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Rail-based direct air carbon capture

delete2022-07-01
delete11
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OA
AI
E
E. Bachman *
A
Alexandra Tavasoli
H
HattonT.Alan (T. Alan Hatton)
C
Christos T. Maravelias
E
Erik Haites
P
Peter Styring
A
Alán Aspuru‐Guzik
J
Jeffrey G. MacIntosh
G
Geoffrey A. Ozin *
DOI:10.1016/j.joule.2022.06.025delete
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摘要

摘要

En 中文
The direct capture of carbon dioxide from the environment is increasingly becoming an urgent necessity to mitigate the worst effects of climate change. However, the high energy demands require creative implementation strategies to minimize the diversion of already-stretched conventional resources toward this cause. To alleviate these issues, creative implementation strategies must be devised to lower the barrier to economic applicability of DAC systems so that they can be widely deployed. To this end, the work described herein presents innovative technology for deploying specially designed, self-contained DAC railcars on both diesel and electrified rail lines outfitted with battery arrays, CO2 direct air capture systems, compression equipment, and ancillary gear that uniquely exploits the substantial sustainable energy generated on-board the train through regenerative braking as well as from solar panels mounted on compatible railcars. The units are equipped with large intakes that extend up into the slipstream of the moving train and collect CO2 feedstock air by fluidic, ramjet-type processes thus obviating the need for the fans required by land-based systems and places no demand on energy or land resources. Unloaded daily at crew change or fueling stops into regular CO2 tank cars, the network will curate delivery of the harvested CO2 to on-route sites for permanent underground sequestration, or delivery to end-users as feedstock for the circular carbon economy. The technology will harvest meaningful quantities of CO2 at far lower costs and has the conservative potential to reach annual productivity of 0.45 gigatons by 2030, 2.9 gigatons by 2050, and 7.8 gigatons by 2075 with each car having an annual capacity of 3,000 tonnes of CO2 in the near term and more as the technology progresses.
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University of Sheffield
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Princeton University
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Vector Institute for Artificial Intelligence
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