This manuscript provides a comprehensive review of the enzymatic hydrolysis of lignocellulosic biomass, emphasizing how chemical composition, structural features, inhibitory compounds, and process configurations collectively influence the conversion of structural polysaccharides into fermentable sugars. Variability among herbaceous, woody, and residual biomasses results in differences in cellulose, hemicellulose, lignin content, and crystallinity, which strongly affect enzyme accessibility. The review discusses key inhibitory mechanisms, including nonproductive cellulase adsorption onto lignin, interference from phenolic derivatives and pretreatment by-products, and inhibition caused by accumulating mono- and oligosaccharides. Process configurations such as SHF, SSF, PSSF, and consolidated bioprocessing are compared, with SSF often achieving superior performance by mitigating end-product inhibition. The manuscript also highlights the growing relevance of computational modeling and simulation tools, which support kinetic prediction, the evaluation of transport limitations, and the optimization of operating conditions in high-solids systems. Additionally, recent advances in artificial intelligence are presented as powerful approaches for modeling nonlinear hydrolysis behavior, estimating kinetic parameters, identifying rate-limiting steps, and improving predictive accuracy in complex bioprocesses. Overall, the integration of experimental insights with advanced modeling, simulation, and AI-based strategies is essential for overcoming current limitations and enhancing the technical feasibility and industrial competitiveness of lignocellulosic bioconversion.

The growing demand for cleaner energy and the reduction of greenhouse gas emissions have highlighted anhydrous ethanol as a key renewable fuel due to its applications as a fuel, gasoline additive, and biodiesel reactant. Traditional dehydration via azeotropic distillation with cyclohexane is energy-intensive and environmentally harmful, prompting the search for safer alternatives. This study modeled and simulated the ethanol dehydration process using monoethylene glycol in Aspen Plus v12.1, implementing conventional control strategies optimized by Luyben’s method, which identified tray 31 as the system’s most sensitive point. Dynamic simulations demonstrated process robustness, with rapid recovery from disturbances in feed and heat duty, maintaining 99.999 wt% ethanol purity and solvent recovery. Additionally, artificial intelligence (AI) models—specifically decision tree, random forest, and LightGBM—were developed to control top product composition using easily measurable variables. These models significantly outperformed linear regression, with the decision tree achieving R² = 0.9970, MAE = 4.19 × 10⁻⁷, and RMSE = 2.20 × 10⁻⁶, maintaining ethanol molar fractions above 0.9996 even under dynamic disturbances. Despite their strong performance, the industrial adoption of AI-based controllers is still limited. However, with the implementation of real-time validation, significant advancements in computational processing capacity, and improvements in techniques to prevent overfitting, artificial intelligence models can become a promising tool for industrial process control. Graphical abstract