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一种基于多模型融合软测量建模方法

ISSN号：0438-1157
期刊名称：《化工学报》
时间：0
分类：TP273[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置]
作者机构：[1]中南大学信息科学与工程学院,湖南长沙410083
相关基金：国家高技术研究发展计划项目（2009AA04Z124）; 国家自然科学基金重点项目（60634020）

关键词：小波变换, 相空间重构, 多模型融合, 软测量, 锌净化, wavelet transform, phase space reconstruction, multi-model fusion, soft sensor model, purification of zinc

中文摘要：

针对锌湿法冶炼净化过程中钴离子浓度LS-SVM软测量建模方法精度低的问题,将最小二乘支持向量机（LS-SVM）和自回归滑动平均模型（ARMA）融合建立钴离子浓度融合软测量模型,首先通过离子浓度序列的小波变换获得序列的低频和高频子序列,对各子序列分别进行相空间重构,并在相空间中分别建立最小二乘支持向量机模型,然后将各模型的输出利用小波重构整合得到钴离子基于LS-SVM软测量结果,利用自回归滑动平均模型对基于LS-SVM模型输出误差信息进行建模,通过对两个模型的融合,获得融合模型的软测量估计值。将该方法应用于锌液净化除钴段入口钴离子浓度的软测量,结果表明该方法比单一的LS-SVM方法具有更好的泛化性能和测量精度,显示出良好的应用潜力。

英文摘要：

As for low forecast accuracy of cobalt ion concentration by least square support vector machine（LS-SVM）method in the purification process of zinc hydrometallurgy,a multi-model fusion soft sensor modeling method based on combination LS-SVM with ARMA model was introduced in purification of zinc.Firstly the series of cobalt ion concentration was decomposed by wavelet transform,the decomposed sub-sequences were reconstructed by phase space reconstruction.Each sub-sequence was modeled by LS-SVM method in phase space,then the output of each model was integrated by wavelet reconstruction.Secondly correction was made for error of LS-SVM modeling output in ARMA model.Finally the output of two models were integrated,the integration value was the estimated value of cobalt ion concentration.The method was applied in prediction of cobalt ion concentration in the entrance of purification process of zinc hydrometallurgy.The results showed that this method had better generalization performance and high prediction accuracy than LS-SVM method,which showed good potential for application.