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models, which described only the limiting step e.g.
detailed description of pH and temperature changes.
I. INTRODUCTION equations for each individual substrate and type of bacteria.
considered as one of the most promising and feasible substrate as a linear combination of different biomass (e.g.
Germany, Phone No.: +49 421 590-52338 improve the operation of biogas plants.
carbohydrates (C) steps of anaerobic digestion.erpublication. It is described by the first (4). The basic structure is shown in Table 2. Furthermore. The comparative characteristics between the biogas model of Blesgen  and Blesgen and Hass  and the proposed model are presented in the Table 1. methanogenic bacteria (Xmeth) convert VFA (VFA) into methane (Me) and total inorganic carbon or carbon dioxide (TIC). The kinetics is 268 www. PS Hydrolysis of simple accessible mono-/oligomers: S - and LS: carbohydrates. For the estimation Blesgen of parameters it was decided to conduct the AD experiments with the most abundant representative Four sub-model: compounds of proteins. The experimental Structure Single model physicochemical conditions referred to the German Standard Procedure VDI reactor and plant 4630 for standardized batch trials . In the first step (hydrolysis) the primary organic compounds (Cp. T) are difficult to test experimentally due to the long-term running of the CH4. CO2 process conditions changes (pH. CH4. The model describes three steps of anaerobic digestion: hydrolysis. P (proteins). Hydrolysis is generally declared as one of the limiting Disintegration of primary substrates: S . The model accounts for 10 Dynamical change of an intermediate product . There are described according to the Monod function. biomass. the individual investigation of the dynamics of the degradation Phases of CH4 of the master biomass and their arbitrary mixture were Liquid and gaseous Gaseous release studied. However.org . proteins and lipids). gelatine and rapeseed oil. the volumetric flow rate of biogas.0 was used to calculate numeric model solutions. proteins (P) (8) and lipids(L) (9) for the acidogenic bacterial group. relative simplicity. Bacterial three basic requirements for the formulated model: cause. model has an improved structural performance with a There are 11 differential equations describing the AD: minimized number of unknowns and assumptions. respectively. effect. July 2015 The aim of this study was to develop a relatively Table 1 Comparative characteristics of the model of simple model which is supposed to represent accurately the Blesgen  and Blesgen and Hass  and the model used following key process variables such as the volume of in this study biogas and methane. Volume-3. CO2. The where S – C (carbohydrates). Issue-7. as substrates carbohydrates (C) (7). and predictive capability . It was translated from Fortran 95 to C++ language and Microsoft Visual Studio 8. the volumetric dynamics of methane concentration and the The model of Properties Proposed model total chemical oxygen demand (COD). acidogenesis. L (lipids). biological. International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869 (O) 2454-4698 (P). Inhibition LCFA. This Dynamical change of acidogenic (2) and methanogenic (3) means. the reduced model has some limitations: pH bacteria (kg s-1): dynamics. temperature and accumulation of ammonia are not included. Finally. CO2. Acid forming bacteria (kg s-1): (Xaci) produce CO2 (total inorganic carbon: TIC) and volatile fatty acids (VFA). A DYNAMICAL MODEL OF ANAEROBIC DIGESTION VFA Numerical modeling is a tool for investigation of the static or dynamic processes without conducting or reducing Fractionation into Total organic the number of experiments. The qualitative and HCO3 -. one should accept that it is still not possible to adopt a general mathematical model Mass balance is not Mass balance is Parameters applicable under all circumstances and completely implied implied representing the overall process of biogas production with all reactions and all parameters of the process. respectively) are hydrolyzed into simple accessible mono-/oligomers (CS.volatile biochemical reactions associated to two bacterial fatty acids (mmol s-1) (10): populations (acidogens and methanogens). proteins (P) (5) and lipids (L) (6) (kg s-1): order reaction kinetics. carbohydrates and lipids: sucrose. growth rate is taken as proportional to substrate uptake. Temperature. Pp. at present. Hydrolysis rate constants are determined by using the The proposed model represents a reformulated version of first order kinetic model (1): the biogas model of Bernard  and a version of Blesgen  and Blesgen and Hass . VFA II. and Lp: primary carbohydrates. Products experiments. and methanogenesis. At that point mathematical models came into heat biomass use. pH. proteins and lipids. The mathematical model has a three-step structure. No speciation carbon quantitative variations of the substrate input and also the CO3 2-.
