Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects

Article


Alam, Gulzar, Ihsanullah, Ihsanullah, Naushad, Mu. and Sillanpää, Mika. 2022. "Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects." Chemical Engineering Journal. 427. https://doi.org/10.1016/j.cej.2021.130011
Article Title

Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects

ERA Journal ID3854
Article CategoryArticle
AuthorsAlam, Gulzar, Ihsanullah, Ihsanullah, Naushad, Mu. and Sillanpää, Mika
Journal TitleChemical Engineering Journal
Journal Citation427
Article Number130011
Number of Pages19
Year2022
PublisherElsevier
Place of PublicationNetherlands
ISSN1385-8947
1873-3212
Digital Object Identifier (DOI)https://doi.org/10.1016/j.cej.2021.130011
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S1385894721015965
Abstract

Artificial intelligence (AI) has emerged as a powerful tool to resolve real-world problems and has gained tremendous attention due to its applications in various fields. In recent years, AI techniques have also been employed in water treatment and desalination to optimize the process and to offer practical solutions to water pollution and water scarcity. Applications of AI is also expected to reduce the operational expenditures of the water treatment process by decreasing the cost and optimizing chemicals usage. This review summarizes various AI techniques and their applications in water treatment with a focus on the adsorption of pollutants. Numerous AI models have successfully predicted the performance of different adsorbents for the removal of numerous pollutants from water. This review also highlighted some challenges and research gap concerning applications of AI in water treatment. Despite several advantages offered by AI, there some limitations that hindered the widespread applications of these techniques in real water treatment systems. The availability and selection of data, poor reproducibility, less evidence of applications in real water treatment are some of the key challenges that need to be addressed. Recommendations are made to ensure the successful applications of AI in future water-related technologies. This review is beneficial for environmental researchers, engineers, students, and all stakeholders in the water industry.

KeywordsArtificial intelligence ; Water treatment ; Adsorption; Machine learning ; Water pollution ; Clean water
Public Notes

Files associated with this item cannot be displayed due to copyright restrictions.

Byline AffiliationsUlster University, United Kingdom
King Fahd University of Petroleum and Minerals, Saudi Arabia
King Saud University, Saudi Arabia
Yonsei University, Korea
Shoolini University, India
University of Southern Queensland
University of Johannesburg, South Africa
Permalink -

https://research.usq.edu.au/item/z018x/applications-of-artificial-intelligence-in-water-treatment-for-optimization-and-automation-of-adsorption-processes-recent-advances-and-prospects

  • 23
    total views
  • 0
    total downloads
  • 2
    views this month
  • 0
    downloads this month

Export as

Related outputs

UV Disinfection sensitivity index of spores or protozoa: A model to predict the required fluence of spores or protozoa
Wang, Zhao, Tang, Walter Z., Sillanpää, Mika and Li, Jinze. 2022. "UV Disinfection sensitivity index of spores or protozoa: A model to predict the required fluence of spores or protozoa." Water Science and Technology. 86 (11), pp. 2820-2833. https://doi.org/10.2166/wst.2022.391