Dry-thermophilic anaerobic digestion of organic fraction of municipal solid waste: Methane production modeling

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    Keywords:Bioprocess engineeringDegradationEnvironmental engineeringFermentationReactor analysis


    OFMSW (average particle size of 1 mm; 0.71 g Volatile Solids/g waste), and OFMSW coming from a com-posting full scale plant (average particle size of 30 mm; 0.16 g Volatile Solids/g waste).

    nic frac

    variables.Therefore, kinetic modeling is increasingly needed for a

    better understanding of the performance of such systems, thedesign and operation of biological waste-treatment systems topredict system stability, efuent quality and waste stabilization(Angelidaki et al., 1993).

    Currently, there are fourmain approaches to anaerobicmodelingwhich help predict the behavior of the reactors (Garcia-Ocha et al.,

    In general, when using a specic waste (with roughly the samecharacteristics) as substrate, a decrease in SRT produces an in-crease in the consumed organic loading rate (OLRC), thus, in thespecic methane production (De la Rubia et al., 2006a,b). However,such a behavior presents a maximum from which the effect is theopposite. Depending on the feeding characteristics (particle sizeand organic matter content mainly), every system presents an opti-mum SRT belowwhich the destabilization of the reactor and a dropin biogas generation and organic matter removal take place.

    The effect SRT has on the efuent organic matter concentrationand methane production and its relation with the fed OLR (both

    Corresponding author.

    Waste Management 32 (2012) 382388

    Contents lists available at


    elsE-mail address: alberto.fdezguelfo@uca.es (L.A. Fdez-Gelfo).waste (OFMSW) has been a growing concern over the last yearsdue to the potential of the technology for fast production of meth-ane and/or hydrogen rich biogas and to save fossil energy (Jewellet al., 1993 and Mata-lvarez et al., 2000). Methane productionrates must be increased to maximize revenues from power gener-ation and hence, to make digestion facilities more protable. Opti-mization can be achieved by means of mathematical models forAD. Such models can be useful to predict both substrate utilizationand methane production rates for different operating conditions,and to determine the optimum and critical values for operation

    tured kinetic models. The last one type of models for dynamicsimulation of AD includes a complex matrix with kinetic constantsfor thedifferent degradation steps of organicmatter. Several authors(Bagley and Brodkorb, 1999; Batstone et al., 2002) have used theaforementioned models to predict the process response to specicoperating conditions in Continuous Stirred Tank Reactors (CSTR).Moreover, the Organic Loading Rate (OLR) and the solids retentiontime (SRT) are the main variables in everyday practice to carry outboth a proper design of reactor and an analysis of the CSTR perfor-mance (Halalsheh et al., 2001; Kiyohara et al., 2000).1. Introduction

    Anaerobic digestion (AD) of orga0956-053X/$ - see front matter 2011 Elsevier Ltd.doi:10.1016/j.wasman.2011.11.002A modication of a widely-validated product-generation kinetic model has been proposed. Resultsobtained from the modied-model parameterization at steady-state (that include new kinetic parame-ters as K, YpMAX and hMIN) indicate that the features of the feedstock strongly inuence the kinetics ofthe process. The overall specic growth rate of microorganisms (lmax) with synthetic OFMSW is 43%higher compared to OFMSW coming from a composting full scale plant: 0.238 d1 (K = 1.391 d1;YpMAX = 1.167 L CH4/gDOCc; hMIN = 7.924 days) vs. 0.135 d1 (K = 1.282 d1; YpMAX = 1.150 L CH4/gDOCc;hMIN = 9.997 days) respectively.Finally, it could be emphasized that the validation of proposed modied-model has been performed

    successfully by means of the simulation of non-steady state data for the different SRTs tested with eachwaste.

