Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)

Sang-Mok Lee, So-Won Choi and Eul-Bum Lee ()
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Sang-Mok 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, 2023, vol. 16, issue 19, 1-33

Abstract: The energy-intensive steel industry, which consumes substantial amounts of electricity, meets its power demands through external electricity purchases and self-generation through the operation of its own generators. This study aimed to optimize boiler combustion efficiency and increase power generation output by deriving optimal operational values for O 2 and CO within the boiler flue gas using machine learning (ML) with the aim of achieving maximum boiler efficiency. This study focuses on the power-generation boilers at steel mill P in Korea. First, 361 types of operation data from power generation equipment were collected and preprocessed. Subsequently, a partial least squares regression (PLSR) algorithm was used to develop a prediction model for O 2 and CO values, known as the Boiler Flue Gas Prediction Model (BFG-PM). The prediction accuracy for O 2 was notably high (83.2%), whereas that for CO was lower (53.4%). Nonetheless, the model’s reliability was high because more than 90% of the predicted values were within a 10% error range. Finally, the correlation of the BFG-PM model was applied to the performance test code (PTC) 4.0 for the boiler efficiency calculations formula, deriving the optimal O 2 and CO control points. Through a simulation, it was verified that the boiler efficiency was improved by controlling the combustion air. In addition, an average increase in boiler efficiency of 0.29% was confirmed by applying it directly to the generator operating on-site. The results of this study are expected to contribute to annual cost savings, with a reduction of USD 217,000 in electricity purchasing costs and USD 19,700 in greenhouse gas emissions trading expenses.

Keywords: machine learning; power plant in steel mill; boiler efficiency; combustion control; flue gas prediction; regression; partial least squares; performance test code 4.0 (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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