Industrial corrosion problems rarely occur in simple and well-defined chemical systems. Process environments typically contain mixtures of acids and salts, oxidizing and inhibiting species, dissolved gases, and are subject to changing hydrodynamic conditions. Under these conditions, corrosion behavior is governed by the interaction of multiple electrochemical processes. Models based on empirical correlations often struggle to remain reliable outside the narrow range of conditions for which data exist, particularly when extrapolated to new chemistries or operating conditions.
To address these challenges, OLI has developed a mechanistic corrosion modeling framework that integrates the Mixed-Solvent Electrolyte (MSE) thermodynamic model with electrochemical kinetics. The framework links solution speciation, transport properties, and electrochemical reactions to predict general corrosion behavior, at steady state, in environments ranging from acidic to neutral and alkaline in the presence of various impurities. Applying this framework to various alloys illustrates how corrosion behavior can be described consistently across a wide range of industrially relevant chemistries.
Thermodynamic Foundation and Electrochemical Module
The MSE framework computes solution chemistry in detail, including pH, species activities, and transport properties over wide concentration ranges and in mixed-solvent systems. These outputs provide the corrosion model with the information needed to evaluate anodic dissolution, passive film formation/breakdown, and cathodic reactions such as hydronium, nitrate, water, oxygen reduction, and more. Because each electrochemical process is altered based on changes in solution chemistry and system conditions, the MSE Corrosion Framework must be parameterized in a structured manner, in which each chemical system is used to tune and constrain a specific aspect of the electrochemical model.
In this blog, the parameterization process used for corrosion-resistant alloys is described by following the progression from simple, well-defined chemical systems to increasingly complex environments, illustrating how individual electrochemical mechanisms are tuned and then used to reproduce corrosion behavior in mixed and multicomponent systems.
Single-Acid Systems
The parameterization process begins with single-acid environments, in order to isolate specific fundamental corrosion mechanisms. Nitric acid systems, for example, are used to constrain parameters associated with passive dissolution and nitrate reduction in oxidizing conditions. In these environments, corrosion rates are often governed by the stability and dissolution rate of the passive film rather than active metal dissolution. Nitric acid corrosion data are used to tune parameters related to the passive current density along with NO3– reduction cathodic reaction. Predicted polarization curves illustrate how nitrate reduction dominates and governs the corrosion potential, while the passive current density controls the corrosion rate (Figure 1). As seen in Figure 1, decreasing the pH (making the environment more aggressive) causes the passive anodic dissolution to shift to higher current densities.
Figure 1. The predicted polarization curves of alloy 316 at room temperature in (a) 10 wt% HNO3 and (b) 80 wt% HNO3. The partial electrochemical processes are defined in the legend.
Sulfuric acid provides a contrasting, non-oxidizing environment in which passivity is progressively lost as acidity increases. This system is used to tune parameters related to the active-passive transition, the critical current density, and hydronium ion reduction kinetics. As seen in Figure 2, the model captures observed trends across sulfuric acid concentration and temperature, including the decrease in corrosion rate at very high acid concentrations where water availability becomes limited. These corrosion behaviors emerge naturally from the coupling of electrochemical kinetics with thermodynamically consistent speciation. Differences between alloys are intrinsically incorporated into the model parameters based on experimental data, ensuring that the distinct corrosion behavior of each alloy is accurately represented. This is illustrated in Figure 3, where alloy 625 exhibits a lower critical current density than alloy 316, which is consistent with its greater resistance to corrosion and highlighting the unique electrochemical behavior of each alloy.
Figure 2. Calculated (lines) and experimental (symbols) corrosion rates of alloy 625 in H2SO4 at different temperatures.
Figure 3. Predicted polarization curves for (a) alloy 625 and (b) alloy 316 at room temperature and 30 wt% H2SO4.
Additional single-acid systems, such as hydrochloric, hydrofluoric, and phosphoric acids, introduce electroactive species that interact strongly with the passive film and introduce additional cathodic reactions. Chloride ions promote depassivation and increase the critical current density, while hydrofluoric and phosphoric acids influence both cathodic reactions and passive dissolution behavior through surface interactions. Together, these environments further refine the anodic and cathodic processes for each alloy, completing the parameter set needed to describe corrosion behavior across a broad range of acidic systems.
Mixed-Acid Systems
Once parameters are established in single-acid systems, the same parameters are used to predict the corrosion rate in mixed-acid environments. In sulfuric-nitric acid mixtures, corrosion behavior reflects the balance between active dissolution and nitrate induced passivation. The model reproduces experimental corrosion rates for alloy 625 in H2SO4-HNO3 solutions (Figure 4). Predicted polarization curves show how the addition of nitric acid shifts the mixed potential into the passive region by introducing nitrate reduction as the dominant cathodic reaction, suppressing active metal dissolution (Figure 5). These results demonstrate how parameters governing nitrate reduction and passivity in the binary nitric acid system also influence the corrosion behavior in mixed acids.
Figure 4. Calculated (lines) and experimental (symbols) corrosion rate of alloy 625 in H2SO4+HNO3 solutions as a function of temperature.
Figure 5. Predicted polarization curves for alloy 625 in (a) 29.2 wt% H2SO4 and (b) 29.2 wt% H2SO4 + 15.8 wt% HNO3 at 80 °C.
Neutral and Alkaline Environments
In neutral and alkaline environments, corrosion behavior is governed by different cathodic processes than in acidic systems. Hydronium reduction becomes less significant, with water reduction controlling corrosion under deaerated conditions and oxygen reduction dominating in aerated environments. These systems are therefore used to constrain the electrochemical parameters associated with water and oxygen reduction.
