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Kwon, Young-Nam
Superior Membrane-based Advanced Research & Technology (SMART) Lab
Research Interests
  • Membrane, Reverse(Forward) osmosis, Membrane distillation, Hydrate-induced ice desalination

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A holistic review on how artificial intelligence has redefined water treatment and seawater desalination processes

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Title
A holistic review on how artificial intelligence has redefined water treatment and seawater desalination processes
Author
Ray, Saikat SinhaVerma, Rohit KumarSingh, AshutoshGanesapillai, MaheshKwon, Young-Nam
Issue Date
2023-01
Publisher
Elsevier BV
Citation
DESALINATION, v.546, pp.116221
Abstract
In the modern era, deep learning (DL), and machine learning (ML), have emerged as potential technologies that are widely applied in the fields of science, engineering, and technology. These tools have been extensively used to optimize seawater desalination and water treatment processes to achieve efficient performance. Indeed, automation has played a key role in redefining the issues of water treatment and seawater desalination. Artificial intelligence (AI) has been developed as a versatile tool for processing data and optimizing smart water services while addressing the issues of monitoring, management, and labor costs. Recently, specific AI tools, such as artificial neural networks (ANNs) and genetic algorithms, have been implemented for self-monitoring and modeling applications in the field of water treatment and seawater desalination. In the present article, the application of AI in the water treatment and seawater desalination sectors is thoroughly reviewed. Additionally, conventional modeling approaches are compared with ANN modeling. Furthermore, the challenges and shortcomings are discussed, along with future prospects. Moreover, the applications of AI mechanisms in data processing, optimization, modeling, prediction, and decision-making during water treatment and seawater desalination processes are underscored. Finally, innovative trends in seawater desalination and water treatment with AI tools are summarized.
URI
https://scholarworks.unist.ac.kr/handle/201301/59986
DOI
10.1016/j.desal.2022.116221
ISSN
0011-9164
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UEE_Journal Papers
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