Mary Kay O'Connor Process Safety Center

Reliability and Maintenance

List of Center Publications

Corrosion

Corrosion in one of the most systemic and historically amongst the most expensive problems for the many sectors of the industry including petroleum refining, mining, transportation, food processing, nuclear industry, offshore oil and gas industry and industrial water systems. Corrosion in equipment and pipeline can lead to a loss of containment event and hence is its research from a process safety perspective is particularly important.

Microbiologically induced corrosion (MIC), a specific form of corrosion, is a phenomenon in which corrosion of a substrate is perpetrated and/or accelerated because of the presence of microorganisms (bacteria and algae) on its surface.

Detection and mitigation of MIC:

At MKOPSC the following research has been conducted:

  • Development of sensor for the detection of microbiologically influenced corrosion: This research is focused on the usage of nanomaterials to create a substrate which would be functionalized by biomolecules to detect specific bio-moieties unique to MIC sites. The goal this to conduct specific and real time/pseudo real time detection of MIC.
  • Integration of electron impedance spectroscopy and microfluidics for investigating microbially influenced corrosion: Development of effective mitigation strategies for MIC requires a fundamental understanding of how biofilms are formed. The aim of this study is to investigate the factors underlying formation and development of dual-culture biofilms using microfluidic flow system. This system is used to develop a correlation between the thickness of the biofilm as measured by confocal laser scanning microscope (CLSM) and impedance measurement from electron impedance spectroscopy (EIS). The effect of hydrodynamic factors like flow rate, shear stress on biofilm dynamics would be investigated.
  • Radio Frequency Identification (RFID) Smart Corrosion coupon: The development of a novel type of smart corrosion coupons using Radio Frequency Identification (RFID) technology for continuous real-time wireless monitoring of corrosion. This will combine the advantages of RFID technology and the corrosion coupon so that a better corrosion monitoring method can be realized. The central idea in this project is to create smart RFID corrosion coupons, which can emit signals indicating the corrosion status of the monitored points on demand, by placing the RFID coupons close to pipeline, which act like normal corrosion coupon.

Bayesian inference in corrosion:

MKOPSC has also applied Bayesian Network in the modeling and assessment of risk arising from corrosion in pipelines and other systems.

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List of Center Publications:

Detection and mitigation of MIC:

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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.

Bayesian inference in corrosion:

  1. 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
  2. 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/handle/1969.1/174151