Abstract
Underground coal gasification (UCG) is a technological process, which converts solid coal into a gas in the underground, using injected gasification agents. In the UCG process, a lot of process variables can be measurable with common measuring devices, but there are variables that cannot be measured so easily, e.g., the temperature deep underground. It is also necessary to know the future impact of different control variables on the syngas calorific value in order to support a predictive control. This paper examines the possibility of utilizing Neural Networks, Multivariate Adaptive Regression Splines and Support Vector Regression in order to estimate the UCG process data, i.e., syngas calorific value and underground temperature. It was found that, during the training with the UCG data, the SVR and Gaussian kernel achieved the best results, but, during the prediction, the best result was obtained by the piecewise-cubic type of the MARS model. The analysis was performed on data obtained during an experimental UCG with an ex-situ reactor.
Highlights
The Support Vector Regression (SVR) model achieved a better performance when using two input variables
Three approaches were examined in order to find the best prediction method for the Underground coal gasification (UCG) data soft-sensing
In the UCG process, it is a complicated to predict some process variables because it is not possible to see the state of the process that runs in an inaccessible environment
Summary
Underground coal gasification (UCG) represents an in-situ controlled combustion of coal where valuable gases (i.e., syngas) are produced. The UCG represents an alternative to traditional coal mining methods. The UCG allows to mine coal from deep coal seams, seams affected by tectonic disturbances, seams with a low grade, or seams that have a thin stratum profile. Various coal types can be gasified, e.g., lignite or bituminous. The UCG offers a low surface damage, low solid waste discharge and lower emissions of SO2, NOx to the air than the traditional coal mining
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