    2011 Elsevier Ltd. All rights reserved.

    tion of municipal solid

    1999): based on kinetic equations (Chen and Hashimoto, 1980;Chen, 1983; Romero Garca, 1991), unstructured and non-segre-gated, unstructured and segregated (Hill, 1983) and, nally, struc-Accepted 3 November 2011Available online 30 November 2011

    been studied in a semi-continuous stirred tank reactor (SSTR). Two types of wastes were used: syntheticDry-thermophilic anaerobic digestion ofwaste: Methane production modeling

    L.A. Fdez-Gelfo a,, C. lvarez-Gallego a, D. Sales b, LaDepartment of Chemical Engineering and Food Technology, Faculty of Science, UniversibDepartment of Environmental Technologies, Faculty of Marine and Environmental Scie

    a r t i c l e i n f o

    Article history:Received 6 July 2011

    a b s t r a c t

    The inuence of particle s(OFMSW) in the overall ki

    Waste M

    journal homepage: www.All rights reserved.ganic fraction of municipal solid

    Romero Garca a

    f Cadiz, 11510 Puerto Real, Cdiz, Spain, University of Cadiz, 11510 Puerto Real, Cdiz, Spain

    and organic matter content of organic fraction of municipal solid wastecs of dry (30% total solids) thermophilic (55 C) anaerobic digestion have

    SciVerse ScienceDirect


    evier .com/ locate/wasman

  • Manmeasured as Dissolved Organic Carbon DOC- or Volatile Solids VS-) can be used to determine the parameters of an adequate ki-netic model, such as the one proposed by Romero Garca (1991),for the prediction of process behavior.

    2. Kinetic model development

    The kinetic modeling helps predict the effect of the main pro-cess variables on system performance. In this sense, the develop-ment of adequate models and their parameterization, by ttingmodel equations to the experimental results obtained in specicassays, is a critical step.

    What is more, since industrial plants for the treatment ofOFMSW by AD are characterized by the use of a wide range ofwaste composition to feed reactors, kinetic modeling plays a keyrole in determining optimum operating conditions.

    In this work, the biogas generation model proposed by RomeroGarca (1991) has been used. Such a model has successfully provedto t experimental results of AD of different organic wastes: winevinasses (Romero Garca, 1991; Prez et al., 2001a, 2001b), sludgesfrom Wastewater Treatment Plant (WWTP) (De la Rubia et al.,2006a, 2006b), and OFMSW (Fernndez et al., 2010).

    The model assumes that the substrate consumption rate (rS) isobtained through the following equation:

    rs lmaxh S S SNB

    h SNB 1

    where, SNB represents the Non-Biodegradable substrate concentra-tion (ML3), S represents the substrate concentration (ML3), h isthe maximum substrate concentration that can be utilized in thebiomass formation including cell material from microorganisms(ML3) and lmax represents the maximum specic growth rate ofthe microorganisms (T1).By including Eq. (1) in the mass balanceon a completely mixed reactor and assuming steady state condi-tions, the model equation used in this work can be obtained:

    S SNB S0 SNBlmaxh2

    where S0 represents the initial substrate concentration (ML3) and hrepresents the solids retention time (T).

    In Eq. (2), as posited by Romero Garca (1991), it was assumedthat parameter h is equivalent to the initial substrate concentrationS0 for continuous operation. Besides, the above equation is basedon the overall growth rate (lmax) of the main microbial groups in-volved on the process and its value is representative of the rate-limiting step.

    Eq. (2) can be modied by assuming that the quantity of non-biodegradable matter is proportional to the total substrate concen-tration in the feeding. Thus, SNB can be expressed as the fraction ofnon-biodegradable substrate (0 6 a < 1) with respect to the initialsubstrate concentration (S0), as in Eq. (3):

    S aS0 S01 almaxh3

    Evidently, the aforementioned equation could be easily linearizedas follows:


    a 1 almax



    Based on Eq. (4) and assuming that substrate is converted nearlystoichiometrically into methane, Eq. (5) can be obtained:

    cCH4 YPS0 S



    S0 aS0 S01almaxhh


    L.A. Fdez-Gelfo et al. /Waste YP S01 ah 11


    5where, cCH4 represents the volumetric methane production rate (ex-pressed as Litres of CH4/Litresreactor/d) and YP the methane yieldcoefcient (expressed as Litres of CH4/g consumed organic matter).

    By linearizing Eq. (5), the following can be obtained:


    YP1 a YP1 almax



    However, according to Leatherbarrow (1990), linearization ofequations implies a change in the tted variables because it mod-ies the random error distribution associated to the aforemen-tioned values, and thus, the hypotheses of the linear- regressiontechniques are invalidated. Nevertheless, non-linear regressiondoes not modify the error distribution since it is not necessary totransform any variables. In this case, the adjustment equation isas follows:

    cCH4 YP1 aS01h 1lmaxh



    Eq. (7) represents product generation model of Romero Garca(1991) and the coefcient YP is assumed to be constant. However,in this work it has been checked that YP is dependent on the SRTused. Such a behavior has also been reported by Bernd Linke(2006), who worked with solid waste from the potato processingindustry in thermophilic range. The author reported a decrease inboth the biogas and methane yield of 23.5% and 14%, respectively,when the feeding OLR was increased from 0.8 to 3.4 gVS/L/d.

