Machine learning approach to investigate high temperature corrosion of critical infrastructure materials.

Muthukrishnan, Ramkumar; Balogun, Yakubu; Rajendran, Vinooth; Prathuru, Anil; Hossain, Mamdud; Faisal, Nadimul Haque

Abstract

Degradation of coatings and structural materials due to high temperature corrosion in the presence of molten salt environment is a major concern for critical infrastructure applications to meet its commercial viability. The choice of high value coatings and structural (construction parts) materials comes with challenges, and therefore data centric approach may accelerate change in discovery and data practices. This research aims to use machine learning (ML) approach to estimate corrosion rates of materials when operated at high temperatures conditions (e.g., nuclear, geothermal, oxidation (dry/wet), solar applications) but geared towards nuclear thermochemical cycles. Published data related to materials (structural and coatings materials), their composition and manufacturing, including corrosion environment were gathered and analysed. Analysis demonstrated that Random Forest Regression (RFR) model is highly precise compared to other models. Assessment indicates that very limited sets of materials are likely to survive high temperature corrosive environment for extended period of exposure. While a higher quality and larger dataset are required to accurately predict the corrosion rate, the findings demonstrated the value of ML’s regression and data mining capabilities for corrosion data analysis. With the research gap in material selection strategies, proposed research will be critical to advancing data analytics approach exploiting their properties for high temperature corrosion applications.

Citation

MUTHUKRISHNAN, R., BALOGUN, Y., RAJENDRAN, V., PRATHURU, A., HOSSAIN, M. and FAISAL, N.H. 2024. Machine learning approach to investigate high temperature corrosion of critical infrastructure materials. High temperature corrosion of materials [online], 101(Suppl 1), pages 309-331. Available from: https://doi.org/10.1007/s11085-024-10312-4

• Journal Article Type: Article
• Acceptance Date: Sep 12, 2024
• Online Publication Date: Sep 22, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Sep 13, 2024
• Publicly Available Date: Sep 20, 2024
• Journal: High temperature corrosion of materials
• Print ISSN: 2731-8397
• Electronic ISSN: 2731-8400
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 101
• Issue: Suppl 1
• Pages: 309-331
• DOI: https://doi.org/10.1007/s11085-024-10312-4
• Keywords: Thermochemical cycles; Materials; Structural parts; Coatings; Degradation; Electrolysis
• Public URL: https://rgu-repository.worktribe.com/output/2475165
• Additional Information: This article has been published with separate supporting information. This supporting information has been incorporated into a single file on this repository and can be found at the end of the file associated with this output.