Modeling of Consolidated Bioprocessing for Bioproduction—A Comprehensive Review

With the rising global population and increasing energy demands, sustainable bioproducts such as bioethanol offer essential alternatives to fossil fuels. Unlike first-generation bioethanol derived from food crops like corn, second-generation bioethanol is produced from lignocellulosic (LC) biomass, a non-food resource that addresses sustainability concerns. Consolidated Bioprocessing (CBP) integrates enzyme production, hydrolysis, and fermentation in a single step, using either microbial consortia or engineered microorganisms, reducing costs and simplifying the process compared to separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF). However, CBP systems are complex due to the dynamic interactions between microbial consortia, metabolic pathways, and process conditions. Addressing these complexities requires advanced modeling techniques that effectively capture non-linear relationships and optimize process parameters. Machine learning-based models have the potential to advance the field of CBP by enabling data-driven approaches to capture complex bioprocess dynamics, improve prediction accuracy, and optimize bioproduct production in CBP systems, thus paving the way toward commercial viability. This review gives an actual overview of relevant key processes CBP, the current state of modeling CBP, its limitations, and the emerging role of machine learning (ML) as a solution to CBP’s modeling challenges. It details recent modeling techniques for CBP, including polynomial models, response surface methodologies, with detailed discussions on regression models and neural network models. In this paper, a summarized review of first-order principle-based modeling approaches as well as data-driven modeling approaches is included, emphasizing advancements that contribute to the scalability and efficiency of CBP for bioproduct production. This review provides new perspectives and insights on the modeling of consolidated bioprocessing for utilizing low-cost lignocellulosic biomass in bioproduction.