Anaerobic digestion model no. 1-based distributed parameter model of an anaerobic reactor: I. Model development

Mu, Shengjing; Zeng, Yingzhi; Wu, Ping; Lou, S.J.; Tartakovsky, B.

doi:10.1016/j.biortech.2007.07.060

Cited by 36 publications

(22 citation statements)

References 17 publications

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“…Analysis of biogas models will depend on digester types to assist process monitoring and control, which in turn can help optimize digester or fermenter operation and maximize biogas production. Therefore, application of the mathematical models is a prerequisite to improve performance of digester, and various models have been developed for the AD process from steady to dynamic models (Batstone et al, 2000;Bernard et al, 2001;Mu et al, 2008;Donoso-Bravo et al, 2009, 2011Tomei et al, 2009). Mu et al (2008) developed no.…”

Section: Process Modeling Monitoring and Control Of Biogas Productionmentioning

confidence: 99%

Decentralized Biogas Technology of Anaerobic Digestion and Farm Ecosystem: Opportunities and Challenges

Wang

2014

Front. Energy Res.

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Long-term economic and environmental concerns have resulted in a great amount of research on renewable sources of biomass and bioenergy to replace fossil fuels in the past decades. Decentralized biogas technology is one of the most potential technologies of biomass and bioenergy by using agricultural waste materials (e.g., animal manure, crop straw, and by-products from food industries) as feedstocks. By-products from biogas production, called digestate, are nutrient rich, which could potentially be reused as green fertilizers in agriculture, thereby providing a sustainable substitute for synthetic fertilizers for farm ecosystem. Thus, the biogas production of anaerobic digestion is win-win option for livestock and crop producers to address issues of waste management and energy supply, and to avoid contamination of surface and ground waters and emissions of odors and greenhouse gases. In this paper, we review biogas production technology and then evaluate environmental effects of digestate used as fertilizer. Finally, we discuss issues of deployment of decentralized biogas technology for farm ecosystem. Economic and technological barriers still exist for large scale deployment of biogas technology in rural region. Two national scale deployments in China and Nepal showed that the operational status of biogas digesters is not optimal and up to 50% of plants are non-functional after a short operation period regardless of the social and economic factors. Main barriers are a wide variation of feedstocks and environmental conditions (e.g., temperature) over space and time. It becomes clear that the experimental conditions of the pilot plants need to be adjusted and calibrated to the local feedstocks and climate. Also, more research needs to be done in cold fermentation technology. Thus, collaboration of all relevant designers, farmers, stakeholders, and regulators is proposed as the way forward, particularly as their complexity has been identified as the major hurdle to the implementation of decentralized biogas production, which can deliver beneficial synergies for the decentralized biogas production in rural region responsible for food production and waste management.

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Section: Process Modeling Monitoring and Control Of Biogas Productionmentioning

confidence: 99%

Decentralized Biogas Technology of Anaerobic Digestion and Farm Ecosystem: Opportunities and Challenges

Wang

2014

Front. Energy Res.

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“…25,26 Therefore, in the growth phase no biofilm washout occurs so that a batch reactor balance is used. When biofilm reaches its maximum biomass concentration a CSTR reactor balance is used.…”

Section: ' Introductionmentioning

confidence: 99%

Multi-Population Model of a Microbial Electrolysis Cell

Pinto

Srinivasan

Escapa

et al. 2011

Environ. Sci. Technol.

