Soft-Sensor Modeling of PVC Polymerizing Process Based on F-GMDH-Type Neural Network Algorithm

Abstract EN

For predicting the conversion velocity of the vinyl chloride monomer (VCM) in the polymerization process of polyvinylchloride (PVC), an improved Group Method of Data Handling- (GMDH-) type neural network soft-sensor model is proposed.

After analyzing the technique of PVC manufacturing process, the auxiliary variables for setting up the soft-sensor model are selected and the experimental data are normalized.

Because the internal standard of the original GMDH-type neural cannot solve the problem of multiple-collinearity problem and the useful variables tend to be prematurely eliminated in the modeling process, a hybrid method combining the regression analysis method and the least squares method is proposed to solve the multiple-collinearity problem.

On the same time, by adopting some optimization experiences in genetic algorithm (GA), the generational crossover combination variables method is proposed to solve the shortcoming of useful variable being eliminated prematurely.

The simulation results show that the proposed soft-sensor model can significantly improve the prediction accuracy of economic and technical indicators in the PVC polymerization process and can meet the real time control requirements of polymerization reactor production process.