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

• Tác giả: Zhiyuan Fu, Xuewen Han, Junwen Pu, Wei Qi, Rongrong Si, Yuhang Sun, Qiong Wang, Zhitong Yao
• Ngôn ngữ: eng
• Thông tin xuất bản: Netherlands : International journal of biological macromolecules , 2025
• Bộ sưu tập: NCBI
• ID: 685304

Tóm tắt

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