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

Journal: International journal of biological macromolecules
PMID: 40064276

Abstract

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.

Authors

  • YuHang Sun
    School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
  • Qiong Wang
    Beijing Meiling Biotechnology Corporation, Beijing, 102600, PR China.
  • Zhitong Yao
    College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Zhiyuan Fu
    School of Resources Environment and Tourism, Anyang Normal University, Anyang 455000, China.
  • Xuewen Han
    Beijing Key Laboratory of Lignocellulosic Chemistry, Beijing Forestry University, College of Materials Science and Technology, Beijing 100083, China.
  • Rongrong Si
    Beijing Key Laboratory of Lignocellulosic Chemistry, Beijing Forestry University, College of Materials Science and Technology, Beijing 100083, China.
  • Wei Qi
    School of Chemical Engineering and Technology, State Key Laboratory of Chemical Engineering, Tianjin University Tianjin 300350 P. R. China mwang@tju.edu.cn.
  • Junwen Pu
    Beijing Key Laboratory of Lignocellulosic Chemistry, Beijing Forestry University, College of Materials Science and Technology, Beijing 100083, China. Electronic address: jwpu@bjfu.edu.cn.

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