Ces travaux ont été réalisés dans le cadre du projet Biodecol2 financé par le programme PSDR Grand Ouest.
7. Références
Amon, T., Amon, B., Kryvoruchko, V., Zollitsch, W., Mayer, K., Gruber, L. 2007. Biogas production from maize and dairy cattle manure-Influence of biomass composition on the methane yield. Agriculture, Ecosystems and Environment 118 (1-4), pp. 173-182.
Appels, L., Lauwers, J., Gins, G., Degrève, J., Van Impe, J., Dewil, R. 2011. Parameter identification and modeling of the biochemical methane potential of waste activated sludge. Environmental Science and Technology 45 (9), pp. 4173-4178.
Buffiere, P., Delgadillo Mirquez, L., Steyer, J.P., Bernet, N., Delgenes, J.P. 2008a. Anaerobic digestion of solid wastes needs research to face an increasing industrial success.
International Journal of Chemical Reactor Engineering 6, art. no. A94.
Buffiere, P., Frederic, S., Marty, B., Delgenes, J.-P. 2008b. A comprehensive method for organic matter characterization in solid wastes in view of assessing their anaerobic biodegradability. Water Science and Technology 58 (9), pp. 1783-1788.
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Buffière, P., Loisel, D., Bernet, N. and Delgenes, J.P. 2006. Towards new indicators for the prediction of solid waste anaerobic digestion properties. Water Science and Technology., 53 (8), 233-241.
Chandler, J.A., Jewell, W.J., Gossett, J.M., Van Soest, P.J. and Robertson, J.B. Predicting methane fermentation biodegradability. Biotechnology and Bioengineering Symposium Series, (1980), 10, 93-107.
Graham and Midgley. 2000. Graphical representation of particle shape using triangular diagrams: an excel spreadsheet method. Earth Surface Processes and Landforms 25, 1473–1477.
Labatut, R.A., Angenent, L.T., Scott, N.R. 2010. Biochemical methane potential and biodegradability of complex organic substrates. Bioresource Technology 102 (3), pp.
2255-2264.
Lesteur, M., Bellon-Maurel, V., Gonzalez, C., Latrille, E., Roger, J.M., Junqua, G., Steyer, J.P. 2010. Alternative methods for determining anaerobic biodegradability: A review.
Process Biochemistry 45 (4), pp. 431-440.
Mottet, A., François, E., Latrille, E.,Steyer, J.P., Déléris, S., Vedrenne, F.,Carrère, H. 2000.
Estimating anaerobic biodegradability indicators for waste activated sludge. Chemical Engineering Journal 160 (2), pp. 488-496.
Nallathambi Gunaseelan V. 2007. Regression models of ultimate methane yields of fruits and vegetable solid wastes, sorghum and napiergrass on chemical composition. Bioressource Technology. 98, 1270-1277.
Parkin, G.F Owen W.F. 1986. Fundamentals of Anaerobic Digestion of Wastewater Sludges.
Journal of Environmental Engineering .12(5), 867-912.
Peu P., Picard S., Girault R., Béline F., Bridoux G. 2011. Prediction of hydrogen sulphide production during anaerobic digestion of organic substrates (soumis à Bioresource Technology)
Raposo, F., Fernández-Cegrí, V., de la Rubia, M. A., Borja, R., Béline, F., Cavinato, C., et al.
(2011). Biochemical methane potential (BMP) of solid organic substrates: Evaluation of anaerobic biodegradability using data from an international interlaboratory study. Journal of Chemical Technology and Biotechnology, 86(8), 1088-1098.
Symons GE, Buswell AM (1933) The methane fermentation of carbohydrates. J. Am. Chem.
Soc. 55: 2028–2039.
Triolo J.M., Sommer S.G., Møller H.B., Weisbjerg M.R., Jiang X. 2011. A new algorithm to characterize biodegradability of biomass during anaerobic digestion: Influence of lignin concentration on methane production potential. In press
Vedrenne, F., Béline, F., Dabert, P., Bernet, N. 2008. The effect of incubation conditions on the laboratory measurement of the methane producing capacity of livestock wastes.
Bioresource Technology 99 (1), pp. 146-155.
