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AuthorJawad, Jasir
AuthorHawari, Alaa H.
AuthorJavaid Zaidi, Syed
Available date2023-05-23T09:39:13Z
Publication Date2021
Publication NameChemical Engineering Journal
AbstractThe freshwater scarcity is causing a major challenge due to the growing global population. The brackish water and seawater are the biggest sources of water on the planet. Therefore, using desalination and water treatment techniques, household and industrial demands can be met. Microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), membrane bioreactor (MBR), and membrane distillation (MD) are some of the membrane processes used in water and wastewater treatment. Artificial intelligence models, such as artificial neural networks (ANN), have recently become a popular alternative to modeling these processes due to several advantages over the conventional model. Therefore, this paper presents a review of ANN models from the last two and a half decades developed for the membrane processes used in wastewater treatment and desalination. Moreover, a complete procedure for the development of two types of ANN models is provided in the paper. The study also discusses the development strategies and comparison of different sorts of ANN models. These models have been applied to several lab-scale, pilot and commercial plants for simulation, optimization, and process control. This work may aid in the development of new ANN models for membrane processes by considering the recent improvements in the field. 2021 Elsevier B.V.
SponsorThis publication was possible by an NPRP grant (NPRP10-0117-170176) from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors. In addition, the authors would like to thank Qatar University for the financial support through grant number QUCG-CAM-19/20-4.
Membrane separation
Neural network model
Wastewater treatment
TitleArtificial neural network modeling of wastewater treatment and desalination using membrane processes: A review
TypeArticle Review
Volume Number419

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