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A review of machine learning models in predicting biogas production

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dc.citedby1
dc.contributor.authorAmran N.A.M.en_US
dc.contributor.authorMohamed H.en_US
dc.contributor.authorRafaai Z.F.M.en_US
dc.contributor.authorYacob N.S.en_US
dc.contributor.authorJunoh H.en_US
dc.contributor.authorShamsuddin A.H.en_US
dc.contributor.authorid57194584787en_US
dc.contributor.authorid57136356100en_US
dc.contributor.authorid58092875800en_US
dc.contributor.authorid57357724400en_US
dc.contributor.authorid56335823200en_US
dc.contributor.authorid35779071900en_US
dc.date.accessioned2025-03-03T07:44:22Z
dc.date.available2025-03-03T07:44:22Z
dc.date.issued2024
dc.description.abstractOne of the main forces advancing Industry 4.0, also known as the fourth industrial revolution, is machine learning (ML). This paper examines the machine learning (ML) models application for biogas production prediction in anaerobic digestion (AD). This study's primary objective is to determine which ML techniques and models are utilised in the AD process. In addition, this study identifies the type of ML techniques, input and output parameters, and software used. Researchers have widely employed a couple of ML models in biogas production. After reviewing the 15 most recent papers, it was discovered that the Artificial Neural Network (ANN) and Adaptive Network-Based Fuzzy Inference System (ANFIS) are the most commonly used types of ML. The most commonly used operating parameters in predicting biogas production were reaction time, temperature, pH, total solids (TS), volatile solids, volatile fatty acids (VFAs), and fixed solids. In conclusion, the review discusses the challenges and prospects of using machine learning in the AD process and provides recommendations for future implementation. ? 2024 Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo60006
dc.identifier.doi10.1063/5.0181016
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85188310136
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85188310136&doi=10.1063%2f5.0181016&partnerID=40&md5=65e31bff11971cf600f291036b2bf8dc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36748
dc.identifier.volume2934
dc.publisherAmerican Institute of Physicsen_US
dc.sourceScopus
dc.sourcetitleAIP Conference Proceedings
dc.titleA review of machine learning models in predicting biogas productionen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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