Bayesian Optimization and Molecular Dynamics Simulations to Guide Protic Ionic Liquid-Based Biorefining for Efficient Lignin Applications

Abstract

The transition to a circular bioeconomy depends on the sustainable use of renewable resources, with forest-based biomass playing a critical role. Lignin, in particular, is a promising feedstock for replacing fossil-derived aromatics due to its unique chemical structure. However, lignin obtained from conventional pulp mills often contains impurities and structural modifications that limit its potential for producing high-value chemicals and materials. In contrast, emerging biorefinery pretreatment strategies enable a "lignin-first" approach, where every major biomass component cellulose, hemicelluloses, and lignin are valorized for targeted applications, allowing the tailored production of bio-based products with improved structural quality. Traditionally, the development of biorefining processes requires extensive experimentation guided largely by empirical advances.In this study, we demonstrate how Bayesian optimization can accelerate the development of a sustainable biomass fractionation process based on the protic ionic liquid (PIL)-triethylammonium hydrogen sulfate ([TEA][HSO4]). Open-source Python-based tools were employed to optimize lignin extraction from softwood and evaluate the influence of key processing parameters, including temperature and process severity, on lignin yield. Molecular dynamics simulations and targeted literature analyses were adopted to identify the optimal trade-offs between lignin recovery and structural properties. Notably, the optimized PIL fractionation process achieved 82% delignification with lignin yields of up to 73%, while consistently producing lignin with low molecular weights because of in situ depolymerization during pretreatment. Furthermore, advanced qualitative characterization was conducted to assess the lignin structure and evaluate the relationship between lignin yield, structural quality, and potential downstream applications.