An Interpretable Time Series Forecasting Model for Predicting NOx Emission Concentration in Ferroalloy Electric Arc Furnace Plants

Youngjin Seol, Seunghyun Lee, Jiho Lee, Chang-Wan Kim, Hyun Su Bak, Youngchul Byun and Janghyeok Yoon ()
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Youngjin Seol: Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
Seunghyun Lee: Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
Jiho Lee: Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
Chang-Wan Kim: School of Mechanical Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
Hyun Su Bak: Particulate Matter Research Center, Research Institute of Industrial Science and Technology (RIST), Gwangyang 57801, Republic of Korea
Youngchul Byun: Particulate Matter Research Center, Research Institute of Industrial Science and Technology (RIST), Gwangyang 57801, Republic of Korea
Janghyeok Yoon: Department of Industrial Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea

Mathematics, 2024, vol. 12, issue 6, 1-22

Abstract: Considering the pivotal role of ferroalloys in the steel industry and the escalating global emphasis on sustainability (e.g., zero emissions and carbon neutrality), the demand for ferroalloys is anticipated to increase. However, the electric arc furnace (EAF) of ferroalloy plants generates substantial amounts of nitrogen oxides (NOx) because of the high-temperature combustion processes. Despite the substantial contributions of many studies on NOx prediction from various industrial facilities, there is a lack of studies considering the environmental condition of the EAF in ferroalloy plants. Therefore, this study presents a deep learning model for predicting NOx emissions from ferroalloy plants and further can provide guidelines for predicting NOx in industrial sites equipped with electric furnaces. In this study, we collected various historical data from the manufacturing execution system of electric furnaces and exhaust gas systems to develop a prediction model. Additionally, an interpretable artificial intelligence method was employed to track the effects of each variable on the NOx emissions. The proposed prediction model can provide decision support to reduce NOx emissions. Furthermore, the interpretation of the model contributes to a better understanding of the factors influencing NOx emissions and the development of effective strategies for emission reduction in ferroalloys EAF plants.

Keywords: NOx emission; electric arc furnace; deep learning; explainable artificial intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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