A Mechanistic Model Based on Statistics for the Prediction of a Converter’s End-Point Molten Steel Temperature

Fang Gao; Dazhi Wang; Yanping Bao; Xin Liu; Lidong Xing; Lihua Zhao

LAPSE

Living Archive for Process Systems Engineering

LAPSE:2023.36592

Published Article

LAPSE:2023.36592

A Mechanistic Model Based on Statistics for the Prediction of a Converter’s End-Point Molten Steel Temperature

Fang Gao, Dazhi Wang, Yanping Bao, Xin Liu, Lidong Xing, Lihua Zhao

September 20, 2023

With the high efficiency and automation of converter smelting, it is becoming increasingly important to predict and control the endpoint temperature of the converter. Based on the heat balance, a model for predicting the molten pool temperature in a converter was established. Moreover, the statistical method of multiple linear regression was used to calculate the converter heat loss coefficient, greatly improving the prediction accuracy of the mechanistic model. Using the model, the oxidation process for each element in the molten pool, the melting processes of scrap, and the flux were also calculated. The model could better approximate the actual smelting process. Data from a 130 t converter were collected to validate the model. When the error ranges were limited to ±20 and ±15 °C, the model hit rates were 96 and 86.7%, respectively.

Record ID

LAPSE:2023.36592

Keywords

converter, endpoint temperature, heat loss coefficient, model, multiple linear regression

Subject

Numerical Methods and Statistics

Suggested Citation

Gao F, Wang D, Bao Y, Liu X, Xing L, Zhao L. A Mechanistic Model Based on Statistics for the Prediction of a Converter’s End-Point Molten Steel Temperature. (2023). LAPSE:2023.36592

Author Affiliations

Gao F: State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
Wang D: State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
Bao Y: State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
Liu X: State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
Xing L: State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China; Technical Support Center for Prevention and Control of Disastrous Accidents in Metal Smelting, University of Science and Technology Beijing,
Zhao L: School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China

Journal Name

Processes

Volume

11

Issue

8

First Page

2233

Year

2023

Publication Date

2023-07-25