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

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