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

## TL;DR

This study shows how to make a biofuel called butyl levulinate from plant materials using a one-step process and a new predictive model.

## Contribution

A new semi-mechanistic kinetic model and a mass-based yield metric for lignocellulosic feedstocks are introduced.

## Key findings

- Maximum BL yields were 49.6 mol% for glucose and 43.4 mol% for cellulose under optimized conditions.
- Both cellulose and hemicellulose-derived sugars contribute to BL formation, supporting whole-biomass utilization.
- A hierarchical surrogate kinetic model accurately predicts BL yields across different feedstocks.

## Abstract

This study reports the production of n-butyl levulinate (BL) as an advanced biofuel and biomass-derived ester via homogenous acid-catalysed butanolysis of four lignocellulosic feedstocks of increasing structural complexity: glucose, cellulose, xylan, and corn cob. A one-pot process valorises the lignocellulosic biomass, improving atom economy, and avoiding multistep derivatisation or protective group strategies. Under optimised conditions, maximum butyl levulinate yields are 49.6 mol% for glucose (170 °C), 43.4 mol% for cellulose (190 °C), 28.8 mol% for corn cob (210 °C), and 8.9 mol% for xylan (210 °C). It is shown that both cellulose- and, for the first time, hemicellulose-derived sugars contributed to butyl levulinate formation, reinforcing the advantage of whole-biomass utilisation over cellulose-focused approaches. A new mass-based yield metric is proposed that accounts for contributions from both carbohydrate fractions, enabling fair performance comparisons across feedstocks. Co-products, including n-butyl formate, n-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.

This study reports the production of n-butyl levulinate, an advanced biofuel and biomass-derived ester, via homogeneous acid-catalysed butanolysis of four lignocellulosic feedstocks: glucose, cellulose, xylan, and corn cob.