Knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatment
Abstract Perikinetic and orthokinetic flocculation are the first steps in drinking water treatment plant (DWTP) and affect all subsequent processes. Leveraging multi-stage water quality parameters, we developed a machine learning (ML) framework for coagulation control that incorporates knowledge emb...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Clean Water |
| Online Access: | https://doi.org/10.1038/s41545-025-00510-1 |
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| _version_ | 1849226681237110784 |
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| author | Yu-Qi Wang Wenchong Tian Hao-Lin Yang Yun-Peng Song Jia-Ji Chen Qiong-Ying Xu Wan-Xin Yin Le-Qi Ding Xi-Qi Li Han-Tao Wang Ai-Jie Wang Hong-Cheng Wang |
| author_facet | Yu-Qi Wang Wenchong Tian Hao-Lin Yang Yun-Peng Song Jia-Ji Chen Qiong-Ying Xu Wan-Xin Yin Le-Qi Ding Xi-Qi Li Han-Tao Wang Ai-Jie Wang Hong-Cheng Wang |
| author_sort | Yu-Qi Wang |
| collection | DOAJ |
| description | Abstract Perikinetic and orthokinetic flocculation are the first steps in drinking water treatment plant (DWTP) and affect all subsequent processes. Leveraging multi-stage water quality parameters, we developed a machine learning (ML) framework for coagulation control that incorporates knowledge embedding (KE) through hyper-parametric constraints on threshold water quality, energy consumption, and economic costs. Random forest (RF) has the best performance among the eight methods with a percentage error of 2.53% and a coefficient of determination of 0.9922. The results of the interpretability analysis show that the model can accurately identify the coagulation demand and balance the removal effect with the energy consumption and economic cost. Through real experimental validation and simulation extrapolation, the RF-KE model can reduce turbidity by 16.36% and dosing cost by 9.64%. This framework reduces economic costs while optimizing water quality through KE and interpretability analyses, providing evidence for the safe and reliable application of future models. |
| format | Article |
| id | doaj-art-d771f362f6a84cf1b86836aae486fe6c |
| institution | Kabale University |
| issn | 2059-7037 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Clean Water |
| spelling | doaj-art-d771f362f6a84cf1b86836aae486fe6c2025-08-24T11:05:45ZengNature Portfolionpj Clean Water2059-70372025-08-018111010.1038/s41545-025-00510-1Knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatmentYu-Qi Wang0Wenchong Tian1Hao-Lin Yang2Yun-Peng Song3Jia-Ji Chen4Qiong-Ying Xu5Wan-Xin Yin6Le-Qi Ding7Xi-Qi Li8Han-Tao Wang9Ai-Jie Wang10Hong-Cheng Wang11State Key Laboratory of Urban-rural Water Resource and Environment, School of Eco-Environment, Harbin Institute of TechnologySchool of Energy and Environment, City University of Hong KongState Key Laboratory of Urban-rural Water Resource and Environment, School of Eco-Environment, Harbin Institute of TechnologyState Key Laboratory of Urban-rural Water Resource and Environment, School of Eco-Environment, Harbin Institute of TechnologyState Key Laboratory of Urban-rural Water Resource and Environment, School of Eco-Environment, Harbin Institute of TechnologyState Key Laboratory of Urban-rural Water Resource and Environment, School of Eco-Environment, Harbin Institute of TechnologyState Key Laboratory of Urban-rural Water Resource and Environment, School of Eco-Environment, Harbin Institute of TechnologyState Key Laboratory of Urban-rural Water Resource and Environment, School of Eco-Environment, Harbin Institute of TechnologyState Key Laboratory of Urban-rural Water Resource and Environment, School of Eco-Environment, Harbin Institute of TechnologyPowerChina Eco- environmental Group Co., LtdState Key Laboratory of Urban-rural Water Resource and Environment, School of Eco-Environment, Harbin Institute of TechnologyState Key Laboratory of Urban-rural Water Resource and Environment, School of Eco-Environment, Harbin Institute of TechnologyAbstract Perikinetic and orthokinetic flocculation are the first steps in drinking water treatment plant (DWTP) and affect all subsequent processes. Leveraging multi-stage water quality parameters, we developed a machine learning (ML) framework for coagulation control that incorporates knowledge embedding (KE) through hyper-parametric constraints on threshold water quality, energy consumption, and economic costs. Random forest (RF) has the best performance among the eight methods with a percentage error of 2.53% and a coefficient of determination of 0.9922. The results of the interpretability analysis show that the model can accurately identify the coagulation demand and balance the removal effect with the energy consumption and economic cost. Through real experimental validation and simulation extrapolation, the RF-KE model can reduce turbidity by 16.36% and dosing cost by 9.64%. This framework reduces economic costs while optimizing water quality through KE and interpretability analyses, providing evidence for the safe and reliable application of future models.https://doi.org/10.1038/s41545-025-00510-1 |
| spellingShingle | Yu-Qi Wang Wenchong Tian Hao-Lin Yang Yun-Peng Song Jia-Ji Chen Qiong-Ying Xu Wan-Xin Yin Le-Qi Ding Xi-Qi Li Han-Tao Wang Ai-Jie Wang Hong-Cheng Wang Knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatment npj Clean Water |
| title | Knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatment |
| title_full | Knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatment |
| title_fullStr | Knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatment |
| title_full_unstemmed | Knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatment |
| title_short | Knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatment |
| title_sort | knowledge embedding and interpretable machine learning optimize comprehensive benefits for water treatment |
| url | https://doi.org/10.1038/s41545-025-00510-1 |
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