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

Abstract EN

For solving the problem that the conversion rate of vinyl chloride monomer (VCM) ishard for real-time online measurement in the polyvinyl chloride (PVC) polymerization productionprocess, a soft-sensor modeling method based on echo state network (ESN) is put forward.

Byanalyzing PVC polymerization process ten secondary variables are selected as input variables of thesoft-sensor model, and the kernel principal component analysis (KPCA) method is carried out onthe data preprocessing of input variables, which reduces the dimensions of the high-dimensionaldata.

The k-means clustering method is used to divide data samples into several clusters as inputs ofeach 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 betweeninput and output variables of the soft-sensor model.

Finally, the weighted summation of outputs ofeach submodel is selected as the final output.

The simulation results show that the proposedsoft-sensor model can significantly improve the prediction precision of conversion rate andconversion velocity in the process of PVC polymerization and can satisfy the real-time controlrequirement of the PVC polymerization process.