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Picture for A Bayesian Network Based Approach to Corrosion Risk Assessment of Radioactive Waste Tanks
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51315-5433-A Bayesian Network Based Approach to Corrosion Risk Assessment of Radioactive Waste Tanks

Product Number: 51315-5433-SG
ISBN: 5433 2015 CP
Author: Narasi Sridhar
Publication Date: 2015
$20.00

The Hanford site contains approximately 55 million gallons of radioactive and chemically hazardous wastes arising from weapons production. This paper will present a Bayesian framework to model the probability of tank failures at Hanford and illustrate its use for tanks that are already known to have failed and leaked waste.
Picture for Case Study: Integrated External Corrosion Management for A 12-Inch Crude Oil Pipeline
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Case Study: Integrated External Corrosion Management for A 12-Inch Crude Oil Pipeline

Product Number: 51323-18970-SG
Author: Keith Parker, Trey Johnston, Christophe Baeté, Michael Snow
Publication Date: 2023
$20.00

The Enbridge External Corrosion Prevention team (ECP) is developing an Integrated External Corrosion Management (IECM) process by which management of external corrosion control systems can be optimized while
maintaining asset integrity and safety. IECM is intended to provide a methodology by which operators can move from a traditional reactive approach to a state-of-the-art proactive strategy commensurate with industry expertise and technology. Here we discuss a case study intended to demonstrate and assess the outcomes of IECM. The subject is a 12-inch diameter crude oil pipeline located in North America. Results of the process reveal opportunities for improvement and efficiencies in operation and maintenance (O&M).
Picture for Probabilistic Assessment of Hydrogen Stress Cracking of Steels and CRA in Sour Environments
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Probabilistic Assessment of Hydrogen Stress Cracking of Steels and CRA in Sour Environments

Product Number: 51324-20409-SG
Author: Narasi Sridhar; Mariano A. Kappes
Publication Date: 2024
$40.00
Hydrogen Stress Cracking (HSC) of steels and CRA has been studied for a long time and it is recognized that the results have significant variability depending on many factors, including test procedures, material microstructures, and environmental variables. Although the standards used for acceptance of a material for sour service are deterministic, the variabilities in the test data as well as field conditions merit probabilistic treatment. This paper provides a Bayesian network model of HSC that connects the variabilities in material, environment, and loading conditions through mechanistic factors related to hydrogen transport, deformation mode, and strain rate. The model tries to encapsulate the state of knowledge in this area and tests the outcomes in terms of probability of HSC. The sensitivity of the model to input variables is assessed.
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