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Chemical process monitoring method based on time sequence multi-block modeling strategy

A chemical process and timing technology, applied in the direction of program control, comprehensive factory control, and comprehensive factory control

Active Publication Date: 2020-11-10
日照市三星化工有限公司
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  • Summary
  • 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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  • Chemical process monitoring method based on time sequence multi-block modeling strategy
  • Chemical process monitoring method based on time sequence multi-block modeling strategy
  • Chemical process monitoring method based on time sequence 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 a time sequence multi-block modeling strategy, and aims 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 thought, the method comprises the following steps of firstly, sampling nodes according to a time sequence, and dividing an augmented matrix or vector into a plurality of variable blocks; and then, optimizing a projection transformation base for each variable sub-block by utilizing the thought of generalized canonical correlation analysis so as to extract the cross correlation between the variable sub-blocks, i.e., extracting the auto-correlation characteristics on the time sequence. In order to comprehensively consider the results given by the monitoring indexes of the multiple models, the method provided by the invention also uses the comprehensive monitoring indexes to respectively monitor the changes of the dynamic and static score information. In addition, the superiority of the method provided by the invention can be verified by a specific embodiment, so that the method provided by the invention is a more superior chemical process dynamic monitoring method.

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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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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