scenarios was firstly demon-

Table 4Simulation scenarios and their specifications of input substrates.

Scenario Organic loading rate(kg ODM/(m3

digester � d))Cow manure (CM) Corn silage

Content (kg) Ratea (%) Content (k

1 1.0 0.049 30 0.1132 2.5 0.122 30 0.2843 4.0 0.194 30 0.4544 5.5 0.267 30 0.6245 7.0 0.34 30 0.7946 2.5 0 0 0.3157 2.5 1.204 100 08 2.5 0 0 09 2.5 0.122 30 0.284

10 2.5 0.093 30 0.18611 2.5 0.075 30 0.12512 2.5 0.063 30 0.08413 2.5 0.054 30 0.05414 2.5 0.048 30 0.03215 2.5 0.122 30 0.28416 2.5 0.1 30 017 2.5 0.25 50 0.2518 2.5 0.21 50 019 2.5 0.11 30 0.128

a Percentage of weight.

strated in the Section 3.1 to demonstrate that a careful selection ofthe values for input characterization and calibration could lead toreasonable simulations.

3.1. Feeding of different single substrates

The first set of simulations presented a description of the gen-eral behavior of the single substrates of corn silage (CS), cow man-ure (CM) and grass silage (GS). After the initial 10-day operationunder the above defined conditions the input was switched to in-puts of 100% corn silage, 100% cow manure and 100% grass silage,respectively. The feeding rates for the three single substrate weredefined as listed in Table 4 (scenarios 6–8) by keeping the organicloading rate constant at 2.5 kg ODM/(m3

digester � d).At the end of the simulation time the processes were assumed

to reach a new steady state. The steady-state values of the simula-tion and data collected from the literatures and calculations arecombined in Table 5. The values that could be found in literaturesvaried strongly depending on substrate characteristics, operationconditions, measurement procedure and other factors. Conse-

(CS) Rapeseed oil (RO) Grass silage (GS)

g) Ratea (%) Content (kg) Ratea (%) Content (kg) Ratea (%)

70 0 0 0 070 0 0 0 070 0 0 0 070 0 0 0 070 0 0 0 0

100 0 0 0 00 0 0 0 00 0 0 0.255 100

70 0 0 0 060 0.031 10 0 050 0.05 20 0 040 0.063 30 0 030 0.073 40 0 020 0.08 50 0 070 0 0 0 0

0 0 0 0.234 7050 0 0 0 0

0 0 0 0.21 5035 0 0 0.128 35

Table 5Comparison of biogas yield and composition between the simulation and the data collected from literatures and calculations.

Simulation after 1 year operation Literature

Biogas yield (NL/kg FM) Methane content (%) Biogas yield (NL/kg FM) Methane content (%)

Corn silage 181.267 53.09 179.902–237.934a 48.71–56.19b

Cow manure 33.577 59.71 28.895–39.540c 52.53–67.66d

average: 61.90d

Grass silage 194.344 54.08 201.741e 53.62e

a Source: adapted from the calculations in (Mähnert, 2007)for loading rates of 1–4 kg ODM/(m3digester � d) to the ODM here of 29.0%.

b Source: numbers from batch experiments in (Amon et al., 2003).c Source: adapted from the calculations in (Mähnert, 2007) for loading rates of 1–3 kg ODM/(m3

digester � d) to the ODM here of 7.6%.d Source: numbers from batch experiments in (Amon et al., 2007).e Source: the values in Table 1 and digestibilities in (Fritz, 2008) were used for the calculation with the values of gas yields and gas composition for proteins, lipids and

carbohydrates from (Baserga, 1998).

10824 H. Zhou et al. / Bioresource Technology 102 (2011) 10819–10828

quently the data arranged in Table 5 give a range of plausible val-ues. As the simulated data could be found basically close to the val-ues given in the literature it could be concluded from thecomparison of both that the simulation results fell into a plausibleand realistic range of methane and biogas yield.

Rapeseed oil was excluded from the simulation as a single sub-strate because the change from the initial operating conditions to100% rapeseed oil led to process failure in the simulation as wellas it would in the real operation. This scenario was to be consid-ered unrealistic.

3.2. Feeding of different organic loading rates

If the initial composition of the feed mixture of 70% weightcorn silage and 30% weight cow manure is kept constant whilethe organic loading rate in the simulations varies, the effects ofa decrease or increase of the organic loading rate can be ob-served. The yield of biogas from the substrates is logically ex-pected to diminish as higher organic loading rates can cause adecrease of the hydraulic retention time of the substrates inthe digester. That means that less of the organic material inthe substrate will be degraded during the retention time andmore of the biogas potential of the substrate can still be foundin the outlet of the digester. The effect on the biogas yield wasconfirmed by the conducted simulations of different organicloading rates of the mentioned substrate mixture (Fig. 3). Onthe other hand it can be seen from Fig. 3 that raising the organicloading rate causes higher absolute biogas production (perm3

digester � d) in the digester.

