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Artificial Intelligence-Driven Approach to Optimizing Boiler Power Generation Efficiency: The Advanced Boiler Combustion Control Model

Author

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  • Kyu-Jeong Lee

    (Graduate Institute of Ferrous and Eco Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
    Energy Technology Section, Investment and Engineering Department, Pohang Iron and Steel Company (POSCO), Pohang 37754, Republic of Korea)

  • So-Won Choi

    (Graduate Institute of Ferrous and Eco Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea)

  • Eul-Bum Lee

    (Graduate Institute of Ferrous and Eco Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
    Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea)

Abstract

The by-product gases generated during steel manufacturing processes, including blast furnace gas, coke oven gas, and Linz–Donawitz gas, exhibit considerable variability in composition and supply. Consequently, achieving stable combustion control of these gases is critical for improving boiler efficiency. This study developed the advanced boiler combustion control model (ABCCM) by combining the random forest (RF) and classification and regression tree (CART) algorithms to optimize the combustion of steam power boilers using steel by-product gases. The ABCCM derives optimal combustion patterns in real time using the RF algorithm and minimizes fuel consumption through the CART algorithm, thereby optimizing the overall gross heat rate. The results demonstrate that the ABCCM achieves a 0.86% improvement in combustion efficiency and a 1.7% increase in power generation efficiency compared to manual control methods. Moreover, the model reduces the gross heat rate by 58.3 kcal/kWh, which translates into an estimated annual energy cost saving of USD 89.6 K. These improvements contribute considerably to reducing carbon emissions, with the ABCCM being able to optimize fuel utilization and minimize excess air supply, thus enhancing the overall sustainability of steelmaking operations. This study underscores the potential of the ABCCM to extend beyond the steel industry.

Suggested Citation

  • Kyu-Jeong Lee & So-Won Choi & Eul-Bum Lee, 2025. "Artificial Intelligence-Driven Approach to Optimizing Boiler Power Generation Efficiency: The Advanced Boiler Combustion Control Model," Energies, MDPI, vol. 18(4), pages 1-45, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:820-:d:1588015
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    References listed on IDEAS

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    1. Wu, Yixi & Wang, Ziqi & Shi, Chenli & Jin, Xiaohang & Xu, Zhengguo, 2024. "A novel data-driven approach for coal-fired boiler under deep peak shaving to predict and optimize NOx emission and heat exchange performance," Energy, Elsevier, vol. 304(C).
    2. Xu, Wentao & Poh, Kimleng & Song, Siheng & Huang, Yaji, 2025. "Research on reducing pollutant, improving efficiency and enhancing running safety for 1000 MW coal-fired boiler based on data-driven evolutionary optimization and online retrieval method," Applied Energy, Elsevier, vol. 377(PA).
    3. Sang-Mok Lee & So-Won Choi & Eul-Bum Lee, 2023. "Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)," Energies, MDPI, vol. 16(19), pages 1-33, September.
    4. Zeng, Yujiao & Xiao, Xin & Li, Jie & Sun, Li & Floudas, Christodoulos A. & Li, Hechang, 2018. "A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant," Energy, Elsevier, vol. 143(C), pages 881-899.
    5. Sinha, Aparna & Das, Debanjan & Palavalasa, Suneel Kumar, 2023. "dClink: A data-driven based clinkering prediction framework with automatic feature selection capability in 500 MW coal-fired boilers," Energy, Elsevier, vol. 276(C).
    6. Yang-Kon Kim & Eul-Bum Lee, 2018. "Optimization Simulation, Using Steel Plant Off-Gas for Power Generation: A Life-Cycle Cost Analysis Approach," Energies, MDPI, vol. 11(11), pages 1-17, October.
    7. Shi, Yan & Zhong, Wenqi & Chen, Xi & Yu, A.B. & Li, Jie, 2019. "Combustion optimization of ultra supercritical boiler based on artificial intelligence," Energy, Elsevier, vol. 170(C), pages 804-817.
    8. Liu, Xingrang & Bansal, R.C., 2014. "Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant," Applied Energy, Elsevier, vol. 130(C), pages 658-669.
    9. de Oliveira Junior, Valter B. & Pena, João G. Coelho & Salles, José L. Félix, 2016. "An improved plant-wide multiperiod optimization model of a byproduct gas supply system in the iron and steel-making process," Applied Energy, Elsevier, vol. 164(C), pages 462-474.
    10. Filipe Neves & Armando A. Soares & Abel Rouboa, 2024. "A Comparison of Different Biomass Combustion Mechanisms in the Transient State," Energies, MDPI, vol. 17(9), pages 1-17, April.
    11. So-Won Choi & Eul-Bum Lee, 2022. "Contractor’s Risk Analysis of Engineering Procurement and Construction (EPC) Contracts Using Ontological Semantic Model and Bi-Long Short-Term Memory (LSTM) Technology," Sustainability, MDPI, vol. 14(11), pages 1-32, June.
    12. Szega, Marcin & Czyż, Tomasz, 2019. "Problems of calculation the energy efficiency of a dual-fuel steam boiler fired with industrial waste gases," Energy, Elsevier, vol. 178(C), pages 134-144.
    13. Rahat, Alma A.M. & Wang, Chunlin & Everson, Richard M. & Fieldsend, Jonathan E., 2018. "Data-driven multi-objective optimisation of coal-fired boiler combustion systems," Applied Energy, Elsevier, vol. 229(C), pages 446-458.
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