Fiyadh, Seef Saadi and Alardhi, Saja Mohsen and Al Omar, Mohamed and Aljumaily, Mustafa M. and Al Saadi, Mohammed Abdulhakim and Fayaed, Sabah Saadi and Ahmed, Sulaiman Nayef and Salman, Ali Dawood and Abdalsalm, Alyaa H. and Jabbar, Noor Mohsen and El-Shafie, Ahmed (2023) A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique. Heliyon, 9 (4). ISSN 2405-8440, DOI https://doi.org/10.1016/j.heliyon.2023.e15455.
Full text not available from this repository.Abstract
Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.
| Item Type: | Article |
|---|---|
| Funders: | Bộ Giáo dục và Ðào tạo [Grant no. B2022-DNA-04] |
| Uncontrolled Keywords: | Adsorption process; Artificial neural network; Water treatment; Environmental modelling; Heavy metals |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 24 Oct 2025 07:40 |
| Last Modified: | 24 Oct 2025 07:40 |
| URI: | http://eprints.um.edu.my/id/eprint/48317 |
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