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A modeling method of natural gas purification process based on scmiukfnn algorithm

A technology of purification process and modeling method, applied in design optimization/simulation, biological neural network model, neural architecture, etc., can solve problems such as energy consumption and production cost increase

Active Publication Date: 2021-07-09
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

In addition, the energy consumption and production costs of this process increase dramatically with the increased circulation of the acid gas absorbent solution

Method used

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  • A modeling method of natural gas purification process based on scmiukfnn algorithm
  • A modeling method of natural gas purification process based on scmiukfnn algorithm
  • A modeling method of natural gas purification process based on scmiukfnn algorithm

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Embodiment Construction

[0065] Glossary

[0066] ScMiUKFNN: Scaled Minimum Unscented Kalman Filter Neural Network, a reduced sampling unscented Kalman filter neural network based on scaling.

[0067] The industrial process modeling method based on the ScMiUKFNN algorithm provided by the present invention includes:

[0068] Step S1: Select the process parameters that affect the desulfurization efficiency and the performance indicators of the desulfurization unit; among them, the process parameters include the flow rate of lean amine liquid entering the tail gas absorption tower, the flow rate of lean amine liquid entering the secondary absorption tower, the processing capacity of raw material gas, and the exhaust gas unit The flow rate of the semi-rich amine liquid returning to the desulfurization unit, the temperature of the amine liquid entering the primary absorption tower, the temperature of the amine liquid entering the secondary absorption tower, the pressure of the flash tank, the steam consump...

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Abstract

The invention discloses a natural gas purification process modeling method based on the ScMiUKFNN algorithm, comprising the following steps: step S1: selecting process parameters that affect the desulfurization efficiency and performance indicators of the desulfurization unit; step S2: collecting the process parameters at a preset time and the data of the performance index; step S3: form a normalized sample set, take a part of the normalized sample set as a training sample, and the remaining part as a test sample; step S4: build a neural network model based on the training sample and the The initial state variable of the neural network model; step S5: use the ScMiUKFNN algorithm to estimate the optimal state variable of the neural network model; step S6: obtain the updated neural network model of the training sample; step S7: obtain the predicted result, and compare the predicted result with The actual output in the test sample is compared, if the comparison result is less than the preset error value, the neural network model is valid; otherwise, the above steps are repeated until the comparison result is less than the preset error value.

Description

technical field [0001] The invention relates to the technical field of purification of high-sulfur natural gas, and more specifically, to a modeling method of natural gas purification process based on ScMiUKFNN algorithm. Background technique [0002] With the rapid growth of demand for clean energy, the demand for natural gas is gradually increasing. However, high-sulfur gas (HSG) contains ten times more acidic gas than normal natural gas, and occupies a considerable proportion in China's gas reservoirs. Due to its toxicity and corrosiveness, high-sulfur gas cannot be used directly, and hydrogen sulfide (H 2 S) and carbon dioxide (CO 2 ), called the desulfurization process of high-sulfur gas. In addition, the energy consumption and production costs of this process increase dramatically with the increase of acid gas absorbent solution circulation. Therefore, reducing energy consumption and operating costs, improving the economic benefits of enterprises, and improving mar...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06N3/04
CPCG06N3/04G06F30/20
Inventor 辜小花王甜唐海红张堃宋鸿飞张兴侯松裴仰军李太福邱奎
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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