Method for determining causal relationship of key variables in complex industrial process

A causal relationship and industrial process technology, applied in the field of determining the causal relationship of key variables in complex industrial processes, can solve the problem of less parameter selection, achieve the effect of reducing interference items, eliminating pseudo-causal relationship, and reducing human judgment

Active Publication Date: 2018-06-15
CENT SOUTH UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to provide a method that is easy to calculate, has less parameter selection, fully considers the coupling problem between key variables, and can solve the causal relationship identification between key variables in a nonlinear complex dynamic system with moderate and strong coupling. The Method of Retrospective Analysis of Process Abnormal Conditions

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  • Method for determining causal relationship of key variables in complex industrial process
  • Method for determining causal relationship of key variables in complex industrial process
  • Method for determining causal relationship of key variables in complex industrial process

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specific Embodiment 1

[0075] The actual operation data of a refinery hydrocracking process in my country will be used as an example below, and the causal relationship of key variables in the hydrocracking process is determined based on the improved convergence cross-mapping algorithm proposed in the present invention, according to figure 1 Execute in the flow chart and give detailed instructions. It should be emphasized that the following description is only exemplary, and the following examples are only some of the key variables in the hydrocracking process, and are not intended to limit the scope of the present invention and its application. The provided method includes the following steps:

[0076] Step 1, collecting historical production data of key variables in the hydrocracking process, and performing sample data preprocessing. That is, for the 7 key variables in the hydrofinishing reaction part of the hydrocracking process whose causal relationship is to be determined, the simplified schema...

specific Embodiment 2

[0090] Taking tobacco shredded production as an example again, tobacco shredded production is a typical intermittent production process, and shredded drying is the most critical process. Leaves shredded as required. The production process is relatively complicated, and the shredded leaves are affected by factors such as moisture and temperature. Therefore, taking the shredded shredded drying process as an example, the causal relationship between key variables in the process is analyzed. The main steps are as follows:

[0091] Step 1: Select five key variables during the silk drying process: inlet moisture, hot air volume, wall pressure in the first zone, wall pressure in the second zone, and outlet moisture, and the length of the sample is 300;

[0092] Step 2, determine the optimal timing embedding dimension E of the reconstructed manifold, the G(k) graph of each key variable is as follows Figure 7 As shown, the optimal timing embedding dimensions of each key variable are 7...

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Abstract

The invention discloses a method for determining the causal relationship of the key variables in a complex industrial process. The optimal time sequence embedding dimension of each key variable is calculated through the pseudo-neighbor thought according to the historical data of the key variables of the causal relationship to be determined in the industrial process; for the two key variables, thecausal relationship is assumed, so that the optimal time sequence embedding dimension of the factor is assumed to be the standard, and a time sequence reconstitution flow shape of the two key variables is constructed, a convergence cross mapping algorithm is used for calculating the convergence cross mapping capability between the two; the capability judgment threshold value of the CCM is determined based on Monte Carlo simulation, so as to judge the correctness of the assumed causal relationship among the key variables, so as to construct a preliminary causal relationship network of the key variables in the industrial process; and a time lag detection method is used for correcting a preliminary causal relationship network to obtain a final key variable causal relationship network. According to the method, the offline data is fully utilized, so that the interference effect on the production process is avoided, and the safety and the economic benefit are improved.

Description

technical field [0001] The invention relates to technical fields such as the analysis of the causal relationship of key variables in the abnormal working condition diagnosis and retrospective analysis of industrial processes, and specifically relates to a method for determining the causal relationship of key variables in complex industrial processes. Background technique [0002] In large and complex industrial systems such as petroleum refining and iron and steel smelting, there are many operating variables in the process, the coupling between variables is serious, and the integration degree is high. For example, the hydrocracking process is a sub-process in the petroleum refining process. It is a processing process in which hydrogen is converted into light oil by hydrogenation, cracking and isomerization reactions of heavy oil through catalyst action at relatively high pressure and temperature. , mainly including four important parts: hydrofining reaction, hydrocracking re...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06F17/18
CPCG06F17/18G06F2218/04
Inventor 王雅琳胡芳香曹跃袁小锋阳春华桂卫华
Owner CENT SOUTH UNIV
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