Targeted conversion of cellulose and hemicellulose macromolecules in the phosphoric acid/acetone/water system: An exploration of machine learning evaluation and product prediction.

The simultaneous hydrolysis of cellulose and hemicellulose involves trade-offs, making precise control of hydrolysis products crucial for sustainable development. This study employed three machine learning (ML) models-Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machines (SVM)-to simulate and predict the yields of xylose (Xyl), furfural (FF), glucose (Glu), 5-hydroxymethylfurfural (5-HMF), and levulinic acid (LA) in a phosphoric acid/acetone/water system. The RF model demonstrated the highest accuracy, with R values between 0.782 and 0.887, and RMSE from 1.740 to 3.370. Key factors affecting the targeted conversion of macromolecules were identified as the solid-liquid ratio, reaction temperature, and acid dosage, with 160 °C recognized as a critical threshold for converting sugars derived from cellulose and hemicellulose into aldehydes and acids. The presence of metal chlorides, particularly AlCl, significantly enhanced the selectivity of reactions and affected the distribution of products. It was found that corncobs are more efficient than bagasse in producing Glu. This study supports precise control over a multivariate system for producing multiple hydrolysis products from hemicellulose and cellulose, paving the way for data-driven optimization of lignocellulosic biomass conversion to high-value chemicals.