Machine learning prediction of bio-oil yield during solvothermal liquefaction of lignocellulosic biowaste

Abstract

Solvothermal liquefaction of biomass has gained attention to produce liquid biofuel and specialized chemicals. In this study, eXtreme Gradient Boosting was applied for predicting the bio-oil yield during solvothermal liquefaction of lignocellulosic biowaste. To establish a precise model for predicting the bio-oil yield, the prediction of the biomass conversion was found to be an intermediate ameliorating variable. The combination of the contents of biochemical components (cellulose, hemicellulose and lignin) with the operating factors (temperature, time, solid loading, solvent polarity and solvent density) provided the best prediction for the biomass conversion (R2 equals to 99.98% for training and 97.67% for test). To predict the yield of bio-oil, introduction of the biomass conversion among inputs improved prediction accuracy (R2 equals to 100% for training and 94.4% for test). The best prediction models developed were interpreted using a game theory-based feature importance and the partial dependence plotting analysis, which provided insights into the biomass conversion pathways and the bio-oil generation mechanism. To assist other researchers in predicting, a graphical user interface was created. This tool will save both resources and time that would otherwise be spent on multiple experimental trials and might not yield useful results.