ISIJ International
Online ISSN : 1347-5460
Print ISSN : 0915-1559
ISSN-L : 0915-1559
Regular Article
A New AdaBoost.IR Soft Sensor Method for Robust Operation Optimization of Ladle Furnace Refining
Hui-Xin TianYu-Dong LiuKun LiRan-Ran YangBo Meng
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2017 Volume 57 Issue 5 Pages 841-850


LF (Ladle Furnace) refining plays an important role during secondary metallurgic process. The traditional LF refining operation relies on the workers’ experience, it is disadvantageous to ensure the stable production, high-quality products and energy saving. A new robust operation optimization method of molten steel temperature based on AdaBoost.IR soft sensor is proposed in LF refining process. Firstly, an intelligent model based on BP (Back Propagation) neural network is established by analyzing the changes of energy during whole refining process of LF as sub intelligent model. Then an AdaBoost.IR is designed for the characters of industrial data, and is suitable for industrial soft sensor modeling. The ensemble soft sensor model is established for realizing the online real time measurement of molten steel temperature with better accuracy by using AdaBoost.IR. Secondly the robust operation optimization model is described by analyzing the process of LF refining based on above AdaBoost.IR soft sensor. And the HPOS-GA is used to solve the optimal operation solution of robust optimization model. The new robust operation optimization of temperature based on AdaBoost.IR is used in 300 t LF of the Baosteel Company. The results of experiments demonstrate the soft sensor can predict the temperature more accuracy and the end temperature of LF refining after robust optimization become more stable.

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© 2017 by The Iron and Steel Institute of Japan
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