JUHEE BAE, YURONG LI
The steel-making process in a Basic Oxygen Furnace (BOF) must meet a combination of target values such as final melt temperature and upper limits of carbon and phosphorus content with minimum material loss. This study aims to enhance precision in reaching the optimal blow end time (cut-off point) for these targets, which is traditionally reliant on operators' experience. Using a comprehensive production dataset, we implement standard machine learning models to predict these endpoint targets. Furthermore, we explore various causes of prediction uncertainty through the analysis of raw data and engineered features. Our findings demonstrate robust prediction hit rates for temperature, carbon, and phosphorus of 88%, 92%, and 89%, respectively. This indicates that by applying machine learning techniques, we can significantly enhance productivity, material and energy efficiency in the BOF process, thereby reducing environmental impact and operational costs.
@article{169b8d2e-509f-4c4e-8d20-a9cfbd3468e2,
title={Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System},
author={JUHEE BAE and YURONG LI},
year={2020},
language={en}
}TY - JOUR TI - Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System AU - JUHEE BAE AU - YURONG LI PY - 2020 LA - en ER -
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