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Industrial non-stationary process soft measurement modeling method based on local weighting factor model

A factor model, locally weighted technique, applied in instrumentation, adaptive control, control/regulation systems, etc., to solve problems such as under-consideration of process dynamics and nonlinearities

Active Publication Date: 2020-01-10
CHINA JILIANG UNIV
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  • Abstract
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Problems solved by technology

Traditional probabilistic latent variable models, such as probabilistic principal component analysis, are mostly static linear methods, and process dynamics and nonlinearity have not been fully considered

Method used

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  • Industrial non-stationary process soft measurement modeling method based on local weighting factor model
  • Industrial non-stationary process soft measurement modeling method based on local weighting factor model
  • Industrial non-stationary process soft measurement modeling method based on local weighting factor model

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Embodiment Construction

[0066] The present invention will be further described below in conjunction with drawings and embodiments.

[0067] The invention aims at the problem of detecting the butane content in the debutanizer, by using the variables that are easy to measure in the process, and using a local weighting factor analysis model, the online soft measurement is performed on the butane content in the process.

[0068] Embodiments of the present invention and its implementation process are as follows:

[0069] Step 1: Collect data of various process variables in debutanizer through distributed control system and real-time database system: training sample set X trian ∈R N×n , store these data in the historical database, and select some data as samples for modeling.

[0070] Step 2: Obtain the butane content value corresponding to the sample used for modeling in the historical database through on-site extraction and offline laboratory analysis as the output Y of the soft sensor model trian ∈R ...

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Abstract

The invention discloses an industrial non-stationary process soft measurement modeling method based on a local weighting factor model. According to the method, a sliding window is introduced, a factoranalysis model is established in each sliding window, an online query sample is input into each sliding window to obtain a local similarity between the query sample and a training sample and local confidence of the query sample in the sliding window, then results of all the sliding windows are integrated to obtain a global weight of each training sample, a weighted average value of a training sample set is calculated according to the global weight, and the query sample is predicted on the basis of the weighted average value to obtain a butane content prediction value of the query sample. According to the invention, the accuracy of the prediction result is improved by establishing a weight relationship between an online measurement sample and the training sample.

Description

technical field [0001] The invention belongs to the field of industrial non-stationary process soft sensor modeling and application, in particular to an industrial non-stationary process soft sensor modeling and on-line detection method based on a local weighting factor model. Background technique [0002] Although the probabilistic model has been rapidly developed in the field of soft sensor modeling and has achieved fruitful results, most of the existing work is based on the assumption that the process is based on a stationary process. In practice, with changes in market demand, production planning Due to adjustments, external disturbances, etc., most industrial processes present non-stationary characteristics, that is, the statistical indicators of some process variables such as mean, variance, and covariance change with time. Industrial non-stationary processes widely exist in industrial production activities, and non-stationary The state of the process changes all the t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 何雨辰张丽芳方靖云王云宋执环严天宏
Owner CHINA JILIANG UNIV
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