Machine Learning-Based Tap Temperature Prediction and Control for Optimized Power Consumption in Stainless Electric Arc Furnaces (EAF) of Steel Plants

Abstract

The steel industry has been forced to switch from the traditional blast furnace to the electric arc furnace (EAF) process to reduce carbon emissions. However, EAF still relies entirely on the operators’ proficiency to determine the electrical power input. This study aims to enhance the efficiency of the EAF process by predicting the tap temperature in real time through a data-driven approach and by applying a system that automatically sets the input amount of power to the production site. We developed a tap temperature prediction model (TTPM) with a machine learning (ML)-based support vector regression (SVR) algorithm. The operation data of the stainless EAF, where the actual production work was carried out, were extracted, and the models using six ML algorithms were trained. The model validation results show that the model with an SVR radial basis function (RBF) algorithm resulted in the best performance with a root mean square error (RMSE) of 20.14. The SVR algorithm performed better than the others for features such as noise. As a result of a five-month analysis of the operating performance of the developed TTPM for the stainless EAF, the tap temperature deviation decreased by 17% and the average power consumption decreased by 282 kWh/heat compared with the operation that depended on the operator’s skill. In the results of the economic evaluation of the facility investment, the economic feasibility was found to be sufficient, with an internal rate of return (IRR) of 35.8%. Applying the developed TTPM to the stainless EAF and successfully operating it for ten months verified the system’s reliability. In terms of the increasing proportion of EAF production used to decarbonize the steel industry, it is expected that various studies will be conducted more actively to improve the efficiency of the EAF process in the future. This study contributes to the improvement of steel companies’ manufacturing competitiveness and the carbon neutrality of the steel industry by achieving the energy and production efficiency improvements associated with the EAF process.

Modeling of Predictive Maintenance Systems for Laser-Welders in Continuous Galvanizing Lines Based on Machine Learning with Welder Control Data

“…Go et al developed a prediction model of rolling bearing water corrosion based on the SVM algorithm by using vibration data obtained from the rolling bearing acceleration sensor to diagnose the failure of the rotating body [12]. Choi et al developed a support vector regression (SVR)-based tap temperature prediction model (TTPM) using EAF operation data to automatically set the amount of power input by predicting tap temperatures in real time in the electric arc furnace (EAF) process of an integrated steelworks [13]. These studies aimed to control this process more precisely and improve productivity by minimizing deviations or errors that occur while engineers control the process.…”

“…Support Vector Machine (SVM)Vibration data obtained from the rolling bearing acceleration sensor Prediction of moisture-induced corrosion in rolling bearings Go et al[12] Support Vector Regression (SVR) Operation data of an electric arc furnace (EAF)Real-time prediction of tap temperature and automatic setting of power input for EAF Choi et al[13] …”

Analysis of the Steelmaking Process via Data Mining and Pearson Correlation

“…On the other hand, machine learning techniques have recently been used to analyze and improve the steelmaking control process [ 6 , 7 , 8 ]. The selected models differ in their degree of sophistication and scope.…”

A Proposed Methodology to Evaluate Machine Learning Models at Near-Upper-Bound Predictive Performance—Some Practical Cases from the Steel Industry

“…As such, there have been numerous studies that have used ML models, which are in fact statistical models, to resolve challenges in a wide range of processes and optimization problems within the steel industry. Predicting the electrical energy (EE) consumption of the electric arc furnace (EAF) [1], the tap temperature of the EAF [2], the temperature of molten steel during treatment in secondary metallurgy [3], end-point prediction of temperature and alloying elements in the basic oxygen furnace (BOF) [4], and prediction of the molten steel temperature in the steel ladle and tundish [5] are several examples of ML models applied in the context of steel process engineering.…”