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C
HAPITRE4 : Optimisation numérique du dimensionnement des unités de méthanisation via la modélisation de la
dégradation des substrats dans le digesteur
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Avant-propos : Ce chapitre est consacré au développement et à la mise en application d’une méthodologie permettant de prédire les rendements de dégradation des substrats dans un digesteur. Son objectif est d’aboutir à une méthode permettant, sur la base d’une caractérisation des substrats et d’un modèle de digestion anaérobie approprié, de prédire le rendement du digesteur, notamment en termes de production de méthane, en fonction des paramètres de dimensionnement de ce dernier. D’un point de vue applicatif, la mise en application de cette méthode devra aboutir à la production de courbes de dimensionnement établissant l’impact de paramètres de dimensionnement, comme le temps de séjour hydraulique sur la production de méthane liée à la dégradation du substrat étudié. Ce chapitre est présenté sous forme de trois publications scientifiques.
La première publication traite du calage des paramètres cinétiques de croissances des biomasses dans le modèle ADM1. Elle est intitulée « ADM1 calibration on degradation kinetics in two inoculums resulting from anaerobic digestion of waste activated sludge and pig manure ». En plus de constituer un préliminaire nécessaire à la suite des développements méthodologiques (modélisation de la dégradation des substrats en batch), cette étude permet de discuter la validité des paramètres par défaut du modèle ADM1.
La seconde publication traite du développement d’une méthodologie de définition des données d’entrée pour le modèle ADM1 (fractionnement). Cette méthode est basée sur une étude des cinétiques de dégradation des substrats lors de tests effectués en batch. Elle permet d’aboutir à un fractionnement cinétique de la DCO des substrats compatible pour l’utilisation en tant que donnée d’entrée pour le modèle ADM1. Cette publication est intitulée « A waste characterisation procedure for ADM1 implementation based on degradation kinetics ».
A la suite de ces développements méthodologiques, la troisième publication présente l’application de la méthode mise au point à un jeu de co-produits organiques. L’objectif de ce travail était de caractériser des co-substrats organiques courants, de modéliser leur comportement dans des digesteurs continus mésophiles et d’en déduire des abaques de dimensionnement prédisant l’impact du temps de séjour hydraulique du digesteur sur les rendements de biodégradation des co-substrats étudiés. Ce travail est présenté sous forme d’un article intitulé : « A specific characterisation of organic wastes to simulate the effect of hydraulic retention time on anaerobic digestion efficiency ».
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Sous-chapitre 4 A:
ADM1 calibration on degradation kinetics in two inoculums resulting from anaerobic digestion of waste activated sludge and pig manure
Girault R.a,b, Bridoux G.c, Nauleau F.c, Poullain C.c, Buffet J.a,b, Sadowski A.G.d, Steyer J.P.e, Batstone D.f, Béline F.a,b
a Cemagref, UR GERE, 17 av. de Cucillé, CS 64427, F-35044 Rennes, France.
(Tel. (+33)2 23 48 21 42 – Fax: (+33)2 23 48 21 15 – e-mail: romain.girault@cemagref.fr)
b Université Européenne de Bretagne, F-35044 Rennes, France
c SAUR, Recherche et développement, Atlantis, 1, av. Eugène Freyssinet, F-78280 Guyancourt, France.
d IMFS de Strasbourg (CNRS-UdS-ENGEES-INSA), France
e3 INRA, UR50, Laboratoire de Biotechnologie de l'Environnement, Avenue des Etangs,Narbonne, F-11100, France.
f University of Queensland, Advanced Water Management Centre, Brisbane, Australia
Abstract
In this study, the ability of the default ADM1 parameter set to simulate biomass growth in mesophilic anaerobic CSTRs fed with waste activated sludge (WAS) and pig slurry (PS) was investigated. Batch experiments were carried out to investigate the degradation kinetics of substrates specific to each main anaerobic digestion stage (acetate, propionate, glucose, amino acids, oleate, cellulose, casein and triolein). The methane production rates obtained in the batch experiments were then simulated with ADM1 and its default parameter set. If necessary, kinetic parameters were estimated to ensure accurate simulation of experimental results. Results revealed that for acetotrophic methanogenesis, default parameter sets are affordable for WAS acclimated inoculum but require calibration for PS acclimated inoculum. Hence, the INH3×km_ac(kgCOD.kgCOD-1.d-1)/Ks_ac(kgCOD.m-3) parameter set was adjusted from 0.090×8/0.15 (default paramaters) to 2.51/0.30, 2.24/0.43 and 2.85/0.69 for three experimental runs using PS acclimated inoculum. Biomass shifts due to the concentration of ammonia explained these discrepancies in the kinetic parameters. Temporal variations in acetotrophic methanogenic activity were also analysed. Concerning propionate acetogenesis, results revealed inconsistencies related to model stoichiometry. Concerning monomer acidogenesis, results showed that biomass activity tests related to the degradation of a pure substrate are not appropriate to investigate overall acidogenic activity. Experimental results enabled comparative analysis of the rate-limiting stages in the anaerobic digestion process using both waste activated sludge and pig slurry.
