Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor

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dc.contributor.authorNair, V.V.
dc.contributor.authorDhar, H.
dc.contributor.authorKumar, S.
dc.contributor.authorThalla, A.K.
dc.contributor.authorMukherjee, S.
dc.contributor.authorWong, J.W.C.
dc.date.accessioned2026-02-05T09:32:58Z
dc.date.issued2016
dc.description.abstractThe 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
dc.identifier.citationBioresource Technology, 2016, 217, , pp. 90-99
dc.identifier.issn9608524
dc.identifier.urihttps://doi.org/10.1016/j.biortech.2016.03.046
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/25909
dc.publisherElsevier Ltd
dc.subjectBackpropagation
dc.subjectBioconversion
dc.subjectBiogas
dc.subjectBioreactors
dc.subjectFatty acids
dc.subjectMethane
dc.subjectMunicipal solid waste
dc.subjectNeural networks
dc.subjectOptimization
dc.subjectVolatile fatty acids
dc.subjectAnaerobic bioreactors
dc.subjectArtificial neural-network based modeling
dc.subjectBiogas production
dc.subjectMATLAB environment
dc.subjectOptimized conditions
dc.subjectOrganic fraction of municipal solid wastes
dc.subjectTotal volatile solids
dc.subjectVolatile fatty acids (VFAs)
dc.subjectAnaerobic digestion
dc.subjectbiogas
dc.subjectmethane
dc.subjectvolatile fatty acid
dc.subjectbiofuel
dc.subjectorganic compound
dc.subjectsolid waste
dc.subjectwaste
dc.subjectanoxic conditions
dc.subjectartificial neural network
dc.subjectbioreactor
dc.subjectbiotechnology
dc.subjectgas production
dc.subjectmunicipal solid waste
dc.subjectoptimization
dc.subjectorganic matter
dc.subjectperformance assessment
dc.subjectvolatile substance
dc.subjectanaerobic bioreactor
dc.subjectanaerobic digestion
dc.subjectanaerobic reactor
dc.subjectArticle
dc.subjectback propagation
dc.subjectgas chromatography
dc.subjectmicrobial consortium
dc.subjectmoisture
dc.subjectnetwork learning
dc.subjectpH
dc.subjectpriority journal
dc.subjectthermal conductivity
dc.subjectvegetable
dc.subjectanaerobic growth
dc.subjectanalysis
dc.subjectbiosynthesis
dc.subjectcity
dc.subjectfood
dc.subjectlaboratory
dc.subjectprincipal component analysis
dc.subjectAnaerobiosis
dc.subjectBiofuels
dc.subjectCities
dc.subjectFatty Acids, Volatile
dc.subjectFood
dc.subjectHydrogen-Ion Concentration
dc.subjectLaboratories
dc.subjectNeural Networks (Computer)
dc.subjectOrganic Chemicals
dc.subjectPrincipal Component Analysis
dc.subjectSolid Waste
dc.subjectWaste Products
dc.titleArtificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor

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