modelling, prediction and factors in the corrosion of ...corrosion.hzs.be/presentations/rob...

Centre for Infrastructure Performance and Reliability

Modelling, prediction and factors in the

corrosion of steels in marine

environments

Robert E. Melchers

1


Outline

• Background

• Models for marine corrosion of steels

• Factors in marine corrosion

• Microbiologically influenced corrosion

• Corrosion of sheet piling

• Chains and moorings for FPSOs

• Corrosion in ships

• Corrosion protection

• Discussion

• Conclusion

2


Motivation

Deterioration of infrastructure

• An increasing issue – safety, costs

• Life-cycle planning and assessment of existing structures

• Need for estimation of likely future deterioration

• Questions for structural engineers:

Loss of strength? loss of safety? when? costs? when to repair?

• Deterioration affects structural safety / reliability

• Fatigue – reasonably well understood – much research

• Wear – mainly mechanical equipment


Deterioration

• Corrosion: major issue for infrastructure:

bridges, industrial facilities, coastal and harbour structures,

shipping, pipelines and tanks, offshore structures

– expected life: 20-100+ years

nuclear waste containers: 10,000 + years.

• Protective/sacrificial coatings, cathodic protection… offer

protection, but not always feasible - e.g. bulk carrier holds, chains...

• Requirements:

1. Predict amount of corrosion now,

2. Predict future long-term corrosion

3. Requires a robust, calibrated model


• expected life tL of interest

• requires models for R(t) => models for corrosion prediction

Corrosion,

pitting

Failure:

Load > Strength

Safety of corroding structures


• ‘Uniform’ corrosion over a

surface – critical for strength

• Easy to use in computations

• Usually consists of pitting

• Pitting – critical for containment

(e.g. pipes, tanks)

• Crevice corrosion – under

deposits, contact areas

Capacity of corroded structures


Prediction of likely corrosionCorrosion texts

• much electro-chemistry & no obvious models

• inaccessible to most engineers

• corrosion initiation & short-term behaviour

Corrosion handbooks

• much field information – anecdotal, no organized data

• no models for prediction

Field testing

• invariably short-term - over-estimates long-term corrosion and pitting

• not useful for long-term models

Electro-chemical tests: (= accelerated tests)

• interpretation = requires expertise

• how to relate results to likely field behaviour ?


• Traditional - a constant corrosion ‘rate’

Inconsistent with observations

• Similarly for other steels

• Similarly for other environments

• Various other models proposed:

• e.g - Atmosphere: power law

- c(t) = A t B

- also not consistent with data.

A better model must include:

- complexity of corrosion ✔- long-term trending ✔- corrosion vs pitting

- variability estimates

Corrosion trends


Factors in corrosion - seawater immersion

Oxygen supply, Salinity, pH, Carbon dioxide,

Carbonate solubility, Pollutants Temperature,

Pressure, Suspended solids, Wave Action,

Water velocity, Bacteria, Biomass

Steel composition

Surface roughness

Size

Coupon edge ratio

Research approach Reduce complexity

• Started with: unpainted mild steel

• ‘at-sea’ and ‘near-surface’ exposures

- ensures full aeration of seawater

- eliminates most chemical factors and physical factors, except:

• Average seawater temperature (T) - governs many processes

• Biological activity - function of nutrients / water pollution

• Other factors later


A robust steel corrosion model (2003)

• Theoretical diffusion requirements – see literature

• Calibrated to field data, also for some other factors - see literature

• Bi-modal characteristic function

• Also for other environments, various steels, Cu alloys, Al alloys

• NOT a corrosion ‘rate’

Long-term

rate


Model calibration – field test data


Model parameter calibration

No pollution, no major bacterial influences

r0 = early corrosion rate

ca = corrosion at tata = idealized transition time

Function of T = mean temperature

Other parameters similarly

Also estimates for uncertainty / variability/ standard deviations …


Influencing factors

• Salinity - related to water hardness – see Corros Sci 2006

- early on, salinity affects mainly pitting

- soft fresh waters can be highly corrosive

- once rusts develop, corrosion interface = stagnant conditions

- => no effect from chlorides / other salts

- (cf. in air, NaCl particles hygroscopic => increases ‘wetness’ time)

• Timing - pushes model (to left or right) in time by about 6 months.

