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›› 2014, Vol. 14 ›› Issue (3): 462-468.

• 系统与集成 • 上一篇    下一篇

基于PSA-SVRM模型的钢铁企业副产煤气消耗量预测

杨波   

  1. 云南省计量测试技术研究院
  • 收稿日期:2014-03-10 修回日期:2014-04-11 出版日期:2014-06-20 发布日期:2014-06-20
  • 通讯作者: 杨波

Consumption Prediction of By-product Gas in Iron and Steel Enterprises Based on PSA-SVRM Model

YANG Bo   

  1. Yunnan Institute of Metrology And Testing Technology
  • Received:2014-03-10 Revised:2014-04-11 Online:2014-06-20 Published:2014-06-20
  • Contact: YANG Bo

摘要: 针对钢铁企业副产煤气消耗量经验模型难以对其进行精确预测的问题,通过分析副产煤气消耗用户及其特点,按不同用户利用支持向量机对副产煤气消耗量进行分类,依托Powell算法、模拟退火法和支持向量回归机各自的性质及特点,构建了副产煤气消耗量预测模型,并依托企业实际数据对模型进行验证. 结果表明,对烧结、炼钢、连铸3个主工序60个步长的测试分类准确率分别为94.8%, 94.9%和100%,模型预测的相对平均误差分别为2.5%, 2.8%和2.1%,表明模型适用于副产煤气消耗量预测. Wilcoxon符号秩检验验证了模型的有效性.

关键词: 副产煤气, 支持向量回归机, 参数优化, Powell算法, 模拟退火法

Abstract: In order to deal with the problem that empirical model of by-product gas consumption in iron and steel enterprises cannot accurately predict the by-product gas consumption, the analysis of by-product consumption user and its characteristics is carried out. Support vector machine is used to classify the by-product gas consumption. It combines Powell calculation, simulated annealing arithmetic calculation, and support vector regression machine (SVRM), a model of PSA (Powell simulated annealing)-SVRM to predict the by-product gas consumption has been established. By-product gas consumption data in some iron and steel enterprises are used in the model. The results show that the prediction accuracy for sintering, steelmaking and continuous casting processes is 94.8%, 94.9% and 100%, respectively, with the relative mean error of 2.5%, 2.8% and 2.1%, respectively, which indicates that this PSA-SVRM model is suitable to prediction of the by-product gas consumption. Wilcoxon sign rank test proves the effectiveness of PSA-SVRM model.

Key words: by-product gas, support vector regression machine, parameter optimization, Powell calculation, simulated annealing

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