Detection and mitigation of MIC:
- Kannan, Pranav, et al. “A Review of Characterization and Quantification Tools for Microbiologically Influenced Corrosion in the Oil and Gas Industry: Current and Future Trends.” Industrial & Engineering Chemistry Research 57.42 (2018): 13895-13922. https://pubs.acs.org/doi/abs/10.1021/acs.iecr.8b02211
- Cui, Yan (2017). Risk Assessment of Pipeline on Third-Party Damage in Oil and Gas Industry with Bayesian Network and Game Theory. Master’s thesis, Texas A & M University. https://oaktrust.library.tamu.edu/handle/1969.1/161410
- Kannan, Pranav (2018). Towards The Development of Biosensors for the Detection of Microbiologically Influenced Corrosion (MIC). Doctoral dissertation, Texas A & M University. https://oaktrust.library.tamu.edu/handle/1969.1/174036
- Kotu, S. P., Erbay, C., Sobahi, N., Han, A., Mannan, S., & Jayaraman, A. (2016). Integration of electrochemical impedance spectroscopy and microfluidics for investigating microbially influenced corrosion using co-culture biofilms. In NACE International Corrosion Conference Proceedings (p. 1). NACE International. https://www.onepetro.org/conference-paper/NACE-2016-7793
- Nicola, S., R.A. Mentzer, M.S. Mannan, “Prediction of Corrosion in Pipes,” Proceedings of 8th Global Congress on Process Safety, Houston, Texas, April 1-4, 2012. https://www.researchgate.net/publication/292834582_Prediction_of_corrosion_in_pipes
Bayesian inference in corrosion:
- Kannan, Pranav, et al. “A systems-based approach for modeling of microbiologically influenced corrosion implemented using static and dynamic Bayesian networks.” Journal of Loss Prevention in the Process Industries (2020): 104108. https://www.sciencedirect.com/science/article/abs/pii/S0950423019304474
- Palaniappan, Visalatchi (2018). Pipeline Risk Assessment Using Dynamic Bayesian Network (DBN) for Internal Corrosion. Master’s thesis, Texas A & M University. https://oaktrust.library.tamu.edu/items/f2311907-de8a-4bf1-9fc4-e77b9f9a5f37
