Regression Models Utilization to the Underground Temperature Determination at Coal Energy Conversion

Abstract

The underground coal gasification represents a technology capable of obtaining synthetic coal gas from hard-reached coal deposits and coal beds with tectonic faults. This technology is also less expensive than conventional coal mining. The cavity is formed in the coal seam by converting coal to synthetic gas during the underground coal gasification process. The cavity growth rate and the gasification queue’s moving velocity are affected by controllable variables, i.e., the operation pressure, the gasification agent, and the laboratory coal seam geometry. These variables can be continuously measured by standard measuring devices and techniques as opposed to the underground temperature. This paper researches the possibility of the regression models utilization for temperature data prediction for this reason. Several regression models were proposed that were differed in their structures, i.e., the number and type of selected controllable variables as independent variables. The goal was to find such a regression model structure, where the underground temperature is predicted with the greatest possible accuracy. The regression model structures’ proposal was realized on data obtained from two laboratory measurements realized in the ex situ reactor. The obtained temperature data can be used for visualization of the cavity growth in the gasified coal seam.

Investigation of Underground Coal Gasification in Laboratory Conditions: A Review of Recent Research

“…For temperature prediction, regression models calculated from measured ex situ data were also proposed. Durdán et al [94] modeled the temperature in a small ex situ reactor using multiple linear regression models. Compared to the previous approach [93], applying thermo-physical parameters to the calculation is unnecessary.…”

Modeling and Control of Energy Conversion during Underground Coal Gasification Process

“…Paper [11] researches the possibility of the model's utilization for temperature prediction in UCG process. Within experimental research, several regression models were proposed that differed in their structures, i.e., the number and type of selected controllable variables as independent variables.…”

“…Improving the prediction of these temperature higher accuracy makes it possible to identify places in the coal seam where coal to transformed, and the underground cavity is formed. In addition, the prediction o seam temperatures allows the development of methods to control the UCG process on modeled temperatures in the coal seam [11]. The proposed models should contribute to developing a methodology for predicting temperatures in a gasified coal seam.…”