A multi-model fusion modeling method based on improved Kalman filter algorithm was presented for soft sensor of key quality index or state variable in the polymerization process. First

a data-driven soft-sensor modeling method was proposed by combining mixtures of kernels principal component analysis (K2PCA) with artificial neural network (ANN). Second

a parallel hybrid model was constructed by fusing the data-driven model with a mechanism model through an improved Kalman filtering algorithm. Moreover

a linear smoothing filter and a model variance updating method were adopted for optimizing the hybrid model

which could enhance performance and improve prediction stability of the hybrid model. The application of the proposed multi-model fusion modeling method in the vinyl chloride polymerization rate prediction verified that the hybrid model was more effective in comparison with single-model cases (thermodynamic mechanism model or K2PCA-ANN model). The proposed multi-model fusion modeling method would provide basic conditions for control and optimization of PVC polymerization process in further research.