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Cellulosic biomass fermentation for biofuel production: Review of artificial intelligence approaches
National University of Sciences and Technology, Pakistan.
National University of Sciences and Technology, Pakistan.
University of Sheffield, United Kingdom.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Chemical Sciences (from 2013).ORCID iD: 0000-0003-4035-181X
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2024 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 189, article id 113906Article in journal (Refereed) Published
Abstract [en]

Scarcity in fossil fuel reserves and their environmental impacts has forced the world towards the production of clean and environment-friendly fuels called biofuels. This review focuses on the importance of different machine learning models and optimization techniques to simulate and optimize process conditions, yield and parameters in the fermentation of cellulosic biomass from fifty recent studies. The superiority of ML models, especially ANN dominance in 70 % of studies with highest coefficient of regression over conventional techniques in the production of bioethanol and biohydrogen is comprehensively reviewed. Research gaps and studies directed toward the usage of most optimum ML models in future are directed after the sensitivity analysis with 5 % variation that suggest the stability of ML models. It is intended to spur further investigation into the development and use of ML models combined with optimization methods and CFD in the fermentation process to produce bioethanol and biohydrogen. 

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 189, article id 113906
Keywords [en]
Cellulosic biomass, Fermentation, Bio-ethanol production, Machine learning, Artificial intelligence, Biofuel Bio-hydrogen
National Category
Bioenergy Renewable Bioenergy Research
Research subject
Chemical Engineering
Identifiers
URN: urn:nbn:se:kau:diva-97432DOI: 10.1016/j.rser.2023.113906ISI: 001110657400001Scopus ID: 2-s2.0-85175523627OAI: oai:DiVA.org:kau-97432DiVA, id: diva2:1813168
Funder
Knowledge Foundation, 20210057Available from: 2023-11-20 Created: 2023-11-20 Last updated: 2024-02-27Bibliographically approved

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Naqvi, Salman Raza

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