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The stochastic nature of pitting corrosion of metallic structures has been widely recognized. It is assumed that this kind of deterioration retains no memory of the past, so only the current state of the damage influences its future development. This characteristic allows pitting corrosion to be categorized as a Markov process. In this paper, two different models of pitting corrosion, developed using Markov chains, are presented. Firstly, a continuous-time, nonhomogeneous linear growth (pure birth) Markov process is used to model external pitting corrosion in underground pipelines. A closed-form solution of the system of Kolmogorov's forward equations is used to describe the transition probability function in a discrete pit depth space. The transition probability function is identified by correlating the stochastic pit depth mean with the empirical deterministic mean. In the second model, the distribution of maximum pit depths in a pitting experiment is successfully modeled after the combination of two stochastic processes: pit initiation and pit growth. Pit generation is modeled as a nonhomogeneous Poisson process, in which induction time is simulated as the realization of a Weibull process. Pit growth is simulated using a nonhomogeneous Markov process. An analytical solution of Kolmogorov's system of equations is also found for the transition probabilities from the first Markov state. Extreme value statistics is employed to find the distribution of maximum pit depths.

A predictive model for pitting corrosion in buried pipelines is proposed. The model takes into consideration the chemical and physical properties of the soil and pipe to predict the time dependence of pitting depth and rate. Maximum pit depths were collected together with soil and pipe data at more than 250 excavation sites over a three-year period. The time dependence of the maximum pit depth was modeled as dmax = κ(t − t0)ν, where t is the exposure time, t0 is the pit initiation time, and κ and ν are the pitting proportionality and exponent parameters, respectively. A multivariate regression analysis was conducted with dmax as the dependent variable and the pipeline age, and the soil and pipe properties as the independent variables. The dependence of κ and ν on the predictor variables was found for the three soil textural classes identified in this study: clay, clay loam, and sandy clay loam. The proportionality parameter κ was found to be primarily influenced by the redox potential, pH value, soil resistivity, and the dissolved ion concentrations. In contrast, the pitting exponent ν was found to be influenced mainly by the pipe-to-soil potential, water content, bulk density, and the pipe coating type. A real-life pipeline integrity assessment is used as a case study to illustrate the application of the proposed model and to show how it can have a positive impact on integrity management programs.

Recently, the authors proposed a new predictive model for pitting corrosion in underground pipelines. The model is based on field measurements of maximum pitting corrosion depth together with local soil and pipeline characteristics. The pitting corrosion data collection was conducted over a three-year period, for onshore buried pipelines operating in southern Mexico. This technical note contains a detailed description of the results of the field measurements, indicating the data entries classified as outlier observations and the textural soil class ascribed to each data entry.

A. Valor,* et al., discuss “Statistical Characterization of Pitting Corrosion—Part 1: Data Analysis” and “Statistical Characterization of Pitting Corrosion—Part 2: Probabilistic Modeling for Maximum Pit Depth,” by R.E. Melchers,*** which were published in Corrosion 61, 7 (2005), p. 655-664 and Corrosion 61, 8 (2005), p. 766-777, respectively. A reply from R.E. Melchers follows.

Calcium soaps are materials that serve a wide range of industrial applications such as softeners, detergents, plasticizers, greases, lubricants, cosmetics, and medicines. In addition, calcium salts of saturated carboxylic acids are of interest because of their presence in the staple food of Mexicans and other Central American people: the corn tortilla. Because of their wide use in industry, a knowledge of the thermal properties of the alkaline metal soaps is of great importance. In the present work, the thermal diffusivity of butyric-Ca, valeric-Ca, caprilic-Ca, undecanoic-Ca, palmitic-Ca, and stearic-Ca salts has been determined by photoacoustics. The thermal diffusivity of these salts shows a linear dependence on the number of carbons in the aliphatic chain, and was found within the range 2.60×10−3 to 1.38×10−2cm2ċs−1, with the highest and lowest values corresponding to butyric-Ca and stearic-Ca, respectively.

Recently, the authors proposed a new predictive model for pitting corrosion in underground pipelines. The model is based on field measurements of maximum pitting corrosion depth together with local soil and pipeline characteristics. The pitting corrosion data collection was conducted over a three-year period, for onshore buried pipelines operating in southern Mexico. This technical note contains a detailed description of the results of the field measurements, indicating the data entries classified as outlier observations and the textural soil class ascribed to each data entry. [PUBLICATION ABSTRACT]

A. Valor,* et al., discuss \"Statistical Characterization of Pitting Corrosion-Part 1: Data Analysis\" and \"Statistical Characterization of Pitting Corrosion-Part 2: Probabilistic Modeling for Maximum Pit Depth,\" by R.E. Melchers,*** which were published in CORROSION 61, 7 (2005), p. 655-664 and CORROSION 61, 8 (2005), p. 766-777, respectively. A reply from R.E. Melchers follows. [PUBLICATION ABSTRACT]

Mew deterministic and stochastic models for external pitting corrosion in underground pipelines can help improve integrity and reliability analyses. This article applies these models to Monte Carlo-simulated and real pipeline pitting corrosion data obtained from field and in-line inspections. The probabilistic description of corrosion pitting can realistically estimate the reliability evolution of cathodically protected, coated pipelines, for which the worst-case corrosion rates commonly recommended in the pipeline-corrosion literature might be exceedingly conservative. A more complete predictive pit-growth model would incorporate microbial corrosion's role, notably the effects of sulfate-reducing bacteria, as well as seasonal fluctuations in soil properties and their influence on the pitting damage process. The results described in this article apply to corroding pipelines in contact with clay, clay loam, and sandy clay loam soils in tropical regions. Caution should be exercised when applying the results to pipelines in contact with these textural classes in nontropical climates.

New deterministic and stochastic models can perdict the evolution of pitting corrosion depth and rate distributions from observed soil properties. Recent reports place pipeline corrosion costs in Canada and the US at $7 billion/year. Corrosion is the second and third leading cause, respectively, of pipeline failures in North America and Europe. Pitting corrosion causes a higher percentage of failures than other corrosion mechanisms. Despite the scientific and technical progress in modeling pitting corrosion in underground pipelines, several issues relevant to this damage mechanism remain and should be properly addressed. This article summarizes the results of recent study aimed at addressing these needs. Part 1, presented here, describes new deterministic and stochastic predictive models for external pitting corrosion in underground pipelines. In the last stage of the study, researchers developed an empirical Markov chain-based stochastic model for predicting the evolution of pitting corrosion depth and rate distributions from the observed properties of the soil.