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Chemical process fault detection method based on generalized canonical variable analysis

A chemical process, typical variable technology, applied in the field of data-driven process monitoring, can solve problems such as lag in the effect of manipulated variables

Pending Publication Date: 2020-11-10
NINGBO UNIV
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Problems solved by technology

For example, the stickiness of pipeline valves will cause the effect of operating variables to lag behind, which may bring about a negative impact on the time series

Method used

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  • Chemical process fault detection method based on generalized canonical variable analysis
  • Chemical process fault detection method based on generalized canonical variable analysis
  • Chemical process fault detection method based on generalized canonical variable analysis

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Embodiment Construction

[0057] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and specific examples of implementation.

[0058] 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.

[0059] 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 monitoring research due to the complexity of the process. The whole TE process inc...

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Abstract

The invention discloses a chemical process fault detection method based on generalized canonical variable analysis, and aims to fully extract canonical correlation of chemical process sampling data ona time sequence and reasonably describe correlation characteristics so as to realize effective monitoring of a fault operation state of a chemical process. Compared with a traditional method, the generalized canonical correlation analysis algorithm involved in the method considers the square of the canonical correlation coefficient, so that the extracted characteristic components meet the maximization requirement of the canonical correlation coefficient. In addition, the characteristics related to the time sequence and the static characteristics are separately considered, and two comprehensive monitoring indexes are correspondingly used for separately implementing online process monitoring. Finally, the superiority of the method in extracting time sequence related characteristics and thereliability of the method in monitoring the operation state of the chemical process are verified in a specific embodiment, so that the method provided by the invention is a more optimal chemical process fault detection method.

Description

technical field [0001] The invention relates to a data-driven process monitoring method, in particular to a chemical process fault detection method based on generalized typical variable analysis. Background technique [0002] Since computer technology is widely used to manage the production, monitoring, scheduling and other scenarios of modern chemical processes, modern chemical processes are gradually moving toward an intelligent development model. In addition, due to the extensive installation and use of advanced instruments and storage devices, chemical process objects can be stored offline and measured online in real time with a large amount of sampling data. These data contain potentially useful information that can reflect the operating status of the production process. A solid data foundation has been laid. Therefore, how to fully and effectively use the sampling data to monitor the fault conditions in the chemical process in real time reflects the digital and intell...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q10/00
CPCG06Q10/0637G06Q10/0633G06Q10/067G06Q10/20
Inventor 张赫葛英辉童楚东
Owner NINGBO UNIV
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