Open AccessA COMPARATIVE STUDY OF DATA-DRIVEN MODELING METHODS FOR SOFT-SENSING IN UNDERGROUND COAL GASIFICATION
Author(s) -
Ján Kačur,
Milan Durdán,
Marek Laciak,
Patrik Flegner
Publication year - 2019
Publication title -
acta polytechnica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.207
H-Index - 15
eISSN - 1805-2363
pISSN - 1210-2709
DOI - 10.14311/ap.2019.59.0322
Subject(s) - underground coal gasification , coal , syngas , heat of combustion , multivariate adaptive regression splines , kriging , process engineering , support vector machine , petroleum engineering , multivariate statistics , engineering , coal mining , regression analysis , computer science , artificial intelligence , waste management , machine learning , nonparametric regression , chemistry , organic chemistry , combustion , hydrogen
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.