Soft Sensor for Polypropylene Melt Index based on Improved Orthogonal Least Squares
A new method to build melt index soft sensor is proposed based on improved orthogonal least squares (IOLS) for nonlinear polypropylene process. OLS model has good generalization and sparseness by combining parameter local regularization and leave-one-out mean square error in cost function. Orthogonal signal correction(OSC) is applied to preprocess OLS model in order to reduce the noise information which is uncorrelated with output variables. Considering multi-grade operation in polypropylene plant, model parameter adaptive updating strategy is presented for updating the OLS model parameters online. The application results on real industrial process data show that IOLS can predict polypropylene melt index more accurately than partial least squares (PLS) and OLS.
Huage Tian Xiaogang Deng Xuemin Tian
College of Information and Control Engineering China University of Petroleum Dongying 257061, China
国际会议
The 8th World Congress on Intelligent Control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)
济南
英文
5881-5885
2010-07-07（万方平台首次上网日期，不代表论文的发表时间）