041 .  (300%) Proteins 0. ) f -1 (1. July 2015 Table 2 Biochemical rate coefficients and kinetic rate equations for carbohydrates. proteins (P) and lipids (L). ysim are the model . ) where . 0. ) g -1 (1. concentrations of CO2 (15) and CH4 (Vol. Change of biogas volume production is integrated from the biogas flow rate (m3 s-1 ): III. proteins and lipids Rate VFA TIC Me a -1 b -1 c -1 d -1 (1. volumetric studies (Table 3).02 .08 .0. Carbohydrates 0.03  0.0. The values for the kinetic coefficients of the inorganic carbon rate (mmol s-1) (12).8 (at 35ºC) Garcia-Heras  Gujer and Zehnder 0.predicted outputs. .org . The solution of the system can be presented as (22): (12) (12) ∑ (22) ̇ (13) where Ψ(θ) is the objective function. The software allows the sharing of any subset of the model parameters to 269 www.1 .25 vary within Batstone et al. θ ̇ represents the parameters to be determined and N is the ̇ number of measurements.2 vary within Batstone et al. ̇ ̇ then it is generally required to introduce weighting factors (16) ( . (1.-%) (16). yexp are the collected measurements.65 Flotats et al.13  0.  calculation of the most probable parameter  was achieved by the Numeric's library Minuit. Volume-3. leading to a weighted least-square criterion . are the maximum bacterial growth rates on proteins (P) carbohydrates (C). an appropriate criterion must be selected for the optimal solution of the model parameter There are 5 algebraic equation calculating the identification.  (100%) Gelatine 0.0.13 Raposo et al.27 ± 0.0 (at 35ºC) Garcia-Heras  type kinetics for growth is considered and the inhibition by Gujer and Zehnder long chain fatty acids is introduced: 0. the molar release of first-order rate of hydrolysis were based on previous CO2 (13) and CH4 (14) (mol s-1). are the half-saturation constants associated with the substrate.1 vary within Batstone et al. (1. ) e -1 (1.25 -0. International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869 (O) 2454-4698 (P).5 . (1.  (100%) Lipids 0.0.4  0. IpL is the inhibition coefficient.7 (at 35ºC) Garcia-Heras  Gujer and Zehnder 0. When the errors of the measurements do not have a constant standard deviation.erpublication. and Estimation and model calibration of the parameters biogas flow rate (17) (m3 s-1): was performed on the basis of least squares procedure by measuring the deviation between the model and real system outputs. Issue-7.2. The (16) ̇ ̇ ̇ Table 3 Literature overview of hydrolysis constant Substrate Khyd [day-1] Reference For acidogens (18-20) and methanogens (21) Monod. ESTIMATION OF PARAMETERS To find the best agreement between simulated and ̇ experimental data.
The robustness of the parameter Inoculum Rapeseed oil 25. Gelatine 11. The equaled 28 days. pressure and amount of clicks made by gas and fructose enter the cell. Subsequently. gelatine (Backfee) and rapeseed oil temperature of 38±0. gelatine .-% of total each experiment was defined by VDI protocol which biogas which was within the experimental range. time.420 Mixture of three substrates 14. thus. The biogas process Blanks without substrate were maintained as control to production stopped after 16th day.-%. VFA are further converted by acetogenic bacteria corrected biogas and CH4 volumes to standard conditions. substrates were measured by drying and calcinating the Subsequently.04 gCODL-1 during 16 measured at the beginning. In the case of the substrates and the inoculum.62 Vol.145 L h-1. rapeseed oil – 8. where they are degraded by counter) was developed in processing and stored in text endoenzymes . the sodium hydroxide unit was omitted.586 estimation resulted from the possibility to restrict the Inoculum Sucrose 25.8 for 2 min at 2 bars. Bremen. the sludge was pre-incubated compared to the experimental data. added to the inoculum sludge. and Total COD (Hach-Lange. the flow rate increased slightly Nabertherm).000 mL).methane were produced. In addition. The total methane and biogas volumes were glucose and fructose which after hydrolysis can enter the estimated by subtracting the volume of the average blank bacterial cell and be degraded. for 24 h (P300. The methane at 38±0. respectively. as production by the inoculum. For the estimation of the biogas production from the AD of sucrose. the biogas was collected in biogas bacterial strains. Hydraulic retention time for volumetric concentration was kept at 52.2°C controlled by a thermostat (Haake (EUCO GmbH). respectively.466 A. The minimum methane concentration was reached at day 9 and showed 50.96 L of The biogas flow rate was quite high at the first day and The following parameters were determined from the reached 0. it excluded critical parameter Inoculum Mixture 23. International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869 (O) 2454-4698 (P). LabVIEW VI automatically and CO2. CODTot determined of samples taken on daily basis.2°C during one week. MATERIALS AND METHODS Rapeseed oil 15. into acetate and H2/CO2.1 shows the data database . it is too complex to enter the cell for some gas solubility.002 L h-1.690 values that caused numerical instability . and for the mixture sucrose through a port in the fermenter cap. sucrose must be hydrolyzed to bags. After filling with the substrate defined according VDI 4630 (2006). Finally. The concentration of the substrates was DC 30/K10) in a water bath. Germany). it dropped down and reached at day 10 samples at 105°C and 550°C. Fig. A 75% NaCl hexoses.31 to 0.600 for each parameter. The discharge of biogas occurred g L-1. Issue-7. glucose temperature. concentrations of methane and carbon dioxide were calculated from the measured corresponding volumes as B.400 identification space. Finally. Once hydrolyzed. The data acquisition (date.2 ml L-1. methanogenic bacteria reproduced it on the screen and saved the data in a MySQL convert acetate and H2 into methane. recording of the biogas production.316 IV. 0. volumes. The outlet tube was -5 g L-1. Lower Saxony. July 2015 minimize the sum of squares. the identification Description COD [g L-1] space of the model parameters can be limited individually Inoculum Gelatine 25.