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• W4224581311 abstract "Carbon capture and utilization (CCU) offer ways to reduce carbon emissions of fossil-fuel-based industries with lower economic penalties. There has been increasing social demand to invest in CCU technologies but their evaluations for further R&D and commercialization are complicated by the lack of consistent standards for data and methodologies, as well as their varying levels of technical maturity and uncertainty. As numerous combinations of capture and conversion technologies are possible in a commercial implementation, it is essential to perform systematic evaluations in terms of their economics and CO2 reduction potential, both quickly and fairly. This work introduces a computer-aided tool called ArKaTAC3 which can be used for such purposes. The latest version of the software includes features for constructing a superstructure network to model various CCU pathway alternatives from given sources to products and identifying optimal ones. Many difficult issues faced in evaluating CCU technologies, including the collection of reliable life cycle inventory data, system boundary specification, multi-objective specification, and consideration of uncertainty can be managed with the tool and its embedded CCU database, thus lowering the barrier to systematic and quantitative analyses by policymakers and practitioners. Two case studies are performed: (i) identification of sustainable CCU pathways from a superstructure comprising various CO2 utilization technologies and (ii) optimization of a CCU supply chain network in the Middle East. These case studies demonstrate the efficacy and flexibility of the newly-developed tool in making policy- and investment-related decisions for CCU." @default.
• W4224581311 created "2022-04-27" @default.
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• W4224581311 date "2022-07-01" @default.
• W4224581311 modified "2023-10-01" @default.
• W4224581311 title "Computer-aided identification and evaluation of technologies for sustainable carbon capture and utilization using a superstructure approach" @default.
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• W4224581311 doi "https://doi.org/10.1016/j.jcou.2022.102032" @default.
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