Zhou WANG, Jian CHANG
This study analyzes the factors affecting end-point manganese content in the BOF steelmaking process, aiming to develop a reliable predictive model. A multiple linear regression model was constructed based on actual production data to predict the end-point manganese content. To enhance prediction accuracy, an artificial neural network (ANN) approach was employed, resulting in the establishment of a backpropagation (BP) neural network model. Furthermore, a combined genetic algorithm and BP neural network model (GA-BP) was developed, integrating the strengths of both methodologies. Comparative validation of these models demonstrated that the GA-BP neural network model achieved the highest prediction accuracy, with hit rates of 90% and 84% for predictive errors within ±0.03% and ±0.025%, respectively. The results indicate that the combined GA-BP model outperforms the traditional regression and BP models, providing a highly accurate and beneficial tool for optimizing manganese content prediction in actual steel production processes.
@article{d471d8ef-6764-4ab6-bb59-9abfb1a8d083,
title={054 Prediction Model of End point Manganese Content for BOF Steelmaking Process},
author={Zhou WANG and Jian CHANG},
year={2026},
language={en}
}TY - JOUR TI - 054 Prediction Model of End point Manganese Content for BOF Steelmaking Process AU - Zhou WANG AU - Jian CHANG PY - 2026 LA - en ER -
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