• Pressure - no evidence of any noticeable effect

• Water velocity – increases corrosion in phases 0-1 mainly before rusts

build-up, thereafter consequential

• Depth – effects from temperature, DO and nutrient levels, velocity?

• Alloying – little effect for small changes in composition

• Size/Area - not an important variable, for moderate-sized objects ….


Effect of alloying

• Literature - effect of alloying contradictory

- changes at some test sites producing different effects to

elsewhere - correlation studies yielded very poor results

• Bi-modal model allowed data to be separated ….

• Note: some compositions influence phases 1 & 2 - others mainly 3 & 4

• Carbon content:

- frequently dismissed, has an effect on phases 3 and 4

- why? bacterial effect of C ?


Effect of seawater velocity

• All texts, papers point to classical result due to LaQue (1948)

• Obtained from laboratory work, short-term (36 days).

• Field results from Swansea Channel show:

• Velocity increases corrosion rate only in first few weeks, then increasing thickness of rust layer gives protection

• Hence curve moves upwards with greater velocity….

Classical New


Pitting

• Observations:

• Pit development =

• Step-wise, cyclic

• Mechanism changes from aerobic to autocatalytic anaerobic corrosion

• Experimental data => pit depth is not a linear function


Microbiologically influenced corrosion Pacific Ocean, Australia

• Similar sites, 100 km apart

- site A – coastal seawater

- site B – bay: higher losses –

WHY?

• water quality testing - high nitrates and

phosphates from nearby agriculture

fertilizer run-off

Port Huemene CA.

• direct evidence of water quality difficult

to find but ...

• anecdotal comments by surf-riders

"…sometimes you have to paddle

across filthy water to get out to the line-up’ …

and ‘brown coloured effluent’ from a local

waste-water treatment plant (Wannasurf 2003).

A

B

Model

Observed


Microbiological corrosion & nutrients

Nutrients are necessary for biological metabolism:

• sulfates - abundant in seawater

• phosphates, phosphorous – abundant, unlikely to be limiting

• organic carbon - almost certainly available in coastal seawater

• ferrous ions – micronutrient - not in seawater – from corrosion of steel

• nitrogen – macronutrient - usually not present in seawater

Our approach (see references)

• MIC is the result of bacterial metabolism

• Depends on nutrient availability

• Nitrogen = critical nutrient (Carlucci 1974, Postgate 1984)

=> Dissolved Inorganic Nitrogen (DIN)

- sources: ammonium, nitrate and nitrite.


Effect of DIN on long-term corrosion

• DIN changes cs and rs in

simplified corrosion model

• Field data: parameter plots,

estimates of uncertainty

• Temperature remains important

• Allows prediction of corrosion

from water quality analysis

(Corros. Sci. 2014)


MIC and pitting corrosion

• MIC typically most severe for pitting corrosion.

• Pitting follows broadly same model as corrosion loss

• Example: Steel tubular bridge piles

• Located through effluent flow from sewerage treatment

Centre for Infrastructure Performance and Reliability21

Accelerated Low Water Corrosion

• High localized corrosion just below

Low Tide level

• ‘First detected’ 1980s

• Major concern for harbours

• MIC suspected ...

• Bacteria present in both affected and

unaffected cases => prediction?

Australian research project:

• Based on: nutrients are critical

• Field exposures at 13 locations

• Steel strips 3, 6m long, 50 x 3mm

• Exposed for up to 3 years

• Microbial identification ignored

• DIN measured in-situ.


• US Navy sites & EPA + other data extends range to 1.2 mg/L

• Good correlation between ALWC effect and local DIN,

• Outcome: estimate likelihood of ALWC from:

- ‘short-term’ field tests (1-2 years)

- DIN concentration in local seawater [See: Corrosion Science, 65: 26-36.]

Results – ALWC vs. DIN


Corrosion of working chains

• Working chains wear at inter-link areas

- but strength reduction more than wear ...

- wear also removes rust layer

• Corrosion continuously in Phase 1 of model ...

• Allow for temperature, salinity, DO, velocity, etc.

• Comparison to data for North Sea ≈ 0.4 mm/yr

• Steel composition ... (from earlier research)

See: Journal of Marine Science and Technology 12: 102-110.

Brunel


Corrosion of offshore mooring chains for FPSOs

FPSO = Floating Production, Storage and Offloading vessels

• Special build, or converted oil tankers

• Remaining “on-station” = critical

• Oil & gas exploitation moving into deeper, Tropical waters (2-3 km

• Corrosion in the Tropics?

