An insight into real-time monitoring and predictive modelling of ultrafiltration pretreatment for flow rate and fouling mitigation in seawater desalination
The growing demand for freshwater, driven by population growth, industrialization, and climate change, has increased global reliance on seawater desalination. While reverse osmosis (RO) remains the primary technology for salt removal, ultrafiltration (UF) plays a critical role as a pretreatment stag...
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| Format: | Article |
| Language: | English |
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Elsevier B.V.
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/120544/ http://psasir.upm.edu.my/id/eprint/120544/1/120544.pdf |
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| author | Katibi, Kamil Kayode Mutalovich, Azimov Abdugani Nawi, Nazmi Mat Iztleuov, Gani M. Sataev, Marat I. Mohd Nasir, Muhammad Adib |
| author_facet | Katibi, Kamil Kayode Mutalovich, Azimov Abdugani Nawi, Nazmi Mat Iztleuov, Gani M. Sataev, Marat I. Mohd Nasir, Muhammad Adib |
| author_sort | Katibi, Kamil Kayode |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The growing demand for freshwater, driven by population growth, industrialization, and climate change, has increased global reliance on seawater desalination. While reverse osmosis (RO) remains the primary technology for salt removal, ultrafiltration (UF) plays a critical role as a pretreatment stage by reducing suspended solids, colloids, and microorganisms contributing to RO membrane fouling. This study focuses exclusively on the predictive modelling and real-time optimization of a UF system operating as a pretreatment component in a seawater desalination plant. Using 426 days of operational data, four Machine learning (ML) models: Tree Regression (TR), Ensemble Learning (ENS), Neural Networks (NN), and Gaussian Process Regression (GPR) were trained to predict UF flow rates and membrane resistance in real time. ENS achieved the best performance with an R² of 0.99 and Root Mean Square Error (RMSE) of 3.08 L/min, closely followed by TR (R² = 0.99, RMSE = 3.27 L/min). NN and GPR yielded R² of 0.98 (RMSE = 4.69 L/min) and R² of 0.97 (RMSE = 5.98 L/min), respectively. Adaptive backwash control guided by these predictions reduced average TMP excursions by 18 % and decreased backwash frequency from one event every 6 h to one every 8 h, improving operational stability and cutting maintenance costs by 12 %. This framework presents a scalable, intelligent approach to fouling mitigation in seawater desalination pretreatment. |
| first_indexed | 2025-11-15T14:48:37Z |
| format | Article |
| id | upm-120544 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:48:37Z |
| publishDate | 2025 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1205442025-10-06T01:55:14Z http://psasir.upm.edu.my/id/eprint/120544/ An insight into real-time monitoring and predictive modelling of ultrafiltration pretreatment for flow rate and fouling mitigation in seawater desalination Katibi, Kamil Kayode Mutalovich, Azimov Abdugani Nawi, Nazmi Mat Iztleuov, Gani M. Sataev, Marat I. Mohd Nasir, Muhammad Adib The growing demand for freshwater, driven by population growth, industrialization, and climate change, has increased global reliance on seawater desalination. While reverse osmosis (RO) remains the primary technology for salt removal, ultrafiltration (UF) plays a critical role as a pretreatment stage by reducing suspended solids, colloids, and microorganisms contributing to RO membrane fouling. This study focuses exclusively on the predictive modelling and real-time optimization of a UF system operating as a pretreatment component in a seawater desalination plant. Using 426 days of operational data, four Machine learning (ML) models: Tree Regression (TR), Ensemble Learning (ENS), Neural Networks (NN), and Gaussian Process Regression (GPR) were trained to predict UF flow rates and membrane resistance in real time. ENS achieved the best performance with an R² of 0.99 and Root Mean Square Error (RMSE) of 3.08 L/min, closely followed by TR (R² = 0.99, RMSE = 3.27 L/min). NN and GPR yielded R² of 0.98 (RMSE = 4.69 L/min) and R² of 0.97 (RMSE = 5.98 L/min), respectively. Adaptive backwash control guided by these predictions reduced average TMP excursions by 18 % and decreased backwash frequency from one event every 6 h to one every 8 h, improving operational stability and cutting maintenance costs by 12 %. This framework presents a scalable, intelligent approach to fouling mitigation in seawater desalination pretreatment. Elsevier B.V. 2025 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/120544/1/120544.pdf Katibi, Kamil Kayode and Mutalovich, Azimov Abdugani and Nawi, Nazmi Mat and Iztleuov, Gani M. and Sataev, Marat I. and Mohd Nasir, Muhammad Adib (2025) An insight into real-time monitoring and predictive modelling of ultrafiltration pretreatment for flow rate and fouling mitigation in seawater desalination. Desalination and Water Treatment, 323. art. no. 101386. pp. 1-11. ISSN 1944-3994; eISSN: 1944-3986 https://www.sciencedirect.com/science/article/pii/S1944398625004023?via%3Dihub 10.1016/j.dwt.2025.101386 |
| spellingShingle | Katibi, Kamil Kayode Mutalovich, Azimov Abdugani Nawi, Nazmi Mat Iztleuov, Gani M. Sataev, Marat I. Mohd Nasir, Muhammad Adib An insight into real-time monitoring and predictive modelling of ultrafiltration pretreatment for flow rate and fouling mitigation in seawater desalination |
| title | An insight into real-time monitoring and predictive modelling of ultrafiltration pretreatment for flow rate and fouling mitigation in seawater desalination |
| title_full | An insight into real-time monitoring and predictive modelling of ultrafiltration pretreatment for flow rate and fouling mitigation in seawater desalination |
| title_fullStr | An insight into real-time monitoring and predictive modelling of ultrafiltration pretreatment for flow rate and fouling mitigation in seawater desalination |
| title_full_unstemmed | An insight into real-time monitoring and predictive modelling of ultrafiltration pretreatment for flow rate and fouling mitigation in seawater desalination |
| title_short | An insight into real-time monitoring and predictive modelling of ultrafiltration pretreatment for flow rate and fouling mitigation in seawater desalination |
| title_sort | insight into real-time monitoring and predictive modelling of ultrafiltration pretreatment for flow rate and fouling mitigation in seawater desalination |
| url | http://psasir.upm.edu.my/id/eprint/120544/ http://psasir.upm.edu.my/id/eprint/120544/ http://psasir.upm.edu.my/id/eprint/120544/ http://psasir.upm.edu.my/id/eprint/120544/1/120544.pdf |