Machine learning technology in biohydrogen production from agriculture waste: Recent advances and future perspectives-Reference-Cited by-同舟云学术

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Machine learning technology in biohydrogen production from agriculture waste: Recent advances and future perspectives

Published:2022-11 Issue: Volume:364 Page:128076
ISSN:0960-8524
Container-title:Bioresource Technology
language:en
Short-container-title:Bioresource Technology

Author:

Kumar Sharma Amit,Kumar Ghodke Praveen,Goyal Nishu,Nethaji S.,Chen Wei-Hsin

Funder

National Institute of Technology Calicut

University of Petroleum and Energy Studies

National Science and Technology Council

Ministry of Science and Technology, Taiwan

Ministry of Education

National Cheng Kung University

Publisher

Elsevier BV

Subject

Waste Management and Disposal,Renewable Energy, Sustainability and the Environment,General Medicine,Environmental Engineering,Bioengineering

Reference80 articles.

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2. Ardabili, S.F., Najafi, B., Shamshirband, S., Bidgoli, B.M., Deo, R.C., Chau, K.W., 2018. Computational intelligence approach formodeling hydrogen production: A review. Eng. Appl. Comput. Fluid Mech. doi:10.1080/19942060.2018.1452296.

3. Machine learning methods for modelling the gasification and pyrolysis of biomass and waste;Ascher;Renew. Sustain. Energy Rev.,2022

4. Artificial neural network based modeling of biomass gasification in fixed bed downdraft gasifiers;Baruah;Biomass Bioenergy,2017

5. The prospect of purple non-sulfur (PNS) photosynthetic bacteria for hydrogen production: The present state of the art;Basak;World J. Microbiol. Biotechnol.,2007

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