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Nickel-base alloys are used in high temperature environments such as heat exchangers and land-based gas turbines where oxidation becomes a significant issue. To mitigate oxidation these alloys contain chromium and aluminum to form protective oxide layers. In dry air the behavior of chromia- and alumina-forming alloys can be predictable. However several high temperature applications contain a significant amount of water vapor which in turn can lead to enhanced corrosion. The effects of water vapor are not completely understood. Hence a comparative study of the oxidation behavior of two nickel-base alloys – UNS N06230 and UNS N07214 - in dry and wet air was undertaken. UNS N06230 a chromia former and UNS N07214 an alumina former were oxidized at 1000°C in dry and wet (15 volume% H2O) air for times of 1 minute 10 minutes 1 hour 5 hours 10 hours and 100 hours using thermogravimetric analysis. The coupons were characterized using X-ray diffraction and scanning electron microscopy coupled with energy dispersive spectroscopy. UNS N06230 performed better overall in wet air when compared to UNS N07214. Mechanisms for this difference will be discussed.
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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