Melt Index Prediction by Neural Soft-Sensor Based on Multi-Scale Analysis and Principal Component Analysis  

作　　者：施健 刘兴高 

机构地区：[1]National Laboratory of Industrial Control Technology,Institute of Systems Engineering,Zhejiang University,Hangzhou 310027,China

出　　处：《Chinese Journal of Chemical Engineering》2005年第6期849-852,共4页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No. 20106008)National HI-TECH Industrialization Program of China (No. Fagai-Gaoji-2004-2080)Science Fund for Distinguished Young Scholars of Zhejiang University (No. 111000-581645).

摘　　要：Prediction of melt index （MI）, the most important parameter in determining the product＇s grade and quality control of polypropylene produced in practical industrial processes, is studied. A novel soft-sensor model with principal component analysis （PCA）, radial basis function （RBF） networks, and multi-scale analysis （MSA） is proposed to infer the MI of manufactured products from real process variables, where PCA is carried out to select the most relevant process features and to eliminate the correlations of the input variables, MSA is introduced to a^quire much more information and to reduce the uncertainty of the system, and RBF networks are used to characterize the nonlinearity of the process. The research results show that the proposed method provides promising prediction reliability and accuracy, and supposed to have extensive application prospects in propylene polymerization processes.熔体流动指数( MI )的预言，在决定产品的最重要的参数“ s 等级和在实际工业进程生产的聚丙烯的质量管理， isstudied.A 小说有主要组分分析( PCA )的软传感器的模型，光线的基础函数( RBF )网络，并且多尺度的分析( MSA )被建议从真实进程变量推断生产产品的 MI ，在 PCA 被执行选择最相关的进程特征并且消除输入变量的关联的地方， MSA 被介绍获得更多信息并且减少系统的不确定性，并且 RBF 网络被用来描绘这进程的非线性。研究结果证明建议方法提供有希望的预言可靠性和精确性，并且想了在丙烯聚合过程有广泛的应用程序前景。

关 键 词：propylene polymerization neural soft-sensor principal component analysis multi-scale analysis 

分 类 号：O621.25[理学—有机化学]