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Corrosion Model Development

Objective

The purpose of this article is to explain how the models used in OLI Studio: Corrosion Analyzer are developed, and to examine the data used to parameterize and validate these models. There are two models – the mixed potential model and the repassivation potential model.

  • The mixed potential model calculates the corrosion potential and corrosion rate for general corrosion.
  • The repassivation potential model calculates the repassivation potential, which, when combined with the corrosion potential from the mixed potential model, helps assess the risk of localized corrosion.

Both these electrochemical modules are built on top of thermophysical modules – either the aqueous thermodynamic (AQ) module or the Mixed-Solvent electrolyte (MSE) module. These thermodynamic modules have been rigorously validated for a wide range of systems, ensuring high fidelity in our predictions.

 

Mixed potential model

The mixed potential model accounts for corrosion across various regimes, including active dissolution, active-passive transition, and passive dissolution1. To accurately predict corrosion rates and corrosion potentials, the model incorporates both anodic reactions, such as metal dissolution, and cathodic reactions, such as the reduction of species like oxygen, water, hydronium ions, etc.

To accurately represent these reactions, several parameters need to be defined:

  • Reversible potentials for both anodic and cathodic half-reactions.
  • Tafel slopes for the electrochemical reactions.
  • Exchange current densities and their temperature dependence.
  • Dependence of passive current density on pH, temperature, and active ions.
  • Critical current density and Flade potential, along with their dependence on pH and temperature.

Experimental data for corrosion rates and potentials across different environments are typically used to parameterize these reactions. A comprehensive approach is required to ensure that the model captures all the interactions and dependencies between species in complex environments. An example of the complexity involved in simultaneously reproducing different complex systems is shown in the figures below. The accurate representation of experimental corrosion rates of alloy 2507 in two mixed acid systems (H₂SO₄ + HNO₃ and HF + HNO₃) is shown. The model needs to accurately predict the effects of pH, temperature, and the appropriate cathodic reactions (hydronium ion reduction and nitrate reduction). Additionally, in the case of HF, surface complexation effects on corrosion rates must also be included.

When data for a specific alloy is limited, insights from other alloys are applied. Extensive validation across multiple alloys ensures that the model predictions for different alloy compositions align with known physical behavior.

Ballal, Deepti, and Andre Anderko. "Modeling Corrosion of Corrosion-Resistant Alloys in Complex Environments in Wide Concentration Ranges." AMPP CORROSION. AMPP, 2024.

Ballal, Deepti, and Andre Anderko. "Modeling Corrosion of Corrosion-Resistant Alloys in Complex Environments in Wide Concentration Ranges." AMPP CORROSION. AMPP, 2024.

Repassivation potential model

The repassivation potential (Erp) used in the localized corrosion model is also calibrated against experimental data2-4. This model accounts for the effects of water and aggressive species such as chlorides, as well as inhibitive species like molybdates, chromates, nitrates, and sulfates. Species that can be either aggressive or inhibitive, like H₂S, are also included due to their significant and sometimes complex effect on repassivation.

For example, when predicting Erp for Alloy 22 in a system with water and chloride ions (using the MSE corrosion model), the parameters describing the dependence of Erp on temperature and the activities of chloride ions and water must be carefully calibrated to match experimental data across a wide range of conditions. When experimental data for a specific alloy is limited, trends based on alloy composition are used to ensure that predictions remain physically reasonable across diverse conditions5. Our models undergo extensive validation to ensure reliable and accurate predictions, even when experimental data is scarce.

 

Conclusion

The mixed potential model and the repassivation potential model are rigorously validated against experimental data and refined using trends from known alloy compositions when data is limited. This ensures that the predictions remain reliable across a wide range of environmental conditions.

References

1. A. Anderko, Modeling of Aqueous Corrosion, in 'Shreir's Corrosion', (ed. T. J. A. Richardson), Amsterdam, Elsevier; 2010),  p. 1585-1629.

2. A. Anderko, N. Sridhar, and D. S. Dunn, "A General Model for the Repassivation Potential as a Function of Multiple Aqueous Solution Species", Corrosion Science  46, 7 (2004), p. 1583-1612.

3. A. Anderko, N. Sridhar, M. A. Jakab, and G. Tormoen, "A General Model for the Repassivation Potential as a Function of Multiple Aqueous Species. 2. Effect of Oxyanions on Localized Corrosion of Fe-Ni-Cr-Mo-W-N Alloys", Corrosion Science  50, 12 (2008), p. 3629-3647.

4. A. Anderko, F. Gui, L. Cao, N. Sridhar, and G. R. Engelhardt, "Modeling Localized Corrosion of Corrosion-Resistant Alloys in Oil and Gas Production Environments: I. Repassivation Potential", Corrosion  71 (2015), p. 1197-1212.

5. A. Eslamimanesh, A. Anderko, and M. M. Lencka, "Prediction of General and Localized Corrosion of Corrosion-Resistant Alloys in Aggressive Environments", NACE CORROSION, (NACE, 2019), NACE-2019-12763.

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