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

Andrzej Kotyra () and Konrad Gromaszek
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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

Energies, 2025, vol. 18, issue 19, 1-17

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.

Keywords: biomass; co-combustion; convolutional neural networks; image 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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