- The inoculum was a seeding sludge blend originating %. Inoculum and substrates characteristics well as the biogas flow rate. Calibration of the experimental data of the anaerobic device  is a gas volume counter based on the low-cost digestion with sucrose gas sensor developed by Liu et al. Initially.071 L h-1. 16 g of sucrose were the volume of the NaOH solution should be neglected. Afterwards.org . and subsequently fermented into VFA files separated by commas. The digesters were manually mixed mono-digestions and finally their mixture: sucrose several times per day and maintained at a constant (Nordzucker AG). in total - connected to a CO2 capture unit (filled with 3M NaOH) 14g L-1. First.007 L h-1 and then stopped at day 16. of experiment 9.18Vol. Volume-3.6 g L-1. and at the end of the days. a pig and cattle manure digestion plant The simulations showed initially a discrepancy (Ritterhude.440 Sucrose 15.2 L of biogas corresponding to 4. Although sucrose is soluble solution (pH 2) served as a sealing liquid for decreasing the in water. The gasUino A. achieved through exoenzymes.erpublication. Hydrolysis of table sugar is samples respectively. the bottle was flushed with 100% N2 gas different substrates was: sucrose -16. Germany) was until 0. In order to reduce the endogenous methane volumes were by 397 mL and 313 mL less.0 g L-1. rapeseed oil -3 g L-1. it increased and reached 53. glucose and fructose. from a wastewater treatment plant (Farge. Starting from the second day till the substrates: total solids (TS) and volatile solids (VS) of the fifth it increased from 0. The simulated biogas and methane produced Germany).67 Vol. In total. Table 4 Characterization of the inoculum and substrates used 270 www. Table 4 summarizes the characteristics of the used when methane recordings were needed. The concentration of and inoculum.  where the Table sugar (sucrose) is a disaccharide consisting of two recordings are adapted to standard conditions. Generated methane and biogas passed V. during 28 days measure biogas and methane production from the sludge. Germany) and sludge from between the experimentally measured biogas and methane corn and silage digesting plant (Osterholz-Scharmbeck. The volumetric experiments. RESULTS AND DISCUSSION through a condensate trap for vapor removal and were recorded by a gas volume sensor (gasUino). The pH was decreased from 15. gelatine -15. Equipment and measurements Triplicate batch experiments were conducted in glass Three different single substrates were tested in batch flasks (1.01 L h-1 to 0.
Fig. Issue-7.5 Vol. maximum at 0. concentration and saturation degree of LCFA . Generally. B. Bacterial degradation of LCFAs begins with adsorption of LCFA by the cell and this can lead to growth inhibition depending on type of bacteria.g. is rate-limiting and the overall proteins degradation is a slow process. Simulated CODTot decrease was slightly faster than the experimental results. for the and for methane about 225 mL. volumetric concentration of CH4 and CO2. After 28 days 7. 29]. In anaerobic environments lipids are hydrolyzed by lipases to glycerol and long-chain fatty acids (LCFA).035 L h1. However. The initial step of fermentation. It is a complex process starting with hydrolyzation by proteolytic enzymes e. After day 25 the biogas Fig. NH3 and S. Fig. hydrolysis.1 to 54. Results for blank biogas and to 0. In terms of CODTot the simulated version showed a somewhat faster degradation as compared to the measured values.96 biogas flow rate and CODTot. and size. CODTot was depleting from 15. The biogas flow different panels show biogas and methane volume rate was increasing within the 6 days and reached its production.8 g L-1 was added into the sludge. the simulations followed the key dynamics of the sucrose AD dynamics. 3 shows the experimental results from the AD of 8 mL L-1 rapeseed oil. It shows the dynamics of the biogas and methane volume production. 1 Experimental and simulation data of anaerobic mono-digestion in batch of 16 g L -1 sucrose.-%. July 2015 The simulated data was in a good agreement with the corresponding experimental measurements: in particular for the biogas and methane volume production and biogas flow rate and. Volume-3.org . into peptides and amino acids which are then acidified into VFA.97 gCOD L-1 during 16 days. International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869 (O) 2454-4698 (P). Calibration of the experimental data of the anaerobic digestion with gelatine Proteins are complex. The Fig. the digestion of lipid matter can cause some problems. volumetric concentration of CH4 and CO2. Gelatine in a mass of 15. C. There fluctuating between 53. Many researches consider LCFA degradation as a ‘‘limiting step’’ for a number of reasons: formation of floating scum which causes limiting bioavailability and becomes toxic for acetogenic and methanogenic bacteria [22. high molecular-weight compounds and are degraded more slowly than carbohydrates.19 L of biogas and 3. protease and peptidase. biogas flow rate and CODTot model proposed the biogas flow rate with a shift to the very beginning of the batch experiment. 2 Experimental data and simulation of anaerobic volumetric methane concentration in total biogas was mono-digestion in batch of 15. 2 shows the experimental results from the AD of gelatine.8 g L-1 gelatine.erpublication. H2. Calibration of the experimental data of the anaerobic digestion with rapeseed oil Lipids are attractive substrates for anaerobic digestion and co-fermentation due to their high putative methane yield.93 L methane were produced. methane formation were subtracted 271 www.
