Multiphysics modelling, quantum chemistry and risk analysis for corrosion inhibitor design and lifetime prediction

Abstract

Organic corrosion inhibitors can provide an effective means to extend the life of equipment in aggressive environments, decrease the environmental, economic, health and safety risks associated with corrosion failures and enable the use of low cost steels in place of corrosion resistant alloys. To guide the construction of advanced models for the design and optimization of the chemical composition of organic inhibitors, and to develop predictive tools for inhibitor performance as a function of alloy and environment, a multiphysics model has been constructed following Staehle's principles of "domains and microprocesses". The multiphysics framework provides a way for science-based modelling of the various phenomena that impact inhibitor efficiency, including chemical thermodynamics and speciation, oil/water partitioning, effect of the inhibitor on multiphase flow, surface adsorption and self-assembled monolayer formation, and the effect of the inhibitor on cathodic and anodic reaction pathways. The fundamental tools required to solve the resulting modelling from a first-principles perspective are also described. Quantification of uncertainty is significant to the development of lifetime prediction models, due to their application for risk management. We therefore also discuss how uncertainty analysis can be coupled with the first-principles approach laid out in this paper.

A DFT study of adsorption of imidazole, triazole, and tetrazole on oxidized copper surfaces: Cu2O(111) and Cu2O(111)-w/o-CuCUS

“…where the subscripts have the same meaning as in eqn (4). The geometries of the standalone ''mol'' and ''slab'' structures were kept the same as in the ''mol/slab'' system.…”

“…Although the atomic scale mechanism of how organic corrosion inhibitors work is usually not known, it is widely accepted that the adsorption of inhibitors onto surfaces represents an important step in achieving the inhibitory effect. 3 From this point of view, it is therefore important to characterize the molecule-surface bonding, although it represents only one aspect towards the atomic-scale understanding of corrosion protection mechanisms (for a more thorough approach, which involves a deconstruction of various relevant elements and their integration into a multiscale model, see the recent paper of Taylor 4 ). Moreover, the interaction of organic molecules with surfaces has also been receiving considerable attention due to its importance in many other technological applications as well as for reasons of scientific curiosity.…”

DFT Study of Azole Corrosion Inhibitors on Cu2O Model of Oxidized Copper Surfaces: I. Molecule–Surface and Cl–Surface Bonding

“…Instead, it represents only one aspect towards an atomic-scale understanding of corrosion protection mechanisms. For a more thorough approach, which involves a deconstruction of various relevant elements and their integration into a multiscale model, see the recent paper of Taylor et al [10]. Another promising approach, aimed towards the rational design of new corrosion inhibitors, utilizes machine-learning methods (e.g., see the recent review of Winkler [11]), with which it is possible to generate reasonably robust and predictive quantitative models, but the success comes at the expense of much lower mechanistic insight compared to computationally intensive physics-based methods [12,13].…”

Materials science: Share corrosion data

“…Highly accurate corrosion simulations could partially or completely replace the time-consuming, environmentally unfriendly, complicated and expensive experimental corrosion tests. For example, quantum chemical simulations are heavily used to evaluate the molecular structures and electronic properties of corrosion inhibitors 5 .…”