國立虎尾科技大學 |

Artificial Intelligence Enhanced Water Desalination.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Artificial Intelligence Enhanced Water Desalination./
作者:	Cao, Zhonglin.
面頁冊數:	1 online resource (110 pages)
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:	click for full text (PQDT)
ISBN:	9798379701314

Artificial Intelligence Enhanced Water Desalination.
Cao, Zhonglin.

Artificial Intelligence Enhanced Water Desalination. - 1 online resource (110 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379701314Subjects--Topical Terms:

1148725
Physical chemistry.
Subjects--Index Terms:

Water desalinationIndex Terms--Genre/Form:

554714
Electronic books.

Artificial Intelligence Enhanced Water Desalination.
LDR:03214ntm a22004097 4500 001 1151821
005 20241118135832.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379701314
035 $a (MiAaPQ)AAI30494163
035 $a AAI30494163
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Cao, Zhonglin. $3 1478653
245 1 0 $a Artificial Intelligence Enhanced Water Desalination.
264 0 $c 2023
300 $a 1 online resource (110 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
500 $a Advisor: Barati Farimani, Amir.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2023.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Physical chemistry. $3 1148725
650 4 $a Nanotechnology. $3 557660
650 4 $a Mechanical engineering. $3 557493
653 $a Water desalination
653 $a Reverse osmosis
653 $a 2D materials
653 $a Nanopore geometries
653 $a Water permeation
655 7 $a Electronic books. $2 local $3 554714
690 $a 0494
690 $a 0652
690 $a 0800
690 $a 0548
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Carnegie Mellon University. $b Mechanical Engineering. $3 1148639
773 0 $t Dissertations Abstracts International $g 84-12B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30494163 $z click for full text (PQDT)