Optimizing low-temperature biogas production from biomass by anaerobic digestion

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<ul><li><p>du</p><p>rci</p><p>Hew19 O</p><p>Keywords:Biogas productionAnaerobic digestion</p><p>eomrodu</p><p>produce biogas by anaerobic digestion using model dairy wastewater sludge as substrate. The Monod</p><p>roducprodu</p><p>been studied experimentally and theoretically for six decades [1].</p><p>be applied to increase the protability of large-scale plants,generate signicant benets without excessive energy use orchemical demand and to scale-up laboratory installations [9,10].Mathematical model-based simulations of bioreactor runs canexplain changes in process variables e biomass, substrate and</p><p>The models found in scientic literature differ in structure andzation techniquesdigestion processoperated in batcho maximize globalIn the continuouse goal is to maxi-aratus per unit of</p><p>ed hydrodynamicprocesses in laboratory-scale plug-ow UASB reactors. They stud-ied process behavior using two-compartment [18] and multiplemixed-compartmentmodels [17]. Laboratory-scale installations areoften used to study process kinetics before scaling-up to full-scaleapplications. Batstone et al. [19] demonstrated that the hydraulicsof laboratory-scale plug ow-type bioreactors may differ signi-cantly from that of full-scale digesters. They recommended thatmixed ow-type models should be used instead of plug-ow re-actors for modeling full-scale bioreactors.</p><p>* Corresponding author. Tel.: 48 89 523 3413; fax: 48 89 523 4469.</p><p>Contents lists availab</p><p>Renewable</p><p>.e ls</p><p>Renewable Energy 69 (2014) 219e225E-mail address: marek@uwm.edu.pl (M. Markowski).Anaerobic digestion is used to treat and recover energy from sludgein wastewater [2], municipal solid wastes [3], agricultural residuesand food processing waste [4e6]. The anaerobic digestion tech-nology offers great potential for rapid disintegration of organicmatter to produce biogas and conserve fossil energy resources [7].There is a growing interest in biogas production, and the number ofbiogas production plants and average plant size continue to in-crease [8].</p><p>Mathematical modeling and optimization techniques have to</p><p>level of complexity [8,11e14]. Different optimican be used to improve the performance of the[15,16]. If a bioreactor (laboratory or technical) ismode (unsteady state), the optimization goal is tbiogas production at the end of each batch.mode of bioreactor operation (steady state), thmize the amount of biogas produced in the apptime.</p><p>Bolle et al. [17] and Singhal et al. [18] describand different kinds of organic residues in anaerobic digesters has tion, should be controlled to guarantee the desired response [11].Continuous-ow bioreactorMathematical modelingOptimization</p><p>1. Introduction</p><p>In the absence of air, biogas is pthrough anaerobic digestion. Biogashttp://dx.doi.org/10.1016/j.renene.2014.03.0390960-1481/ 2014 Elsevier Ltd. All rights reserved.maximizes the amount of biogas produced per unit of time. Total biogas production derived from thetheoretically optimized reactor in the calculation model was 1.6 times higher than that derived for theexperimental bioreactor. The methane fraction in biogas increased from 64.5% to 71.2% after optimiza-tion, whereas the carbon dioxide fraction in biogas decreased from 34.5% to 27.8%. The optimization ofthe intermediate cylinder of the digester signicantly increased total biogas production (by up to 160%)in comparison with the output noted before optimization.</p><p> 2014 Elsevier Ltd. All rights reserved.</p><p>ed by micro-organismsction from wastewater</p><p>product concentrations e accompanied by temperature changesinside an apparatus. They can also describe the inuence of thenutrient feeding rate on substrate digestion and explain howprocess parameters, including time, concentration and composi-Available onlineapproach was used to nd the optimal diameter of the two cylinder-separated stages of the reactor thatAccepted 21 March 2014 optimal geometric parameters of the digester. A continuous-mode two-stage bioreactor was applied toOptimizing low-temperature biogas proanaerobic digestion</p><p>Marek Markowski a,*, Ireneusz Bia1obrzewski a, MaMiros1aw Krzemieniewski b</p><p>a Faculty of Engineering, University of Warmia and Mazury in Olsztyn, 10-718 Olsztyn,b Faculty of Environmental Sciences, University of Warmia and Mazury in Olsztyn, 10-7</p><p>a r t i c l e i n f o</p><p>Article history:Received 7 November 2012</p><p>a b s t r a c t</p><p>The inuence of selected glow-temperature biogas p</p><p>journal homepage: wwwction from biomass by</p><p>n Zielinski b, Marcin Debowski b,</p><p>eliusza 14, Polandlsztyn, Oczapowskiego 5, Poland</p><p>etric bioreactor parameters on the performance of continuous-ow-typection from biomass by anaerobic digestion was studied to determine the</p><p>le at ScienceDirect</p><p>Energy</p><p>evier .com/locate/renene</p></li><li><p>In the present study, a continuous-mode two-stage bioreactorwas applied to produce biogas by anaerobic digestion using model</p><p>Fig. 1. Experimental design diagram: a) two-stage mixed ow reactor; 1. Chamberhydrolyzer, with full mixing, 2. Methanogenic