Initially. Volume-3. It is assumed rate ranged between 0.0 K reactor 272 www. Biogas and Yield factor for CH4 methane volume production.045 inhibiting acetogens and methanogens but not in such an L h-1 and dropped then. The mean volumetric methane concentration was further decreasing until day 26 of the experiment.62 L methane were constant at 75. volumetric concentration of 0.63 to 20. Within in biogas was increasing till day 10 and was subsequently 28 days of AD.1 -2 m3 Temperature in the 311.22 kg·kg-1 degradation Yield factor for VFA production from 0. The hydrolysis took nearly 6 days.05 mol·kg-1 Blank biogas and methane formation were subtracted Volume of the reactor 1. The volumetric methane concentration after 25 days which was also measured earlier .1 10 -6 s -1 proteins Maximum uptake rate for 3. The maximum biogas production was achieved biogas production.01 kg·m-3 VFA Yield factor VFA Fig.5 kg·m-3 carbohydrates Yield factor for primary carbohydrates 0.56 10 -6 s -1 lipids Maximum uptake e rate 5. it was challenging to describe the inhibition Table 5 Kinetic parameters used in the model for AD of biogas production by LCFA which caused delay in of mixture: gelatine.55 kg·kg-1 lipids degradation Yield factor VFA 0.erpublication.3 10 -6 s -1 proteins Half-saturation constant 5. 6 g L1 gelatine. Definition Value Unit data matched the experimental data (Fig.35 kg·kg-1 degradation mono-digestion in batch of 8 ml L -1 rapeseed.4 Vol.22 L.43 g COD L-1 . Issue-7. It was oil. 3). Within the first five days COD Tot aggressive manner as compared to mono-AD of rapeseed was increasing from 10. Generally.50 kg·kg-1 proteins degradation Yield factor for VFA 0. International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869 (O) 2454-4698 (P). July 2015 production was stopped. The inhibition of biogas substrates it was decided to take the arbitrary substrate production mediated by LCFA through hydrolysis lasted mixture with 5 g L-1 sucrose. The total amount of produced D. biogas flow rate and CODTot are displayed. gelatine and rapeseed oil From the data it was possible to judge that the hydrolytic For the final experiment using the mixture of all three step took nearly 5 days.002 L h-1. The first 15 days the biogas flow produced. However.2 kg·m-3 lipids Yield factor primary 0. Hypothetically. LCFA concentration became rapeseed oil. 3 ml L -1 until day 18. in total a substrate concentration of 14 g L-1 favorable after bacterial growth and consequently also for (Fig. rapeseed oil biogas and CH4 production.0.96 kg·kg-1 production from lipids Maximum uptake rate for 8. Five days later the that the inhibition of LCFA slowed down the AD by flow strongly increased and reached its maximum at 0.9 10 -6 s -1 carbohydrates some regarding the biogas flow rate.20 10 -6 s -1 VFA Half-saturation constant 0.-%. The introduction of an inhibition factor brought positive results and the simulated Para.65 kg·kg-1 carbohydrates Hydrolysis constant for 5. Half-saturation constant 6.68 kg·kg-1 production from protein Hydrolysis constant for 4. 3 Experimental data and simulation of anaerobic 0. Calibration of the experimental data of the anaerobic biogas and methane was 6. meter there was some mismatches observed at the beginning in Hydrolysis constant for the graph regarding the volumetric concentrations and 7. sucrose. 8.006 . 4). the Maximum uptake rate for simulations followed the obtained dynamics of the 4.0 kg·m-3 proteins Yield factor for primary 0.org .552 mol·kg-1 production from VFA CH4 and CO2. respectively.6 10 -6 s -1 for lipids Half-saturation constant 3. Inhibition coefficient 0.9 L and 5.14 L biogas and 4. digestion with sucrose.2 10 -6 s -1 carbohydrates rapeseed oil AD.
