AI-DRIVEN OPTIMIZATION OF AGRICULTURAL RESIDUE BIOREFINERIES FOR SIMULTANEOUS PRODUCTION OF BIOFUELS, PLATFORM CHEMICALS, AND VALUE-ADDED BIOMATERIALS

Abstract

A shift to circular bioeconomy requires the sustainable utilization of agricultural residues, which are available in the annual global scale of about 998 MT. Lignocellulosic biorefineries continue to struggle with the issues of feedstock heterogeneity and recalcitrance, energy intensive pretreatments and the economic viability of single-product systems. This review focuses on the disruptive impact of artificial intelligence (AI) and machine learning (ML) in enhancing the performance of integrated biorefineries for the co-production of biofuels, platform chemicals, and value-added biomaterials. Complex models, such as artificial neural networks (ANNs), eXtreme Gradient Boosting (XGB), Random Forest (RF), Support Vector Regression (SVR), and bio-inspired metaheuristics, were developed for predictive modeling, real-time process control, and multi-objective optimization of the biorefinery process from feedstock pretreatment to biopolymer product recovery. AI-guided pretreatment processes suppress feedstock variability as well as inhibitor production, while ML algorithms maximize enzymatic saccharification, fermentation kinetics, and thermochemical conversion route. In platform chemicals synthesis, hybrid machine learning (ML) schemes integrated with thermodynamic screening enables high-throughput catalyst screening and biphasic system parameter optimization for 5-HMF, furfural and levulinic acid synthesis. For biomaterials, transfer learning and neural network models are used to improve processes such as extraction of nanocellulose, macromolecular tailoring of lignin, and fermentation of polyhydroxyalkanoates. Digital twins, techno-economic analysis, and life cycle assessment, at the systems level, provide the mechanisms for dynamic flow routing based on market values and carbon efficiency objectives. Data heterogeneity, small dataset issues, and model interpretability are also challenged by a number of methods such as transfer learning, physics-informed neural networks (PINNs), and explainable AI (XAI) frameworks. The review ends with stating that AI-based optimization is a game changing technology that empowers autonomous, economically feasible, and environmentally sustainable biorefineries.