A mechanistic and a probabilistic model for predicting and analyzing microbiologically influenced corrosion

Dawuda, Abdul-Waris (2019) A mechanistic and a probabilistic model for predicting and analyzing microbiologically influenced corrosion. Masters thesis, Memorial University of Newfoundland.

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The complexities inherent in Microbiological Influenced Corrosion (MIC) requires a thorough understanding of the mechanisms involved when attempting to predict its rate. Even though mechanistic models have been developed in recent MIC studies, these models rarely analyze factors influencing pit depth and corrosion rate predicted. The objective of this work is to improve MIC prediction by quantitatively analyzing the factors influencing the predicted pit depth and corrosion rates. Therefore, this work presents a mechanistic and a probabilistic model which predicts corrosion rates, pit depth propagation, and analyzing influential factors in a MIC process. The mechanistic approach presents a model based on the direct contact extracellular electron transfer mechanism and nutrient limitation for microbial metabolism. The mechanistic model investigates the impact of redox intermediaries embedded in the cell structure of electroactive biofilms on corrosion rates. The mechanistic model also analyzes the effect of biofilm thickness limiting nutrient availability for corrosive microbiological organisms. The probabilistic approach presents a Bayesian network model which predicts the maximum corrosion rate in a process system. The probabilistic model analyzes the most critical factors affecting the corrosion rate predicted using Importance and Sensitivity analysis. The predictions obtained by both models were consistent with MIC rates in case studies and experimental studies. We also discovered that, redox properties of electroactive biofilms pose a significant threat to asset integrity as opposed to corrosion caused by sulfate reduction, in the case of Sulfate Reducing Bacteria (SRB).

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/14274
Item ID: 14274
Additional Information: Includes bibliographical references.
Keywords: Microbial Corrosion, Corrosion, Bayesian, Bacteria, MIC
Department(s): Engineering and Applied Science, Faculty of
Date: October 2019
Date Type: Submission
Library of Congress Subject Heading: Microbiologically influenced corrosion--Mechanical properties.

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