Polypropylene melt index predicating method based on multiple priori knowledge mixed model
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The invention discloses a polypropylene melt index predicating method based on a multiple priori knowledge mixed model, which fully explores and utilizes priori knowledge of a polypropylene industrial site, and is used for organically integrating various priori knowledge, embedding the priori knowledge into a multilayer perceptron neural network in a non-linear equality constraint form, and optimizing a network weight number by means of a particle swarm optimization algorithm based on an augmented Lagrange multiplier constraint processing mechanism. Based on the multiple priori knowledge neural network model, the multiple priori knowledge neural network model is organically integrated with a polypropylene melt index simplification mechanism model into a harmonic average mixed soft-measuring model. The multiple priori knowledge mixed soft-measuring modeling method has good fitting prediction ability, and is capable of enhancing model extrapolation capacity and realizing good unity of model extrapolation and prediction accuracy of polypropylene melt indexes. Besides, the method is capable of avoiding zero gain and gain inversion and guaranteeing safety in practical polypropylene melt index quality closed-loop control application.