KPCA-ESN Soft-Sensor Model of Polymerization Process Optimized by Biogeography-Based Optimization Algorithm

Abstract

For solving the problem that the conversion rate of vinyl chloride monomer (VCM) is hard for real-time online measurement in the polyvinyl chloride (PVC) polymerization production process, a soft-sensor modeling method based on echo state network (ESN) is put forward. By analyzing PVC polymerization process ten secondary variables are selected as input variables of the soft-sensor model, and the kernel principal component analysis (KPCA) method is carried out on the data preprocessing of input variables, which reduces the dimensions of the high-dimensional data. Thek-means clustering method is used to divide data samples into several clusters as inputs of each submodel. Then for each submodel the biogeography-based optimization algorithm (BBOA) is used to optimize the structure parameters of the ESN to realize the nonlinear mapping between input and output variables of the soft-sensor model. Finally, the weighted summation of outputs of each submodel is selected as the final output. The simulation results show that the proposed soft-sensor model can significantly improve the prediction precision of conversion rate and conversion velocity in the process of PVC polymerization and can satisfy the real-time control requirement of the PVC polymerization process.