IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i4p820-d1588015.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/4/820/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/4/820/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. 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.
    11. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
    2. Wang, Zhi & Peng, Xianyong & Zhou, Huaichun & Cao, Shengxian & Huang, Wenbo & Yan, Weijie & Li, Kuangyu & Fan, Siyuan, 2024. "A dynamic modeling method using channel-selection convolutional neural network: A case study of NOx emission," Energy, Elsevier, vol. 290(C).
    3. Sergio García García & Vicente Rodríguez Montequín & Henar Morán Palacios & Adriano Mones Bayo, 2020. "A Mixed Integer Linear Programming Model for the Optimization of Steel Waste Gases in Cogeneration: A Combined Coke Oven and Converter Gas Case Study," Energies, MDPI, vol. 13(15), pages 1-25, July.
    4. Xu, Wentao & Huang, Yaji & Song, Siheng & Yue, Junfeng & Chen, Bo & Liu, Yuqing & Zou, Yiran, 2023. "A new on-line combustion optimization approach for ultra-supercritical coal-fired boiler to improve boiler efficiency, reduce NOx emission and enhance operating safety," Energy, Elsevier, vol. 282(C).
    5. Yuan, Yuxing & Na, Hongming & Chen, Chuang & Qiu, Ziyang & Sun, Jingchao & Zhang, Lei & Du, Tao & Yang, Yuhang, 2024. "Status, challenges, and prospects of energy efficiency improvement methods in steel production: A multi-perspective review," Energy, Elsevier, vol. 304(C).
    6. Gavirineni Naveen Kumar & Edison Gundabattini, 2022. "Investigation of Supercritical Power Plant Boiler Combustion Process Optimization through CFD and Genetic Algorithm Methods," Energies, MDPI, vol. 15(23), pages 1-28, November.
    7. Chandrakant Nikam, Keval & Jathar, Laxmikant & Shelare, Sagar Dnyaneshwar & Shahapurkar, Kiran & Dambhare, Sunil & Soudagar, Manzoore Elahi M. & Mubarak, Nabisab Mujawar & Ahamad, Tansir & Kalam, M.A., 2023. "Parametric analysis and optimization of 660 MW supercritical power plant," Energy, Elsevier, vol. 280(C).
    8. Xi, Han & Wu, Xiao & Chen, Xianhao & Sha, Peng, 2021. "Artificial intelligent based energy scheduling of steel mill gas utilization system towards carbon neutrality," Applied Energy, Elsevier, vol. 295(C).
    9. 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.
    10. Lei Han & Lingmei Wang & Hairui Yang & Chengzhen Jia & Enlong Meng & Yushan Liu & Shaoping Yin, 2023. "Optimization of Circulating Fluidized Bed Boiler Combustion Key Control Parameters Based on Machine Learning," Energies, MDPI, vol. 16(15), pages 1-23, July.
    11. Qianchao Wang & Hongcan Xu & Lei Pan & Li Sun, 2020. "Active Disturbance Rejection Control of Boiler Forced Draft System: A Data-Driven Practice," Sustainability, MDPI, vol. 12(10), pages 1-18, May.
    12. Cui, Jia & Fu, Tianhe & Yang, Junyou & Wang, Shunjiang & Li, Chaoran & Han, Ni & Zhang, Ximing, 2025. "An active early warning method for abnormal electricity load consumption based on data multi-dimensional feature," Energy, Elsevier, vol. 314(C).
    13. Heydar Maddah & Milad Sadeghzadeh & Mohammad Hossein Ahmadi & Ravinder Kumar & Shahaboddin Shamshirband, 2019. "Modeling and Efficiency Optimization of Steam Boilers by Employing Neural Networks and Response-Surface Method (RSM)," Mathematics, MDPI, vol. 7(7), pages 1-17, July.
    14. Chen Ya & Zhang Xintian & Liu Haoxiang, 2021. "Investigating the Impact of Capacity Utilization on Carbon Dioxide Emission: Evidence from China’s Iron and Steel Industry," Journal of Systems Science and Information, De Gruyter, vol. 9(6), pages 681-703, December.
    15. Li, Xinli & Wang, Yingnan & Zhu, Yun & Yang, Guotian & Liu, He, 2021. "Temperature prediction of combustion level of ultra-supercritical unit through data mining and modelling," Energy, Elsevier, vol. 231(C).
    16. Jiang, Sheng-Long & Wang, Meihong & Bogle, I. David L., 2023. "Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization," Applied Energy, Elsevier, vol. 350(C).
    17. Hee-Kwan Shin & Jae-Min Cho & Eul-Bum Lee, 2019. "Electrical Power Characteristics and Economic Analysis of Distributed Generation System Using Renewable Energy: Applied to Iron and Steel Plants," Sustainability, MDPI, vol. 11(22), pages 1-27, November.
    18. Zetterholm, J. & Ji, X. & Sundelin, B. & Martin, P.M. & Wang, C., 2017. "Dynamic modelling for the hot blast stove," Applied Energy, Elsevier, vol. 185(P2), pages 2142-2150.
    19. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    20. Xue, Wenyuan & Lu, Yichen & Wang, Zhi & Cao, Shengxian & Sui, Mengxuan & Yang, Yuan & Li, Jiyuan & Xie, Yubin, 2024. "Reconstructing near-water-wall temperature in coal-fired boilers using improved transfer learning and hidden layer configuration optimization," Energy, Elsevier, vol. 294(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:820-:d:1588015. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.