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

• 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. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Industriell bioteknik -- Bioenergi (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Industrial Biotechnology -- Bioenergy (hsv//eng)

LANTBRUKSVETENSKAPER  -- Annan lantbruksvetenskap -- Förnyelsebar bioenergi (hsv//swe)

AGRICULTURAL SCIENCES  -- Other Agricultural Sciences -- Renewable Bioenergy Research (hsv//eng)

Nyckelord

Cellulosic biomass

Fermentation

Bio-ethanol production

Machine learning

Artificial intelligence

Biofuel Bio-hydrogen

Chemical Engineering

Kemiteknik

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