Abnormity detection and diagnosis method for non-gaussian dynamic high-sulfur natural gas purification process

A process abnormality and diagnostic method technology, applied in the direction of electrical testing/monitoring, testing/monitoring control systems, instruments, etc., can solve problems such as failure to detect failures in time

Active Publication Date: 2015-05-27
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0006] The purpose of the present invention is to overcome the deficiency that the existing technology cannot detect the occurrence of faults in time, and provide a non-Gaussian dynamic high-sulfur natural gas purification pr...

Method used

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  • Abnormity detection and diagnosis method for non-gaussian dynamic high-sulfur natural gas purification process
  • Abnormity detection and diagnosis method for non-gaussian dynamic high-sulfur natural gas purification process
  • Abnormity detection and diagnosis method for non-gaussian dynamic high-sulfur natural gas purification process

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Embodiment 1

[0049] Such as figure 1 As shown, a non-Gaussian dynamic high-sulfur natural gas purification process abnormal detection and diagnosis method is carried out according to the following steps:

[0050] Step 1: Randomly collect n groups of high-sulfur natural gas purification process data to form the original measurement sample set X=[x 1 ,x 2 ,...,x n ]∈R m×N , each sample contains N independent high-sulfur natural gas purification process parameter sampling values.

[0051] Step 2: Preprocess the sample data and select the effective data that can best reflect the actual characteristics of the production process; specifically: eliminate samples with missing parameters in the collected data, and ensure that the samples meet the company's purification gas technical indicators, and the obtained data is X *=[x 1 ,x 2 ,...,x n ]∈R m×n , n is the number of samples collected after processing, n

[0052] Step 3: Analyze the autoregressive model of the data X* obtained in st...

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Abstract

The invention discloses an abnormity detection and diagnosis method for a non-gaussian dynamic high-sulfur natural gas purification process. The diagnosis method comprises the following steps: randomly collecting high-sulfur natural gas purification process data to form an original measurement sample set; preprocessing the data; determining the time delay order of the model by analyzing an industrial process autoregression model, then projecting the data to a nuclear independent element space, and realizing abnormity detection by monitoring whether the statistics of T2 and SPE corresponding to an independent element is beyond the control limit set under the normal state or not; finally, calculating a first-order partial derivative of the statistics of T2 for original variables, drawing the contribution chart of the first-order partial derivative, and thus realizing abnormity diagnosis. The method can detect failure occurrence in time and retrospects the reason of process operating parameters which cause failures, so that decision-making reference is provided for system failure investigation and recovery and monitoring in the nonlinear, dynamic and non-gaussian process is realized.

Description

technical field [0001] The invention belongs to fault detection and diagnosis technology in the desulfurization production process of high-sulfur natural gas, and relates to a non-Gaussian dynamic high-sulfur natural gas purification process. Background technique [0002] The industrial process of purification and desulfurization of high-sulfur natural gas is complex, with many process parameters, affected by uncertain factors such as temperature, pressure, flow rate, equipment aging and raw gas processing capacity, it is a typical chemical system with complex nonlinear dynamic characteristics. The purification and desulfurization process of high-sulfur natural gas mainly includes the following parts: the MDEA solution in the main absorption tower absorbs the acidic component H 2 S and CO 2 , hydrolysis reactor removal (COS), regeneration tower MDEA solution cycle regeneration and heat exchange process, the specific process flow process such as figure 2 shown. Once the sy...

Claims

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Application Information

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IPC IPC(8): G05B23/02
CPCG05B23/0281
Inventor 张利亚李太福李景哲邱奎裴仰军辜小花
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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