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

Author

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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)

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.

Suggested Citation

  • Żaklin Grądz & Waldemar Wójcik & Baglan Imanbek & Bakhyt Yeraliyeva, 2025. "Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion," Energies, MDPI, vol. 18(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:258-:d:1563075
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    References listed on IDEAS

    as
    1. Mollo, Malebo & Kolesnikov, Andrei & Makgato, Seshibe, 2022. "Simultaneous reduction of NOx emission and SOx emission aided by improved efficiency of a Once-Through Benson Type Coal Boiler," Energy, Elsevier, vol. 248(C).
    2. Oleksandra Hotra & Svitlana Kovtun & Oleg Dekusha & Żaklin Grądz, 2021. "Prospects for the Application of Wavelet Analysis to the Results of Thermal Conductivity Express Control of Thermal Insulation Materials," Energies, MDPI, vol. 14(17), pages 1-15, August.
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