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dc.contributor.authorEzzatzadegan, L
dc.contributor.authorYusof, R
dc.contributor.authorMorad, NA
dc.contributor.authorShabanzadeh, P
dc.contributor.authorMuda, NS
dc.contributor.authorBorhani, TN
dc.date.accessioned2021-09-06T11:07:29Z
dc.date.available2021-09-06T11:07:29Z
dc.date.issued2021-04-11
dc.identifier.citationEzzatzadegan, L., Yusof, R., Morad, N.A., Shabanzadeh, P., Muda, N.S. and Borhani, T.N. (2021) Experimental and Artificial Intelligence Modelling Study of Oil Palm Trunk Sap Fermentation. Energies, 14(8):2137. https://doi.org/10.3390/en14082137en
dc.identifier.issn1996-1073en
dc.identifier.doi10.3390/en14082137en
dc.identifier.urihttp://hdl.handle.net/2436/624314
dc.description© 2021 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/en14082137en
dc.description.abstractFive major operations for the conversion of lignocellulosic biomasses into bioethanol are pre-treatment, detoxification, hydrolysis, fermentation, and distillation. The fermentation process is a significant biological step to transform lignocellulose into biofuel. The interactions of biochemical networks and their uncertainty and nonlinearity that occur during fermentation processes are major problems for experts developing accurate bioprocess models. In this study, mechanical processing and pre-treatment on the palm trunk were done before fermentation. Analysis was performed on the fresh palm sap and the fermented sap to determine the composition. The analysis for total sugar content was done using high-performance liquid chromatography (HPLC) and the percentage of alcohols by volume was determined using gas chromatography (GC). A model was also developed for the fermentation process based on the Adaptive-Network-Fuzzy Inference System (ANFIS) combined with particle swarm optimization (PSO) to predict bioethanol production in biomass fermentation of oil palm trunk sap. The model was used to find the best experimental conditions to achieve the maximum bioethanol concentration. Graphical sensitivity analysis techniques were also used to identify the most effective parameters in the bioethanol process.en
dc.description.sponsorshipThis work supported by the Ministry of Education Malaysia through a Research University Grant of the University Technology Malaysia (UTM) (Award Number: Rk430000.7743.4J010).en
dc.formatapplication/pdfen
dc.languageen
dc.language.isoenen
dc.publisherMDPIen
dc.relation.urlhttps://www.mdpi.com/1996-1073/14/8/2137en
dc.subjectfermentationen
dc.subjectoil palm trunk sapen
dc.subjectneuro-fuzzyen
dc.subjectANFISen
dc.subjectparticle swarm optimization (PSO)en
dc.titleExperimental and artificial intelligence modelling study of oil palm trunk sap fermentationen
dc.typeJournal articleen
dc.identifier.eissn1996-1073
dc.identifier.journalEnergiesen
dc.date.updated2021-09-01T18:11:30Z
dc.date.accepted2021-04-06
rioxxterms.funderMinistry of Education Malaysiaen
rioxxterms.identifier.projectRk430000.7743.4J010en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2021-09-06en
dc.source.volume14
dc.source.issue8
dc.source.beginpage2137
dc.source.endpage2137
dc.description.versionPublished version
refterms.dateFCD2021-09-06T11:07:18Z
refterms.versionFCDVoR
refterms.dateFOA2021-09-06T11:07:30Z


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