Effects of hydration parameters on chemical properties of biocrudes based on machine learning and experiments

To investigate the effects of temperature and biomass concentration of Hydrothermal liquefaction (HTL) on chemical properties of biocrudes, machine learning (ML) was used to predict the weight of hydration parameters on the properties of biocrudes. The elemental compositions, molecular weights, functional groups, thermal degradation, molecular structure of biocrudes were studied. The optimum yield of biocrudes was 65% and the highest heat value reached up to 34.28 kJ/g, showing comparable fuel properties. It was found that the hydration temperature significantly affects the elemental components, functional groups and molecular weight and structures of biocrudes. In addition, biomass concentration also affect the functional groups and structures of biocrudes. ML results indicated that Support Vector Machine Linear Kernel method is suitable for heat value prediction.