Growing global energy demand and concerns over climate change and fossil fuel depletion have increased interest in sustainable bioproducts such as ethanol. Unlike first-generation (1G) ethanol derived from food crops (e.g., corn), second-generation (2G) ethanol is produced from lignocellulosic biomass, an abundant non-food resource that addresses key sustainability concerns. Consolidated bioprocessing (CBP) integrates enzyme production, hydrolysis, and fermentation into a single step, using either microbial consortia or engineered microorganisms, thereby simplifying the process and potentially reducing costs compared with separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF). However, CBP systems are complex due to dynamic interactions among microbial communities, metabolic pathways, and process conditions. Addressing this complexity requires modeling approaches that capture nonlinear relationships and support robust process optimization. Machine learning (ML)-based models offer data-driven tools to represent complex bioprocess dynamics, improve predictive accuracy, and optimize bioproduct formation, thereby supporting progress toward commercial viability. Although CBP can be applied to a range of bioproducts, this review primarily focuses on lignocellulosic ethanol and closely related biofuels. The review provides a comprehensive overview of key CBP processes, the current state of CBP modeling, major limitations, and the emerging role of ML in addressing modeling challenges. It summarizes recent modeling techniques for CBP, including polynomial models and response surface methodologies, and discusses regression and neural network approaches in detail. Both first-principles and data-driven modeling strategies are considered, highlighting advances that can improve the scalability and efficiency of CBP for bioproduction. Overall, this review offers perspectives on modeling-enabled pathways for utilizing low-cost lignocellulosic biomass in sustainable bioprocessing.
Yeboah et al. (Thu,) studied this question.