Developing a hybridized thermodynamic and data-driven model for catalytic supercritical water gasification of biomass for hydrogen production

Supercritical water gasification (SCWG) of biomass offers a promising pathway for sustainable energy production. This study presents a novel approach that combines an artificial intelligence (AI) technique with the Gibbs minimization (GM) method to enhance the GM approach by involving the catalyst effect on the gasified yield. The proposed strategy incorporates a modified Gibbs energy calculation, incorporating a delta term that quantifies the influence of the catalyst's properties on the Gibbs free energy. The delta term is determined using experimental data, including characteristics of biomass and the employed catalysts. An artificial neural network (ANN) model is developed to predict the delta value and is integrated with the GM method to improve the estimation of gasified product yields in the presence of a catalyst. The ANN model establishes correlations between the catalyst specifications such as pore volume, specific surface area, and pore diameter, and delta value. The trained model can predict the delta term, allowing the catalyst's role to be considered within the GM technique. This integration addresses a limitation of the conventional thermodynamic model, which typically ignores the catalyst's influence. The developed ANN model exhibits high accuracy, with an R2 value of 0.92 and a mean squared error (MSE) of 0.042. The hybridized GM approach substantially improves the hydrogen yield prediction compared to the basic model, enhancing (reducing) the MSE and mean absolute error by 98% and 93%, respectively. Furthermore, sensitivity analysis reveals that increasing temperature and reducing pressure contribute to improved hydrogen production. Additionally, lower biomass concentrations are shown to be beneficial for enhancing the hydrogen yield.