File Download

There are no files associated with this item.

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)
Related Researcher

권영남

Kwon, Young-Nam
Read More

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

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

Author(s)
Ray, Saikat SinhaVerma, Rohit KumarSingh, AshutoshGanesapillai, MaheshKwon, Young-Nam
Issued Date
2023-01
DOI
10.1016/j.desal.2022.116221
URI
https://scholarworks.unist.ac.kr/handle/201301/59986
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.
Publisher
Elsevier BV
ISSN
0011-9164
Keyword (Author)
Machine learning (ML)Artificial neural networks (ANNs)DesalinationWater treatmentArtificial intelligence (AI)
Keyword
NEURAL-NETWORK MODELWASTE-WATERTREATMENT-PLANTOSMOSISPERFORMANCEPREDICTIONREMOVALSYSTEMSOPTIMIZATIONSIMULATION

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.