Soft-measurement for polymerization conversion rate of SBR based on kernel function PLS models

Aiming at the enterprise demand that polymerization conversion rate needs to be online high precision predicted for styrene butadiene rubber(SBR),considering the complexity of actual working condition and the disadvantages of partial least squares(PLS)algorithm for its nonlinear processing power,PLS models with single or mixed kernel function are created separately and used to forecast the SBR polymerization conversion rate.The simulation results show that Kernel PLS models can all meet the enterprise requirements,which mean the ratio of polymerization conversion rate prediction absolute error greater than 1.5 is not more than 10 % of the total samples.Especially,the mixed Kernel PLS model shows more excellent properties because of its both local and global characteristics.