Application of Fuzzy Neural Networks in Combustion Process Diagnostics

Żaklin Grądz (), Waldemar Wójcik, Konrad Gromaszek, Andrzej Kotyra, Saule Smailova, Aigul Iskakova, Bakhyt Yeraliyeva, Saule Kumargazhanova and Baglan Imanbek
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Żaklin Grądz: Department of Electronics and Information Technology, Lublin University of Technology, Nadbystrzycka St. 38D, 20-618 Lublin, Poland
Waldemar Wójcik: Department of Electronics and Information Technology, Lublin University of Technology, Nadbystrzycka St. 38D, 20-618 Lublin, Poland
Konrad Gromaszek: Department of Electronics and Information Technology, Lublin University of Technology, Nadbystrzycka St. 38D, 20-618 Lublin, Poland
Andrzej Kotyra: Department of Electronics and Information Technology, Lublin University of Technology, Nadbystrzycka St. 38D, 20-618 Lublin, Poland
Saule Smailova: School of Digital Technology and Artificial Intelligence, D. Serikbayev East Kazakhstan State Technical University, Protozanov St. 69, 070004 Ust-Kamenogorsk, Kazakhstan
Aigul Iskakova: Departament of Automation and Control, Satbayev University, Satpaev St. 22a, 050013 Almaty, Kazakhstan
Bakhyt Yeraliyeva: Faculty of Information Technology, M.Kh. Dulaty Taraz Regional University, Tole Bi St. 40, 080000 Taraz, Kazakhstan
Saule Kumargazhanova: School of Digital Technology and Artificial Intelligence, D. Serikbayev East Kazakhstan State Technical University, Protozanov St. 69, 070004 Ust-Kamenogorsk, Kazakhstan
Baglan Imanbek: Faculty Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Al-Farabi Ave. 71, 050040 Almaty, Kazakhstan

Energies, 2023, vol. 17, issue 1, 1-19

Abstract: Coal remains one of the key raw materials used in the energy industry to generate electricity and heat. As a result, diagnostics of the combustion process is still an important topic of scientific research. Correct implementation of the process allows the emission of pollutants into the atmosphere to be kept at a compliant level. Therefore, it is important to conduct the process in a manner that will not exceed these standards. A preliminary analysis of the measurement signals was carried out, and signal predictions of flame intensity changes were determined using the autoregressive moving average (ARMA) model. Different fuzzy neural network architectures have been investigated. Binary and multi-class classifications of flame states were conducted. The best results were obtained from the ANFIS_grid partition model, producing an accuracy of 95.46% for binary classification and 79.08% for multi-class classification. The accuracy of the recognition of flame states and the high convergence of the determined predictions with measurement signals validate the application of the proposed approach in diagnosing or controlling the combustion process of pulverized coal and its mixtures with biomass. Expert decisions determine the range of acceptable states.

Keywords: flame intensity; flame state classification; fuzzy neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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