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Damage Review and Risk Assessment of Midstream Amine Processing Units

Authors: Brian McNeill, Staff Engineer II; Shane Kelliher, Staff Engineer I

For those who had the opportunity to watch last week’s webinar presented by Brian and Shane, this article expands on the benefits of risk-based inspection (RBI) for amine processing units and offers a multidisciplinary approach. It begins with an overview of the process and rich/lean amine loading, identifies key damage mechanisms and areas of concern, and proposes options for corrosion mitigation.

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Are Dents Really a Problem?

Authors: Kevin Haley, P.E., Consulting Engineer I; Daniel Spring, Ph.D., Consulting Researcher II & Consulting Engineer II

Pipelines are susceptible to dents. There are currently several assessment procedures available for evaluating this damage mechanism and determining whether a dent poses a viable threat to the integrity of a component. In this article, Kevin and Daniel discuss common analysis methods for the critical failure mechanisms, potential failure mechanisms such as creep, complex dent configurations, and highlight the value of using a Level 3 finite element analysis (FEA) to determine whether the dent is truly a problem.

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Pipeline Monitoring and Leak Detection: Essential Technologies and Practices

Author: Rohan Oak, P.E., Senior Engineer II

Due to length and complexity, midstream pipelines are prone to leaks. In this article, Rohan provides a detailed overview of the technologies and practices used in pipeline monitoring and leak detection. In addition, he discusses the importance of leak detection programs for the long-term operation of pipeline infrastructure.

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How Playing Battleship Helps with CML Optimization

How Playing Battleship Helps with CML Optimization

As a child, many of us played the game Battleship. But did you know this game provides a great introduction to Bayesian updating and corrosion monitoring location (CML) optimization? Using the Battleship analogy, Branden Stucky introduces the Bayesian Optimized Asset Reliability (BOAR) assessment and provides insight into how to combine expert knowledge with collected data to identify corrosion rates.

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