Machine learning-based prediction and optimization of the cellulose conversion process for levulinic acid productionOA

Levulinic acid(LA)is a promising platform product with wide industrial applications.In recent years,the efficient conversion of cellulose into LA has become a research hotspot.However,traditional experimental optimization methods are often time-consuming and inefficient,therefore limiting its further upgrading.In this study,we integrated multidimensional data,including reaction conditions,solvent properties,and physicochemical characteristics of metal salts,to construct a systematic dataset.Six machine learning models,decision tree,Gradient Boosting Regression,K-Nearest Neighbors,multilayer perceptron,random forest,and support vector machine,were developed to predict LA yield.Among them,the gradient boosting regression model achieved the best performance,with a testset determination coefficient of 0.94 and the lowest root-mean-square error.SHapley Additive exPlanations and partial dependence analysis revealed that water fraction,catalyst dosage,and reaction temperature were the key factors influencing LA formation.By integrating the gradient boosting model with the particle swarm optimization algorithm,RuCl_(3) was identified as an efficient catalyst under high-temperature and short-reaction-time conditions.This study demonstrates the potential of applying the machine learning method in cellulose conversion research,and provides a data-driven strategy and theoretical guidance for the efficient and green production of LA.