End temperature prediction of molten steel in RH based on integrated case-based reasoning

被引：0
|
作者：
Feng K. [1 ]
Xu A.-J. [1 ]
He D.-F. [1 ]
Wang H.-B. [2 ]
机构：
[1] School of Metallurgy and Ecology Engineering, University of Science and Technology Beijing, Beijing
[2] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing
来源：
Feng, Kai (fengkai-show@163.com) | 2018年 / Science Press卷 / 40期
关键词：
Case-based reasoning; Genetic algorithm; Molten steel temperature; Prediction; Ruhrstahl-Heraeus;
D O I：
10.13374/j.issn2095-9389.2018.s1.023
中图分类号：
学科分类号：
摘要：
In regards to the end temperature prediction of molten steel in RH refining, an integrated case-based reasoning (CBR) method based on multiple linear regression (MLR) and genetic algorithm (GA) was proposed. Firstly, MLR was used to intelligently simplify the number of attributes to modify the lack of methods in the accurate selection of influencing factors in general CBR method. Secondly, GA was used to optimize the attribute weights in order to resolve the lack of attribute weights calculation method for similarity computation in case retrieval. Lastly, the end temperature prediction of molten steel in RH refining was realized based on the simplified influencing factors and optimized weights, and using grey relational degree (GRD) in case retrieval. Testing was performed based on the actual production data in RH refining in steelmaking plant, and comparison between MLR method, BP neural network, general CBR method and integrated CBR method was carried out. The results show that integrated CBR method has better prediction accuracy than MLR method, BP neural network and general CBR method in multiple temperature ranges. © All right reserved.
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页码：161 / 167
页数：6
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