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Propylene polymerization production process optimal soft survey instrument and method based on genetic algorithm optimization BP neural network

A BP neural network and optimal soft-sensing technology, applied in biological neural network models, gene models, electrical program control, etc., can solve problems such as low measurement accuracy and easy to be affected by human factors

Inactive Publication Date: 2008-12-03
ZHEJIANG UNIV
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] In order to overcome the shortcomings of the existing propylene polymerization production process that the measurement accuracy is not high and is easily affected by human factors, the present invention provides an online measurement, automatic optimization of online parameters, fast calculation speed, automatic model update, strong anti-interference ability, High-precision soft-sensing instrument and method for optimal melting index of propylene polymerization production process based on genetic algorithm optimization BP neural network

Method used

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  • Propylene polymerization production process optimal soft survey instrument and method based on genetic algorithm optimization BP neural network
  • Propylene polymerization production process optimal soft survey instrument and method based on genetic algorithm optimization BP neural network
  • Propylene polymerization production process optimal soft survey instrument and method based on genetic algorithm optimization BP neural network

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Embodiment 1

[0077] refer to figure 1 , figure 2 with image 3 , an optimal soft sensor instrument for propylene polymerization production process based on genetic algorithm optimization BP neural network, including propylene polymerization production process 1, field intelligent instrument 2 for measuring easily measurable variables, control station 3 for measuring operating variables, The DCS database 4 for storing data and the melt index soft measurement value display instrument 6, the on-site intelligent instrument 2 and the control station 3 are connected to the propylene polymerization production process 1, and the on-site intelligent instrument 2 and the control station 3 are connected to the DCS database 4, The soft sensor instrument also includes the optimal soft sensor model 5 of the genetic algorithm optimized BP neural network, the DCS database 4 is connected to the input end of the optimal soft sensor model 5 based on the genetic algorithm optimized BP neural network, the T...

Embodiment 2

[0144] refer to figure 1 , figure 2 with image 3 , an optimal soft sensor method for propylene polymerization production process based on genetic algorithm optimization BP neural network, the soft sensor method mainly includes the following steps:

[0145] 1), for the propylene polymerization production process object, according to the process analysis and operation analysis, select the operational variables and easily measurable variables as the input of the model, and the operational variables and easily measurable variables are obtained from the DCS database;

[0146] 2) Preprocessing the sample data, centering the input variables, that is, subtracting the average value of the variables; then pre-whitening the input variables, that is, variable decorrelation, and applying a linear transformation to the input variables; through the independent component analysis method, Recover the basic source signal from the centered and pre-whitened linear mixture data;

[0147] 3), ...

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Abstract

A propylene polymerization production process optimal soft-measurement meter based on genetic algorithm optimized BP neural network comprises a propylene polymerization production process, a site intelligent meter, a control station, a DCS databank used for storing data, an optimal soft measurement model based on genetic algorithm optimized BP neural network, and a melting index soft-measurement value indicator. The site intelligent meter and the control station are connected with the propylene polymerization production process and the DCS databank; the optimal soft-measurement model is connected with the DCS databank and the soft-measurement value indicator. The optimal soft measurement model based on genetic algorithm optimized BP neural network comprises a data pre-processing module, an ICA dependent-component analysis module, a BP neural network modeling module and a genetic algorithm optimized BP neural network module. The invention also provides a soft measurement method adopting the soft measurement meter. The invention can realize on-line measurement and on-line automatic parameter optimization, with quick calculation, automatic model updating, strong anti-interference capability and high accuracy.

Description

technical field [0001] The invention relates to an optimal soft measuring instrument and a method, in particular to an optimal soft measuring instrument and a method for a propylene polymerization production process based on genetic algorithm optimization of a BP neural network. Background technique [0002] Polypropylene is a thermoplastic resin produced by propylene polymerization. The most important downstream product of propylene, 50% of the world's propylene and 65% of my country's propylene are used to make polypropylene. It is one of the five general-purpose plastics. Daily life is closely related. Polypropylene is the fastest growing general-purpose thermoplastic resin in the world, second only to polyethylene and polyvinyl chloride in total. In order to make my country's polypropylene products have market competitiveness, develop impact copolymer products, random copolymer products, BOPP and CPP film materials, fibers, and non-woven fabrics with good balance of rigi...

Claims

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Application Information

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IPC IPC(8): G05B19/418C08F10/06C08F2/00G06N3/12G06N3/02
CPCY02P90/02
Inventor 刘兴高楼巍
Owner ZHEJIANG UNIV
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