    In this sense, a mathematical empirical relationship is proposedby the authors in this work to explain the relationship between YPand the SRT. The observed dependence of both parameters suggesta Monod-type curve, as expressed in Eq. (8).

    Yp YpMAX h hMINK h hMIN 8

    where YpMAX is the maximum methane production (expressed as LCH4/gDOCc), hMIN is the minimum SRT (expressed as days) belowwhich the system would become unstable and K is a Monod-typeconstant whose value is the difference (hhMIN), expressed as days,for which Yp reaches half of its maximum value.

    By analogy with the Monod equation, the value of K may beassociated with the ability of microorganisms to produce methanefrom substrate. Thus, the high values of the aforementionedparameter indicate that microorganisms present greater difcultyin the synthesizing methane from degraded organic matter.

    The mathematical relation between YP and SRT depends on thetype of substrate. Thus, a mathematical relation must be obtainedfor each case. Last but not least, from the introduction of such anexpression into the equation of product generation model ofRomero Garca (1991) the Modied Product Generation Model(MPGM) can be obtained.

    For continuous processes that have not reached steady-state ofoperation and taking into account the material balance for an idealmixed reactor, the equations resulting from the extension of themodel of Romero Garca (1991) are:

    SS0; h; t S0 St0 SSS SSS S0 St0 elMAX1ht

    St0 SSS S0 St0 elMAX1ht9

    SSS a S0 S0 1 almax h


    where, St = 0 represents the substrate concentration when new con-ditions are imposed on the system (ML3), SSS represents the sub-

    agement 32 (2012) 382388 383strate concentration of the system under Steady-State conditions(ML3) and t represents the operating time elapsed since the newconditions were imposed on the system (t).

  • w/w), cabbage (5.3% w/w), orange (4.9% w/w), apple (4.9% w/w),bread (3.5% w/w) and paper (55.8% w/w).

    It is important to emphasize that, as shown in Table 1, com-pared to the OFMSWSYN, the OFMSWFSP had a low content of organ-ic matter (measured as Volatile Solids and dissolved organiccarbon). According to Pavan et al. (2000), OFMSWFSP can be classi-ed as non-easily biodegradable, since the VS/TS ratio is lower than0.7. In this study, the VS/TS ratio is 0.22, which, based on theassumptions of the authors, implies the following: rst, the systemcan work with a high organic loading rate without the occurrenceof acidication problems, and, second, the biogas and methaneproduction is considerably smaller compared with the productionachieved from an easily biodegradable substrate.

    Table 1Physicochemical characterization of wastes.

    Analytical parameter OFMSWSYN OFMSWFSP

    pH 7.78 7.98Density (kg/m3) 750 650Alkalinity (gCaCO3/L) 4.29 18.13Ammonia (gNH3-N/L) 1.68 0.79Total nitrogen (g/kg) 23.0 29.0Total solids (g/g sample) 0.90 0.71Volatile Solids (g/g sample) 0.71 0.16Fixed solids (g/g sample) 0.19 0.55

    Management 32 (2012) 382388Knowing the value of the substrate concentration in the efu-ent, the methane generation may be estimated since the modelof Romero Garca (1991) considers that the methane productivityis directly proportional to the consumed organic loading rate.

    cCH4 YP S0 S



    Combining the expressions 8, 9 and 11, the following expressioncan be obtained for non steady-state process:

    cCH4 1h

    YpMAXh hMINK h hMIN

    S0S0 St0 SSS SSSSt0e lMAX1h tSt0 SSS S0 St0e lMAX1h t

    " #

    12On the basis of the previously developed model for steady-state

    and non steady-state operations, the two main aims of the paperare:

    1. To obtain the kinetic parameters of the dry-thermophilic anaer-obic digestion in order to determine the inuence of the type ofwaste in the overall kinetic process. To reach this objective, theequations of the MPGM for steady-state operation will beapplied to the experimental data of methane productivity.