• SCORCH-JIP established to investigate

• UoN corrosion tests in Tropics, etc.

24


SCORCH – Field installation of samples


FPSO Mooring chains in the Tropics

• Current chain design guidelines for corrosion =

North Sea experience

• Steel chains: 76 to 152+ mm diam.

• UoN experimental program - no surprises

• Field recoveries of corroded chain

- some showed very deep, large pits

• Caused much industry concern

26


FPSO Mooring chains in the Tropics

• Very deep pitting of steel chain observed - off the coast of West Africa,

in Timor Sea …

• Much > than expected from temperature

• Led to detailed field investigations – incl. water quality

• Showed very high DIN in local seawater >> in normal seawater

• High DIN traced to water pollution

• Several chain links scanned -> computer models

• Measured pit depths:

• Overall consistent with MIC research findings

75 mm diam. steel 20+ mm pitting

27


Corrosion in ship ballast tanks

• Traditionally - ballast tanks were unpainted

• Ballast tanks – detritus at base

• Investigations: used coupons

• Australian Navy frigate instrumented

• Relative humidity – difficult – short periods

• Temperature sensors OK

• Some correlation {see Trans RINA A 148:77; CS 50(12)3296)


Corrosion in holds of bulk carrier ships

• Aggressive cargoes (iron ore, coal) – very abrasive

• Protective coatings not used for holds

• Environment: humid, high temperatures

• [Other influences: bottom plate damage from grabs / front-end loaders]

• Research questions: corrosion aggressiveness of coal, iron ore?

• Cargo hold of an operational bulk carrier instrumented, monitored

• Also lab simulation tests

Results:

• Fine particles critical in pitting (cf. sands)

• Dilute chemistry/salts = not important

• Consistent with classical observations, soils

[see Corr Sci 44(11) 2549; 44(12)2665; Marine Structures 16(8)547]


Ships and offshore vessels - interior corrosion

• New project (2017- ) ARC Linkage grant

• Corrosion often occurs at welds, sharp edges

• Also bilges, areas of deposits

• Effect on structural reliability / safety


Corrosion at welds

• Considerable differences of pitting

depth severity in seawater

• HAZ = most severe pit depths

• Not a constant pitting corrosion

‘rate’

• Step-wise progression


Corrosion Protection 1.• Protective coatings (are not impermeable)

• Cathodic Protection (a) sacrificial anodes

(b) impressed current

• Both can offer protection but care required

• A certain degree of ‘black magic’ ( – driven by service providers?)

• “Protection” is not always feasible - e.g. bulk carrier holds, chains...

• Protective measures require maintenance !

• Sacrificial steel allowance may be more cost effective – in life-time cost

assessments (increasingly being used for infrastructure)

• Prime example: ship hold plating – 10% loss rule => replacement

Understanding of corrosion protection possibilities and limitations,

requires a good insight into corrosion processes … particularly pitting…

• s


Corrosion protection 2.

Protective coatings

• Good protection needs good surface

preparation (no salt deposition)

• No pin-hole pitting, no “holidays”

• Coating must be well maintained

=> timely recoating !

• Coating life = ?

• Very uncertain, depends on who predicts

• Coating over rusts, even cleaned, unlikely to be successful

• Reason – cannot clean out the microscopic pits – corrosion can

continue there without oxygen, including microbiological corrosion


Corrosion protection 3. Impressed current

• works by providing electrons from current rather than the steel

• + => alkaline rusts => (CaCO3) surface deposition (from seawater)

• not liked too much by bacteria

• may not inhibit corrosion inside pits … hence not useful once corrosion has started!

• system must be maintained – otherwise …

Sacrificial anodes

• generally a robust system

• another metal (e.g. zinc. Al anodes) provide source of electrons rather than the steel

• many anodes and continuous replacement => maintenance issue.

Galvanizing

• same principle - only lasts until sacrificial (Zn type) coating is lost.