after which it decreased and production completely stopped at day 26. The present experiments yielded 655 ml CH4 per g VS. the volumetric was accurately described by the model. The experimental results during the next five days were in The total theoretical biogas production is equal to good agreement with the simulated dynamics of the CH4 8. gelatine and rapeseed oil. Fig.228 L while VCH4 was 4. 5. with average CH4 %. VI. Parameterization became are given in the Table 5. There could be several mathematical model compared to the observed reasons why the measured biogas is less than the experimental data was about 20% in most cases or showed theoretically predicted potential: the bacterial population of complete agreement. Volume-3. Direct model validation was tested on the AD of the chosen substrates mixture. S. up the bacterial biomass (about 5-10% of substrate) during AD . The predicted probity of the proposed biogas volume is shown in Fig. The kinetic parameters used in the model from experimental data simpler. Starting from the day 6. However. CONCLUSION A three-step model was suggested to describe AD for Fig.mass of the substrate (VS added). This applies also to the kinetic coefficients concerning could be that more organic material was consumed to build the bacterial activity. The additional concentration of CH4 and CO2 and. The summary of the total methane and was observed. The set of chosen kinetic parameters is given in (23) Table 5. The present mean yield of CH4 matched published results and was equal to 310 mL CH4 per g VS.erpublication. One more reason AD. however. there is slight difference in volume experimental yield with the theoretical volumes in CH4 (53 production of methane during the days 7-17. July 2015 constant at 58.-%. The parameterized model followed well the progress of the where BG Tot (L) or CH4 Tot is biogas (or CH4) total (L). Issue-7. the biogas The theoretical methane and biogas yield were flow rate was smoothly increasing and reached its calculated based on the following formula (23): maximum at 0. R and mix - of CH4 was lower in the experiment during days 15-23.org . International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869 (O) 2454-4698 (P). Besides. G. the volumetric concentration m . of acetogenic and methanogenic microorganisms caused a 6 g L-1 gelatine. in total -14 g L-1). the results of Hansen et al. 3 ml L-1 rapeseed oil. Afterwards mL per g VS) and in biogas (80 mL per g VS) there is a complete agreement between simulated and measured data difference of yield.74 L. 5 Cumulative biogas and methane. The methane yield produced from AD of sucrose varied in between 240 and 360 mL CH4 per g VS .  corresponded to a higher production of 800-900 ml CH4 per g VS. Volumes of blank biogas and methane formation due to the aim to make the estimation of model parameters were subtracted. sucrose. easy to handle and the validation of the calibrated model satisfy our intention to predict the biogas dynamics only by 273 www. experimental data.025 L h-1 on day 17. BMP tests with gelatine were carried out by Hansen et al. Another reason could be that some part of the substrate was not accessible for the microorganisms. the biogas flow rate are degree of reduction compared to the model of Blesgen is shown. Comparing the concentration. The inhibition by LCFA results of AD in batch of mixed substrates (5 g L-1sucrose. which limit the AD.8 Vol. 4 Comparison of simulated and experimental sucrose.  and 100-150 mL CH4 per g VS were produced which is lower compared to 205 mL CH 4 /g VS obtained here. suggesting that the model can be used the inoculum was not initially diverse as compared to the for the relatively accurate prediction of the dynamics of sludge used for the mono . decrease of hydrolysis rate and slower biogas production The volume of biogas and methane.fermentations. produced in AD batch tests In earlier studies it has been reported that biochemical methane potential (BMP) of rapeseed oil showed 704±13 ml CH4 per g VS . respectively. gelatine. rapeseed oil and mixture.
