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A review on emerging technologies and machine learning approaches for sustainable production of biofuel from biomass waste

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BIOMASS & BIOENERGY
卷 180, 期 -, 页码 -

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PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.biombioe.2023.106997

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Anaerobic treatment; Artificial intelligence; Bioalcohol; Biofuel; Biohydrogen; Genetic modification

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Proper treatment and disposal of biomass waste is crucial to prevent environmental deposition and its negative impacts. Biofuel has emerged as a potential alternative to fossil fuels, reducing carbon emissions and meeting global energy demands. This review examines different biomass waste conversion techniques and explores the production of biofuels with zero carbon emissions. Research on anaerobic treatment, metabolic engineering, and artificial intelligence has been conducted to enhance biofuel production efficiency.
Biomass waste must be treated and disposed off properly, as it concerns the possibility of deposition in the environment. This could worsen the ecosystem by promoting the growth of pathogens, producing poisonous fumes, releasing ammonia and other dangerous gases into the atmosphere. Therefore, researchers are exploring different direct biomass waste applications. One such application is biotechnological options with financial and environmental benefits like lowering the consumption of fossil fuels, reducing harmful emissions, accelerating the synthesis of low-cost raw materials, and fostering an environment that is favourable for a broad range of microorganisms. In recent years, biofuel has emerged as the primary source of bio-energy by replacing fossil fuels that can meet the global energy demands and lower greenhouse gas (GHG) emissions. This review explores the potential of biomass waste conversion techniques and examines the production of biofuels to achieve zero carbon emissions. Critical research has also been done to improve the anaerobic treatment method of the biochemical conversion method. Apart from these, the advancements in the study of metabolic engineering techniques and artificial intelligence in the production of biofuel and strategies along with microbial chassis to enhance production of biofuel in an efficient manner were also studied. Finally, challenges and future perspectives of biofuels were delineated.

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