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Predictive Analytics and Machine Learning in Integrated External Corrosion Management

Managing external corrosion, especially for underground assets, is a significant challenge dating back to the first underground pipeline in 1865. The very first issue of the journal, CORROSION, featured a headline story on this subject. This subject is fundamental for corrosion engineers and pipeline operators.

Product Number: 51323-19393-SG
Author: Thomas Hayden, Joseph Mazzella, Keith Parker, Christophe Baete
Publication Date: 2023
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Integrated External Corrosion Management (IECM) is a novel framework developed for pipeline operators to model, identify, and optimize external corrosion risk and costs using a data-driven approach. Over the last decade, Machine Learning (ML) has transformed industries from consumer technology to product design to industrial systems. In corrosion, the Association for Materials Protection and Performance (AMPP) has added a symposium for specialists designing and optimizing machine learning algorithms detection and management. This work is not about a specific algorithm or technology set. Instead, this work presents a framework for incorporating the output of a predictive algorithm with an IECM framework. This work considers the interplay between in-line inspection (ILI), direct assessment, close interval surveys, and mechanistic modeling. Lastly, this work describes an external corrosion management system that is fully "observable", an environment where the state of any component in a pipeline system can either be directly observed or inferred in near real-time.

Integrated External Corrosion Management (IECM) is a novel framework developed for pipeline operators to model, identify, and optimize external corrosion risk and costs using a data-driven approach. Over the last decade, Machine Learning (ML) has transformed industries from consumer technology to product design to industrial systems. In corrosion, the Association for Materials Protection and Performance (AMPP) has added a symposium for specialists designing and optimizing machine learning algorithms detection and management. This work is not about a specific algorithm or technology set. Instead, this work presents a framework for incorporating the output of a predictive algorithm with an IECM framework. This work considers the interplay between in-line inspection (ILI), direct assessment, close interval surveys, and mechanistic modeling. Lastly, this work describes an external corrosion management system that is fully "observable", an environment where the state of any component in a pipeline system can either be directly observed or inferred in near real-time.

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Estimating Corrosion Rates for Underground Pipelines: A Machine Learning Approach

Product Number: 51319-13456-SG
Author: Joseph Mazzella, Len Krissa, Thomas Hayden, Haralampos Tsaprailis
Publication Date: 2019
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