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Corrosion prediction method of petrochemical equipment based on extreme learning machine

An extreme learning machine and equipment corrosion technology, which is applied to computer parts, instruments, biological neural network models, etc., can solve problems such as difficulty in guaranteeing accuracy and slow learning speed

Active Publication Date: 2018-12-04
XI'AN PETROLEUM UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

For example, traditional neural networks (such as BP algorithm) are prone to fall into local optimum, slow learning speed, and difficult to guarantee accuracy.

Method used

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  • Corrosion prediction method of petrochemical equipment based on extreme learning machine
  • Corrosion prediction method of petrochemical equipment based on extreme learning machine
  • Corrosion prediction method of petrochemical equipment based on extreme learning machine

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Embodiment Construction

[0030] The implementation of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0031] refer to figure 1 , a petrochemical equipment corrosion prediction method based on extreme learning machine, including the following steps:

[0032] Step 1: Monitor corrosion data; in refineries, select sensitive parts of petrochemical equipment that are susceptible to corrosion as monitoring points according to different corrosion mechanisms in the process, for example, the top of the catalytic fractionation tower, the top of the atmospheric and vacuum device, the upper part of the transfer line, High-pressure air cooler outlet pipeline; After selecting the corrosion monitoring point, determine the installation location of the corrosion measuring instrument, monitor the corrosion data in real time, collect and transmit the corrosion data, and save the corrosion data in the corrosion database.

[0033] Step 2: Establish a corrosion datab...

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Abstract

Corrosion prediction method of petrochemical equipment based on extreme learning machine, monitor corrosion data of refinery, establish corrosion database, use long-term accumulated corrosion data as sample preprocessing, select corrosion influencing factors PH, CL-, H2S, NH3N as input, corrosion product Fe2+ and Fe3+ as output, establish a three-layer neural network to train the sample data, obtain the extreme learning machine petrochemical equipment corrosion prediction model, input the corrosion data of on-site monitoring into the prediction model, and obtain the corrosion prediction value of Fe2+ and Fe3+, the present invention can compare The relationship between corrosion influencing factors and corrosion results can be well expressed, the corrosion status of petrochemical equipment can be understood according to the predicted value, and the corrosion of petrochemical equipment can be effectively controlled and prevented by adjusting process parameters.

Description

technical field [0001] The invention relates to petrochemical equipment corrosion protection technology, in particular to a petrochemical equipment corrosion prediction method based on an extreme learning machine. Background technique [0002] Corrosion exists in various industries of the national economy. Corrosion has caused huge losses to the national economy, and the economic losses caused by corrosion account for 3%-5% of the country's total national economic output value this year. Petrochemical refining is in a high temperature and high pressure environment, which is toxic, harmful, flammable and explosive, and corrosion is particularly prominent in the petrochemical industry. In addition, with the high sulfur content of imported crude oil and the development of many oilfields in my country, the quality of crude oil is deteriorating day by day. The water content, sulfur content, salt content, heavy metal content and acid value of crude oil are all increasing, which a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/02
CPCG06N3/02G06F18/214
Inventor 李皎周三平吴莹
Owner XI'AN PETROLEUM UNIVERSITY