Part of this study was part of the BIODECOL2 project supported by the French PSDR (Pour et Sur le Développement Régional) Program. The authors thank Saur Research and Development for funding some sections of this study.
7. References
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APHA, 1998. Standard Methods for the Examination of Water andWastewater, 20th ed.
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Batstone D.J., Keller J., Angelidaki I., Kaluzhnyi S.V., Pavlostatis S.G. Rozzi A., Sanders W.T.M., Siegrist H., Vavilin. 2002. Anaerobic Digestion Model No.1, Scientific and Technical Report No.13, IWA Publishing, ISBN: 1 900222 78 7
Batstone, D.J., Pind, P.F., Angelidaki, I. 2003. Kinetics of thermophilic, anaerobic oxidation of straight and branched chain butyrate and valerate. Biotechnology and Bioengineering 84 (2), 195-204
Batstone, D.J., Tait, S., Starrenburg, D. 2009. Estimation of hydrolysis parameters in full-scale anerobic digesters. Biotechnology and Bioengineering 102 (5), pp. 1513-1520 Buendía, I.M., Fernández, F.J., Villaseñor, J., Rodríguez, L. 2008. Biodegradability of meat
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Ekama, G.A., Dold, P.L., Marais v., G.R. 1986. Procedures for determining influent COD fractions and the maximum specific growth rate of heterotrophs in activated sludge systems. Water Science and Technology 18 (6), pp. 91-114.
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Girault et al. in progress. Degradation kinetics in two anaerobic inoculums issued from waste activated sludge and pig manure digestion aiming at ADM1 calibration.
Kleerebezem R., Van Loosdrecht M.C.M. (2006) Waste characterization for implementation in ADM1. Water Science and Technology 54 (4), 157-174.
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Lucas T., Le Ray D., Peu P., Wagner M., Picard S. 2007. A new method for continuous assessment of CO2 released from dough baked in ventilated ovens Journal of Food Engineering, 81 (1), 1-11.
Mata-Alvarez, J., Dosta, J., Macé, S., & Astals, S. (2011). Codigestion of solid wastes: A review of its uses and perspectives including modeling.Critical Reviews in Biotechnology,31 (2), 99-111.
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(2009). An ASM/ADM model interface for dynamic plant-wide simulation. Water Research 43(7), 1913-1923
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Wichern, M., Gehring, T., Fischer, K., Andrade, D., Lübken, M., Koch, K., Gronauer, A., Horn, H. 2009. Monofermentation of grass silage under mesophilic conditions:
Measurements and mathematical modeling with ADM 1. Bioresource Technology 100 (4), 1675-1681.
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Sous-chapitre 4 C:
A specific characterisation of organic wastes to simulate the effect of hydraulic retention time on anaerobic digestion efficiency
Girault R.a,b, Bridoux G.c, Nauleau F.c, Poullain C.c, Buffet J.a,b, Sadowski A.G.d, 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
Abstract
The aim of this article is to characterize the most common organic wastes from agro-industries, agriculture and urban collection to investigate the effect of hydraulic retention time (HRT) on the efficiency of anaerobic digestion according to the characteristics of each waste.
A recently developed method (“anaerobic respirometry”) was used to define a set of input state variables for the “Anaerobic Digestion Model n°1” (ADM1) adapted to each substrate, including hydrolysis rates. The efficiency of CSTR with different HRTs was then analysed by modelling each substrate. The results obtained for 21 common organic wastes from agro-industries, agriculture and urban collection differed significantly depending on the substrate.
For substrates like bovine blood or greasy sludge, HRTs need to be long enough to ensure reactor stability (no accumulation of volatile fatty acids and no decrease in pH). Hence, if the HRT applied is longer than 14 days, this parameter has no impact on the CSTR efficiency. On the other hand, for substrates like bovine slurry or rumen content, the HRT has a major impact on the efficiency of its degradation in a CSTR. Indeed, for rumen, an HRT of 48 days is required to reach 80% of maximum methane production in a CSTR.
Keywords
Anaerobic digestion; organic wastes; modelling; fractionation; ADM1, hydraulic retention time
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1. Introduction
Anaerobic digestion is increasingly used in the treatment of organic wastes from the agro-food industry, agriculture, wastewater treatment plants (WWTP) and urban collection.
This treatment allows energy to be recovered from organic wastes and reduces the environmental impact of waste management. Optimum design is required to optimize the production of biogas with respect to investment costs. In addition, maximum biodegradation of the substrates in the anaerobic digester is required to minimize potential methane emission (global warming potential of 21) during the storage of the residual digestate and, consequently, to optimize the balance of greenhouse gas emissions of the process.
Consequently, the design strongly impacts the economic and environmental balance of anaerobic digestion processes. Design guidelines are available for well-known anaerobic digestion processes such as sewage sludge or pig slurry digestion. However, anaerobic digestion processes are being increasingly used for a wide range of organic wastes mainly with the aim of energy recovery (Alatriste-Mondragón et al., 2006; Mata-Alvarez et al., 2011). The optimal design of an anaerobic CSTR, particularly in terms of hydraulic retention time (HRT), is strongly dependent on the characteristics of the influent, which are very diversified for industrial organic wastes. One way of optimizing digester design according to the characteristics of the influent is using a model to predict reactor yield according to its design and to the characteristics of the substrates. This can save money and time in comparison with experimental trials. Nevertheless, it requires (i) characterization of the desired substrate in terms of fractionation of organic matter, in accordance with the input state variables of the model and (ii) an appropriately calibrated model. The most commonly used model is the “Anaerobic Digestion Model n°1” (ADM1, Batstone et al., 2002). Several methods have been developed to determine a suitable fractionation suitable for the substrate concerned: (i) physical-chemical analysis (Lübken et al., 2007, Wichern et Al;, 2008), (ii) elemental analysis (Kleerebezem et al., 2006; Zaher et al., 2009a), (iii) the conversion of output from another model into input for ADM1 (Copp et al., 2003; Vanrolleghem et al., 2005; Zaher et al., 2007; Rousseau et al., 2008; Nopens et al., 2009), (iv) online fractionation in a continuous monitoring reactor (Batstone et al., 2009; Girault et al., 2010) and (v)
“anaerobic respirometry” (Yasui et al. 2008; Girault et al., submitted). The principle of
“anaerobic respirometry” is the identification of COD fractions and of kinetic parameters
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the methane production rate (MPR) obtained in batch experiments in which anaerobic degradation of the substrate takes place. The main advantage of this method is the use of kinetic criteria for substrate fractionation in addition to biochemical characteristics. Unlike other fractionation methods based on physical-chemical analysis, this method also takes into account the biodegradability kinetics that result from biochemical and accessibility criteria.
For each substrate, this method, which is described in detail in Girault et al. 2011, (submitted), enables the determination of a set of input state variables for the ADM1 model and the hydrolysis constants related to the degradation of COD fractions for which hydrolysis is rate limiting. In the present paper, anaerobic respirometry was applied to a wide range of organic wastes from agro-industry, agriculture, WWTP and urban collection. A set of input state variables for ADM1 were determined for each substrate. These results were then used to simulate anaerobic digestion of each waste and to provide numerical design data. This study provides design data in terms of the effect of HRT on the methane production rate of each substrate. The results can be thus used to determine the optimal design for each type of waste.