Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104284
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dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorRen, Jen_US
dc.date.accessioned2024-02-05T08:47:49Z-
dc.date.available2024-02-05T08:47:49Z-
dc.identifier.issn0960-8524en_US
dc.identifier.urihttp://hdl.handle.net/10397/104284-
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.rights© 2017 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Ren, J. (2018a). GM(1,N) method for the prediction of anaerobic digestion system and sensitivity analysis of influential factors. Bioresource Technology, 247, 1258–1261 is available at https://doi.org/10.1016/j.biortech.2017.10.029.en_US
dc.subjectAnaerobic digestionen_US
dc.subjectGM(1,N)en_US
dc.subjectGrey theoryen_US
dc.subjectMethane yielden_US
dc.subjectModellingen_US
dc.titleGM(1,N) method for the prediction of anaerobic digestion system and sensitivity analysis of influential factorsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1258en_US
dc.identifier.epage1261en_US
dc.identifier.volume247en_US
dc.identifier.doi10.1016/j.biortech.2017.10.029en_US
dcterms.abstractAnaerobic digestion process has been recognized as a promising way for waste treatment and energy recovery in a sustainable way. Modelling of anaerobic digestion system is significantly important for effectively and accurately controlling, adjusting, and predicting the system for higher methane yield. The GM(1,N) approach which does not need the mechanism or a large number of samples was employed to model the anaerobic digestion system to predict methane yield. In order to illustrate the proposed model, an illustrative case about anaerobic digestion of municipal solid waste for methane yield was studied, and the results demonstrate that GM(1,N) model can effectively simulate anaerobic digestion system at the cases of poor information with less computational expense.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationBioresource technology, Jan. 2018, v. 247, p. 1258-1261en_US
dcterms.isPartOfBioresource technologyen_US
dcterms.issued2018-01-
dc.identifier.scopus2-s2.0-85031496822-
dc.identifier.eissn1873-2976en_US
dc.description.validate202402 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberISE-0723-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6788405-
dc.description.oaCategoryGreen (AAM)en_US
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