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

Kyu-Jeong Lee, So-Won Choi and Eul-Bum Lee ()
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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
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

Energies, 2025, vol. 18, issue 4, 1-45

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.

Keywords: steel mill; steam power plant; burners; boiler combustion optimization; combustion control; by-product gases; gross heat loss prediction; advanced boiler combustion control model; machine learning; random forest; classification and regression tree (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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