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    Optimization of Sink Locations in Carbon Integration Networks

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    Date
    2022-01-01
    Author
    Alnouri, Sabla Y.
    Sarigol, Ilkan
    Al-Mohannadi, Dhabia
    Jaber, Hadi
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    Abstract
    The detrimental environmental impacts of climate change that are a result of high atmospheric CO2 concentrations have prompted global efforts to limit the continuous increase in greenhouse gas (GHG) emissions. Many are now relying on the deployment of various carbon capture, utilization and storage (CCUS) methods which have been found reliable for reducing CO2 levels in the atmosphere. Various industries are shifting towards the decarbonisation of their operations through the use of a combination of various CCUS activities. This could involve the conversion of CO2 into value-added chemicals, the utilization of CO2 for enhanced oil recovery (EOR), the injection of CO2 into geological formations and/or oceans, the biological fixation of CO2, and other similar activities. Such operations are often referred to as CO2 “sinks”. It is often crucial for industries to identify which CCUS operations to deploy, especially when faced with many choices, since factors such as the cost of implementation and the sink efficiency play a significant role in the sink selection process. In this work, a mathematical model that helps identify optimal CO2 sink locations within industrial clusters is developed and utilized. Identifying optimal sink locations is an important factor that needs to be considered as part of a CCUS network planning problem. The proposed mathematical model was found to be very useful for identifying optimal CCUS sink selections, and their respective locations. A total of 22.7% of savings in transportation costs have been realized within the network, when compared to the case of having sink locations prescribed upfront.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85135508536&origin=inward
    DOI/handle
    http://dx.doi.org/10.1016/B978-0-323-95879-0.50152-1
    http://hdl.handle.net/10576/53739
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    • GPC Research [‎501‎ items ]

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