Fig. 3. Biogas yield at different organic loading rates (OLRs, in

3.3. Different feeding compositions

The biogas yield as well as the biogas composition dependstrongly on the utilized substrate, as shown in Table 5. Corn silageas a substrate rich in carbohydrates is known to reach a high yieldof biogas but a low content of methane in the biogas. In contrastthe methane content for cow manure reaches around 60% of thebiogas, while the yield of biogas on the substrate is comparablylow. In most of the biogas plants mono-substrate is not used asfeeding material, and the final input is a mixture of differentmaterials. The composition and yield of biogas vary in terms ofthe utilized co-substrates. Fig. 4 shows the effects of some possiblemixtures of substrates fed to a biogas plant. The simulation repro-duced the known fact that higher proportions of corn silage lead tomore biogas yield. Grass silage also showed a similar influence.Therefore, these substrates might be chosen to raise the absolutebiogas production of biogas plants. Higher percentages of cowmanure on the other hand could result in higher contents ofmethane in the biogas.

3.4. Feeding of rapeseed oil

According to Baserga (1998) digestible lipids could result in abiogas yield of 1250 L/kg ODM and a methane content up to 68%in the biogas. As rapeseed oil is considered basically consisting ofpure digestible lipids those values would approximately be valuesthat can be expected from the anaerobic digestion of rapeseed oil.The biogas yield of 1053 L/kg ODM and the methane content of70.7% of the biogas stated by Lübken et al. (2007) roughly con-

kg ODM/(m3digester � d)) with original substrate mixtures.

Fig. 4. Effects of different mixtures of input substrates on A-biogas yield and B-methane content.

H. Zhou et al. / Bioresource Technology 102 (2011) 10819–10828 10825

firmed the above assumption. The spontaneous change with a pureaddition of rapeseed oil is not considered realizable because of thehigh amount of ODM, especially the high energy density of the oilcompared to other substrates as well as the composition of the oil.Partial feeding of rapeseed oil with a limited percentage of thewhole input ODM are assumed practicable to a certain degree.Thus simulations with different percentages of the whole feedingmaterial were performed to clarify the influences of continuous ra-peseed oil feeding. Starting with the initial substrate combinationof 30% weight of cow manure and 70% weight of corn silage thecorn silage was replaced by rape seed oil in steps of 10% weightup to 50% weight of rapeseed oil while maintaining the organicloading rate constant. Fig. 5 shows the simulated development ofbiogas yield and methane content of the biogas for the different in-put combinations with rapeseed oil.

The strongly increasing influence on biogas yield could clearlybe identified as expected. The same applied to the methanecontent. For substrate combinations of up to 40% weight of rape-seed oil (with 30% weight of corn silage and 30% weigh of cow

manure) stable operation could be observed for longer than1 year. The cases with higher oil percentages led to process fail-ure which was identified by a strong drop in the methane pro-duction (Fig. 5). The process failure might be due to a slow pHdecline observed at the beginning of the process. The declinemight at some point result in the death of biomass. The deathwould be manifested in a sudden increase of acetic, propionicand other fatty acids. The higher the percentage of oil in the sub-strate combination was the earlier the failure occurred. The sim-ulated scenarios led to the assumption that oil addition to thefeeding is applicable within certain limits and can lead to highermethane production. The processes would collapse or sometimescould not keep stable for a long time (more than 1 year) underthe direct change to continuous high feeding rates of rapeseedoil to the anaerobic digestion even if the oil addition did not ex-ceed limits. A solution to prevent process failure might be tokeep occasional and also continuous feeding of oil from close tothe limited amounts. It is also supposed that a slow increase ofthe percentage of oil in the input could be helpful to strengthen

Fig. 5. Variations of A-biogas yield and B-methane content with the increase on the contents of rapeseed oil in the input mixtures.

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process stability by leaving the biocenosis time for the adaptationto the operation conditions.

