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THE INFLUENCE OF INSULATION MATERIALS ON CORROSION UNDER INSULATION

Picture for THE INFLUENCE OF INSULATION MATERIALS ON CORROSION UNDER INSULATION
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The designer of industrial equipment and piping has three weapons in the fight against corrosion
under insulation (CUI). The first and primary defense against CUI is a high quality, immersion
grade coating. The second is a properly designed and installed weather barrier jacketing. The third
and, arguably, least understood element is the choice of insulation material. This paper will explore
the ways in which insulation materials influence CUI behavior, presenting results from both
laboratory and field-testing on seven industrial insulation materials and one composite system.
The materials tested were calcium silicate, expanded perlite, cellular glass, mineral wool (both
regular and water-repellent grade), and two types of flexible aerogel blanket material -Pyrogel
XT and Cryogel Z

Product Number: MPWT19-15362
Author: John Williams, Owen Evans
Publication Date: 2019
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Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets for which the oil and gas industry is not immune. Over the last few decades, both downstream and upstream industry segments have recognized the magnitude of CUI and challenges faced by the industry in its ability to handle CUI risk-based assessment, predictive detection and inspection of CUI. It is a concern that is hidden, invisible to inspectors and prompted mainly by moisture ingress between the insulation and the metallic pipe surface. The industry faces significant issues in the inspection of insulated assets, not only of pipes, but also tanks and vessels in terms of detection accuracy and precision. Currently, there is no reliable NDT detection tool that can predict the CUI spots in a safe and fast manner. In this study, a cyber physical-based approach is being presented to identify susceptible locations of CUI through a collection of infrared data overtime. The experimental results and data analysis demonstrates the feasibility of utilizing machine-learning techniques coupled with thermography to predict areas of concern. This is through a simplified clustering and classification model utilizing the Convolutional Neural Networks (CNN). This is a unique and innovative inspection technique in tackling complex challenges within the oil and gas industry, utilizing trending technologies such as big data analytics and artificial intelligence.
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