    2. Next, the results obtained from the parameterization of thekinetic equations in steady-state will be used to simulate andvalidate the MPGM, at non steady-state conditions, throughthe methane productivity data for the different SRTs tested witheach waste.

    3. Materials and methods

    3.1. Software

    To t kinetic equations to the experimental data and determinethe kinetic parameters of the model, linear and non-linear regres-sions have been applied using a statistical software program (Stat-graphics Plus v. 5.0 Professional Edition). The non-linear regressionis based on minimization-square-residues algorithm of Marquardt(1963).

    3.2. Experimental equipment

    A 5 L reactor without biomass recycling with a working volumeof 4.5 L was used (Fig. 1). The thermophilic conditions (55 C) weremaintained by circulating water through the jacket from a thermo-static water bath (7 L). The reactor was equipped with a dischargeball valve and several input/output ports located at the top: a stir-ring paddle (stirring rate of 13 rpm), pH probe, biogas outlet, feedinlet and two extra pH controllers. In this type of reactor, theHydraulic Retention Time (HRT) and the Solid Retention Time(SRT) are equal.

    The pH was adjusted using an on/off controller and correctionswere carried out by means of 5 N NaOH and 1 N H3PO4 solutions.The pH was maintained stable in the 6.58 range, which is suitablefor methanogenic microorganisms.

    About the feeding regime, the SSTR reactor was fed once a day.The TS concentration of the feeding was adjusted to 30% (which ischaracteristic of dry anaerobic digestion) by adding tap water.

    3.3. Feedstock

    Two types of OFMSW were used: synthetic waste (OFMSWSYN),with an average particle size of 1 mm and prepared following sug-

    384 L.A. Fdez-Gelfo et al. /Wastegestions of Martin et al. (1997) to provide the nutritional require-ments of the main microorganisms involved in the process, and theindustrial one, with an average particle size of 30 mm. The indus-trial OFMSW coming from the trommel of the full scale treatmentplant Las Calandrias located at Jerez de la Frontera, Cdiz- Spain.This OFMSW will be called as OFMSWFSP, where FSP means fullscale plant. The physicochemical characterization of both wastesis displayed in Table 1.

    The synthetic waste was employed in order to avoid the distor-tion in the process caused by the changes in feed compositionwhen industrial feeding is used and, therefore, evaluate as theanaerobic digestion is inuenced by this aspect. About the compo-sition of the OFMSWSYN, the main constituents were potato (6.2%

    Fig. 1. Semi-continuous stirred tank reactor (SSTR).Dissolved organic carbon (mg/g) 112.6 39.8Total volatile fatty acids (mgAcH/ L) 1440 32.65

  • 3.4. Methodology

    As regards to OFMSWSYN, an initial sequence of four SRTs (20,15, 10 and 8 days) was tested once reactor stabilization at 25-day SRT was obtained. The start-up and stabilization stages arefully described by Fdez-Gelfo et al. (2010). The imposed organicloading rate during each SRT, expressed as mgDOC/L day andmgVS/L day, is shown in Table 2.

    Once destabilized the reactor at 8-days SRT, the OFMSWFSP be-gan to be fed to the digester. In this case, the next sequence of fourSRTs was 15, 13, 12 and 10 days (see Table 2).

    sample volume was 1 mL and the operational conditions were as1

    analyzed, as in the case of the DOC, from the ltrate supernatantobtained by means of a lixiviation (10 g of digested waste in100 ml of Milli-Q water during 20 min) of the efuent samples.


    K 1:391days r2 0:999

    Table 3Variables in the methane production model with OFMSWSYN.

    h(days) DOCINFLUENT(g/L)


    cCH4 (L CH4/LREACTORd)

    Yp (L CH4/gDOCC)

    20 28.075 10.303 0.93 1.0515 28.075 10.806 1.15 0.9710 28.075 14.803 0.93 0.708 28.075 10.475 0.13 0.06

    L.A. Fdez-Gelfo et al. /Waste Management 32 (2012) 382388 385follows: 7 min at 55 C; ramped at 27 C min until 150 C; detec-tor temperature: 255 C; injector temperature: 100 C. The carrierwas helium and the ow rate used was 30 ml min1. A standardgas (by Carburos Metlicos, S.A; composition: 4.65% H2; 5.33%N2; 69.92% CH4 and 20.10% CO2) was used for the systemcalibration.

