Autopsy Results and Inorganic Fouling Prediction Modeling Using Artificial Neural Networks for Reverse Osmosis Membranes in a Desalination Plant
Nowadays, reverse osmosis (RO) desalination has become a highly effective and economical solution to address water scarcity worldwide. The membranes used in this type of separation are influenced by both pre-treatment operations and feed water quality, leading to fouling, a complex phenomenon respon...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Eng |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4117/6/5/98 |
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| Summary: | Nowadays, reverse osmosis (RO) desalination has become a highly effective and economical solution to address water scarcity worldwide. The membranes used in this type of separation are influenced by both pre-treatment operations and feed water quality, leading to fouling, a complex phenomenon responsible for reducing treatment performance and shortening membrane lifespan. In this study, an autopsy of a RO membrane from the Boujdour plant was performed, and a fouling prediction tool based on transmembrane pressure (TMP) was developed using MATLAB/Simulink (R2015a) with an artificial neural network (ANN) model. The impact of membrane fouling on treatment performance was also examined through one year of monitoring. A detailed analysis of the fouled membrane was conducted using SEM/EDS techniques to characterize the fouling on the membrane’s surface and cross-section. The results revealed significant fractures on the membrane surface, with fouling predominantly consisting of organic deposits (characterized by a high oxygen concentration of 39.69%) and inorganic fouling, including Si (7.99%), Al (2.79%), Mg (1.56%), Fe (1.27%), and smaller quantities of K (0.87%), S (0.36%), and Ca (0.12%). The ANN model for predicting transmembrane pressure was successfully developed, achieving a high R<sup>2</sup> value of 92.077% and a low mean square error (MSE) of 0.005657. This predictive model demonstrates the ability to forecast future TMP cycles based on historical data. The research provides a detailed understanding of the types of fouling affecting RO membranes and contributes to the development of preventive strategies to mitigate membrane fouling. |
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| ISSN: | 2673-4117 |