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103

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Multi-population model of a microbial electrolysis cellOrganic matter conversion to hydrogen in a microbial electrolysis cell (MEC) offers a number of advantages in comparison to H 2 production by water electrolysis, which requires a significant energy input, and to fermentative H 2 production, which has a limited yield of not more than 25%.1À3 Intensive MEC research in recent years has led to significant improvements in the volumetric rate of H 2 production, cathode materials, MEC design, and operating conditions, yet the overall performance remains relatively low. 2,4 One solution for the complex problems posed by MEC research is to develop a mathematical model that can describe the dynamics of chemical oxygen demand (COD) consumption and H 2 production in a MEC. This model can then be used to optimize the MEC operational parameters and design, thus facilitating the development of a full-scale MEC-based wastewater treatment process. Although several microbial fuel cell (MFC) models have been developed 5À9 and an anodic compartment model has been recently presented, 10 to our best knowledge a MEC model capable of simulating H 2 production from complex organic matter has not yet been reported. However, MFC models that can describe the competition between electricigenic and methanogenic microorganisms for acetate have already been presented. 5,7,11 The anaerobic degradation process has also been extensively studied and modeled (e.g., 12À14).This work presents a simple dynamic model of a MEC developed with the objective to simulate H 2 production from wastewater for process design, optimization, and control applications. Furthermore, the model application is illustrated by analyzing the influence of the substrate feed rate (organic load) and applied voltage on COD removal and H 2 production.' MATERIALS AND METHODS Analytical Methods. Chemical oxygen demand of synthetic wastewater (sWW) was estimated according to Standard Methods. 15Both total COD (tCOD) and soluble COD (sCOD) values were analyzed. Acetate, propionate, and butyrate were analyzed using a gas chromatograph. The total concentration of volatile fatty acids (VFAs) was calculated with respect to the COD equivalent of each component. Gas production in the MEC anodic and cathodic chambers was measured online using glass U-tube bubble counters interfaced with a data acquisition system. The gas composition was measured using a gas chromatograph. A detailed description of all analytical methods used in the study can be found in Tartakovsky et al. 16MEC Design, Operation, and Characterization. Three membraneless MECs (MEC-1, MEC-2, and MEC-3) with 50-mL anodic and H 2 -collection compartments were constructed from nylon plates. The anodes were made of 5-mm-thick carbon felt measuring 10 cm Â5 cm (SGL Group, Wiesbaden, Germany). Gas diffusion cathodes with a Ni load of 0.2À0.3 mg cm À2 were used in all MECs and prepared as described in Manuel et al. ABSTRACT: This work presents a multi-population dynamic model of a microbial electrolysis cell (MEC). The model ...

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“…Batstone et al, (2005) developed a distributed parameter model by combining the ADM1 kinetics with the Takacs clarifier model (Takacs et al, 1991), which approximates a UASB reactor by using several layers, i.e., reactor hydrodynamics was simplified. In contrast, Mu et al, (2008) presented a comprehensive distributed parameter model, which combines the biotransformation kinetics of ADM1 with the axial dispersion transport model. They used a hyperbolic tangent function to describe biomass distribution within a one-compartment model.…”

Section: Tecno Lógicasmentioning

confidence: 99%

Anaerobic digestion modeling: from one to several bacterial populations

Ramírez-Rivas¹

2011

TecnoL.

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Los sistemas de digestión anaeróbica son procesos complejos que desafortunadamente a menudo sufren de inestabilidad. A fin de diseñar, optimizar y operar eficientemente estos sistemas es necesario desarrollar estrategias apropiadas de control. Estas estrategias requieren, en general del desarrollo de modelos matemáticos. El proceso de digestión anaerobia comprende una red compleja de reacciones secuenciales y paralelas de naturaleza bioquímica y fisicoquímica. Generalmente, estas reacciones contienen un paso en particular, denominado reacción-limitante el cual siendo el más lento, limita la tasa de reacción del proceso global. Los primeros intentos del modelado de la digestión anaerobia condujeron a modelos que describen este paso limitante. Sin embargo, en una amplia gama de condiciones de funcionamiento, el paso limitante no siempre es el mismo. Este puede depender de las características de las aguas residuales, de la carga hidráulica, de la temperatura, etc. Es evidente que la "hipótesis del paso limitante" conduce a modelos simples y fácilmente utilizables. Sin embargo, estos modelos no describen muy bien el comportamiento del digestor, especialmente en condiciones transitorias de funcionamiento. Este trabajo revisa el estado del arte en modelización de la digestión anaerobia. Damos una breve descripción de los modelos clave de digestión anaerobia que se han desarrollado hasta el momento para describir sistemas con crecimiento de biomasa, incluyendo el modelo de digestión anaerobia No 1 (ADM1) de la Asociación Internacional del Agua (IWA) e identificamos las áreas que requieren esfuerzos futuros de investigación.

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Anaerobic digestion model no. 1-based distributed parameter model of an anaerobic reactor: I. Model development

Cited by 36 publications

References 17 publications

Decentralized Biogas Technology of Anaerobic Digestion and Farm Ecosystem: Opportunities and Challenges

Decentralized Biogas Technology of Anaerobic Digestion and Farm Ecosystem: Opportunities and Challenges

Multi-Population Model of a Microbial Electrolysis Cell

Anaerobic digestion modeling: from one to several bacterial populations

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