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Determination of Coal and Biomass Co-Combustion Process States Using Convolutional Neural Networks

Author

Listed:
  • Andrzej Kotyra

    (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)

Abstract

The paper presents the application of high-speed flame imaging combined with convolutional neural networks (CNNs) for determining different states of biomass–coal co-combustion in terms of thermal power and excess air coefficient. The experimental setup and methodology used in a laboratory-scale co-combustion system are described, highlighting tests conducted across nine defined operational variants. The performance of several state-of-the-art CNN architectures was examined, focusing particularly on those achieving the highest classification metrics and exploring the dependence of input image resolution and applying a transfer learning paradigm. By benchmarking various CNNs on a large, diverse image dataset without preprocessing, the research advances intelligent, automated control systems for improved stability, efficiency, and emissions control, bridging advanced visual diagnostics with real-time industrial applications. The summary includes recommendations and potential directions for further research related to the use of image data and machine learning techniques in industry.

Suggested Citation

  • Andrzej Kotyra & Konrad Gromaszek, 2025. "Determination of Coal and Biomass Co-Combustion Process States Using Convolutional Neural Networks," Energies, MDPI, vol. 18(19), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:19:p:5219-:d:1762735
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    References listed on IDEAS

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    1. Chen, Ting & Zhao, Yuanfang & Huang, Shenda & Jin, Yiying & Wang, Meizhen & Feng, Huajun & Yin, Jun, 2025. "Influence of papermaking biomass co-firing on operation energy efficiency and gas emission stability of a coal-fired thermal power plant: A case study," Energy, Elsevier, vol. 327(C).
    2. Han, Zhezhe & Hossain, Md. Moinul & Wang, Yuwei & Li, Jian & Xu, Chuanlong, 2020. "Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network," Applied Energy, Elsevier, vol. 259(C).
    3. Joachim Kozioł & Joanna Czubala & Michał Kozioł & Piotr Ziembicki, 2020. "Generalized Energy and Ecological Characteristics of the Process of Co-Firing Coal with Biomass in a Steam Boiler," Energies, MDPI, vol. 13(10), pages 1-12, May.
    4. Tóth, Pál & Garami, Attila & Csordás, Bernadett, 2017. "Image-based deep neural network prediction of the heat output of a step-grate biomass boiler," Applied Energy, Elsevier, vol. 200(C), pages 155-169.
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