AI Prediction of H₂ Production in a Biomass Supercritical Water Gasification Fluidized Bed under Pulsating Inlets

Abstract

Supercritical water gasification (SCWG) is a promising approach with the advantages of high gasification rate, hydrogen-rich gas production, and environmental friendliness. In this work, a computational fluid dynamic-discrete element method (CFD-DEM) model is proposed to investigate and optimize the process of supercritical water (SCW) biomass gasification. The effect of pulsating SCW inlets on gasification and artificial neural network prediction of H 2 yields were conducted. The sinusoidal SCW inlet affects the bubble behavior in the bed, but has little effect on the heat transfer. When the sinusoidal period is 1.0 s, the H 2 yields are higher. Based on the BP neural network model, the H 2 yields were predicted and the optimized operating conditions are specified. This study will provide a theoretical foundation and guidance for industrial applications.

Multiphase Transportation, Conversion, & Utilization of Energy in Chemical Engineering: A Special Issue for MTCUE-2022

“…The results showed that a high surface area and pore volume of biochar resulted in an enhanced CO 2 capture capacity. Wang et al proposed a novel model to investigate and optimize the process of SCW biomass gasification. They studied the effects of pulsating SCW inlets on gasification and conducted artificial neural network to predict H 2 yields.…”