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Artificial Intelligence Enhanced Wat...

Cao, Zhonglin.

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Artificial Intelligence Enhanced Water Desalination.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial Intelligence Enhanced Water Desalination./
作者:	Cao, Zhonglin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	110 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
標題:	Physical chemistry. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30494163
ISBN:	9798379701314

Artificial Intelligence Enhanced Water Desalination.
Cao, Zhonglin.

Artificial Intelligence Enhanced Water Desalination. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 110 p.

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2023.

Water scarcity is currently affecting the lives of billions of people all around the world, and the situation is worsening. Reverse osmosis (RO) water desalination is used as a predominant industrial solution of water scarcity. RO water desalination is to apply pressure on saline water toward a permeable membrane, while the membrane can filter out unwanted ions and allow fresh water to pass through. Traditional polymeric membranes used in the RO process are generally energy-inefficient because of the low water permeability. In recent years, 2D materials such as graphene with artificially-created nanopores have been widely researched as more efficient substitutions for traditional membranes. There are two factors that cast influence on the desalination performance of 2D materials: the material and the nanopore geometry. In this work, various 2D materials are compared for their performances in RO desalination using molecular dynamics (MD) simulations. Physical reasons behind the superior performances of materials such as MoS2, MXene, and metal-organic frameworks are unveiled. Harnessing the power of artificial intelligence, a deep reinforcement learning (DRL) model is trained to rapidly optimize graphene nanopore geometry by balancing the water permeation/ion rejection trade-off. DRL optimized nanopore geometries inspire the creation of more uniquely-shaped nanopores with much higher water permeation than regular circular nanopore without compromising the ion rejection capability. Lastly, neural network is used predict the ion concentration profile under nanoconfinement. It serves as a faster and accurate surrogate for molecular dynamics simulation to deepen our understanding about the electrical double layer in nanoconfined space, which is a critical physical phenomenon associated with RO desalination.

ISBN: 9798379701314Subjects--Topical Terms:

1981412
Physical chemistry.
Subjects--Index Terms:

Water desalination

Artificial Intelligence Enhanced Water Desalination.
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300 $a 110 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
500 $a Advisor: Barati Farimani, Amir.
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520 $a Water scarcity is currently affecting the lives of billions of people all around the world, and the situation is worsening. Reverse osmosis (RO) water desalination is used as a predominant industrial solution of water scarcity. RO water desalination is to apply pressure on saline water toward a permeable membrane, while the membrane can filter out unwanted ions and allow fresh water to pass through. Traditional polymeric membranes used in the RO process are generally energy-inefficient because of the low water permeability. In recent years, 2D materials such as graphene with artificially-created nanopores have been widely researched as more efficient substitutions for traditional membranes. There are two factors that cast influence on the desalination performance of 2D materials: the material and the nanopore geometry. In this work, various 2D materials are compared for their performances in RO desalination using molecular dynamics (MD) simulations. Physical reasons behind the superior performances of materials such as MoS2, MXene, and metal-organic frameworks are unveiled. Harnessing the power of artificial intelligence, a deep reinforcement learning (DRL) model is trained to rapidly optimize graphene nanopore geometry by balancing the water permeation/ion rejection trade-off. DRL optimized nanopore geometries inspire the creation of more uniquely-shaped nanopores with much higher water permeation than regular circular nanopore without compromising the ion rejection capability. Lastly, neural network is used predict the ion concentration profile under nanoconfinement. It serves as a faster and accurate surrogate for molecular dynamics simulation to deepen our understanding about the electrical double layer in nanoconfined space, which is a critical physical phenomenon associated with RO desalination.
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653 $a Water permeation
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