Modelling of Microbiologically Influenced Corrosion · corrosion interface •The corrosion...

CENTRE FOR RISK, INTEGRITY AND SAFETY ENGINEERING WWW.MUN.CA/ENGINEERING/RESEARCH/CRISE

Modelling of Microbiologically Influenced

Corrosion

Centre for Risk, Integrity, and Safety Engineering (C-RISE)

Presenter:

Nonso Ezenwa


Problem Statement

• Since the first theory was proposed to explain microbiologically influenced corrosion in 1934, numerous studies have been done on the subject. Yet, much remains unknown about MIC. Some of what is unclear is as a consequence of the complex role that biofilm plays in MIC. That is the focus of this work.

2


A twin-track approach

• Microbiologically influenced corrosion (MIC) refers to corrosion that is initiated or accelerated by microorganisms (Borenstein, 1994)

• Improving our understanding of MIC can be achieved by two approaches: investigating the mechanism and the effects of MIC

• Investigating the mechanism of MIC involves trying to understand how MIC occurs. This requires a study of microscopic interactions occurring at the corrosion interface. This is achieved here by molecular modelling

• Investigating the effects of MIC involves an understanding of pit growth. This is achieved by deterministic modelling

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The impact of FeS on MIC

• The most important causative agents are SRPs

• While there are a few models used to predict MIC pitting, none of these incorporates the effect of FeS on the corrosion interface

• From the Gu-Zhao-Nesic model - 𝑃𝑅 = 1.16𝑖𝑎and studies in microbial fuel cell technology, a proposition can be made

• This is 𝑖 ∝ 𝑉𝐹𝑒𝑆,

• Where i=current density; VFeS=volume of FeS deposited

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Assumptions

• Corrosion pits formed are cylindrical in shape

• Pit formation occurs mostly downwards into the metal surface rather than across the metal surface (Davis, 2000). Thus, the cross-sectional area of the pit is assumed to be constant.

• The predominant product of SRB corrosion is FeS. Hence, 𝑉𝑝 = 𝑉𝐹𝑒𝑆

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An integrated model

• Through mathematical solutions, we obtain a time-dependent MIC pitting model:

• 𝑥 = 𝑃𝑒1.16𝐵𝑡

• Where x=pit depth; P and B are MIC constants; and t=time

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Validation using oil tubing data (Mohd et

al, 2009)

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0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

0 5 10 15 20 25

Pit

de

pth

(m

m)

Age (years)


Experiment versus Molecular

Modelling (Hehre, 2000)

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Molecular modelling of MIC

• To model MIC at the molecular level is in fact to model the corrosion interface

• The corrosion interface comprises the substrate, the adsorbates and the medium

• The first step in creating a model is to determine the substrate and adsorbates

• One of the most stable Fe indices is chosen as the substrate. Here, Fe(110) is chosen

• The adsorbates are identified from previous studies as O2

and HS-

• Because the biofilm is composed primarily of water (95% by some estimates), water is considered the medium

9


Assumptions

• At neutral pH, HS- is the dominant sulfide species in the bulk solution surrounding the corrosion interface (Xu, 2013)

• The constitution of the biofilm is mostly water

• The presence of dissolved oxygen in the biofilm does not alter the metabolic pathway of SRP activities

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Modelling phases

• The modelling of MIC was done in two phases:

• Phase 1: Identifying preferred adsorption sites on the Fe(110) surface for the docking of the identified adsorbates using the adsorption locator module in Material Studio.

• Phase 2: Constructing the molecular model of the corrosion interface. Determining the most stable structure of this interface by performing molecular optimization

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HS- and O2 docked on Fe(110)

surface

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Optimized structure of

solvated (HS- + O2) on Fe(110)

13


Validation using MD at two

boundary temperatures

• Transportation of heavy crude oil in the

trans-pacific pipeline is reported to take

place between two boundary

temperatures – 291.8K and 355.2K

(Shauers et al, 2000; Dunia and Edgar,

2012)

• These temperatures were used in

molecular dynamics simulation for model

validation

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Bond length and reactivity

• Many experiments have sought to show the

correlation between bond length and reactivity,

and this concept is used here for validation

(Jones and Kirby, 1986).

15


Validation using MD

simulation results

Bond length O2 (in

Angstroms)

Bond length HS-

(in Angstroms)

Optimized structure 4.554 4.445

MD at 291.8K 5.413 3.260

MD at 355.2K 4.480 18.089

16


Acknowledgements

• Dr. Faisal Khan

• Dr. Kelly Hawboldt

• Dr. Richard Eckert

• Dr. Torben Skovhus

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Thank you!


Thank

you!

19

Genome Canada

Genome Alberta

Genome Atlantic

Alberta Innovates

InnoTech Alberta

Natural Resources Canada

Mitacs

Innovate NL

Baker Hughes, a GE

Company

Bioclear Microbial Analysis

BP

DNV GL

Dow Microbial Control

Enbridge

Husky Energy

Kinder Morgan

Luminultra

NALCO

Champion

OSP

PeroxyChem

Shell

Schlumberger

Suez

Suncor


References

• Borenstein, S.W. (1994). Microbially Influenced Corrosion Handbook. Cambridge,England: Woodhead Publishing.

• Davis, J.R. (2000). Corrosion: Understanding the basics. OH, US: ASM International

• Hehre, W.J. and Shusterman, A.J. (2000). Molecular modelling in undergraduate Chemistry education. USA: Wavefunction Inc.

• Mohd, H.M., and Paik, J.K. (2013). Investigation of the corrosion progress characteristics of offshore subsea oil well tubes. Corrosion Science, 67: 130-141

• Xu, D (2013). Microbiologically influenced corrosion: Mechanisms and mitigation (PhD dissertation). Russ College of Engineering and Technology, Ohio University, Ohio, USA

• Jones, P.G. and Kirby, A.J. (1986). Multiple bond length and reactivity correlations. Journal of the Chemical Society, Chemical Communications, 0: 444-445

• Shauers, D., Sarkissian, H., Decker, B., Wilkerson, B., and Mecham, T. (2000). California line beats odds, begins moving viscous crude oil. Oil and Gas Journal, 98(15); 54-63

• Dunia, R., and Edgar, T.F. (2012). Study of heavy crude oil flows in pipelines with electromagnetic heaters. Energy Fuels, 26: 4426-4437

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