Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion

Żaklin Grądz (), Waldemar Wójcik, Baglan Imanbek and Bakhyt Yeraliyeva
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Żaklin Grądz: Department of Electronics and Information Technology, Lublin University of Technology, Nadbystrzycka 38D, 20-618 Lublin, Poland
Waldemar Wójcik: Department of Electronics and Information Technology, Lublin University of Technology, Nadbystrzycka 38D, 20-618 Lublin, Poland
Baglan Imanbek: Faculty Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Al-Farabi Avenue 71, Almaty 050040, Kazakhstan
Bakhyt Yeraliyeva: Faculty of Information Technology, Taraz University Named After M.Kh. Dulaty, Suleymenova St. 7, Taraz 080000, Kazakhstan

Energies, 2025, vol. 18, issue 2, 1-14

Abstract: In the European Union, coal consumption in the power industry has been declining over time. Energy sources such as renewable energy, nuclear energy, and natural gas are being used on an increasing scale. Despite this, fossil fuels continue to be an important pillar of the energy industry in many countries around the world. There are various methods for diagnosing the combustion process, and one of them is based on a fibre-optic system for monitoring changes in flame intensity. Thanks to its innovative design, it allows information to be extracted from the flame under conditions of high temperatures and high dusting. The article presents an analysis of measurement signals for the recognition of states of flame intensity resulting from changes in the operating point of a power boiler. Trends in the flame that occur during the combustion process, which exceed the ranges specified by experts, can cause disturbances in combustion stability. The measurement data after preprocessing were classified using artificial neural networks to determine the conditions for flame stability. Based on the recurrent neural network models used, a classification accuracy of more than 99% was achieved. This allowed for the recognition of flame states in the combustion process.

Keywords: combustion process; flame; recurrent neural networks; classification (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: 2025
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