: rate describing the acidogenesis proteins : rate describing the acidogenesis lipids  S. Decemeber). 45(10). Mosbaek. January). pp. Identifying anaerobic digestion models using simultaneous : half-saturation constant lipids [kg·m-3] batch experiments. Deublein. L. Mathematical modelling of anaerobic reactors ADM1: Anaerobic Digestion Model no. Biofuels. Steyer. J WPCF. Germany Me: methane : yield factor for primary carbohydrates degradation [kg·kg-1]  A. Method for Education and Research (BMBF) for the financial support determination of methane potentials of solid organic waste. : rate describing the hydrolysis of carbohydrates collection of material data. 5. pp. Sanders. I. Water Sci and Technol. M. Wouwer. Water Sci Technol. teaching tool for students or young specialists to study an influence of initial inputs on the efficiency of the biogas REFERENCES process generation. J.C. pp. Vavilin. A. J. Steinhauser. J. I. Bremen. Hoboken. process kinetics and : volume reactor [m3] modelling of anaerobic digestion of complex wastes. Colomer.H.  T.erpublication. Water Sci. accessed 2014-08-19 14:20. Blesgen. Aceves- : maximum uptake rate for carbohydrates [s-1] Lara. A. Biogas from waste and renewable sources. : yield factor VFA production from lipids [kg·kg-1] : yield factor for CH4 production from VFA [mol·kg-1] : hydrolysis constant for carbohydrates [s-1]  D. CODTot: total chemical oxygen demand [kg COD·m-3] Sci and Biotechnol. International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869 (O) 2454-4698 (P). 2003. Jacobs University Bremen. Martin. VFA: volatile fatty acids [mol·s-1] University of Bremen. : hydrolysis constant for proteins [s-1] : hydrolysis constant for lipids [s-1]  A. : half-saturation constant proteins [kg·m-3] March). 1-79. Benoit. Garcia-Heras. L. VS: volatile solids [g·L-1] (2002. : maximum uptake rate for proteins [s-1] identification and validation in anaerobic digestion: A review. Efficient biogas production : yield factor for primary proteins degradation [kg·kg-1] through process simulation. Jansen. sampling. January). G. Stability and control of anaerobic digestion. J." John Wiley& : rate describing the acidogenesis carbohydrates Sons Inc. H. Reactor sizing. Donoso-Bravo. 17021*10 (Anaerobdetektiv) and School of Engineering and Science. Biomethanization of the organic Fraction of : molecular weight of carbon dioxide [kg·mol-1] Municipal Organic Solid waters. 5347-5364. Fernández. fermentation tests. Environ Eng and Manag J. Xaci: acid forming bacteria [kg·m-3] Xmeth: methanogenic bacteria [kg·m-3] TIC: total inorganic carbon [mol·s-1]  A. M. Bastone. F. S. J. T. Angelidaki. 57-71. TS: total solids [g·L-1] Pavlostathis. Issue-7. Dochain. Basic data bioenergy. Keller. LS: accessible lipids [kg·m-3] Biotechnol Bioeng.de/media/downloadable/files/samples/b/a/basisda Abbreviations ten_9x16_2013_web_neu2. 40. pp. Rozzi. Falk. pp. A review Agron Sustain Dev. (2010. Jacobs University Bremen. Cécile.G. Fabien. Schmidt. The IWA Anaerobic Digestion Model No 1 Cp: primary carbohydrates [kg·m-3] (ADM1). (2009) PhD Thesis. V. (2013). Rodrıguez. M. Graef. (2011. March). Weinheim. (1974. Faculty of Chemistry and Biology. October). pp. C. 1 treating domestic wastewater: Rational criteria for model use. "The microbiology of anaerobic digesters. E. Palatsi. Christensen. pp.(Ed. CS: accessible carbohydrates [kg·m-3] (2001. P. Bruno.chain fatty acids oTS: organic total solids [g·L-1]  J. pp. Pp: primary proteins [kg·m-3] Lp: primary lipids [kg·m-3]  O. The model can be applied as a batch experimental setup. E. Wiley-VCH. ch. B. J. (2011. D. 2008. Hansen. pp. 15(8). 24(4). Hadj-Sadok. grateful to Dr. Energy Fuels 24(9). Technol. July 2015 adjustment of three master substrates (proteins.  Agency for Renewable Resources.-P. 6-18. Gujer. AD: anaerobic digestion  J. Bastone. T. Andrews. J. Angelidaki. 127–167. Rostock Available: http://mediathek. 45(17). LCFA: long . Harry Falk for his help in the building of the carbohydrates and lipids).pdf. Z. Genovesi. : rate describing methanogenesis  W. 75(4). 46(4). J. C. Model selection. (2010. Berlin: Beuth Verlag GmbH : rate describing the hydrolysis of proteins : rate describing the hydrolysis of lipids  M. 424-438. and A. 65-73. : half-saturation constant VFA [kg·m-3] : inhibition coefficient [mol·kg-1]  J. : maximum uptake rate for VFA [s-1] : rate of acidogens production on carbohydrates [kg·m-3·s-1] : rate of acidogens production on proteins [kg m-3·s-1]  H. pp. Envir. 313-318.fnr.F. The authors are 274 www. W. 3rd APPENDIX edn. Bernard. 4721-4727. May). pp. Gerardi. Hass. S. : yield factor for VFA production from carbohydrates [kg·kg-1] greenhouse gases and climate change. 