An Integrated Experimental–Simulation Framework for Multi-Parameter Optimization of Ball Milling in Lignocellulosic Bioethanol Production

Corn stover is an abundant, low-cost, and representative lignocellulosic agricultural residue for bioethanol production, but its recalcitrant structure requires effective pretreatment to improve downstream conversion performance. Ball milling technology has been widely used to pretreat lignocellulosic biomass, where regulating particle size distribution enhances lignin utilization and subsequent ethanol production. However, systematic optimization of ball milling operating parameters remains insufficiently investigated. To bridge the gap between bench-scale experimentation and process-level performance prediction, this study establishes an integrated experimental and simulation framework. Ball milling experiments are conducted to systematically vary key operating parameters, and particle morphology is precisely quantified through optical imaging coupled with digital image analysis. Empirical correlations between these operating conditions and particle size characteristics are derived through polynomial fitting. These correlations are then embedded into an Aspen Plus process model for bioconversion performance evaluation. Evaluation from this integrated framework reveals that system-level optimization of the pretreatment process effectively reduces mean particle size, narrows size distribution, and increases specific surface area. By systematically evaluating the operating space, an optimal window that yields a 2.97% improvement in glucose conversion is identified. This work provides practical guidance and a generalizable framework for optimizing mechanical pretreatment to maximize target product yields.