chamber with downside ow, 3.Methanogenic chamber with upside ow, arrows indicate ow direction in the reactor;</p><p>M. Markowski et al. / Renewable Energy 69 (2014) 219e225220dairy wastewater sludge as substrate. The ow rate of the liquidphase at the inlet was kept constant, therefore, liquid ow veloc-ities in each stage of the digester were constant and determined bythe cylinders internal-to-external diameter ratios in each stage ofthe reactor. As part of a mechanistic framework for investigatingmass and energy balances, a set of model equations was adapted todetermine the optimal value of the cylinders diameter with twoseparate reaction stages to maximize the amount of biogas pro-duced per unit of time. Optimization methods were also used toestimate the parameters in model equations, and simulation pa-rameters, which were elaborated by simple experiment or found inliterature, were applied. Therefore, the aim of this study was todetermine the inuence of a bioreactors geometric parameters oncontinuous-ow-type low-temperature biogas production frombiomass by anaerobic digestion and to determine the optimalvalues of the digesters selected geometric parameters. The benetsof applying the general ADM1 model for the optimization ofanaerobic digestion are obvious. The ADM1model consists of manydifferential equations and various coefcients need to be accuratelydetermined, therefore, vast efforts (laboratory and programmingwork) are required to ensure the models effectiveness [20,21].Since the main aim of the present study was to investigate thepossibility of optimizing methane production based on selectedgeometrical characteristics (radius of the internal cylinder) of thebioreactor as the decision variables, a simplied version of well-established anaerobic digestion models was used in the study.The main aim of this study was to develop a simple but effectivemathematical model of anaerobic biomass digestion and to use thatmodel to optimize biogas production efciency. A similar attemptcould be made with the application of a full ADM1 model.</p><p>2. Materials and methods</p><p>2.1. Raw materials and sample preparation</p><p>The studywas conducted on anaerobic sludge from an anaerobicdairy wastewater treatment plant. Anaerobic sludgewas adapted toprocess conditions over a period of 60 days. The sampled dairywastewater was produced from milk powder in the amount of 1 gof milk powder per 1 l of water. The organic compound load onreactor volume was C 1 g COD/l, and the adopted hydraulicretention time (HRT) was 1 day. The main indicators of rawwastewater pollution were determined at: COD 1000 22 mg/l,BOD5 676 14 mg/l, Ntot 65 4 mg N/l, Ptot 19 2 mg P/l.</p><p>The most important and most sensitive fermenting micro-organisms include Archaea of the methanogenic phase. They areresponsible for the vast part of methane production, mainly fromacetic acid. The predominant microbial species in the testedsludge belonged to the generaMethanosarcina andMethanosaeta.</p><p>2.2. Experimental setup</p><p>The study was conducted in a two-stage variable ow reactorshown in Fig. 1. The reactor consisted of concentric chambersserving as the internal hydrolyzer, and two other chambers actedas methanogenic reactors. An intermediate cylinder-separateddownow and upow suspension zones in the methanogenic partof the bioreactor. Raw sewage was pumped to the hydrolyzer(volume of 20 l). A recirculating pump was used to ensureb) diagram of liquid ow and velocity distribution inside a methanogenic chamber; c)computational model of the methanogenic part of the bioreactor.</p></li><li><p>specic microbial growth rate. The model is described by</p><p>ablecomplete mixing in the hydrolyzer. The suction pump pipe waslocated 5 cm below liquid level in the tank. Recycled sludge andraw substrate were placed at the bottom. The inow to meth-anogenic chambers was located at the top of the hydrolyzer. Thispart of the reactor was characterized by top-down ow: substrateowed from top to bottom and from bottom to top. This part ofthe reactor relied on plug ow. Methanogenic chambers had thevolume of 40 l each. The bioreactor was operated continuously atconstant mass ow rate at the inlet of the ow meter. Thedescending and ascending parts of the hydrolyzer had differentcross-sections, and different ow rates were observed in eachsection of the reactor.</p><p>2.3. Instrumentation</p><p>Biogas production efciency was measured on-line using theAALBORG ow meter (USA). The qualitative composition ofbiogas was determined with the use of the 430 LXi Gas Dataanalyzer (UK). The content of methane, CH4, carbon dioxide, CO2,nitrogen N2 and oxygen, O2 was analyzed. Gas quality measure-ments were performed automatically eight times a day. Thequality of efuent owing out of the reactor was analyzed daily.COD (HacheLange tests, dichromate oxidation method accordingto AWWA standards), total suspended solids (gravimetricmethod; OX 35 moisture analyzer) and pH were determined(WTW 340 pH analyzer). The changes in biomass concentrationsinside the reactor and the depth of individual reactor chamberswere measured every 10 days. Mesophilic temperature of33 2 C was maintained in the outer chamber. The study wasconducted for 60 days after the determination of the quality ofliquid efuent produced by the bioreactor. The operating pa-rameters remained stable throughout the study, and COD valuesdid not differ by more than 5% between three consecutivemeasurements.