Keywords
Anaerobic digestion, modelling, ADM1, calibration
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1. Introduction
Modelling of anaerobic digestion is widely used to improve our understanding of processes and to interpret phenomena in research projects, as well as to optimise and benchmark full-scale processes. The most widely used model is the IWA Anaerobic Digestion Model n°1 (ADM1) (Batstone et al., 2002). For mechanistic modelling of biochemical processes using defined models, two crucial steps are normally required: (i) fractionation and characterisation of the influent (definition of the influent composition according to the model state input variables) and (ii) parameter estimation to ensure the optimal fit of the model.
Parameters may need to be changed due to differences in the biological kinetic response (Straub et al., 2006) or due to accessibility or stoichiometry of the input material (Batstone et al., 2009). While a mechanistic model is normally of greater predictive and interpretative value than a stochastic model, one major disadvantage is that mechanistic biological models such as the ADM1 are normally over-specified in terms of parameters. In addition, only a limited subset of parameters can usually be estimated due to over-correlation. Some authors do not change the parameters at all and use the default parameter set of ADM1 (Batstone et al., 2002) to simulate anaerobic CSTRs. In these cases, predictive ability will be limited, especially under unusual conditions (Girault et al., 2011). It is particularly common that only primary parameters are adjusted for substrates for which hydrolysis is the main rate-limiting step (Vavilin et al., 2008). In these cases, many authors adjust only the hydrolysis or disintegration constants to correctly simulate methane production (Gali et al., 2009; Ramirez et al., 2009; Thamsiriroj and Murphy, 2011). This reduces the domain of validity of the model since nothing specific is known about intermediate steps, limiting analysis of alternative configurations and substrates. This is particularly true when investigating process stability, which is driven by VFA accumulation.
To estimate ADM1 kinetic parameters, different types of experiments can be used: (1) Static results from a continuous reactor (Boubaker and Ridha, 2006;Lübken et al., 2007; Wett et al., 2007) but in this case, the kinetic parameters are often poorly identifiable due to over-correlation between stoichiometric and kinetic parameters (Bernard et al., 2006); (2) Dynamic results from a full-scale reactor (Batstone et al., 2004; Wichern et al., 2009; Koch et al., 2010). In this case, the quality of the results depends on normal variations in the process, which may not be high enough to obtain a good identification of the parameters; (3) Dynamic
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results from a continuous reactor after specific substrate pulses (Batstone et al., 2003; Kalfas et al., 2006). This cannot be applied at full scale, and is expensive at laboratory scale due to the frequency of sample collection; (4) Dynamic results of specific batch experiments (Feng et al., 2006; Yasui et al., 2008; Girault et al., 2011). In a suitable laboratory set up, batch tests are not only the cheapest and require the least effort to obtain parameters with a high degree of confidence, since batch tests are short, provide copious data, and are fully dynamic.
Nevertheless, determination of the initial state of batch experiments is crucial, mainly in terms of specific biomass concentrations according to the model’s state variables. Indeed, the effect of initial specific biomass concentrations on simulation results is often fully correlated with kinetic parameters, such as maximum uptake rate.
The objective of this study was to evaluate variations in ADM1 kinetic parameters related to biomass growth in mesophilic CSTRs. To this end, degradation kinetics of specific substrates were assessed in two common anaerobic inoculums one acclimated to pig manure slurry (PS) and the other to waste activated sludge (WAS) both in batch reactors. The parameter sets were estimated using the ADM1 as modelling platform.