Abstract:

A high-precision on-line method of predicting melt index of propylene polymerization based on principal component analysis (PCA) and adaptive particle swarm optimization (APSO) is proposed to overcome the high correlation characteristics and high nonlinear characteristics in the propylene polymerization process.APSO is employed to get better search efficiency and higher precision than classical particle swarm optimization (PSO), and PCA is applied to reduce the complexity of the statistical model.A new method of optimizing both structure and parameters of radial basis function (RBF) network is also proposed.The validity of these methods is demonstrated through practical data in real factory, and research result shows higher precision and shorter computing time than before.