3.5. Discussion of methodology

In this paper the predictions of the modified ADM1 model usingthe calibrated kinetic parameters and the given values for most ofthe model coefficients were able to reflect the trends of anaerobicdigestion of selected agricultural substrates. Generally speakingmodel-based process simulation of anaerobic digestion for biogasproduction as well for wastewater treatment (Santos et al., 2010)allows experiments to be conducted on a virtual basis and therebycan help to avoid costly and time-intensive experiments in prac-tice. In this way many different possibilities and operation scenar-ios for experiments, as well for biogas plants in operation, can beevaluated by model calculations, potential results can be predictedand decisions may be made on a more founded basis. Utilization ofmodels for education issues may also be helpful for better processunderstanding and handling. Despite the numerous advantages,conclusions drawn based on the simulations should be handledcautiously.

A well calibrated model is an essential requirement to derivereasonable statements from the performed simulations. The model

in this case was mainly calibrated on biogas and methane produc-tion while additional data were not sufficient for calibration due tothe limitation of the continuous data available. In addition, the -calibration of the model, mainly on the two kinetic parameters,was done manually by visual comparison of experimental results.The quality of model results depends strongly on calibration of ki-netic parameters. As Koch et al. (2010) pointed out, the calibrationhowever is quite subjective and requires experience since two per-sons would possibly estimate parameters differently with the sameexperimental data set. A new procedure to assess the quality ofsimulations compared to observed data will be considered. There-fore, the calibration of the model will still spare the room for theimprovement on the simulation in the future research.

It is often required to estimate the values that were input intothe model for substrate characteristics such as COD, biodegradablefraction of the COD, NfE and NH4AN as these have a substantialinfluence on the gas composition and model predictions. If themodel is to be used as a decision-making and planning-supporttool for biogas production from selected agricultural substrates, itwould benefit from more careful characterization of inflow sub-strates. As a matter of fact, it is increasingly important to assessthe quality of the inflow substrates in terms of their biochemicalmethane potential (BMP) which is a measure of the anaerobic

H. Zhou et al. / Bioresource Technology 102 (2011) 10819–10828 10827

biodegradability of a substance. The BMP of the substrate coulddetermine the feeding rate into a continuously running digesterand consequently predict the biogas production which could be areference for the simulation results as well as the measured data.Also, it could be used to estimated the total disintegration rate con-stants of a substrate (Galí et al., 2009). Thus it could be consideredas a basic data of a substrate for anaerobic digestion. A newly studyusing near infrared spectroscopy (NIRS) as an indirect and rapidmethod to assess the BMP of meadow grasses (Raju et al., 2011)has given a new option for the determination of the BMP of asubstrate. NIRS scanning of a prepared sample takes less thana few minutes is a non-invasive rapid analytical technique with-out using chemicals, and might be a good tool for rapid BMPassessment.

Results of simulated scenarios could be verified with literaturedata for biogas parameters when the calibrated model is imple-mented. However the predictions from the simulations should behandled considering their origin of basic data as a final verificationof most of the simulations with real operational data could not berealized. Moreover simulations can hardly cover all aspects of aprocess or be better than the data used for their calibration. There-fore, results derived from simulations should be verified by realdata whenever possible even though the simulations are very help-ful in many applications. In addition, all simulations demonstrated(except the calibration and validation) were based on the mathe-matically and theoretically perfect conditions. That meant no ef-fects of the errors from measurement, stirring, feeding, etc werecovered. The even lines in the shown graphs (Figs. 3–5) were theresults of these perfect conditions.

One example for the application of model-based research is thedevelopment of control strategies for the anaerobic digestion pro-cesses which has been currently carried out at the Institute for San-itary Engineering, Water Quality and Solid Waste Management ofthe University of Stuttgart (Löffler et al., 2011). The observationof other parameters besides the gas data might be substantiallyhelpful to draw conclusions more reasonably if the model is furthercalibrated on the other parameters.

4. Conclusions

The modified ADM1 was used for the anaerobic digestion of dif-ferent agricultural substrates (cow manure, corn silage, grass silageand rape oil) with different feeding compositions and loading rates.With a detailed mathematic characterization of inflow substratesand the model calibration mainly on two kinetic parameters andvalidation using a real lab-scale digester, the model showed a goodperformance on predicting the stable-state anaerobic digestion ofagricultural substrates for biogas production. For the single sub-strates the statement could be verified with literature data, whilefor other combinations of substrates only plausibility was availableto roughly confirm the obtained results. Nevertheless, it could beshown that a wide range of possibilities can be examined withthe help of a model. These predictions can contribute to makingoperational decisions in agricultural biogas plants or assisting theplanning of experiments in research.

Acknowledgements

The authors are most grateful for the financial support from theMinistry of Science, Research and the Arts Baden-Württemberg,Germany.

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