Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor
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Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH<inf>4</inf>) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, vegetable waste and yard trimming. An organic loading between 40 and 120 kg VS/m3 was applied in different runs of the bioreactor. The study was aimed to focus on the effects of various factors, such as pH, moisture content (MC), total volatile solids (TVS), volatile fatty acids (VFAs), and CH<inf>4</inf> fraction on biogas production. OFMSW witnessed high CH<inf>4</inf> yield as 346.65 L CH<inf>4</inf>/kg VS added. A target of 60–70% of CH<inf>4</inf> fraction in biogas was set as an optimized condition. The experimental results were statistically optimized by application of ANN model using free forward back propagation in MATLAB environment. © 2016 Elsevier Ltd
Description
Keywords
Backpropagation, Bioconversion, Biogas, Bioreactors, Fatty acids, Methane, Municipal solid waste, Neural networks, Optimization, Volatile fatty acids, Anaerobic bioreactors, Artificial neural-network based modeling, Biogas production, MATLAB environment, Optimized conditions, Organic fraction of municipal solid wastes, Total volatile solids, Volatile fatty acids (VFAs), Anaerobic digestion, biogas, methane, volatile fatty acid, biofuel, organic compound, solid waste, waste, anoxic conditions, artificial neural network, bioreactor, biotechnology, gas production, municipal solid waste, optimization, organic matter, performance assessment, volatile substance, anaerobic bioreactor, anaerobic digestion, anaerobic reactor, Article, back propagation, gas chromatography, microbial consortium, moisture, network learning, pH, priority journal, thermal conductivity, vegetable, anaerobic growth, analysis, biosynthesis, city, food, laboratory, principal component analysis, Anaerobiosis, Biofuels, Cities, Fatty Acids, Volatile, Food, Hydrogen-Ion Concentration, Laboratories, Neural Networks (Computer), Organic Chemicals, Principal Component Analysis, Solid Waste, Waste Products
Citation
Bioresource Technology, 2016, 217, , pp. 90-99