Neural network method for the modeling of SS 316L elbow corrosion based on electric field mapping-Reference-Cited by-同舟云学术

Neural network method for the modeling of SS 316L elbow corrosion based on electric field mapping

Published:2022-05-19 Issue:4 Volume:40 Page:383-391
ISSN:2191-0316
Container-title:Corrosion Reviews
language:en
Short-container-title:

Author:

Memon Azhar M.¹^ORCID,Salman Umar T.²,Hamzat Abdulhammed K.³,AlHems Luai M.¹

Affiliation:

1. Applied Research Center for Metrology, Standards and Testing , Research Institute, King Fahd University of Petroleum and Minerals , Dhahran 31261 , Saudi Arabia

2. Electrical Engineering Department , King Fahd University of Petroleum and Minerals , Dhahran 31261 , Saudi Arabia

3. Mechanical Engineering Department , King Fahd University of Petroleum and Minerals , Dhahran 31261 , Saudi Arabia

Abstract

Abstract Stainless steel is known for its superior corrosion resistance in industrial applications. In this work, corrosion modeling of stainless steel 316L is presented using artificial neural networks. The experimental setup consists of a loop containing stainless steel elbow with simulated seawater of known concentration continuously flowing at a specific flow rate, thus allowing to study the effect of flow dynamics and salt concentration on corrosion. Electric field mapping setup is used to collect the voltage and current information along with the temperature of the elbow section. In addition to modeling, characteristics of the observed scale deposits are also studied in-depth and briefly reported in this work.

Publisher

Walter de Gruyter GmbH

Subject

General Materials Science,General Chemical Engineering,General Chemistry

Link

https://www.degruyter.com/document/doi/10.1515/corrrev-2021-0057/pdf

Reference24 articles.

1. Ahmed, W.H., Bello, M.M., El-Nakla, M., and Al-Sarkhi, A. (2012). Flow and mass transfer downstream of an orifice under flow accelerated corrosion conditions. Nucl. Eng. Des. 252: 52–67. https://doi.org/10.1016/j.nucengdes.2012.06.033.

2. Ahmed, W.H., Bello, M.M., El-Nakla, M., Al-Sarkhi, A., and Badr, H.M. (2014). Experimental investigation of flow accelerated corrosion under two-phase flow conditions. Nucl. Eng. Des. 267: 34–43. https://doi.org/10.1016/j.nucengdes.2013.11.073.

3. Alahmed, A.S., Taiwo, S.U., Abido, M.A., and Almuhaini, M.M. (2019). Intelligent flexible priority list for reconfiguration of microgrid demands using deep neural network. In: IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia). IEEE, Chengdu, China, pp. 21–24.

4. Andritsos, N. and Karabelas, A.J. (2003). Calcium carbonate scaling in a plate heat exchanger in the presence of particles. Int. J. Heat Mass Tran. 46: 4613–4627. https://doi.org/10.1016/s0017-9310(03)00308-9.

5. Choi, Y.-S., Nesic, S., and Ling, S. (2011). Effect of H2S on the CO2 corrosion of carbon steel in acidic solutions. Electrochim. Acta 56: 1752–1760. https://doi.org/10.1016/j.electacta.2010.08.049.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Neural network based corrosion modeling of Stainless Steel 316L elbow using electric field mapping data;Scientific Reports;2023-08-11

2. Novel assessment of in-operations mapped stainless steel elbow pipe internal wall for detection of corrosion and scale deposition;International Journal of Corrosion and Scale Inhibition;2022-12-02