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.
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.
出处
《过程工程学报》
CAS
CSCD
北大核心
2014年第3期462-468,共7页
The Chinese Journal of Process Engineering
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
by-product gas
support vector regression machine
parameter optimization
Powell calculation
simulated annealing arithmetic calculation
