Enhanced prediction of ammonia nitrogen levels in reverse osmosis brine from a full-scale water reclamation plant using machine learning
Water scarcity has led to the increased use of reclaimed water as a sustainable resource. However, reclaimed water generates byproducts like reverse osmosis brine, which can harm aquatic ecosystems if discharged directly. Effective monitoring and prediction of effluent quality are crucial. This stud...
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Elsevier
2025-09-01
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| Series: | Water Research X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589914725000830 |
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| author | Chun Yee Joey Tang Yuming Chen Pei-Hsun Wu Chao-Chin Chang Chang-Ping Yu |
| author_facet | Chun Yee Joey Tang Yuming Chen Pei-Hsun Wu Chao-Chin Chang Chang-Ping Yu |
| author_sort | Chun Yee Joey Tang |
| collection | DOAJ |
| description | Water scarcity has led to the increased use of reclaimed water as a sustainable resource. However, reclaimed water generates byproducts like reverse osmosis brine, which can harm aquatic ecosystems if discharged directly. Effective monitoring and prediction of effluent quality are crucial. This study focuses on a full-scale water reclamation plant (WRP) in Taiwan, which includes treatment units of both the wastewater treatment processes and the water reclamation processes, with comprehensive and detailed data across all treatment units—data rarely available in similar studies. Typical machine learning techniques, including shallow learning and deep learning, were systematically applied, along with automated machine learning frameworks, to predict ammonia nitrogen levels in reverse osmosis brine. The Long Short-Term Memory (LSTM) model outperformed other algorithms, achieving an R-squared value of 0.96 (mean absolute percent error (MAPE) of 2.6 %) with accurate peak level prediction using comprehensive treatment data and an R-squared value of 0.82 (MAPE of 20.5 %) using only influent wastewater quality and operational parameters. For one-day-ahead predictions, the LSTM model achieved an R-squared value of 0.64 (MAPE of 21.3 %). These findings demonstrate the potential of machine learning in full-scale WRP to provide accurate and early predictions, assisting plant operators in decision-making and reducing the risk of eutrophication in nearby rivers. |
| format | Article |
| id | doaj-art-9aefedbda3c74cf5af7a06c46f9ca03d |
| institution | Matheson Library |
| issn | 2589-9147 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Water Research X |
| spelling | doaj-art-9aefedbda3c74cf5af7a06c46f9ca03d2025-07-26T05:23:53ZengElsevierWater Research X2589-91472025-09-0128100384Enhanced prediction of ammonia nitrogen levels in reverse osmosis brine from a full-scale water reclamation plant using machine learningChun Yee Joey Tang0Yuming Chen1Pei-Hsun Wu2Chao-Chin Chang3Chang-Ping Yu4Graduate Institute of Environmental Engineering, National Taiwan University, Taipei 10617, R.O.C, TaiwanDepartment of Electrical and Computer Engineering, University of Washington, Seattle, 98195, USAGraduate Institute of Environmental Engineering, National Taiwan University, Taipei 10617, R.O.C, TaiwanDepartment of Safety, Health and Environmental Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 824, R.O.C, Taiwan; Corresponding authors at: Address: No. 1, University Rd., Yanchao Dist., Kaohsiung City 824, Taiwan.Graduate Institute of Environmental Engineering, National Taiwan University, Taipei 10617, R.O.C, Taiwan; Corresponding authors at: Address: No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan.Water scarcity has led to the increased use of reclaimed water as a sustainable resource. However, reclaimed water generates byproducts like reverse osmosis brine, which can harm aquatic ecosystems if discharged directly. Effective monitoring and prediction of effluent quality are crucial. This study focuses on a full-scale water reclamation plant (WRP) in Taiwan, which includes treatment units of both the wastewater treatment processes and the water reclamation processes, with comprehensive and detailed data across all treatment units—data rarely available in similar studies. Typical machine learning techniques, including shallow learning and deep learning, were systematically applied, along with automated machine learning frameworks, to predict ammonia nitrogen levels in reverse osmosis brine. The Long Short-Term Memory (LSTM) model outperformed other algorithms, achieving an R-squared value of 0.96 (mean absolute percent error (MAPE) of 2.6 %) with accurate peak level prediction using comprehensive treatment data and an R-squared value of 0.82 (MAPE of 20.5 %) using only influent wastewater quality and operational parameters. For one-day-ahead predictions, the LSTM model achieved an R-squared value of 0.64 (MAPE of 21.3 %). These findings demonstrate the potential of machine learning in full-scale WRP to provide accurate and early predictions, assisting plant operators in decision-making and reducing the risk of eutrophication in nearby rivers.http://www.sciencedirect.com/science/article/pii/S2589914725000830Reverse osmosis brineFull-scale water reclamation plantMachine learningAmmonia nitrogenOne-day-ahead predictions |
| spellingShingle | Chun Yee Joey Tang Yuming Chen Pei-Hsun Wu Chao-Chin Chang Chang-Ping Yu Enhanced prediction of ammonia nitrogen levels in reverse osmosis brine from a full-scale water reclamation plant using machine learning Water Research X Reverse osmosis brine Full-scale water reclamation plant Machine learning Ammonia nitrogen One-day-ahead predictions |
| title | Enhanced prediction of ammonia nitrogen levels in reverse osmosis brine from a full-scale water reclamation plant using machine learning |
| title_full | Enhanced prediction of ammonia nitrogen levels in reverse osmosis brine from a full-scale water reclamation plant using machine learning |
| title_fullStr | Enhanced prediction of ammonia nitrogen levels in reverse osmosis brine from a full-scale water reclamation plant using machine learning |
| title_full_unstemmed | Enhanced prediction of ammonia nitrogen levels in reverse osmosis brine from a full-scale water reclamation plant using machine learning |
| title_short | Enhanced prediction of ammonia nitrogen levels in reverse osmosis brine from a full-scale water reclamation plant using machine learning |
| title_sort | enhanced prediction of ammonia nitrogen levels in reverse osmosis brine from a full scale water reclamation plant using machine learning |
| topic | Reverse osmosis brine Full-scale water reclamation plant Machine learning Ammonia nitrogen One-day-ahead predictions |
| url | http://www.sciencedirect.com/science/article/pii/S2589914725000830 |
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