Polypropylene melt index predicating method based on multiple priori knowledge mixed model

Abstract

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.

Description

technical field [0001] The invention relates to the field of soft sensor prediction of polypropylene industrial process, in particular to a method for soft sensor prediction of polypropylene melt index based on multiple prior knowledge mixed models. Background technique [0002] Polypropylene (PP) is a polymer made from propylene as a monomer. Polypropylene is a general-purpose plastic with excellent performance, which is widely used in packaging, manufacturing, textile and many civil consumption fields. The melt index is one of the main quality indicators of polypropylene products, which reflects the flow properties of the resin and thus determines the grade of the resin. There are many grades of polypropylene resins, ranging from several to hundreds. For polypropylene production units, due to the frequent changes in the working conditions during the operation of propylene polymerization, it poses a severe challenge to the modeling and control of melt index in order to pr...

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Problems solved by technology

[0008] The purpose of the present invention is to provide a polypropylene based on multiple prior knowledge neural network and simple mechanism hybrid model in order to overcome the shortcomings of existing pure mechanism models such as low accuracy and pure data-driven model extrapolation ability and safety performance cannot be guaranteed. Soft-sensing forecasting method for melt index

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