Anaerobic Digestion of Solid Wastes Needs Research to Face an Increasing Industrial Success

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<ul><li><p>INTERNATIONAL JOURNAL OF CHEMICALREACTOR ENGINEERING</p><p>Volume 6 2008 Article A94</p><p>Anaerobic Digestion of Solid Wastes NeedsResearch to Face an Increasing Industrial</p><p>Success</p><p>Pierre Buffiere Liliana Delgadillo Mirquez Jean Philippe Steyer</p><p>Nicolas Bernet Jean Philippe Delgenes</p><p>Institut national des sciences appliquees de Lyon, pierre.buffiere@insa-lyon.frUniversidad de Ibague, liliana del iq@yahoo.comInstitut national de la recherche agronomique, steyer@supagro.inra.frInstitut national de la recherche agronomique, bernet@supagro.inra.frInstitut national de la recherche agronomique,</p><p>ISSN 1542-6580</p><p>Brought to you by | National Chung-Hsing UniversityAuthenticated |</p><p>Download Date | 3/20/14 11:59 PM</p></li><li><p>Anaerobic Digestion of Solid Wastes Needs Researchto Face an Increasing Industrial Success</p><p>Pierre Buffiere, Liliana Delgadillo Mirquez, Jean Philippe Steyer, NicolasBernet, and Jean Philippe Delgenes</p><p>Abstract</p><p>Anaerobic digestion of solid wastes is an emerging solution for both wastemanagement and energy production. The high complexity of the process is mostlyattributed to the absence of descriptors for the design and the prediction of sucha process. This paper presents an approach for the description of organic mat-ter based on several biochemical parameters, established on 22 different organicwastes. The lignocellulosic content is the most important parameter for the pre-diction of anaerobic biodegradability and methane production; in addition, theknowledge of the carbohydrate, lipid and protein contents is also crucial andmakes possible a prediction of the intrinsic kinetics of the reaction.</p><p>KEYWORDS: anaerobic digestion, solid waste, biodegradability, biochemicalcomposition, kinetics, modelling</p><p>The authors would like to thank the French Agency for Environment and Energy (ADEME)for funding this work, and the EU ALFA LaBioProc project for permitting Liliana Delgadilloto complete her MSc thesis at INRA-LBE. The authors are also most thankful to D. Batstone(Univ. of Queensland, Australia) for his valuable comments and to Dr. C. Rosen and U. Jeppsson(Department of Industrial Electrical Engineering and Automation, Lund University, Sweden) forproviding their Matlab implementation of ADM1.</p><p>Brought to you by | National Chung-Hsing UniversityAuthenticated |</p><p>Download Date | 3/20/14 11:59 PM</p></li><li><p>1. INTRODUCTION </p><p>In Europe, anaerobic digestion is emerging spectacularly. Indeed, this process is a real alternate solution to landfilling or incineration. The amount of waste treated has continuously increased at a 25% annual growth rate over the last ten years. Nevertheless, increasing needs in terms of research are required to face this emerging industrial success. Anaerobic digestion of solid wastes is a process for which the scientific community still requires tools for an accurate evaluation of performance: process design is generally done by rule-of-thumb, according to criteria that cannot be extrapolated. For instance, the reaction yield is not clearly defined yet for anaerobic digestion of solids. Consequently, the operation of existing plants is particularly difficult due to the lack of accurate indicators. This could have a serious impact on the unit performance, and, to a greater extent, on the credibility of this treatment solution (De Baere, 2006). </p><p>The main reason for that is the extreme complexity of the process: on the one hand, anaerobic digestion is a combination of many biological reactions; on the other hand, solid wastes are very heterogeneous in composition, size, shape, which makes the development of appropriate descriptors difficult. In addition, reactors for solid waste digestion are very large units (usually more than 1000 m3) working under a very high solid loading (the term dry digestion is used for some of the reactors for which the reaction medium does not flow) and under uncontrolled mixing conditions. </p><p>The most common indicator of digester performance is the amount of methane produced per mass unit of solid or volatile solid VS (Rintala and Jrvinen, 1996). The methane potential of various types of wastes is expressed with this criterion (Owens and Chynoweth, 1993; Chynoweth et al., 1993; Gunaseelan, 2004; Jokela et al., 2005). This description is often sufficient to compare the digestibility of waste having the same nature, or different reactor designs with a given waste (Marique et al., 1989). Nevertheless, the description of organic matter degradation by the VS content alone is limited. As pointed out by Mata-Alvarez et al. (2000), there is a need for a better understanding of the degradation kinetics in relation to the biochemical composition. In addition, methane productivity not only depends on the amount of degraded volatile solids, but also on the nature of the solid: carbohydrates, proteins or fats have different methane