    Individual VFA (acetic, propionic, iso-butyric, butyric, iso-vale-ric, valeric, iso-caproic, caproic and heptanoic) levels were

    Table 2Initial organic loading rate (OLR0) used in the different experimental stages.

    Type ofwaste

    Stage OLR0

    gDOC/Ld gVS/Ld SRT(days)

    Operation time(days)

    OFMSWSYN 1 1.404 8.862 20 602 1.872 11.817 15 453 2.808 17.725 10 484 3.509 22.156 8 24

    OFMSWFSP 5 0.701 2.929 15 456 0.809 3.380 13 39For both wastes, each SRT was maintained for at least threeperiods in order to reach stable operation. The operation time ofeach SRT is detailed in Table 2.

    It is worth pointing out that the organic loading rate exceededlimit value suggested by Angelidaki et al. (2006) for a successfulstart-up. In fact, along stages 3 and 4, when the OFMSWSYN wasfed, the organic loading rate expressed as Volatile Solids (VS) washigher than 15 gVS/L/d.

    3.5. Sampling and analysis

    The following analytical determinations were used for wastecharacterizations and process monitoring and control: Total Solids(TS), Volatile Solids (VS), alkalinity, pH, Dissolved Organic Carbon(DOC), ammonium and Volatile Fatty Acids (VFA). To verify the sys-tems performance, all the parameters were analyzed once a dayand determinations were performed according to Standard Meth-ods (APHA-AWWA-WEF, 1995). TS, VS, pH, alkalinity and ammo-nium were determined directly from digested OFMSWSYNsamples. The DOC concentration was analyzed from the ltratesupernatant obtained by means of a lixiviation (10 g of digestedwaste in 100 ml of Milli-Q water during 20 min) of the efuentsamples. Samples for the DOC analysis were further lteredthrough a 0.47 lm glass ber lter.

    The volume of gas produced in the reactor was measured di-rectly by using a high precision ow gas meter WET DRUM TG0.1 (mbar) Ritter through the Tedlar bag. The gas composition(hydrogen, methane and carbon dioxide) was determined by gaschromatography (SHIMADZU GC-14 B) with a stainless steel col-umn packed with Carbosive SII (diameter of 3.2 mm and lengthof 2 m) and a thermal conductivity detector (TCD). The injected7 0.877 3.661 12 368 1.052 4.393 10 30According to Fdez-Gelfo et al. (2011a), the critical solid reten-tion time (SRTCR) has been dened as the tested SRT that causes asignicant decrease in the daily methane production. In the case athand, the value obtained for hMIN (7.92 days) was very close to theSRTCR obtained experimentally (8 days).

    Consequently, the destabilization of the system (Table 3) and aclear drop in the methane production and organic matter removaltake place.4.1.1. Synthetic OFMSW (OFMSWSYN)By using non-linear regression of Eq. (8) to t experimental data

    (Fig. 2), the following values were obtained:

    YpMAX 1:167LCH4=gDOCc hMIN 7:924days4.1. Proposed ratio Y vs. SRT: parametrization of the new functionThe samples for the VFA analysis were further ltered through a0.22 lm Teon lter. VFA levels were determined by gas chroma-tography (SHIMADZU GC-17 A) with a ame ionization detectorand a capillary column lled with Nukol (polyethylene glycol mod-ied by nitro-terephthalic acid). The temperatures of the injectionport and detector were 200 C and 250 C, respectively. Heliumwas the carrier gas at 50 ml min1. In addition, nitrogen gas wasused as make up at 30 ml min1 ow rate. The total VFA was calcu-lated as the sum of individual VFA levels.

    4. Results and discussion

    The evolution of methane production vs. the SRT for each waste,expressed as LCH4/LREACTORd and LCH4/gDOCc, is displayed in Table3. Each SRT was maintained at least for 3 times to ensure the sys-tem stabilization.Fig. 2. Average YP (expressed as litres of CH4 per gram of consumed DOC) versusSRT (expressed as days) for OFMSWSYN.

