Bioethanol can be efficiently produced from lignocellulosic biomass via two-phase processes, consisting of enzymatic hydrolysis and fermentation. To enhance economic and energy efficiency, a system combining separate hydrolysis and fermentation of biomass with a direct-ethanol solid oxide fuel cell (SOFC) is proposed in this work. The system comprises six units: a pretreatment reactor unit, a conditioning unit, a high-solids hydrolysis unit, a seed train unit, an ethanol recovery unit, and an SOFC unit. Exergy analysis based on a thermodynamic model indicates a total exergy efficiency of approximately 0.72. Within the high-solids hydrolysis unit, one piece of equipment exhibits the lowest exergy efficiency of 0.21, at a biomass flux of 71,510 kg/h. The other main exergy destruction exists in the conditioning unit and is followed by seed train unit, accounting for 5.61 and 2.77 of total exergy destruction ratios, respectively. In addition, the tentative parametric analysis for reaction kinetics is performed with varying reaction orders. The results indicate that ammonia gas in a specific unit can follow first- or second-reaction order, whereas acetic acid and sulfuric acid exhibit zero-reaction order, due to the gradual conversion of cellulose to glucose. This work provides key insights for the practical design and operation of the proposed separate hydrolysis and fermentation–SOFC system.

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.