Bio-inspired MXene membranes for enhanced separation and anti-fouling in oil-in-water emulsions: SHAP explainability ML
Optimizing membrane performance for efficient water treatment is crucial for sustainable development and environmental protection, aligning with UN SDGs. This study involves experimental design, statistical reliability of small data, and explainable machine learning (ML) using SHAP (Shapley Additive...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Cleaner Water |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950263224000395 |
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| author | Nadeem Baig Sani I. Abba Jamil Usman Ibrahim Muhammad Ismail Abdulazeez A.G. Usman Isam H. Aljundi |
| author_facet | Nadeem Baig Sani I. Abba Jamil Usman Ibrahim Muhammad Ismail Abdulazeez A.G. Usman Isam H. Aljundi |
| author_sort | Nadeem Baig |
| collection | DOAJ |
| description | Optimizing membrane performance for efficient water treatment is crucial for sustainable development and environmental protection, aligning with UN SDGs. This study involves experimental design, statistical reliability of small data, and explainable machine learning (ML) using SHAP (Shapley Additive Explanations). The research uses ML models and statistical tests to ensure data reliability and stationarity and investigate various membranes’ fouling and separation efficiency (MX-CM, PDMX-CM, and SPDMX-CM). Stationarity tests, including the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests, revealed that MX-CM is stationary at level (I(0)), while PDMX-CM and SPDMX-CM required first differencing (I(1)) to achieve stationarity. SHAP analysis showed that in the fouling study, higher values of PDMX-CM and MX-CM positively influenced model predictions, with SHAP values of +0.09 for Cycle, −0.06 for PDMX-CM, and −0.06 for MX-CM. In the separation efficiency study, Cycle had a neutral impact (0.00), PDMX-CM had a slight positive effect, and MX-CM had a slight negative impact. These findings highlight the importance of ensuring data stationarity and utilizing SHAP for model explainability in predicting membrane performance. Accurate preprocessing and model interpretation enhance decision-making and optimization in membrane fouling and separation efficiency studies, ensuring robust and reliable ML models. |
| format | Article |
| id | doaj-art-007ddb2565444af7993be01e7de40acf |
| institution | OA Journals |
| issn | 2950-2632 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cleaner Water |
| spelling | doaj-art-007ddb2565444af7993be01e7de40acf2025-08-20T01:57:55ZengElsevierCleaner Water2950-26322024-12-01210004110.1016/j.clwat.2024.100041Bio-inspired MXene membranes for enhanced separation and anti-fouling in oil-in-water emulsions: SHAP explainability MLNadeem Baig0Sani I. Abba1Jamil Usman2Ibrahim Muhammad3Ismail Abdulazeez4A.G. Usman5Isam H. Aljundi6Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaDepartment of Chemical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia; Water Research Centre, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia; Corresponding author at: Department of Chemical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia.Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaDepartment of Pure and Industrial Chemistry, Faculty of Science, Sokoto State University, P.M.B. 2134, Along Birnin Kebbi Road, Sokoto, NigeriaInterdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaOperational Research Centre in Healthcare, Near East University, Nicosia, CyprusInterdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; Department of Chemical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaOptimizing membrane performance for efficient water treatment is crucial for sustainable development and environmental protection, aligning with UN SDGs. This study involves experimental design, statistical reliability of small data, and explainable machine learning (ML) using SHAP (Shapley Additive Explanations). The research uses ML models and statistical tests to ensure data reliability and stationarity and investigate various membranes’ fouling and separation efficiency (MX-CM, PDMX-CM, and SPDMX-CM). Stationarity tests, including the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests, revealed that MX-CM is stationary at level (I(0)), while PDMX-CM and SPDMX-CM required first differencing (I(1)) to achieve stationarity. SHAP analysis showed that in the fouling study, higher values of PDMX-CM and MX-CM positively influenced model predictions, with SHAP values of +0.09 for Cycle, −0.06 for PDMX-CM, and −0.06 for MX-CM. In the separation efficiency study, Cycle had a neutral impact (0.00), PDMX-CM had a slight positive effect, and MX-CM had a slight negative impact. These findings highlight the importance of ensuring data stationarity and utilizing SHAP for model explainability in predicting membrane performance. Accurate preprocessing and model interpretation enhance decision-making and optimization in membrane fouling and separation efficiency studies, ensuring robust and reliable ML models.http://www.sciencedirect.com/science/article/pii/S2950263224000395environmental sustainabilityclean water accessmachine learningMXenereliability analysisSHAP |
| spellingShingle | Nadeem Baig Sani I. Abba Jamil Usman Ibrahim Muhammad Ismail Abdulazeez A.G. Usman Isam H. Aljundi Bio-inspired MXene membranes for enhanced separation and anti-fouling in oil-in-water emulsions: SHAP explainability ML Cleaner Water environmental sustainability clean water access machine learning MXene reliability analysis SHAP |
| title | Bio-inspired MXene membranes for enhanced separation and anti-fouling in oil-in-water emulsions: SHAP explainability ML |
| title_full | Bio-inspired MXene membranes for enhanced separation and anti-fouling in oil-in-water emulsions: SHAP explainability ML |
| title_fullStr | Bio-inspired MXene membranes for enhanced separation and anti-fouling in oil-in-water emulsions: SHAP explainability ML |
| title_full_unstemmed | Bio-inspired MXene membranes for enhanced separation and anti-fouling in oil-in-water emulsions: SHAP explainability ML |
| title_short | Bio-inspired MXene membranes for enhanced separation and anti-fouling in oil-in-water emulsions: SHAP explainability ML |
| title_sort | bio inspired mxene membranes for enhanced separation and anti fouling in oil in water emulsions shap explainability ml |
| topic | environmental sustainability clean water access machine learning MXene reliability analysis SHAP |
| url | http://www.sciencedirect.com/science/article/pii/S2950263224000395 |
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