Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening

Hydrogen production from wet organic wastes through supercritical water gasification (SCWG) promotes sustainable development. However, it is always time-consuming and expensive to achieve optimal SCWG conditions and suitable catalysts for different wastes to produce H2-rich syngas. Herein, we developed a unified machine learning (ML) framework to predict syngas composition from SCWG processes inclusive of non-catalytic and catalytic systems. The neural network (NN) model which was the core of the framework exhibited generalizable and satisfactory accuracy (R2 > 0.85) for all systems evaluated. The model also aided in the screening of catalyst and provided optimal conditions for accelerating experiments to produce H2-rich syngas by maximizing H2 yield and minimizing CO2 yield. ML model-based exploration found that SCWG temperature and solid content in the feedstock were the two most important factors affecting syngas composition. ML-based optimization suggested that Fe-based catalyst exhibited a greater potential to promote SCWG of wet wastes under optimal operational conditions. Besides, a web-based graphic user interface was developed by embedding the developed NN model for free access to the SCWG scientific community.