The model captures the evolution of corrosion potential as a function of pH for alloy 625 under deaerated conditions, illustrating the transition from hydronium-controlled to water-controlled cathodic behavior (Figure 6). Predicted polarization curves show how the H3O+ and H2O cathodic reduction reactions transition to be the dominant cathodic reaction as pH increases (Figure 7).
Figure 6. Calculated (lines) and experimental (symbols) Ecorr for alloy 625 as a function of pH (in deaerated solutions) at 25 °C.
Figure 7. Predicted polarization curves for alloy 625 at (a) pH=0, (b) pH=0.8, and (c) pH=7, at 25 °C in deaerated solutions.
In aerated systems, the influence of dissolved oxygen (DO) and mass transport is captured by combining electrochemical kinetics with transport properties calculated by the MSE framework. The variation of corrosion potential with dissolved oxygen concentration under static and agitated conditions is shown in Figure 8, while the predicted polarization curves show how O2 concentration and flow regime affect the liming current densities of the O2 reduction cathodic reaction (Figure 9). At very low oxygen concentrations, the limiting current density for oxygen reduction is lower than the passive current density. Therefore, water reduction becomes the controlling cathodic process, as shown in the predicted polarization curve (Figure 9a). As DO concentration increases, the limiting current for oxygen reduction exceeds the passive current density, intersects with it, and becomes the dominant cathodic reaction (Figure 9b). At complete agitation, all mass transfer limitations are removed and O2 becomes the dominant cathodic reaction governing the corrosion potential (Figure 9c).
Figure 8. Ecorr for alloy 316 as a function of dissolved oxygen (DO) concentration at 22 °C for different NaCl concentrations under static and fully agitated conditions.
Figure 9. Predicted polarization curves for alloy 316 in 0.6 M NaCl at 22 °C under: (a) DO = 24 ppb (static conditions), (b) DO = 630 ppb (static conditions), and (c) DO = 24 ppb (complete agitation).
Parameterization Workflow: From Simple Systems to Complex Chemistry
The overall parameterization process of the MSE Corrosion Framework is summarized schematically in Figure 10, which illustrates the stepwise progression used to establish model parameters. The workflow begins with single-acid systems each used to constrain specific electrochemical processes, including passive dissolution, the active-passive transition, individual cathodic reactions, and the influence of electroactive species on anodic behavior. Once defined, these parameters are used to predict corrosion behavior in binary mixtures and ultimately in multicomponent environments. The dashed arrows in the workflow indicate how corrosion predictions for each acid mixture depend on parameters defined in earlier systems, illustrating the high degree of dependence and interconnection between different systems. Additional systems are then used to capture the influence of various species on corrosion behavior for different alloys, including salts, acid gases, organic acids, oxidizing species, etc.
Figure 10. Workflow for the MSE corrosion model parametrization, illustrating the progression from single-acid systems (HNO3, H2SO4, HCl, HF, H3PO4) to binary mixtures and eventually to more complex environments containing gases, salts, and oxidizing species. Dashed arrows indicate dependencies between acid mixtures and their single-acid constituents.
The applicability of this approach becomes most evident in multicomponent environments containing multiple species and impurities. Using parameters established in single and binary component systems, the model can reproduce experimental corrosion rates for chemically complex mixtures, demonstrating how corrosion behavior in complex systems can be built systematically from simpler foundations. To illustrate this Figure 11 shows good alignment between calculated and experimental corrosion rates for alloy 316 in multicomponent environments containing hexafluorosilicic acid (H2SiF6). This prediction in complex environments highlights the ability of the framework to extrapolate to conditions where experimental data is sparse or unavailable, making it a valuable predictive tool for material selection and asset integrity management.
Figure 11. Calculated (lines) and experimental (symbols) corrosion rates of alloy 316 in multicomponent systems. (a) Corrosion rate as a function of H2SiF6 concentration at 85 °C in 33 wt% H3PO4 and 10 wt% H2SO4 solutions, (b) Corrosion rates as a function of temperature in mixtures containing H3PO4, H2SO4, Cl–, HF, and H2SiF6.
Conclusion
By integrating rigorous thermodynamics with mechanistic electrochemical modeling, OLI’s MSE Corrosion Framework provides a predictive tool for describing corrosion behavior across a wide range of chemical environments. This first-principles model enables corrosion predictions that remain grounded in underlying chemistry and applicable to conditions commonly found in industrial environments. For more information, or to consult and share ideas, feel free to contact us at https://www.olisystems.com/contact-us
For additional information on the corrosion model, please check these publications:
D. Ballal and A. Anderko, “Modeling Corrosion of Corrosion-Resistant Alloys in Complex Environments in Wide Concentration Ranges”, AMPP CORROSION, (AMPP, 2024), AMPP-2024-20731
A. Eslamimanesh, A. Anderko, and M. M. Lencka, “Theoretical Study of Corrosion of Corrosion-Resistant Alloys in Chemical Processes Environments Including Mixed Acids and Salts Using a Mechanistic Model”, AMPP CORROSION, (AMPP, 2021), AMPP-2021-16479.
A. Anderko, Modeling of Aqueous Corrosion, in ‘Shreir’s Corrosion’, (ed. T. J. A. Richardson), Amsterdam, Elsevier; 2010, p. 1585-1629.
A. Anderko, P. McKenzie, and R. D. Young, “Computation of Rates of General Corrosion Using Electrochemical and Thermodynamic Models”, Corrosion 57, 3(2001), p. 202-213.