A COMPARATIVE STUDY OF DATA-DRIVEN MODELING METHODS FOR SOFT-SENSING IN UNDERGROUND COAL GASIFICATION

Authors

  • Ján Kačur Institute of Control and Informatization of Production Processes, Faculty BERG, Technical University of Kosice http://orcid.org/0000-0003-1498-447X
  • Milan Durdán Institute of Control and Informatization of Production Processes, Faculty BERG, Technical University of Kosice
  • Marek Laciak Institute of Control and Informatization of Production Processes, Faculty BERG, Technical University of Kosice
  • Patrik Flegner Institute of Control and Informatization of Production Processes, Faculty BERG, Technical University of Kosice

DOI:

https://doi.org/10.14311/AP.2019.59.0322

Keywords:

underground coal gasification, syngas calorific value, underground temperature, time series prediction, machine learning, soft-sensing

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.

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Published

2019-08-31

Issue

Vol. 59 No. 4 (2019)

Section

Articles

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How to Cite

Kačur, J., Durdán, M., Laciak, M., & Flegner, P. (2019). A COMPARATIVE STUDY OF DATA-DRIVEN MODELING METHODS FOR SOFT-SENSING IN UNDERGROUND COAL GASIFICATION. Acta Polytechnica, 59(4), 322-351. https://doi.org/10.14311/AP.2019.59.0322