KERNEL PRINCIPAL COMPONENT ANALYSIS: RADIAL BASIS FUNCTION NEURAL NETWORKSBASED SOFT-SENSOR MODELING OF POLYMERIZING PROCESS OPTIMIZED BY CULTURAL DIFFERENTIAL EVOLUTION ALGORITHM

For forecasting the key technology indicator convention velocity of vinyl chloride monomer (VCM) in the polyvinylchloride (PVC) polymerizing process, a soft-sensor modeling method based on radial basis function neural networks (RBFNN) is proposed. First, a kernel principal component analysis (KPCA) method is adopted to select the auxiliary variables of the soft-sensing model in order to reduce the model dimensionality. Then the structure parameters of the RBFNN are optimized by the cultural differential evolution (CDE) algorithm to realize the nonlinear mapping between input and output variables of the discussed soft-sensor model. In the end, simulation results show that the proposed model can significantly enhance the predictive accuracy and robustness of the technical and economic indexes and satisfy the real-time control requirements of PVC polymerizing production process.