Model Predictive Control of UCG: An Experiment and Simulation Study

Abstract

Underground coal gasification (UCG) is a potential technology that enables to mine coal without traditional mining equipment. The coal is gasified deep in underground and produced syngas is processed on the surface. The most important technical problem in UCG is unstable quality of syngas and control. This paper proposes advanced control based on an adaptive predictive controller. The maintaining of desired calorific value depends on flow rates of gasification agents injected to the underground geo-reactor and controlled exhaust. The paper proposes a physical model of UCG technology and applies a method of multivariate adaptive regression splines (MARS) to model the gasification process. This method satisfactorily approximates nonlinearity in the process variables. The paper proposes adaptive model predictive control (MPC) using online model estimation and applied it on the MARS model of UCG that imitates the real process. The results have shown that optimization of manipulation variables can replace manual control in UCG. Getting better quality of syngas depends on setpoints, optimized manipulation variables, and constraints used in MPC. In simulations, the adaptive MPC has shown better performance in comparison with manual and PI control.