Soft-sensing research on the gas phase ethylene polymerization in fluidized bed reactor based on DPCA-RBF network

Gas phase ethylene polymerization in fluidized bed reactor is a complicated process, involving the characteristics of high dimension, nonlinear, dynamic and strong noise, and it' s hard to measure the quality variables. To solve the problem of soft-sensing of the key quality variables on line, we first use DPCA to extract the principal component variables from the process variables aiming at eliminating noise and pertinence, reveal the dynamic identity between process variables, and then use RBF modeling method to establish the network topology between principal component variables and quality variables. We adopt PCA-RBF model and DPCA-RBF model on the function data and industrial real-time data respectively, and find that the modeling method of DPCA-RBF is better than the methods of PCA-RBF and RBF when nonlinear, dynamic, noise, and pertinence exist in the modeling data. Thereby, DPCA-RBF modeling method is more suitable for the soft-sensing of industrial real-time variables.