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A chemical process monitoring method based on time-series multi-block modeling strategy

A chemical process and strategy technology, applied in the direction of program control, comprehensive factory control, electrical program control, etc., to achieve the effect of superior chemical process dynamic monitoring method

Active Publication Date: 2022-03-18
日照市三星化工有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, an implementation framework that unifies multi-block modeling with dynamic process monitoring has rarely been constructed

Method used

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  • A chemical process monitoring method based on time-series multi-block modeling strategy
  • A chemical process monitoring method based on time-series multi-block modeling strategy
  • A chemical process monitoring method based on time-series multi-block modeling strategy

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Embodiment Construction

[0040] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and specific examples of implementation.

[0041] Such as figure 1 As shown, the present invention discloses a chemical process monitoring method based on a time-series multi-block modeling strategy. The following describes the specific implementation process of the method of the present invention and its superiority over existing methods in conjunction with a specific chemical process object.

[0042] Table 1: TE process monitoring variables.

[0043]

[0044]

[0045] The application object is from the chemical production process of Tennessee-Eastman (TE) in the United States. The TE process is an actual process flow of the Eastman chemical production workshop. The schematic diagram of the process is as follows figure 2 shown. At present, the TE process has been widely used as a standard experimental platform for process operation status monitor...

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Abstract

The invention discloses a chemical process monitoring method based on time series multi-block modeling strategy, aiming to establish an integrated implementation framework of multi-block modeling and dynamic process monitoring, so as to implement effective dynamic process monitoring. Different from the traditional dynamic process modeling idea, the method of the present invention firstly samples the nodes according to the time series, and divides the augmented matrix or vector into multiple variable blocks; then, using the idea of ​​generalized canonical correlation analysis, optimizes the A projection transformation base, so as to extract the cross-correlation between variable blocks, can extract the autocorrelation feature on the time series. In order to comprehensively consider the results given by the multi-model monitoring indicators, the method of the present invention also uses comprehensive monitoring indicators to monitor the changes of dynamic and static score information respectively. In addition, the superiority of the method of the present invention will be verified in specific implementation cases, thereby illustrating that the method of the present invention is a more superior method for dynamic monitoring of chemical process.

Description

technical field [0001] The invention relates to a data-driven process monitoring method, in particular to a chemical process monitoring method based on a time series multi-block modeling strategy. Background technique [0002] Due to the extensive use of computer-aided systems in the modern chemical industry, process objects can be stored offline and measured online in real time. Massive sampling data contains potentially useful information that can reflect the operating status of the production process. Therefore, how to fully and effectively use the sampling data to monitor the process operation status reflects the level of digital management of the modern chemical process. In the past ten years, both academia and industry have invested a lot of manpower and material resources in the research of data-driven process monitoring technology. Among them, statistical process monitoring is the method technology that has been studied the most, and principal component analysis (Pr...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B19/418
CPCG05B19/41885G05B2219/32339Y02P90/02
Inventor 张赫葛英辉童楚东
Owner 日照市三星化工有限公司
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