potential. Consequently, the biochemical composition of the input is very important for anaerobic digestion (Christ et al., 2000; Sanders, 2001). Moreover, the chemical nature of individual components matters: considering the degradation of carbohydrates, it is well known that cellulose is much more difficult to degrade than sugar monomers (Noike et al., 1985, Scherer et al., 1990, Scherer et al., 2000). The fibre content is thus a relevant parameter for solid waste degradation. The reference work by Chandler et al. (1980) showed that the overall </p><p>1Buffiere et al.: Anaerobic Digestion of Solid Waste</p><p>Brought to you by | National Chung-Hsing UniversityAuthenticated |</p><p>Download Date | 3/20/14 11:59 PM</p></li><li><p>anaerobic biodegradability was directly proportional to the fibre content. The degradation of organic matter exhibits a negative relation to the lignin amount during aerobic composting (Komilis and Ham, 2003) or anaerobic treatment (Pareek et al., 1998; Hartmann et al., 2000), even in landfill conditions (Eleazer et al., 1997). Nevertheless, Tong et al. (1990) could not derive any relationship between the sole lignin content and the methane productivity of herbaceous and woody biomass. Therefore, the biochemical composition, both in terms of protein, carbohydrate and fat content, and in terms of soluble/fibre content could be seen as the appropriate option for anaerobic digestion (Peres et al., 1992). Attempts for establishing a unified typology of solids with regards to their properties are reported by Hansen et al. (2003) and more recently by Gunaseelan (2007); several relations based on multilinear regression have been proposed, but none could cover the whole range of solids tested. </p><p>The present paper summarises 3 years of experimental investigations over a wide range of mixed municipal, industrial and kitchen wastes, including systematic composition measurements and anaerobic batch degradation tests. We propose a typology of the solid wastes based composition criteria. We will see how this description can be used to establish a correlation between the composition and the ultimate methane potential of (almost) any kind of organic wastes. This approach is also used to define an actual reaction yield in order to characterise the performance of a given reactor (Buffire et al., 2006). We propose a dynamic modelling of the anaerobic degradation of the solid waste: we will see if the typology used remains relevant for the prediction of the reaction kinetics by means of generic or specific simulation tools, such as the IWA anaerobic digestion model N1 (Batstone et al., 2002). </p><p>2. MATERIALS AND METHODS </p><p>The samples tested are food and kitchen wastes that have been collected, grouped by category and stored at 20C: fruit (apple, banana, citrus), and vegetable (potatoes, carrots, lettuce) peelings; grass, cooked rice and pasta, bread, fish, meat, coffee waste, office paper and cardboard. Industrial (rabbit slaughterhouse) and municipal wastes (5 prior and 1 after digestion) were also tested. A total of 22 samples were thus characterised. </p><p>2.1 Organic matter characterisation and anaerobic biodegradability assessment </p><p>The samples were characterised according to the following parameters: total (TS) and volatile solids (VS), fibre content, total chemical oxygen demand (COD), total nitrogen (TKN), proteins, lipids and total carbohydrates. Total and volatile </p><p>2 International Journal of Chemical Reactor Engineering Vol. 6 [2008], Article A94</p><p>Brought to you by | National Chung-Hsing UniversityAuthenticated |</p><p>Download Date | 3/20/14 11:59 PM</p></li><li><p>solids measurements were performed on fresh products. The other parameters were measured on freeze-dried samples milled and sieved with a 1 mm grid. The fibre content was determined according to Van Soest and Wine (1967). Proteins were measured according to the Lowry method (Lowry et al., 1961), and total sugars are measured with the anthrone reduction method (Clegg, 1956). Lipids are extracted with the conventional Soxhlet method with petroleum ether (40-60C). The biochemical methane potential (BMP) assays are derived from Owens and Chynoweth (1993) and from Angelidaki and Sanders (2004). We use 7 thermophilic (55C) reactors with an active volume of 3.5 litres each, filled with synthetic growth medium containing nutrients and trace elements, and inoculated with anaerobic thermophilic sludge coming from a stock reactor. Biomass concentration in the tests is comprised between 2 and 4 gVS/L, and the substrate to inoculum ratio is 0.5 on VS basis. The gas production is measured with an electronic volumetric gas counter (based on water displacement) and periodically analysed for composition by gas chromatography. A blank (biomass alone) and a positive standard (biomass + cellulose) were done for each run to correct for endogenous methane production. The only difference with commonly used BMP assays is that waste addition is repeated three times in the same reactor (instead of 3 triplicates in parallel) to account for the adaptation of the