  • Man4.1.2. OFMSW coming from a composting full scale plant (OFMSWFSP)On closer inspection of the experimental values of average YP

    for OFMSWFSP (Table 4) vs. the SRT (Fig. 3), a very similar relation-

    Table 4Variables in the methane production model with OFMSWFSP.




    cCH4 (L CH4/LREACTORd)

    Yp (L CH4/gDOCC)

    15 10.525 7.425 0.19 0.9013 10.525 8.024 0.16 0.8412 10.525 8.160 0.13 0.6810 10.525 7.192 N.D. N.D.

    N.D. not-detected.

    Fig. 3. Average YP (expressed as litres of CH4 per gram of consumed DOC) versusSRT (expressed as days) for OFMSWFSP.

    386 L.A. Fdez-Gelfo et al. /Wasteship to the one obtained for OFMSWSYN was observed. Similarly, bynon-linear regression of YP vs. SRT (based on Eq. (8)), the parame-ters YpMAX, hMIN and K can be estimated:

    YpMAX 1:150LCH4=gDOCc hMIN 9:997daysK 1:282days r2 0:996

    Again, the value of hMIN (9.997 days) obtained was very similarto the experimental SRTCR (Table 4): 10 days for this type of waste.For synthetic and industrial OFMSW, the SRTCR values determinedby non-linear regression were 7.924 days and 9.997 days, respec-tively. Such values indicate that hMIN is inuenced by the type ofwaste. Considering that in the kinetic of the AD process the rate-limiting step is generally the hydrolysis stage (Chulhwan et al.,2005), this fact may be associated with transfer-matter limitationand microbial colonization of the particles of the wastes. Thus,higher hMIN values may be due to higher average particle size ofthe waste, as occurs with the OFMSWFSP.

    By contrast, the YpMAX values obtained for both wastes were al-most equal (1.167 for OFMSWSYN and 1.150 L CH4/gDOCc forOFMSWFSP), which may suggests that the methane generationyield, in term of organic matter removal expressed as DOC, is inde-pendently of waste source (synthetic or industrial) and its biode-gradability and, therefore, this value could be considered like astoichiometric ratio.

    In addition, the observed difference between the K values forboth wastes was lower than 8%, i.e. the values are not signicantlydifferent. Taking into account that K parameter is representative ofthe microbial afnity to methane production from organic matterdegradation, this nding indicates that, although both wastes pres-ent different particle size and organic matter content, the nature ofthe organic matter is the same and, therefore, the afnity of themicroorganisms versus waste are analogous.4.2. Application of the modied product generation model (MPGM)

    Table 5 shows the values obtained for lmax and a from the non-linear regression of the Modied Product Generation Model(MPGM), which ts the Yp dependence on the SRT, to experimentaldata of methane production for both wastes: synthetic and indus-trial OFMSW.

    The specic growth rate of the microorganisms (lmax) washigher when OFMSWSYN was fed to the system: 0.238 d1 forOFMSWSYN vs. 0.135 d1 for OFMSWFSP. In batch processes, AD ofMSW shows a spontaneous sequential phase separation and thespecic growth rate of every microbial group may be characterizedin the same run (lvarez Gallego, 2005). However, it is very impor-tant to emphasize that the lmax value obtained in this work repre-sents the global maximum specic growth rate for the overallmicrobial populations involved in the process since the systemwas operated under semi-continuous regime. Thus, such a valueis representative of the microbial group rate-limiting of theprocess.

    On the other hand, the value of a was lower when the OFMSW-SYN was fed: a reasonable nding taking into account the composi-tional characterization of the wastes (Table 1). The OFMSWSYNpresented higher organic matter concentration compared to theOFMSWFSP: 0.71 gVS/g for the synthetic waste and 0.16 gVS/g forthe industrial one. The value of parameter a obtained for OFMSW-SYN and OFMSWFSP was 0.164 and 0.452 respectively. The afore-mentioned results indicate that the biodegradable fraction ofsuch a waste is 83.6%. Therefore, it is a substrate with high biode-gradability compared with the industrial OFMSWwhich presents abiodegradable fraction of 57.4%.

    The values of lmax obtained in this study using synthetic andindustrial OFMSW match up the literature values for the differentmicrobial populations (Fdez-Gelfo et al., 2011b). As can be seen,the values of lmax in this study are very similar to those obtainedby lvarez Gallego (2005) in batch dry-thermophilic anaerobicdigestion of OFMSW. The author reported that the maximum spe-cic growth rate for the hydrolytic and acidogenic populations ran-ged between 0.08 and 0.18 d1, and for methanogenic acetate-utilizing Archaeas between 0.230.28 d1.

