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An integrated real-time learning industrial process soft measurement modeling method based on multi-objective optimization

A multi-objective optimization and industrial process technology, applied in the field of integrated real-time learning soft sensor modeling based on multi-objective optimization, to achieve the effect of dealing with nonlinearity and improving computational efficiency

Active Publication Date: 2019-06-04
KUNMING UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is how to improve the quality of the historical sample database in the soft sensor modeling technology and how to reduce the complexity of the model while improving the prediction accuracy of the model

Method used

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  • An integrated real-time learning industrial process soft measurement modeling method based on multi-objective optimization
  • An integrated real-time learning industrial process soft measurement modeling method based on multi-objective optimization
  • An integrated real-time learning industrial process soft measurement modeling method based on multi-objective optimization

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Embodiment 1

[0028]Embodiment 1: as figure 1 As shown, an integrated real-time learning industrial process soft-sensing modeling method based on multi-objective optimization includes the following steps. The first step is to collect industrial process data through a distributed control system or offline detection method, and construct a soft-sensing modeling method for soft-sensing modeling database. Through the mechanism analysis of the industrial process, the auxiliary variables related to the predictor variables are determined.

[0029] Step 2: Normalize the samples in the database and divide them into training sets (D train ∈R J×Q ), validation set (D validate ∈R K×Q ) and the test set (D test ∈R T×Q ). The training set is used for model training, the validation set is used for model parameter optimization, and the test set is used for model performance evaluation.

[0030] The third step: use the multi-objective evolutionary optimization method (NSGA-II algorithm) to optimize ...

Embodiment 2

[0048] Example 2: The following will be further described in conjunction with the industrial process of the debutanizer. The debutanizer is a part of the device for desulfurization and naphtha separation in the industrial refining process, and its goal is to minimize the concentration of butane at the bottom of the tower. However, it is still difficult to realize real-time online detection of butane concentration at present. The on-line prediction of butane concentration by soft sensing method can effectively improve the desulfurization efficiency of debutanizer. According to the mechanism analysis, the x 1 Tower top temperature; x 2 pressure at the top of the tower; x 3 Return flow at the top of the tower; x 4 Outflow of top product; x 5 The sixth layer tray temperature; x 6 tower low temp 1;x 7 The seven monitoring variables of tower bottom temperature 2 are used as auxiliary variables to construct the soft sensor model, and the output variable is butane concentration....

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Abstract

The invention discloses an integrated real-time learning industrial process soft measurement modeling method based on multi-objective optimization, and belongs to the field of industrial process softmeasurement modeling. The invention aims to solve the common problems of redundancy and process nonlinearity of industrial data. An evolutionary multi-objective optimization method is adopted to optimize input variables and model structures in a historical sample database, variables irrelevant to quality variables or weakly relevant to the quality variables are removed, the sample quality of the database is improved, and meanwhile, the relation between the model complexity and the prediction precision is effectively balanced. Besides, a part of samples similar to the query samples are selectedfrom the optimized historical sample library to construct a local extreme learning machine model, and a selective integration strategy is adopted to integrate the Pareto optimal solution obtained through multi-objective optimization, so that the nonlinear problem of the industrial process can be effectively solved. By optimizing the modeling data structure and the extreme learning machine model structure, the prediction precision and the calculation efficiency of industrial process soft measurement modeling are improved.

Description

technical field [0001] The invention belongs to the field of industrial process soft sensor modeling, in particular to an integrated real-time learning soft sensor modeling method based on multi-objective optimization. Background technique [0002] In the modern industrial production process, in order to ensure that the product quality meets the increasingly stringent production indicators, it is necessary to conduct real-time online detection of some key process variables or quality variables, so as to realize real-time control of the production process. However, the actual industrial production process is sometimes in harsh environments such as high temperature, high pressure, and strong corrosiveness, so the requirements for sensor performance are extremely strict, and high-performance sensors often have problems such as high cost and difficult maintenance. In addition, Long offline analysis time is another important factor restricting the real-time control of the product...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N20/00
Inventor 金怀平潘贝
Owner KUNMING UNIV OF SCI & TECH
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