667-682. Mailier. (2006). M. pp. Germany : rate of methanogens production on VFA [kg·m-3·s-1] : half-saturation constant carbohydrates [kg·m-3]  X. Dynamical model development and parameter PS: accessible proteins [kg·m-3] identification for an anaerobic wastewater treatment process. A. Kalyuzhnyi. Flotats. V. Illa. April). March). Volume-3. Waste to Anna Schneider through the FHProfUnt /Project Az/FkZ Manag. 339-346. 393–400. (2010. PhD Thesis. V. A. Zehnder. B. Blesgen. (2003. A. November). A. V. The authors thank to German Federal Ministry for H. Siegrist. VDI Handbook Energietech. Marca. In: Mata- TR: temperature in the reactor [K] Alvarez. A. J. Water : maximum uptake rate for lipids [s-1] Res. (2004. Monitoring the anaerobic digestion : rate of acidogens production on lipids [kg·m-3·s-1] process. D. 9(3).Characterization of the substrate. New Jersey. J. Conversion processes in ACKNOWLEDGMENT anaerobic digestion.). : yield factor for VFA production from protein [kg·kg-1] 3(1). J.org . D. : the ideal gas constant [J·mol−1·K−1] : temperature of gas [K]  German Engineers Association (2006) VDI 4630: Fermentation of : the pressure of the gas [Pa] organic materials . (1983). C. : yield factor primary lipids degradation [kg·kg-1] : yield factor VFA degradation [kg·kg-1]  B.
Science Foundation). 189-198. since 2005. M. Creamer. J Chem Technol Biotechnol. 1214-1214. pp. Barth. K. 4044–4064. ecology. Pörtner. 153. He remains a professor at the University of Würzburg. Hill. and R.) Künstliche Lipidmembranen. another honorary doctorate. 2010). M. A.C. M.-F. Bordeaux.Hass. Raposo. Bioresour Technol. a Member in the French–German Graduate College.  F. In: Smith WH.D. 49(10). in Biotechnology. both sponsored  F. pp. of recognition to the pharmacological structure (SFB 487.com/journals/bmri/2014/731731/abs/. 86(8). (2014. chemical kinetics.B. Wiley-VCH. modeling and simulations. pore-forming peptides and proteins. De la Rubia. Korjik . and biochemistry of plants under stress (SFB 251. (August. F. 2012). J WPCF. Boe.M. (2014. F. Florian Kuhnen has main teaching focus on environmental chemistry. biotechnology at the University of Würzburg. Matlab. February). 1987–1999). Journal of Chemical Technology and expertise: Biogas/ anaerobic fermentation Biotechnology. B. V. Previously.291.T. 2002. ISBN 3-527-30775-3 pp. U. B. I. A. Water Sci Columbia in Vancouver. Available: 1549. Demirer. Christakopoulos. and. C.334. Olsson. I. studied in Albert. London. Wilke. Journalof Appl. (2009.N. he was awarded an honorary  C. Lebuhn. A dynamic model for Chromatography (HPLC). G. February). and. C. August). Since 2009. V. pp. J. Angelidaki. F. Furtwangen University are developing Kuhnen. and. Bioresour Technol. 2159–2168. of organic matter High Performance Liquid 275 www. Simulink. nuclear magnetic resonance in vivo and in vitro for the study Andrews. (2013.Master Since 2012 Professor Hass teaches degree in Biotechnology. he manure and organic waste at centralized biogas plants: process obtained his Ph.Hass Design of experiments  L. 2007-2008 . Falk. C. V. and K.W. Volker C. at the prestigious University College London in the field of Sustainable Ludwigs-Universität Freiburg (Basic Bioprocess Engineering. pp. Chemie Ingenieur Technik. July). Rintala. F. Wolf. Publication: A. Bongards. 1088– Benz's research interests include the periplastic structure and organization 1098. pp. of cell membranes and other biological membranes. M. G. Benz has held the Wisdom Professorship at the Beline. Appl Biochem Biotechnol. Würzburg with majors inmicrobiology. Co-digestion of University of Würzburg. including: the molecular basis of 83(1). with control and optimization using computational intelligence methods. J. Nielsen. programming. Platas. F. he was a visiting professor at University of British anaerobic reactor towards maximum biogas production. Mechanism. 13–26. in 1977. by the Umeå University's Faculty of Automatisierungstech 57. 99(10). Cavinato. Elsevier. McLoone. Barnett (1988a. Biological of biomedical basic elements (Member in the DFG-Graduate College. pp. Experimental ethanol and protein production.  P." 2nd edn. seit 2000). International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869 (O) 2454-4698 (P). A treatment approach. production of methane from biomass. Frank JR (Eds. (2011. Control of an 1982. Kougias. K.hindawi. Universitätsverlag Konstanz. From 2011 till present she is doing her PhD project with focus Applied Sciences Jena and the University into modeling and simulation of the biogas process generation. James. signal transduction and membrane transport (SFB 176. and physics at the  H. Benz became a full professor of Technol. sustainable biotechnological processes and Chemie Ingenieur Technik (August. he with M. Benz was recognised with the Gay-Lussac/Humboldt Award de la Ministère de recherche français for his role in the development of a Franco–German collaboration. 