Echo state networks based predictive model of vinyl chloride monomer convention velocity optimized by artificial fish swarm algorithm

Conversion rate in the Polyvinylchloride (PVC) polymerizing process has a certain influence on the molecular weight of PVC, porosity, absorption rate of plasticizer, vinyl chloride monomer (VCM) residue and thermal stability. Therefore, a predictive model based on echo state networks (ESN) method optimized by the artificial fish swarm algorithm (AFSA) is proposed to predict the conversion velocity. Firstly, the hot balancing mechanisms of polymerizer and the influenced factors of convention rate of VCM are analyzed in details. Then the auxiliary variables of the predictive model kernel are selected by using the kernel principal component analysis method for reducing the model dimensionality. Thirdly, the structure parameters of the ESN are optimized by the AFSA to realize the nonlinear mapping between input and output variables of the discussed soft-sensor model. The artificial fish swarm behaviors, such as foraging, swarming, chasing, random, are introduced in details. Finally, simulation results show that the proposed model can significantly enhance the predictive accuracy and robustness of the technical index and satisfy the real-time control requirements of PVC polymerizing production process.