RBF neural network prediction of convention velocity in polymerizing process based on K-means clustering

For forecasting the key technology indicator convention velocity of vinyl chloride monomer (VCM) in the polyvinylchloride (PVC) polymerizing process, a predictive model based on radial basis function neural networks (RBFNN) is proposed. Firstly, kernel principal component analysis (KPCA) method is adopted to select the auxiliary variables of soft-sensing model in order to reduce the model dimensionality. Then the structure parameters of the RBFNN are optimized by the c K-means clustering method. 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.