Self-adaptive quality forecasting method based on incremental support vector regression

A support vector regression and self-adaptive technology, applied in the direction of comprehensive factory control, instrumentation, comprehensive factory control, etc., can solve the problem that the model cannot guarantee long-term application, and achieve the effect of improving update efficiency and reducing update frequency

Inactive Publication Date: 2019-08-16
ZHEJIANG UNIV
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Problems solved by technology

In this way, the model obtained by offline training cannot be guaranteed to be suitable for a long time, so the research on the online learning method of support vector regression has important theoretical significance and practical value

Method used

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  • Self-adaptive quality forecasting method based on incremental support vector regression
  • Self-adaptive quality forecasting method based on incremental support vector regression
  • Self-adaptive quality forecasting method based on incremental support vector regression

Examples

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Embodiment

[0052] The following is a specific example of a debutanizer to illustrate the performance of the incremental support vector regression model. The debutanizer is a commonly used standard industrial process platform for verification of soft-sensing modeling algorithms. Debutanizer is one of the devices in the refining process, the flow chart is as follows figure 2 As shown, the purpose of this device is to remove propane and butane from naphtha gas. Butane content at the bottom of the tower is a very important key indicator. In order to improve the control quality of the butane tower A soft-sensing model is established for the bottom butane content.

[0053] Table 1 shows the 7 auxiliary variables selected for the key quality variable butane content, and describes certain variables, which are tower top temperature, tower top pressure, reflux flow, next-stage flow, and sensitive plate Temperature, bottom temperature and bottom pressure.

[0054] Table 1: Input variable description ...

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Abstract

The invention discloses a self-adaptive quality forecasting method based on incremental support vector regression, which is used for adapting to a complex and changeable multi-working-condition production process, realizes the differential learning of a newly added sample by utilizing the KKT condition of the self-adaptive quality forecasting method based on incremental support vector regression on the basis of the original support vector regression model, carries out the incremental learning on samples with information, keeps the model unchanged for samples without new information, and can reduce the updating frequency of the model while ensuring the generalization capability of the model. The self-adaptive quality forecasting method based on incremental support vector regression not onlycan effectively face the nonlinearity in the actual industrial process, but also can continuously update the model aiming at the time-varying characteristic in the process, and the problem of the updating efficiency of the model is improved to a certain extent, so that the purpose of self-adaptive quality prediction is achieved.

Description

Technical field [0001] The invention belongs to the field of industrial process control and soft measurement, and relates to an adaptive quality prediction method based on incremental support vector regression. Background technique [0002] There are many variables that cannot or are difficult to directly measure in industrial processes. These variables are usually closely related to product quality and are parameters that must be monitored. Soft measurement technology is an effective method to solve this problem. It has been obtained in industrial processes. widely used. In recent years, data-driven soft-sensing modeling methods have received widespread attention. Among them, support vector regression is based on the principle of structural risk minimization, which has solved the problems of small samples, nonlinearity and local minima, and has been widely used in many research fields. The current support vector regression learning algorithms are mostly offline and batch proce...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418
CPCG05B19/41885G05B2219/32339Y02P90/02
Inventor 葛志强杨泽宇宋执环
Owner ZHEJIANG UNIV
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