Butyl levulinate production from lignocellulose with mechanistic learning by hierarchical surrogate kinetic modelling

-butyl acetate, and furfural, are formed in appreciable quantities, offering opportunities for integrated valorisation within a biorefinery framework. Feedstock complexity was found to increase the thermal energy demand required to reach the equivalent conversion. To interpret and generalise the experimental data, a fourth-generation, mass-conserved, semi-mechanistic surrogate kinetic model was developed, based on a learning principle of hierarchical molecular group additivity. The model accurately predicts yields across all feedstocks and is readily adaptable to other lignocellulosic feedstocks in alcoholysis systems. Overall, this work establishes an experimentally validated route to butyl levulinate production that couples high carbon efficiency with a predictive process design tool, advancing the commercial viability of biomass-derived fuels within sustainable, integrated biorefineries.