Machine learning prediction of deep eutectic solvents pretreatment of lignocellulosic biomass

The deep eutectic solvent pretreatments of lignocellulosic biomass were investigated by machine learning methods. Principal component analysis, partial least squares, linear regression, optimized gradient boosting, artificial neural networks, and random forests were used for reveal mechanisms and inner interaction relationships. The dependences of the pretreatment effect on the variables of the reaction conditions, the DES properties and the lignocellulosic biomass properties were analyzed. The influences of various variables on pretreatment process and partial dependence of lignin removal and carbohydrate recovery were studied. The temperature in the reaction conditions, the hydrophilicity in the DES characteristic parameters, and the hemicellulose content of raw lignocellulose components were the top three most influential factors for the changes in lignin removal, which accounted for 30%, 11%, and 6%, respectively. © 2023