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Multi-target modeling method for complex industrial process

A technology of industrial process and modeling method, applied in the direction of instrument, adaptive control, control/regulation system, etc., to achieve the effect of small modeling error

Inactive Publication Date: 2010-11-24
ZHEJIANG UNIV
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Problems solved by technology

This method is suitable for solving complex multi-index modeling problems of single-input single-output process and multi-input multi-output process

Method used

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  • Multi-target modeling method for complex industrial process
  • Multi-target modeling method for complex industrial process
  • Multi-target modeling method for complex industrial process

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Embodiment Construction

[0026] In order to better understand the technical solution of the present invention, further description will be made below in conjunction with the accompanying drawings and specific embodiments.

[0027] A typical pH neutralization process such as figure 1 shown. Acid solution, buffer solution and lye solution undergo neutralization reaction in the reaction tank, and flow q with alkali solution 3 Control output pH. figure 1 in, q 1 ,q 2 ,q 3 and q 4 are the flow rates of acid solution, buffer solution, lye solution and output solution; W a1 , W a2 , W a3 and W a4 are the charge balance factors of acid solution, buffer solution, lye solution and output solution; W b1 , W b2 , W b3 and W b4 are the material balance factors of acid solution, buffer solution, lye solution and output solution respectively; h is the liquid level height, A is the reactor area, C v is the valve coefficient, pK 1 and pK 2 Both are logarithmic values ​​of equilibrium coefficients; pH ...

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Abstract

The invention relates to a multi-target modeling method for a complex industrial process, which comprises the following steps of: acquiring input and output sampling data of a modeled process; determining a first target and a second target of a modeling problem; randomly generating an initial population, wherein each individual in the population expresses a first component part parameter of a T-S fuzzy recursive neural network by using an integer string consisting of four integers corresponding to a DNA basic group and the initial population is used as the current population; calculating a network second component part parameter corresponding to the network first component part represented by each individual by using a recursive least square method; calculating a fitness degree of each individual of the current population; executing selection, crossing and variation operations on the individuals in the current population to obtain a new generation population; and selecting a final population and taking the T-S fuzzy recursive neural network corresponding to any one of the individuals of the final population as a model of the modeled process. Compared with the traditional genetic algorithm, the modeling method provided by the invention can effectively improve the modeling precision.

Description

technical field [0001] The invention relates to a complex industrial process multi-objective modeling method, which is a modeling method for complex nonlinear systems and belongs to the technical field of process control. Background technique [0002] Establishing high-precision models of complex industrial processes is one of the important means to realize the optimization of production processes. Traditional mathematical modeling methods, such as mechanism modeling methods, are often difficult to meet the modeling accuracy requirements for complex industrial processes, especially for complex systems with severe nonlinearity. Therefore, in recent years, researchers have begun to use nonlinear modeling tools, such as artificial neural networks, to establish nonlinear system models. The artificial neural network has strong adaptive learning, self-organization, function approximation and other capabilities, and does not require prior knowledge of the modeled process. It is su...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 王宁陈霄陶吉利
Owner ZHEJIANG UNIV
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