ISSN:1003-8620

CN:42-1243/TF

主管:中信泰富特钢集团股份有限公司

主办:大冶特殊钢有限公司

特殊钢 ›› 2009, Vol. 30 ›› Issue (6): 4-6.

• 试验研究 • 上一篇    下一篇

基于支持向量机回归的铸坯表面目标温度设定

高凤翔1,王长松1,吴秀永1,陈晓2,李珂2   

  1. 1北京科技大学机械电子工程系,北京100083;
    2安阳钢铁公司,安阳455004
  • 收稿日期:2009-05-03 出版日期:2009-12-01 发布日期:2022-11-14
  • 作者简介:高凤翔(1974-),男,博士研究生,连铸过程自动化与智能 控制。
  • 基金资助:
    “十一五”国家科技支撑计划资助项目 (2006BAE03A06)

Setting of Object Surface Temperature of Casting Slab Based on Support Vector Regression

Gao Fengxiang1 , Wang Changsong1 , Wu Xiuyong1 , Chen Xiao2 , Li Ke2   

  1. 1Mechanics-Electronic Engineering Dept,University of Science and Technology, Beijing 100083 ;
    2 Anyang Iron and Steel Co Ltd, Anyang 455004
  • Received:2009-05-03 Published:2009-12-01 Online:2022-11-14

摘要: 提出了一种多输入多输出支持向量机回归算法,利用冶金技术人员计算的目标温度设定表,设定实时二冷区铸坯表面目标温度。200 mm×1 534 mm 16Mn钢板坯连铸试验结果表明,在训练样本相同时,支持向量机训练时间为3.2 s,预测目标温度误差为±1℃,BP神经网络训练时间为23.5 s,预测目标温度误差为±2℃,多输入多输出支持向量机回归算法优于BP神经网络算法,能够根据工艺变化情况,实时改变目标温度,为实现连铸动态控制提供了条件,有助于提高铸坯的质量。

关键词: 铸坯表面目标温度, 二次冷却, 支持向量机回归

Abstract: A multi-input and multi-outpul support vector regression algorithm is introduced to predict the real-time object surface temperature of casting slab in secondary cooling zone by using the given object teinperaiure schedule calculated by metallurgical technicians. Test results of casting 200 mm x 1 534 mm slab of steel 16Mn show that with same trainingspecimen number, the training time by support vector regression algorithm is 3. 2 s with error of predict object temperature ± 1 ℃ , while the training time by BP ( back propagation) neural networks algorithm is 23. 5 s with error of predict object temperature ± 2 ℃ , and the multi-input and output support vector regression aigorithm is better than BP neural networks algorithm to alter the object surface temperature of slab in real time according to the variety of process, therefore it is available to dynamic-control the casting process and increase the quality of slab.

Key words: Object Surface Temperatures of Slab, Secondary Cooling, Support Vector Regression