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    Carbon Dioxide Transport Pipeline Systems: Overview of Technical Characteristics, Safety, Integrity and Cost, and Potential Application of Digital Twin

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    Date
    2022-02-16
    Author
    Sleiti, Ahmad K.
    Al-Ammari, Wahib A.
    Vesely, Ladislav
    Kapat, Jayanta S.
    Metadata
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    Abstract
    Carbon dioxide transport from capture to utilization or storage locations plays key functions in carbon capture and storage systems. In this study, a comprehensive overview and technical guidelines are provided for CO2 pipeline transport systems. Design specifications, construction procedures, cost, safety regulations, environmental and risk aspects are presented and discussed. Furthermore, challenges and future research directions associated with CO2 transport are sorted out including the large capital and operational costs, integrity, flow assurance, and safety issues. A holistic assessment of the impurities' impacts on corrosion rate and phase change of the transported stream is required to improve pipeline integrity. The influence of impurities and the changes in elevation on the pressure drop along the pipeline need to be further investigated to ensure continuous flow via accurate positioning of pumping stations. Although the long-experience in oil and gas pipeline industry forms powerful reference, it is necessary to develop particular standards and techno-economic frameworks to mitigate the barriers facing CO2 transport systems. Digital twins (DT) have potential to transform CO2 transport sector to achieve high reliability, availability, and maintainability at lower cost. Herein, an integrated fivecomponent robust DT framework is proposed for CO2 pipeline transport systems and the future directions for DT development are insinuated. Data-driven algorithms capable of predicting system's dynamic behavior still need to be developed. The data-driven approach alone is not sufficient and low-order physics-based models should operate in tandem with the updated system parameters to allow interpretation and result's enhancing. Discrepancies between dynamic system models, anomaly detection, and deep learning (ADL) require in-depth localized off-line simulations.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85140090252&origin=inward
    DOI/handle
    http://dx.doi.org/10.1115/1.4053348
    http://hdl.handle.net/10576/51755
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    • Mechanical & Industrial Engineering [‎1465‎ items ]

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