    According to the lvarez Gallego (2005) and the values of lmaxobtained, when OFMSWSYN is fed to the system, methanogenesis

    Table 5Results from the MPGM of Romero Garca (1991).


    equation:cCH4 YpMAX hhMIN KhhMIN

    1 aS0 1h 1lmaxh2

    Parameter r2

    OFMSWSYN lmax = 0.238 d1

    a = 0.1640.999

    OFMSWFSP lmax = 0.135 d1

    a = 0.4250.996

    a The adjustment equation has been obtained combining Eqs. (7) and (8).

    agement 32 (2012) 382388from acetate can be considered the rate-limiting step of the processrather than the hydrolytic stage. Such a fact can be associated withthe small particle size of the aforementioned waste (1 mm), whichencourages hydrolysis and solubility of the organic matter. Basedon the aforementioned assumptions, when the OFMSWFSP is fed,hydrolysis and acidogenesis of the waste may be considered therate-limiting step of the process; a reasonable nding taking intoaccount that the average particle size of such OFMSW is 30 timeshigher than that of the synthetic waste.

    4.3. Modied product generation model (MPGM) validation

    The application of MPGM in steady-state to the experimentaldata has allowed the parameterization of the equations and,

  • ManFig. 4. Comparison between the accumulated methane values predicted by Eq. (12),based on MPGM, and experimental results when OFMSWSYN is used.

    Fig. 5. Comparison between the accumulated methane values predicted by Eq. (12),based on MPGM, and experimental results when OFMSWFSP is used.L.A. Fdez-Gelfo et al. /Wastetherefore, it has been possible to obtain the values of the kineticcoefcients. These coefcients allow predicting the behavior thatshould make the system when operating conditions are modied,i.e. the system has not reached steady-state conditions.

    Therefore, the values of these coefcients allow checking thevalidity of the model since they have a particular physical andmicrobiological signicance and, therefore, they cannot take ran-dom values.

    In this way, the simulation of the process at non steady-statewas developed in order to determine the behavior of the systemwhen synthetic and industrial OFMSW are fed to reactor at differ-ent SRTs.

    In the Figs. 4 and 5 are shown the comparison between the accu-mulated methane values predicted by Eq. (12), based on MPGM,and experimental results when OFMSWSYN and OFMSWFSP are fedto the system and the SRT is modied.

    As can be seen, the Eq. (12) ts to experimental data and, hence,it can be concluded that MPGM is valid to describe successfully thebiomethanization process of both OFMSWs, independently of itsorganic matter content and particle size.

    5. Conclusions

    The application of the MPGM has allowed the characterizationof the overall specic growth rate of the microorganisms (lmax)for dry-thermophilic biomethanization of synthetic and industrialOFMSW. The validation of the MPGM has been performed success-fully by means of the simulation of non-steady state data for thedifferent SRTs tested with each waste.

    From the kinetic analysis may be concluded that the type ofwaste (particle size and organic matter content) signicantly inu-ences the hMIN parameter and, consequently, the lmax. However,the obtained values of YpMAX and K have been analogous for

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    This work was supported by the Ministerio de Ciencia e Innova-cin of Spain (Projects CTM2007-62164/TECNO and CTM2010-17654), the Consejera de Innovacin, Ciencia y Empresa of theJunta de Andaluca, Spain (Project P07-TEP-02472), the EuropeanRegional Development Fund (ERDF) and the Ministerio de Educa-cin y Ciencia of Spain (Project NovEDAR_Consolider CSD2007-00055).


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    Dry-thermophilic anaerobic digestion of organic fraction of municipal solid waste: Methane production modeling1 Introduction2 Kinetic model development3 Materials and methods3.1 Software3.2 Experimental equipment3.3 Feedstock3.4 Methodology3.5 Sampling and analysis

    4 Results and discussion4.1 Proposed ratio YP vs. SRT: parametrization of the new function4.1.1 Synthetic OFMSW (OFMSWSYN)4.1.2 OFMSW coming from a composting full scale plant (OFMSWFSP)

    4.2 Application of the modified product generation model (MPGM)4.3 Modified product generation model (MPGM) validation

    5 ConclusionsAcknowledgementsReferences


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