</p><p>2.4. Calculations</p><p>In recent decades, the eld of microbial growth kinetics hasbeen dominated by the semi-empirical model proposed by Monod[22]. The Monod model introduced the concept of growth-controlling or limiting substrate. In analyses of microbial growthdynamics, the Monod model is applied to determine the lineardependency between the microbial growth rate and the concen-trations of bacteria with specic growth rates as the proportioncoefcient written in exponential form:</p><p>dXdt</p><p> mX (1)</p><p>where:</p><p>m mmaxS</p><p>KM S(2)</p><p>where: parameter m is the specic growth rate, mmax can be denedas the increase in biomass per unit of time under optimal feedingconditions (no limiting nutrients), and KM is the substrate con-centration at which the growth rate of organisms is substrate-limited to half the prevailing maximum value. Many differentmodels for predicting anaerobic digestion have been proposed inrecent years [23e28]. This study investigates the ability of theMonodmodel to predict bacterial growth during anaerobic biomassdigestion.</p><p>The link between microbial growth and substrate consumption</p><p>M. Markowski et al. / Renewdue to mass formation can be described by formulas (3) and (4).from biomass by anaerobic digestion was derived from equa-tions (1)e(10) and formulated by accounting for Monod-typedSdt</p><p> 1YX=S</p><p>dXdt</p><p>(3)</p><p>where yield coefcient YX/S is dened as:</p><p>YX=S dXdS</p><p>(4)</p><p>and is assumed to be constant.The end product of the analyzed process is biogas. The kinetics</p><p>of product formation can be calculated based on the kinetics ofsubstrate degradation and bacterial growth, respectively. Differentbiogas production models [29,30] rely on the assumption made byGaden [31] that the product results mainly from primary energymetabolism and is generated when the substrate is degraded.Consequently, kinetic equation (5) can be used to describe productformation:</p><p>dPdt</p><p> YP=XdXdt</p><p>(5)</p><p>where yield coefcient YP/X is dened as:</p><p>YP=X dPdX</p><p>(6)</p><p>It was also assumed that the generation of heat from microbialgrowth can be described with the use of formula (7):</p><p>dEdt</p><p> YE=XdXdt</p><p>(7)</p><p>where:</p><p>YE=X dEdX</p><p>(8)</p><p>A macroscopic analysis of the energy balance and the terms forconduction and generation of heat from microbial growth pro-duced formula (9):</p><p>rCpvTvt</p><p> divl$gradT YE=XdXdt</p><p>(9)</p><p>It was assumed that microbial growth was an adiabatic process,therefore, the energy balance equation (9) was simplied asfollows:</p><p>rCpvTvt</p><p> YE=XdXdt</p><p>(10)</p><p>In a continuous-ow-type bioreactor, it can be assumed thatsimultaneous microbial growth, substrate consumption, productformation and the temperature inside the apparatus are deter-mined by location along the axial coordinate of a bioreactor. Ifthis is the case, the time derivatives in kinetic equations may bereplaced by the product of spatial derivatives and ow rates,which is valid if the system behaves like a plug-ow system. Themathematical model of continuous-ow-type biogas production</p><p>Energy 69 (2014) 219e225 221formula (11):</p></li><li><p>to perform computer simulations of low-temperature biogas pro-</p><p>Ptotal 1L</p><p>ZL</p><p>0</p><p>Px dx (16)</p><p>where L is the distance between the inlet and the outlet of themethanogenic part of the bioreactor. Total biogas production maydepend on the radius (Ri) of the intermediate cylinder. The opti-mization procedure was applied to derive the value of Ri thatguarantees the maximum value of total biogas production. Theoptimal value of Ri was derived as the solution to the followingconstrained optimization problem:</p><p>maxRi</p><p>PtotalRi (17)</p><p>with respect to Ri, subject to the set of constraints dened inequations (12)e(15) and inequalities (18):</p><p>Rlb Ri Rub (18)</p><p>able Energy 69 (2014) 219e225duction from biomass by anaerobic digestion in a continuous-ow-type column.</p><p>8&gt;&gt;&gt;:</p><p>Xz 0 XinSz 0 SinPz 0 0Tz 0 Tin</p><p>(13)</p><p>2.5. Optimization</p><p>All parameters, excluding mmax and KM, in models (11)e(13)were considered as known. The values of mmax and KM were esti-mated by simulating the process of low-temperature microbialgrowth in a continuous-ow-type apparatus. The inlet values ofbiomass concentration (X), substrate concentration (S), productconcentration (P) and the temperature of the liquid mixture (T), aswell as the outlet values of X and P were known, therefore, theoptimization procedure was used to estimate mmax and KM. Theobjective function Jout was dened as the difference between theestimated (Ssim) and known (Sexp) values of S at the outlet of theapparatus, and it was written in the following form:</p><p>Joutmmax;KM Sexp Ssim</p><p>2 (14)The values of mmax and KM were derived as the solution to the</p><p>following optimization problem (15):</p><p>minmmax;KM</p><p>Joutmmax;KM (15)</p><p>with respect to mmax and KM, subject to the constraint dened inequations (11)e(13). The optimization...</p></li></ul>