Discussion - 1

Bi-modal model for corrosion progression is pervasive

• Demonstrates changes in the corrosion process as it develops

• Offers explanations for the mechanisms involved

Implications

• Short-term field tests - results are misleading for longer-term corrosion

• Electrochemical tests – need to mimic actual conditions – but these

may not be known a priori – difficulties in interpreting the results –

expertise required

• Influence of MIC (if it occurs) is mainly longer term (cf. lab tests = short-

term)


Discussion - 2

• For MIC … the presence of bacteria etc. is not enough (‘who is there’

does not tell us very much)

• Now know that severe long-term marine corrosion (pitting) is

correlated with elevated levels of necessary nutrients

• For seawater corrosion the critical nutrient is Dissolved Inorganic

Nitrogen (DIN)

• Allows to obviate the question ‘who does what?‘


Discussion - 3

• All corrosion ultimately results from differences in potential between

points on (wet) surfaces

• Caused by imperfections, inclusions, grain boundaries etc.

• Also, at a larger scale, caused by surface deposits and mill-scale

• And, also, bacterial influences


Conclusion

• Corrosion is not a linear function of time (i.e. not a corrosion ‘rate’)

• Bi-modal model reflects long-term data trends

• Many influences can now be ‘explained’ (sometimes quantitatively)

• Includes influence of MIC – mainly a longer term effect

• Models are helpful to:

- interpret field test data

- extrapolate data to predict longer-term corrosion

- interpret localized corrosion - e.g. the craters in the chain links!

- development of focussed prevention strategies

• Helps in understanding potential for protective measures.


Acknowledgements

Financial support:

Australian Research Council (ARC) for support of basic research (incl. Professorial

Fellowships 2004-8, 2009-13, DORA Research Fellowship 2014-6)

SCORCH-JIP (Project Manager: AMOG Consulting, Melbourne, Australia)

SKM-Merz (Jacobs)

Pacific-ESI

Defence Science and Technology Group

Australian, UK and US water utilities.

BIOCOR ITN network (European Community's Seventh Framework Programme

FP7/2007-2013). Project website: www.biocor.eu

Research support:

The University of Newcastle, Australia

Port Arthur Heritage site, NSW Fisheries Taylors Beach and many other coastal site

owners

Plus - A great team of colleagues, research associates and research students

http://www.biocor.eu


[email protected]

Thank you


Microbiological influenced corrosion (MIC)

• Long history of ‘evidence’ of MIC

• Mainly sulphate-reducing bacteria (SRB) - others also (e.g. IOB)

• Bacteria require appropriate conditions, nutrients, energy

• In biofilm and within rust layers, occur in colonies, act interactively

Detection

• Culturing techniques - e.g. BART culturing kits - industrial use

• APT (adenosine tri-phosphate) = residue of living things

• DNA - Not cheap, requires expertise


Observations • Trends and Gumbel plot interpretations

• Steel - discussed previously in detail (see literature)

• Aluminium trend = new, many other examples similar

• Aluminium pit depth interpretation = new, some other examples, similar

• Steel – microbiological corrosion likely involved in seawater, spray

• Aluminium – toxic to bacteria, no known bacterial involvement in

corrosion

• Corrosion mechanism – most likely the autocatalytic nature of crevice

and pitting corrosion under anoxic conditions (cf. ‘under-deposit

corrosion)…


Discussion – Practical Aspects

• Model provides sound basis for practical simplifications.

• Rather than an empirical-only approximation, as all other ‘models’

• Short-term model:

- only r0 of interest

• Long-term model:

- only rs and cs are of interest

r0

csrs


Conclusion

• Corrosion is a complex function of time

• Long-term corrosion trends relevant for infrastructure applications

• Can now account for water temperature, salinity, wear, water velocity…

• Models are helpful to:

- interpret field test data

- extrapolate data to predict longer-term corrosion

- interpret localized corrosion - e.g. the craters in the chain links!

- development of focussed prevention strategies.


Master new slides

• Text omashev (1966): expect similarities with atmospheric corrosion

- wetter longer => more corrosion

• Gupta & Gupta (1974): wetness of metal surface ≠ soil moisture

• von Wolzogen Khur & van der Vlugt (1934): microbiologically

influenced corrosion (MIC) in soil corrosion

• Melchers & Jeffrey (2013) importance of nutrients for MIC

• Heyn & Braun (1908), Brasher (1967), Mercer & Lumbard (1995):

- dilute salt solutions have no significant influence on corrosion in

(near-) stagnant conditions

- soil moisture is essentially stagnant -> relevance of soil chemistry?

• Soil in contact with the pipe usually ≠ the native soil profile

• Corrosion under non-uniform deposits / metal contact can be severe

• None considered previously in soil corrosion modelling

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