2013) Virtual biotechnology and plant bioreactor cultivation for operator training and simulation: application to physiology/biophysics. R.S. Benz is the solid poultry slaughterhouse waste – a review. Benz was a visiting professor at State University of New York at Stony Brook in 1980 and  J.Kuhnen. H. he was honoured. possibilities and limitations. 88 (12).Kuhnen. S. L. (August. Issue-7.Brüning and C. studies in biology (2004-2007). Teaching Areas: biochemical engineering. continued system dynamics and process automation. In 2011. the regulatory membrane proteins: from the mechanism Methane from biomass. Smith. Rova. 2012) Foam suppression in overloaded manure-based biogas reactors using Model-based optimal control of biotechnological cultivations - antifoaming agents. F. Modelle für biologische Membranen. pp. Besides his work at the HFU he teaches as an adjunct professor at the University of Bremen as well as Jacob's visiting professor Yann Barbot. and. supervisor. C and C++.  P. (2003. data analysis.] In pp. Fernandez-Cegri. Li. Benz (February. Publications: M. In 2007. Mattiasson.) Bacterial and Eukaryotic Porins. Medicine. pp. Läuger at University of Konstanz as his pp. Hass. Benz has been a Member of the European Graduate College. C. (2004. biophysical processes and the molecular basis of membrane proteins in microorganisms and  E. Tsapekos. Matsakas. 4(10). 1549- production. Function. Hass studied at the Hamburg Anna Schneider studied in University of Technology and Chemical Belarusian State University from 2001 Engineering at the University of and graduated in 2006 with Diploma Birmingham Biochemical Engineering. 1989–1992). ISBN  J.Hass.82(9) pp. July). May). 2129. A. 109(1-3). Roland Benz studied studied mathematics.95-105. S. Modeling the Dynamics of the Biogas Process. in biology. Liu. O. February). July 2015  D. Salminen. Mandenius (May. pp. (1980. Since 2003. 2014) Thermo-acidic pretreatment of marine brown algae 2143. Benz R. Anaerobic digestion of organic higher organisms. Munk. In 1984. Structure.org .erpublication. Available: http://link. Publication: Y. simulation of animal waste digestion. http://www. In 1986. Barbot. Ide. Borja. V.) 1992–1999). modeling and simulation. From 2008 bioprocess engineering at the Hochschule she continued studies in Jacobs Furtwangen University in the Faculty of Universities Bremen and graduated Medical and Life Sciences. In 1972.84(8). Pörtner. by the DFG. R. October). et al. 27 (1). (2008. 1339- 1340. London. Gerlach. studies in Julius-Maximilians-Universität sustainable industrial and pharmaceutical bioprocess. P. Volume-3. Fucus vesiculosus to increase methane production—a disposal principle for macroalgae waste from beaches. Publication: I. bio-refineries. he obtained his Habilitation in Biophysics. chemical thermodynamics. 638-649. Bioresour Technol leader of several research projects. his alma mater. Shiralipour. Ye Chen Jay.Sc degree in Molecular held professorships at the University of Biology. Phycology. (1977. Goto. Inhibition of 3-87940-142-X. with Bioprocess Tainer. A Heisenberg  M. interlaboratory study.127-139. 84(8)pp.springer. His research focuses on the fermentation and process monitoring. chemistry. (2002. (2004. Effenberger.M. 50(11). July). Agricultural Fellow of the Deutsche biogas production in Germany -from practice to microbiology basics. anaerobic of Bremen. Biogas plant doctorate by the University of Barcelona. physiology. Forschungsgemeinschaft (DFG) (German Energy Sustain and Soc. 2006. Angelidaki.V. Biochemical Jacobs University Bremen and has been a research fellow at the Rudolf methane potential (BMP) of solid organic substrates: evaluation of Virchow Center and the DFG Research Center for Experimental anaerobic biodegradability using data from an international Biomedicine. "Statistical methods in experimental physics. with Peter imbalances and limitations. BioMed Res Int. May).C. Chemie Ingenieur Evaluation of dried sweet sorghum stalks as raw material for methane Technik. anaerobic digestion process: A review. R. March). M.C. World Scientific. 198-205. and P.H. Publications: Benz R. S. G.com.
Supplementing pineapple pulp waste with urea and metal ions enhances biogas production.

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