Corrosion of equipment by corrosive media is widespread in the processing of inferior crude oil. In hydroprocessing reactor effluent systems, corrosive media are very destructive to heat exchangers and air coolers during flow and cooling because of the high-temperature and -pressure environment. A fire and explosion in the air cooler or heat exchanger are highly likely when their tubes leak. Currently, there are no effective direct detection and prediction means to evaluate the corrosion risk in real time, creating significant hidden threats to the safe operation of the equipment. Therefore, this paper proposes a condition expansion method based on a Gaussian distribution. The distribution laws of characteristic corrosion parameters under various working conditions were studied, and the corrosion risk of the equipment was evaluated. A three-layer back-propagation neural network model is constructed to predict the characteristic corrosion parameters. After testing, the model is shown to have superior predictive accuracy and generalization performance. It can also meet the demand for real-time equipment corrosion prediction. The proposed method can serve an essential role in guiding engineers to take correct and timely prevention and control measures for different degrees of corrosion to reduce losses.
Skip Nav Destination
Article navigation
1 August 2022
Research Article|
June 01 2022
Predictive Study of Flow-Accelerated Corrosion Characteristic Parameters Based on the Neural Network
Yong Gu;
Yong Gu
*Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Search for other works by this author on:
Mingxiang Wang;
Mingxiang Wang
*Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Search for other works by this author on:
Haozhe Jin
Haozhe Jin
‡
*Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China.
‡Corresponding author. E-mail: [email protected].
Search for other works by this author on:
‡Corresponding author. E-mail: [email protected].
Online ISSN: 1938-159X
Print ISSN: 0010-9312
© 2022, AMPP
2022
CORROSION (2022) 78 (8): 751–764.
Citation
Yong Gu, Mingxiang Wang, Haozhe Jin; Predictive Study of Flow-Accelerated Corrosion Characteristic Parameters Based on the Neural Network. CORROSION 1 August 2022; 78 (8): 751–764. https://doi.org/10.5006/4034
Download citation file:
Citing articles via
Suggested Reading
Model for Estimating of Flow-Accelerated Corrosion Rates through Pipe Bend in Nuclear Power Plants
CONF_MAR2013
Effects on Flow-Accelerated Corrosion of Oleylpropanediamine Under Single-Phase Water Conditions Pertinent to Power Plant Feedwater
CORROSION (December,2019)
SCC Analysis of Austenitic Stainless Steels in Chloride-Bearing Water by Neural Network Techniques
CORROSION (August,1992)
Flow-Assisted Corrosion of Carbon Steel Under Neutral Water Conditions
CORROSION (August,2007)