Research On The Methods Of Soft-sensing For Polymerization Conversion Rate Of SBR

As a kind of synthetic rubber, Styrene-butadiene Rubber(SBR) has been widelyused in our daily life and military areas, because its good performances can be insteadof natural rubber in many ways. The real-time monitoring of polymerizationconversion rate is a difficulty for SBR production. At present, laboratory analysismethods are used to control the index in many domestic enterprise. As its serious delayproblem, not only the effects don’t very well but also lots of resources are waste. Thedevelopment of the soft measurement technology provides a good approach for thesolution the problems.Articles are not much on polymerization conversion rate by the technology. Justthe production of SBR, although the process is complex, its working points are relativestability, there are strong relevance between the secondary variables and the precisionprediction for polymerization conversion rate. According to the object, this paper putforward three kinds of soft sensor modeling method based on nuclear functionthoughts.First of all, a soft sensor for SBR Polymerization Conversion Rate Based onKPCA-LSSVM Model. Considering the complexity of actual working condition andthe prediction accuracy requirement of enterprise, it used kernel principal componentanalysis (KPCA), which has a strong ability of nonlinear feature extraction, to processthe data firstly, took the result as input of the least squares support vector machines(LSSVM) model, which has these characteristics, such as small sample and goodgeneralization ability, etc, and established the model for SBR polymerization conver-sion rate.Secondly, these soft-sensing issues studied based on more kernels technology forpolymerization conversion rate of SBR. The KPCA was used to process the data firstly.And the results were taken as input of the radial basis function (RBF) model. Conside-ring the defect that RBF having a single kernel was difficult to describe complexproblems accurately, the Wavelet kernel having a ability of time-frequency and localcharacterization was introduced, and a mixture kernel (MK) was constructed byGaussian and Wavelet kernels to remedy for the defect.Finally, this paper provides soft-sensing for polymerization conversion rate ofSBR based on kernel function PLS models. considering the complexity of actualworking condition and the disadvantages of partial least squares (PLS) algorithm for its nonlinear processing power, and the kernel function introduced could increase itsnonlinear processing power. Then PLS models with single or mixed kernel functionwere created separately and used to forecast the SBR polymerization conversion rate.The simulation results showed that the three kinds of models could all meet theenterprise requirements. KPCA used for complex data processing can provide moreaccurate data information for the subsequent model. Wavelet nuclear has a good localcharacterization ability. The PLS modeling method based on hybrid kernel function,not only can better description of the complex object characteristics, remove noises,but also consider the correlation between the input and output at feature extraction. Inthe same group of data simulation results, models based on the kernel function havehighest prediction accuracy and best effects. Show that these kinds of models are moresuitable for the object modeling.