Data-Driven Approach Using Supervised Learning for Predicting Endpoint Temperature of Molten Steel in the Electric Arc Furnace

This study presents the development of a decision support system that focuses on predicting the endpoint temperature of molten steel to manage the process of an electric arc furnace more systematically. The decision support system leverages a data-driven approach that consists of the following modules: 1) a data preprocessing module that specifically includes raw data filtering, feature engineering, and outlier detection; 2) a feature selection module based on domain knowledge; 3) regression modeling module that employs a supervised learning algorithm to forecast an endpoint temperature; and 4) sensitivity analysis module to identify the correlation between input and output metric. The applicability of the system is demonstrated through a validation study using real-world operational data from Hyundai Steel located in Pohang, South Korea. The validation results show that the endpoint temperatures predicted by the system are evenly scattered to a perfect-fit line within 5% errors of the actual temperatures. The results also indicate that CaO, power, and melting score have the most significant impact on the endpoint temperature, in which temperature decreases as CaO increases and increases as the power and melting score increase.