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An effective industrial process dynamic optimization system and method capable of controlling variable parameterization

A technology of control variables and dynamic optimization, applied in the directions of comprehensive factory control, comprehensive factory control, electrical program control, etc., can solve problems such as poor optimal solution stability

Inactive Publication Date: 2010-06-30
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] In order to overcome the shortcomings of the existing industrial process dynamic optimization system and method that it is difficult to find the optimal solution accurately and quickly, and the stability is poor, the present invention provides an optimal solution that can accurately find large-scale nonlinear dynamic optimization problems and System and method for industrial process dynamic optimization with stable, fast and effective control variable parameterization

Method used

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  • An effective industrial process dynamic optimization system and method capable of controlling variable parameterization
  • An effective industrial process dynamic optimization system and method capable of controlling variable parameterization
  • An effective industrial process dynamic optimization system and method capable of controlling variable parameterization

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Embodiment 1

[0072] refer to figure 1 , figure 2 , an effective control variable parameterized industrial process dynamic optimization system, including field intelligent instrument 2 connected with industrial process object 1, DCS system and host computer 6, said DCS system consists of data interface 3, operation station 4, The database 5 is formed; the field intelligent instrument 2 is connected to the data communication network, the data communication network is connected to the data interface 3, the data interface 3 is connected to the field bus, and the field bus is connected to the operation station 4, the database 5 and the upper computer 6 connection, the host computer 6 includes:

[0073] The initialization module 8 is used to initialize the data and initially set the control vector parameters. The specific steps are as follows:

[0074] (2.1) The time domain t∈[t 0 , tf] are evenly divided into N segments: [t 0 , t 1 ], [t 1 , t 2 ],...,[t N-1 , t N ], where t N =tf, t...

Embodiment 2

[0101] refer to figure 1 and figure 2 , an effective control variable parameterized industrial process dynamic optimization method, the dynamic optimization method is implemented according to the following steps:

[0102] 1) Specify dynamically optimized state variables and control variables in the DCS system, and set the upper and lower boundaries u of the control vector according to the conditions of the actual production environment and operating restrictions min , u max and the sampling period of the DCS, and the historical data of the corresponding variables in the DCS database 5, the upper and lower boundary values ​​of the control variables u min , u max sent to the host computer.

[0103] 2), in the initializing module 8 of upper computer, carry out initializing process to the data that DCS inputs, carry out initial setting to control vector parameter, complete according to the following steps:

[0104] (2.1) The time domain t∈[t 0 , tf] are evenly divided into ...

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Abstract

An effective industrial process dynamic optimization system capable of controlling variable parameterization comprises a field intelligent instrument, a DCS system and an upper computer which are connected with an industrial process object. The upper computer mainly comprises an initialization module for initializing data and control vector parameter; a control variable parameterization module for discretizing the control variable of dynamic optimization problem and describing control variable u (t) with N independent control parameters; an ODE solving module for solving the system of ordinary differential equations of dynamic optimization problem, obtaining the value of state vector and corresponding object function value and transmitting to a NLP module; and a NLP solving module for solving nonlinear programming problem obtained by the control variable parameterization module. The invention further provides the effective industrial process dynamic optimization method capable of controlling the variable parameterization. The invention can find the optimal solution of large-scale nonlinear dynamic optimization problem accurately and has stable and quick convergence.

Description

technical field [0001] The invention relates to the optimization field, in particular to an effective control variable parameterized industrial process dynamic optimization system and method. Background technique [0002] Dynamic optimization of industrial process is the core of process simulation technology and an important link in the optimal design, operation and control of chemical process. From the 1960s to the present, the development of dynamic optimization (also often called optimal control) in the field of theoretical research and practical application is very impressive. Technologies that will continue to have a significant impact on the process industries. [0003] The application fields of dynamic optimization are very extensive. A typical engineering online application is to solve the optimization problem in nonlinear model predictive control (Nonlinear Model Predictive Control, NMPC). Modern industrial applications require that the dynamic process model solve...

Claims

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Application Information

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IPC IPC(8): G05B19/418
CPCY02P90/02
Inventor 刘兴高陈珑
Owner ZHEJIANG UNIV
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