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

A technology based on prior knowledge and melt index, applied in the field of soft sensor prediction of polypropylene industrial process, can solve the problems of pure data-driven model extrapolation ability and safety performance cannot be guaranteed, and the accuracy of pure mechanism model is not high

Active Publication Date: 2012-07-25
ZHEJIANG UNIV
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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

Method used

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

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Embodiment Construction

[0074] The present invention will be further described below in conjunction with the accompanying drawings.

[0075] refer to figure 2 , image 3 , a polypropylene melt index prediction method based on multiple prior knowledge mixed models, the specific implementation method is as follows:

[0076] (1) Offline modeling

[0077] First off-line initialization to establish a polypropylene melt index prediction model based on multiple prior knowledge mixed models, the specific process is as follows:

[0078] 1) From a loop-type liquid phase propylene bulk polymerization field device of a Chinese petrochemical company ( figure 1 ), collect the data needed to establish the soft sensor model through two ways. According to the production process and reaction mechanism of double-loop liquid phase propylene bulk polymerization, the auxiliary variable of the soft sensor model is determined to be the hydrogen concentration of the two loops , Hydrogen feed amount , Feed amoun...

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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...

Claims

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Application Information

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IPC IPC(8): G06F17/50
Inventor 苏宏业娄海川谢磊古勇侯卫锋荣冈
Owner ZHEJIANG UNIV
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