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51317--9489-Stochastic Modeling of Non-Uniform Corrosion of Carbon and Low Alloy Steel during Chemical Cleaning

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A stochastic model of the overall cleaning process and consequent corrosion was developed for an ethylenediaminetetraacetic acid (EDTA) based cleaning process. The model includes: (1) a chemical reaction engineering model(s), (2) a finite-element analysis (FEA) and (3) a Markov model of non-uniform corrosion sites.

Product Number: 51317--9489-SG
ISBN: 9489 2017 CP
Author: Charles Marks
Publication Date: 2017
Industry: Science of Corrosion
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Chemical cleaning of industrial process equipment can result in corrosion of carbon and low alloy components. Whether in the power generation or chemical process industry predicting and monitoring of such corrosion is a critical aspect of the overall chemical cleaning project. Normally laboratory qualification testing is performed to establish the expected corrosiveness of the process. Electrochemical techniques such as LPR ER ZRA or ECN are then used in the field during the cleaning to follow the corrosion on-line. Corrosion coupons can also be installed prior to the cleaning and examined upon completion. However both on-line monitoring and examination of coupons often involves judgment in extrapolating/interpreting the results and estimating the corrosion of the surfaces actually being cleaned.In this work a stochastic model of the overall cleaning process and consequent corrosion was developed for EDTA-based cleaning solvent used for boiler or PWR steam generator cleanings. The model includes: (1) a chemical reaction engineering model(s) of the bulk solution chemistry (2) a finite-element analysis (FEA) based model of the galvanic corrosion phenomena that occur at dissimilar metal interfaces (3) independent consideration of general and non-uniform (pitting-like) corrosion and (4) a Markov model of non-uniform corrosion sites based on experimentally derived probability distributions for incubation birth progression and death of such sites. The overall cleaning and corrosion processes are then modeling with a Monte Carlo simulation to allow confidence levels for probability of exceeded specified corrosion allowances on actual components based on results of process parameters (e.g. deposit composition pH chelant concentration temperature time) coupled with on-line corrosion monitoring measurements. The consequences of such corrosion on pressure boundary integrity including fatigue corrosion was assessed using structural mechanics FEA models.

Key words: chemical cleaning, pitting, stochastic modeling

Chemical cleaning of industrial process equipment can result in corrosion of carbon and low alloy components. Whether in the power generation or chemical process industry predicting and monitoring of such corrosion is a critical aspect of the overall chemical cleaning project. Normally laboratory qualification testing is performed to establish the expected corrosiveness of the process. Electrochemical techniques such as LPR ER ZRA or ECN are then used in the field during the cleaning to follow the corrosion on-line. Corrosion coupons can also be installed prior to the cleaning and examined upon completion. However both on-line monitoring and examination of coupons often involves judgment in extrapolating/interpreting the results and estimating the corrosion of the surfaces actually being cleaned.In this work a stochastic model of the overall cleaning process and consequent corrosion was developed for EDTA-based cleaning solvent used for boiler or PWR steam generator cleanings. The model includes: (1) a chemical reaction engineering model(s) of the bulk solution chemistry (2) a finite-element analysis (FEA) based model of the galvanic corrosion phenomena that occur at dissimilar metal interfaces (3) independent consideration of general and non-uniform (pitting-like) corrosion and (4) a Markov model of non-uniform corrosion sites based on experimentally derived probability distributions for incubation birth progression and death of such sites. The overall cleaning and corrosion processes are then modeling with a Monte Carlo simulation to allow confidence levels for probability of exceeded specified corrosion allowances on actual components based on results of process parameters (e.g. deposit composition pH chelant concentration temperature time) coupled with on-line corrosion monitoring measurements. The consequences of such corrosion on pressure boundary integrity including fatigue corrosion was assessed using structural mechanics FEA models.

Key words: chemical cleaning, pitting, stochastic modeling

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