Lignocellulosic bioethanol production: a review on pretreatment strategies, biofuel separation, and artificial intelligence/machine learning − based sustainable optimization

Abstract

• Lignocellulosic biomass as a sustainable feedstock for bioethanol production. • Physical, Chemical, biological, and nanotechnological–assisted pretreatment. • Enzymatic hydrolysis and types of fermentation process in bioethanol production. • Various separation mechanisms used and strategies to minimize inhibitor production. • Artificial intelligence and Machine Learning techniques such as ANN, RSM, and LCA. Bioethanol production from lignocellulosic biomass, such as agricultural residues, forestry waste, and energy crops, is an abundant, renewable, and non-food-competing resource that has gained attention as a sustainable alternative to fossil fuels. The conversion process typically involves three stages: pretreatment, enzymatic hydrolysis, and fermentation. Pretreatment using physical, chemical, biological, and nanotechnological-assisted methods is essential to break down the rigid biomass structure and enhance enzyme accessibility. In the subsequent enzymatic hydrolysis, cellulolytic enzymes convert cellulose and hemicellulose into fermentable sugars. Fermentation, carried out by microorganisms such as Saccharomyces cerevisiae, Zymomonas mobilis, or Pichia stipitis, subsequently convert these sugars into ethanol. Despite its potential, lignocellulosic bioethanol production faces challenges such as high pretreatment costs, enzyme inefficiency, and the presence of fermentation inhibitors. The use of statistical and optimization tools such as Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) has been widely used to enhance bioethanol production. ANN provides robust predictive modelling by handling complex, non-linear interactions, while RSM enables efficient experimental design, identification of factor interactions, and optimization with fewer trials. Combining these methods improves ethanol yield, minimizes inhibitors, and enhances process efficiency. Recent advancements in metabolic engineering, microbial strain development, and integrated bioprocessing approaches have contributed to improving ethanol yield and process efficiency. This review explores recent progress in bioethanol production from lignocellulosic biomass, focusing on technological innovations, challenges, and future research directions aimed at enhancing the economic viability of lignocellulosic bioethanol as a renewable fuel source.

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