Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects
Article
| Article Title | Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects |
|---|---|
| ERA Journal ID | 3854 |
| Article Category | Article |
| Authors | Alam, Gulzar, Ihsanullah, Ihsanullah, Naushad, Mu. and Sillanpää, Mika |
| Journal Title | Chemical Engineering Journal |
| Journal Citation | 427 |
| Article Number | 130011 |
| Number of Pages | 19 |
| Year | 2022 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 1385-8947 |
| 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. |
| Keywords | Artificial 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 Affiliations | Ulster 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 |
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
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