Flue gas CO2 capture via electrochemically mediated amine regeneration: System design and performance

Wang, Miao; Rahimi, Maryam; Kumar, Amit; Hariharan, Subrahmaniam; Choi, Wonyoung; Hatton, T. Alan

doi:10.1016/j.apenergy.2019.113879

Cited by 79 publications

(89 citation statements)

References 29 publications

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“…As an alternative approach, the conventional temperature swing step in the amine absorption process can be replaced by metal ion substitution in an electrochemical cell [29,30,301]. Reprinted from [302], Copyright (2019), with permission from Elsevier. The conventional thermal swing is replaced with an electrochemical step.…”

Section: Electrochemical Enhancement Of Amine Based Absorptionmentioning

confidence: 99%

Electrochemical carbon dioxide capture to close the carbon cycle

Sharifian

Wagterveld²,

Digdaya

et al. 2021

Energy Environ. Sci.

283

196

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Section: Electrochemical Enhancement Of Amine Based Absorptionmentioning

confidence: 99%

Electrochemical carbon dioxide capture to close the carbon cycle

Sharifian

Wagterveld²,

Digdaya

et al. 2021

Energy Environ. Sci.

283

196

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“…In this paper, Reynolds and Froude numbers are not considered as they are the product of the superficial velocity of gas or liquid phase in combination with other constant values. Besides, pressure gradient [39] and holdup [40] are also dependent parameters of superficial velocities of gas & liquid, which are not considered in this modelling work.…”

Section: Feature Selectionmentioning

confidence: 99%

Prediction of two-phase flow patterns in upward inclined pipes via deep learning

Lin

Liu

Lao

et al. 2020

Energy

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The industrial process involving gas liquid flows is one of the most frequently encountered phenomena in the energy sectors. However, traditional methods are practically unable to reliably identify flow patterns if additional independent variables/parameters are to be considered rather than gas and liquid superficial velocities. In this paper, we reported an approach to predict flow pattern along upward inclined pipes (0 ~ 90°) via deep learning neural networks, using accessible parameters as inputs, namely, superficial velocities of individual phase and inclination angles. The developed approach is equipped with deep learning neural network for flow pattern identification by experimental datasets that were reported in the literature. The predictive model was further validated by comparing its performance with well-established flow regime forecasting methods based on conventional flow regime maps. Besides, the intensity of key features in flow pattern prediction was identified by the deep learning algorithm, which is difficult to be captured by commonly used correlation approaches.

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“…6,8,9 Although quinone-based nucleophiles have been widely studied, [8][9][10][11] other organic compounds such as bipyridine 12 and thiolates 13 have also been investigated. Other electrochemical approaches have recently been emerged, including electrochemically mediated amine regeneration [14][15][16][17] and membrane capacitive deionization. 18,19 These systems offer potentially lower energy consumption for CO 2 capture and have led to a growing interest in using electrochemical processes as a promising alternative separation technique.…”

Section: Introductionmentioning

confidence: 99%

Bench-scale demonstration of CO₂ capture with an electrochemically driven proton concentration process

et al. 2020

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Flue gas CO2 capture via electrochemically mediated amine regeneration: System design and performance

Cited by 79 publications

References 29 publications

Electrochemical carbon dioxide capture to close the carbon cycle

Electrochemical carbon dioxide capture to close the carbon cycle

Prediction of two-phase flow patterns in upward inclined pipes via deep learning

Bench-scale demonstration of CO₂ capture with an electrochemically driven proton concentration process

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Flue gas CO2 capture via electrochemically mediated amine regeneration: System design and performance

Cited by 79 publications

References 29 publications

Electrochemical carbon dioxide capture to close the carbon cycle

Electrochemical carbon dioxide capture to close the carbon cycle

Prediction of two-phase flow patterns in upward inclined pipes via deep learning

Bench-scale demonstration of CO2 capture with an electrochemically driven proton concentration process

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Product

Resources

About

Bench-scale demonstration of CO₂ capture with an electrochemically driven proton concentration process