sludge to the organic waste. In some of the experiments (last substrate addition), liquid samples were taken and analysed for soluble degradation products (COD, volatile fatty acids). Sludge adaptation has a slight influence on the total methane produced, but has a strong influence on the response curve and on the dynamics, which is important for kinetic data assessment. Indeed, we found that two reactors running in parallel with cellulose gave BMP values of 346.66 and 344.310 mLCH4/gcellulose respectively on 3 successive substrate addition. The BMP, is expressed as mlCH4(STP)/gVS. Knowing the COD/VS ratio, it is possible to estimate the overall biodegradability, since the methane/COD yield is constant (350 mlCH4(STP)/gCOD). The biodegradability BD is thus defined as the ratio between the methane produced and the maximal amount that would have been obtained if all the COD were converted to methane (equation 1). </p><p>)/(350)/(mlCH STP4,</p><p>gVSgCODxCODgVSBMP</p><p>BDwaste</p><p>= (1) </p><p>3Buffiere et al.: Anaerobic Digestion of Solid Waste</p><p>Brought to you by | National Chung-Hsing UniversityAuthenticated |</p><p>Download Date | 3/20/14 11:59 PM</p></li><li><p>Figure 1: Conversion process in anaerobic digestion as used in the model ADM1 (from Batstone et al., 2002). </p><p>2.2 ADM1 modelling </p><p>Mathematical modelling of anaerobic digestion processes has been extensively investigated and developed during the last 3 decades. One of the most sophisticated, advanced but also complex model is the IWA Anaerobic Digestion Model No. 1 (ADM1) published by the IWA Task Group for Mathematical Modelling of Anaerobic Digestion Processes in 2002 (Batstone et al., 2002). ADM1 is a structured model with disintegration and hydrolysis, acidogenesis, acetogenesis, and methanogenesis steps. An overview of the model structure is shown in Figure 1. Extracellular solubilisation steps are divided into disintegration and hydrolysis, of which the first converts composite particulate substrate to inert matter, particulate carbohydrates, proteins and lipids. The second is enzymatic hydrolysis converting particulate carbohydrates, proteins and lipids to monosaccharides, amino acids and long chain fatty acids (LCFA) respectively. Disintegration is mainly included to describe degradation of composite particulate material with lumped characteristics, while the hydrolysis steps were introduced to describe well defined, relatively pure substrates (such as cellulose, starch and protein feeds). All disintegration and hydrolysis processes are represented by first order kinetics. Two separate groups of acidogens degrade monosaccharides and amino acids to mixed organic acids, hydrogen and carbon dioxide. The organic </p><p>4 International Journal of Chemical Reactor Engineering Vol. 6 [2008], Article A94</p><p>Brought to you by | National Chung-Hsing UniversityAuthenticated |</p><p>Download Date | 3/20/14 11:59 PM</p></li><li><p>acids are subsequently converted to acetate, hydrogen and carbon dioxide by acetogenic groups that utilise LCFA, butyrate and valerate (one group for the two substrates), and propionate. The hydrogen produced by these organisms is consumed by a hydrogenotrophic methanogenic group, and the acetate by an acetotrophic methanogenic group. Substrate uptake Monod-type kinetics are used for all biochemical reactions. Death of biomass is represented by first order kinetics, and dead biomass is maintained in the system as composite particulate material. Inhibition functions include pH (all groups), hydrogen (acetogenic groups) and free ammonia (acetotrophic methanogens). pH inhibition is implemented as empirical equations, while hydrogen and free ammonia inhibition are represented by non-competitive functions. Mechanisms included to describe physicochemical processes are acid-base reactions (to calculate hydrogen ion, free ammonia, and carbon-dioxide concentrations), and liquid-gas transfer. As a differential and algebraic equation (DAE) set, there are 26 dynamic state variables, 19 biochemical kinetic processes, 3 gas-liquid transfer kinetic processes and 8 implicit algebraic variables. As a differential equation (DE) set, there are 32 dynamic state concentration variables and additional 6 acid-base kinetic processes. </p><p>3. RESULTS AND DISCUSSION </p><p>3.1 Waste composition </p><p>The most important composition parameters that we analysed are listed in Table 1. The first interesting issue is the relatively wide variation of the COD/VS ratio (between 1.19 for rice and 1.53 for fish). This is mostly due to the difference in composition, since carbohydrates, proteins and lipids have different energetic values. As can be seen from the data, it is quite difficult to get an accurate distribution of organic matter between the compartments. Only the fibre content (gravimetric method) leads to 100% closure. In the table, we give the hemicellulose (HEMI) and ADF (cellulose + lignin) fractions, the rest (1-HEMI-ADF) being the neutral detergent soluble part, which regroups water soluble matter and some of the organic content of t...</p></li></ul>