A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification

Abstract Recent advances in machine learning (ML) have witnessed a profound interest and application in the domain of waste to energy. However, their black-box nature renders challenges for ubiquitous acceptance. To address this issue, we developed a novel and first-of-its-kind hybrid data-driven and mechanistic modelling approach for hydrothermal gasification (HTG) of wet waste, in which a gradient boost regressor (GBR) integrated optimization model was first developed to predict and optimize the yield of syngas from HTG of wet waste, and then the predictions of the GBR model were validated and interpreted via mechanistic simulations in Aspen Plus. Results showed that the GBR model had a prediction performance with test R2 > 0.90. GBR-based feature analysis identified that reaction temperature and feedstock solid content were the two significant features necessary to achieve high H2 yield in syngas. Moreover, the GBR-based optimization provided optimal process conditions for H2-rich syngas production, which was validated over mechanistic simulations in Aspen Plus with an error of less than 20%. Interpretation from the mechanistic simulation revealed that steam and dry methane reforming and CO2 methanation were the most significant reactions in the overall HTG process, responsible to produce H2-rich syngas. Integrated modelling approach as presented in this study shows how data-driven and mechanistic models complement each other and can aid the acceleration of experimental design of HTG by ML in general.