Cellulosic biomass fermentation for biofuel production: Review of artificial intelligence approaches

Muhammad Hamza Naveed, Muhammad Nouman Aslam Khan, Muhammad Mukarram, Salman Raza Naqvi, Abdullah Abdullah, Zeeshan Ul Haq, Hafeez Ullah and Hamad Al Mohamadi

Renewable and Sustainable Energy Reviews, 2024, vol. 189, issue PB

Abstract: 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.

Keywords: Cellulosic biomass; Fermentation; Bio-ethanol production; Machine learning; Artificial intelligence; Biofuel; Bio-hydrogen (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1364032123007645
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:189:y:2024:i:pb:s1364032123007645

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2023.113906

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

More articles in Renewable and Sustainable Energy Reviews from Elsevier
Bibliographic data for series maintained by Catherine Liu ().