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A new super austenitic stainless steel has been developed for use in highly corrosive wet environments.The new alloy, Alloy 35Mo, has the general chemical composition 27Cr-35Ni-6.5Mo-0.28N and PRE>52,suggesting high resistance towards chloride induced localized corrosion. The Alloy 35Mo has excellentcorrosion resistance in chloride bearing environments, and has also shown surprising mechanicalproperties with a combination of high yield strength, excellent ductility and low hardness. The mainapplications for the new grade will initially be heat exchanger tubing, small dimension piping and hydraulic& instrumentation tubing. Target industries are refineries, chemical and petrochemical plants and offshoreinstallations. This paper discusses Alloy35Mo, its properties, and a comparison with existing,competing grades
Update on recent experiences from laboratory tests performed at service conditions for geothermal applications. Incorporated in a catalogue of suitable materials for applications geothermal power plants. Users shall be enabled to have a basis for designing such facilities.
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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.
An advanced grade of super-austenitic stainless steel with a reduced content of nickel offers a significant economic advantage over nickel-base CRA’s. As the alloy is readily fabricated by conventional techniques, it is an excellent candidate for a variety of applications in the chemical, petrochemical, mining, oil and gas, and refining industries.