Efficient Surrogate-Assisted Parameter Analysis for Coal-Supercritical Water Fluidized Bed Reactor with Adaptive Sampling

Abstract

Supercritical water fluidized beds (SCWFBs) are promising and efficient reactors for the gasification of coal in supercritical water. The understanding and investigation of multi-phase flows as well as the gasification process usually rely on time-consuming experiments or numerical simulations, which prohibit fast and full exploration of the single and coupled effects of the operation and geometric parameters. To this end, this paper builds an efficient surrogate-assisted parameter analysis framework for the SCWFB reactor. Particularly, (1) it establishes a steady numerical simulation model of the SCWFB reactor for the subsequent analysis; and (2) it employs a Gaussian process surrogate modeling via efficient adaptive sampling to serve as an approximation for predicting the carbon conversion efficiency (CE) of the reactor. Based on this parameter analysis framework, this paper investigates the effects of five independent parameters (the mass flow rate of supercritical water, mass flow rate of the coal slurry, temperature of supercritical water, temperature of the outer wall and reactor length) and their interactions on the reaction performance in terms of the carbon conversion efficiency (CE). We found that the CE increases as a function of the temperature of supercritical water, the temperature of the outer wall and the reactor length; while it decreases as a function of the mass flow rate of supercritical water and the mass flow rate of the coal slurry. Additionally, the global sensitivity analysis demonstratesthat the influence of the temperature of the outer wall exerts a stronger effect than all the other factors on the CE, and the coupled interaction among parameters has a slight effect on the CE. This research provides useful guidance for scaled-up designs and optimization of the SCWFB reactor.