A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs)
Membrane fouling is a major hurdle to effective pressure-driven membrane processes, such as microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO). Fouling refers to the accumulation of particles, organic and inorganic matter, and microbial cells on the membrane’s...
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2023
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| Online Access: | http://hdl.handle.net/11073/25299 |
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| _version_ | 1864513438417420288 |
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| author | Abuwatfa, Waad Hussein |
| author2 | AlSawaftah, Nour Majdi Darwish, Naif A. Pitt, William G. Husseini, Ghaleb |
| author2_role | author author author author |
| author_facet | Abuwatfa, Waad Hussein AlSawaftah, Nour Majdi Darwish, Naif A. Pitt, William G. Husseini, Ghaleb |
| author_role | author |
| dc.creator.none.fl_str_mv | Abuwatfa, Waad Hussein AlSawaftah, Nour Majdi Darwish, Naif A. Pitt, William G. Husseini, Ghaleb |
| dc.date.none.fl_str_mv | 2023-08-25T08:40:38Z 2023-08-25T08:40:38Z 2023-07-24 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Abuwatfa, W. H., AlSawaftah, N., Darwish, N., Pitt, W. G., & Husseini, G. A. (2023). A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs). In Membranes (Vol. 13, Issue 7, p. 685). MDPI AG. https://doi.org/10.3390/membranes13070685 2077-0375 http://hdl.handle.net/11073/25299 10.3390/membranes13070685 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | https://doi.org/10.3390/membranes13070685 |
| dc.subject.none.fl_str_mv | Artificial neural networks (ANNs) Fouling Prediction Simulation Membranes |
| dc.title.none.fl_str_mv | A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs) |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Membrane fouling is a major hurdle to effective pressure-driven membrane processes, such as microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO). Fouling refers to the accumulation of particles, organic and inorganic matter, and microbial cells on the membrane’s external and internal surface, which reduces the permeate flux and increases the needed transmembrane pressure. Various factors affect membrane fouling, including feed water quality, membrane characteristics, operating conditions, and cleaning protocols. Several models have been developed to predict membrane fouling in pressure-driven processes. These models can be divided into traditional empirical, mechanistic, and artificial intelligence (AI)-based models. Artificial neural networks (ANNs) are powerful tools for nonlinear mapping and prediction, and they can capture complex relationships between input and output variables. In membrane fouling prediction, ANNs can be trained using historical data to predict the fouling rate or other fouling-related parameters based on the process parameters. This review addresses the pertinent literature about using ANNs for membrane fouling prediction. Specifically, complementing other existing reviews that focus on mathematical models or broad AI-based simulations, the present review focuses on the use of AI-based fouling prediction models, namely, artificial neural networks (ANNs) and their derivatives, to provide deeper insights into the strengths, weaknesses, potential, and areas of improvement associated with such models for membrane fouling prediction. |
| format | article |
| id | aus_62aa99d9e954b873fcd425bdf1083984 |
| identifier_str_mv | Abuwatfa, W. H., AlSawaftah, N., Darwish, N., Pitt, W. G., & Husseini, G. A. (2023). A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs). In Membranes (Vol. 13, Issue 7, p. 685). MDPI AG. https://doi.org/10.3390/membranes13070685 2077-0375 10.3390/membranes13070685 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/25299 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | MDPI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs)Abuwatfa, Waad HusseinAlSawaftah, Nour MajdiDarwish, Naif A.Pitt, William G.Husseini, GhalebArtificial neural networks (ANNs)FoulingPredictionSimulationMembranesMembrane fouling is a major hurdle to effective pressure-driven membrane processes, such as microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO). Fouling refers to the accumulation of particles, organic and inorganic matter, and microbial cells on the membrane’s external and internal surface, which reduces the permeate flux and increases the needed transmembrane pressure. Various factors affect membrane fouling, including feed water quality, membrane characteristics, operating conditions, and cleaning protocols. Several models have been developed to predict membrane fouling in pressure-driven processes. These models can be divided into traditional empirical, mechanistic, and artificial intelligence (AI)-based models. Artificial neural networks (ANNs) are powerful tools for nonlinear mapping and prediction, and they can capture complex relationships between input and output variables. In membrane fouling prediction, ANNs can be trained using historical data to predict the fouling rate or other fouling-related parameters based on the process parameters. This review addresses the pertinent literature about using ANNs for membrane fouling prediction. Specifically, complementing other existing reviews that focus on mathematical models or broad AI-based simulations, the present review focuses on the use of AI-based fouling prediction models, namely, artificial neural networks (ANNs) and their derivatives, to provide deeper insights into the strengths, weaknesses, potential, and areas of improvement associated with such models for membrane fouling prediction.Dana Gas Endowed Chair for Chemical EngineeringAmerican University of SharjahMDPI2023-08-25T08:40:38Z2023-08-25T08:40:38Z2023-07-24Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfAbuwatfa, W. H., AlSawaftah, N., Darwish, N., Pitt, W. G., & Husseini, G. A. (2023). A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs). In Membranes (Vol. 13, Issue 7, p. 685). MDPI AG. https://doi.org/10.3390/membranes130706852077-0375http://hdl.handle.net/11073/2529910.3390/membranes13070685en_UShttps://doi.org/10.3390/membranes13070685oai:repository.aus.edu:11073/252992024-08-22T12:05:21Z |
| spellingShingle | A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs) Abuwatfa, Waad Hussein Artificial neural networks (ANNs) Fouling Prediction Simulation Membranes |
| status_str | publishedVersion |
| title | A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs) |
| title_full | A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs) |
| title_fullStr | A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs) |
| title_full_unstemmed | A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs) |
| title_short | A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs) |
| title_sort | A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs) |
| topic | Artificial neural networks (ANNs) Fouling Prediction Simulation Membranes |
| url | http://hdl.handle.net/11073/25299 |