Machine Learning Assisted Chemical Process Parameter Mapping on Lignin Hydrogenolysis

Yin Liu, Shuo Cheng and Jeffrey Scott Cross ()
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Yin Liu: Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, Tokyo 152-8550, Japan
Shuo Cheng: Department of Transdisciplinary Science and Engineering, School of Environment and Society, Tokyo Institute of Technology, Tokyo 152-8550, Japan
Jeffrey Scott Cross: Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, Tokyo 152-8550, Japan

Energies, 2022, vol. 16, issue 1, 1-13

Abstract: Lignin depolymerization has been studied for decades to produce carbon-neutral chemicals/biofuels and biopolymers. Among different chemical reaction pathways, catalytic hydrogenolysis favors reactions under relatively mild conditions, while its yield of bio-oil and high-value aromatic products is relatively high. In this study, the influence of reaction parameters on lignin hydrogenolysis are discussed by chemical process parameter mapping and modeled using three different machine learning algorithms based upon literature experimental data. The best R2 scores for solid residue and aromatic yield were 0.92 and 0.88 for xgboost, respectively. The parameter importance was examined, and it was observed that lignin-to-solvent ratio and average pore size have a larger impact on lignin hydrogenolysis results. Finally, the optimal conditions of lignin hydrogenolysis were predicted by chemical process parameter mapping using the best-fit machine learning model, which indicates that further process improvements can potentially generate higher yields in industrial applications.

Keywords: lignin hydrogenolysis; machine learning; LightGBM; XGBoost; CatBoost; chemical process parameter mapping (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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