Nonlinear-Control-Oriented Modeling of the Multi-Variable Underground Coal Gasification Process for UCG Project Thar: A Machine Learning Perspective

Abstract

The Underground Coal Gasification (UCG) process is a complex and multi-physics phenomenon, thus making it difficult to develop a mathematical model that encapsulates all dynamical aspects. In this regard, data-driven modeling techniques offer a reliable alternative for prediction, control, and optimization of dynamical systems, but their application in UCG is still in the early stages. This work aims to bridge this gap by implementing three cutting-edge nonlinear identification structures: Non-linear Autoregressive with Exogenous Inputs (NARX), Hammerstein–Wiener (HW), and State-Space Neural Networks (SSNN) on the UCG process to obtain a multivariable control-oriented model. The contributions include synthesizing an excitation signal for data acquisition, outlining the non-linear system identification procedure, and comparing predictive capabilities using statistical tools. The simulation results demonstrate a rigorous comparison of various techniques for the heating value and flowrate of the syngas, which are the outputs of the UCG process. The results of the analysis show that NARX outperforms other structures in statistical metrics, with MAE, RMSE, and Best fit values of 1.51,1.9, and 0.9, respectively, for the heating value; and 0.25,0.31, and 0.94, respectively, for the flowrate. Consequently, the outputs of the NARX model are compared with the experimental data obtained from the UCG project Thar, which show a good match for both outputs.