Ján Kaˇ cur, Patrik Flegner
The basic oxygen steelmaking process (BOS) faces the issue of the absence of information about the melt temperature and the carbon concentration in the melt. Although deterministic models for predicting steelmaking process variables are being developed in metallurgical research, machine-learning models can model the nonlinearities of process variables and provide a good estimate of the target process variables. In this paper, five machine learning methods were applied to predict the temperature and carbon concentration in the melt.
@article{fddedac8-a2ec-48bf-a200-a9c8d59773fc,
title={047 Prediction of Temperature and Carbon Concentration in Oxygen Steelmaking by Machine Learning A Comparative Study},
author={Ján Kaˇ cur and Patrik Flegner},
year={2026},
language={en}
}TY - JOUR TI - 047 Prediction of Temperature and Carbon Concentration in Oxygen Steelmaking by Machine Learning A Comparative Study AU - Ján Kaˇ cur AU - Patrik Flegner PY - 2026 LA - en ER -
Rajesh Jha, Bimal Kumar Jha
This book describes the application of artificial intelligence (AI) and machine learning (ML) concepts to develop predictive models that can be used t
Unknown, Unknown
This study focuses on the fundamental characteristics of solid iron, which is predominantly composed of iron atoms and provides a basis for understand
Unknown, Unknown
This chapter discusses metal casting processes, highlighting the diversity and common characteristics among them. The objective is to elucidate the fu
This paper addresses the challenge of assessing the feasibility of wind power plant projects at sites with insufficient or no local historic wind data
Important advances in electrochemical engineering technology over the last three decades have fostered the development of a lternative methods to alle