Corrosion prediction

Poulson, B. Plant Corrosion, Predictions of Materials Performance, eds. J. E. Strutt and J. Nickolls, Ellis Horwood, Chichester, (1987)... 

Westcott, C., Williams, D. E., Croall, I. F., Patel, S. and Bernie, J. A. The development and application of integrated expert systems and databases for corrosion consultancy. In Plant Corrosion Prediction of Materials Performance, Ibid. 

H. McArthur, Corrosion Prediction and Prevention in Motor Vehicles. Ellis Horwood, Chichester, WestSessex, England, 1988. 

E. Polarization Curve Measurements for Galvanic Corrosion Prediction... 

An example of the use of EIS measurements to make long-term corrosion predictions for polymer coated metals is shown in Fig. 46 (110). In this plot, the ASTM D610 and D714 visual rankings of corrosion damage after 550 days of exposure in simulated seawater are plotted against a protection index, < (/), determined after only 10 days of exposure. The damage protection index is determined from the breakpoint frequency, /, as... 

In a study of zinc-coated steel covered with a polymer topcoat, the mechanism of topcoat delamination was elucidated with high spatial resolution [216]. Depending on the details of the defect and the composition of the corroding atmosphere, the rate and type of delamination could be described. A similar study with a coated iron surface has been reported [217]. A comparison of results obtained with SKP, electrochemical impedance measurements and cyclic voltammetry with respect to validity as a corrosion prediction tool has been reported [218]. 

The corrosion rate of metals is determined by estabhshed potential difference, soil conductivity, and relative anodic and cathodic areas [18—20]. Composite polarization diagrams are used to predict galvanic current. They consist of potentiostatic cathodic and anodic polarization curves for different metals and alloys in deaerated 1 N H2SO4 and aerated 3% NaCl. Galvanic corrosion prediction for longer time periods from data obtained in short time periods is not accurate due to surface conditions and impurities. 

H.P. Hack, J.R. ScuUy, Galvanic corrosion prediction using long- and short-term polarization curves. 

The de Waard-Milliams model is a well-known modeH - used in industry (such as subsea pipeline corrosion) to predict corrosion, and it is the cornerstone of commercially available corrosion prediction software packages such as Cassandra. Despite its applicability in industry, a significant disadvantage of this model is that it does not consider MIC. In 2002, a NACE paper was published in which the described models were related to various mechanisms from sweet corrosion, sour corrosion, and organic acid corrosion to oxygen corrosion and MIC. Obviously, it is the model describing MIC that concerns us here. 

B.F.M. Pots, R.C. John, I.J. Rippon, M.J.J. Simon Thomas, S.D. Kapusta, M.M. Girgis, T. Whitham. Improvements on de Waard-Milliams corrosion prediction and applications to corrosion management. Paper No. 02235, Corrosion 2002, NACE, Houston, TX, 2002. 

Most models focus on specific forms of corrosion and hence cannot be used in all environments with equal confidence. These models are usually fashioned and tweaked to either fit as accurately as possible to field data and results (which in themselves may not be entirely accurate for several reasons) or to fit results of laboratory tests, which may be similar in trend but frequently differ in value from observations made in the field. Neither model attempts to use a combination of numerical lab data and knowledge and heuristic field data and experience to make a prediction. Another approach to the corrosion prediction problem is to develop a model that does not result in a single equation for the whole domain of the parameter value but rather focuses on utilizing a set of equations to extract a prediction from data values that are as similar as possible to those of the problem. This reduces the domain of generalization of the equations since for each new problem, a new set of data values (which are similar to those of the problem) are used to extract a corrosion rate value. 

Various models are available for the purpose of corrosion prediction. These models can be broadly classified into four categories mechanistic models, empirical models, semiempirical models, and neural net models. Some models are purely empirical models based on lab experiments and field data, while others are mechanistic models of different physicochemical transport processes involved in corrosion. 

Neural nets are generally used where it is difficult to develop an analytical model such as in prediction or pattern recognition problems. Neural net models for corrosion prediction are an extension of empirical models. They too are not based on any theoretical background, with constants used in them representing best-fit parameters based on their training data set [1]. 

Other methods include use of intelligent pigging as well as corrosion prediction models developed by C. De Waard and some other modifications that have been published and commercialized by several other investigators. However, after prediction and/or detection of corrosion incidents inside the pipelines, the most cost-effective method of control is the use of corrosion inhibitors. These are usually amine based and are thus water dispersible. They are usually blended with vapour phase inhibitors and probably some flow enhancers. 

Other approaches to active corrosion prediction utilizing thermochemical calculations [8,17,18] require the experimental determination of effective parameters. They show both the importance of physical boundary conditions and the extremely low level of partial pressures at which active corrosion is potentially dangerous. 

Abstract Corrosion is basically the oxidation of metals, where electrons are transferred between oxidant and reductant. Therefore, corrosion is generally composed of redox reactions and should be analyzed from the viewpoint of electrochemistry. In this chapter, we describe the basic concept of electrochemistry and how various corrosion aspects can be explained by this discipline. We focus particularly on the equilibrium side because it could suggest possibilities that might be useful for corrosion prediction. The close relationship between redox reactions and corrosion are explained and stressed qualitatively and quantitatively. 

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