Calibrating activated sludge models through hyperparameter optimization: a new framework for wastewater treatment plant simulation
Abstract Traditional calibration of Activated Sludge Models (ASM) is often manual, expert-dependent, and inefficient. This study introduces a hyperparameter optimisation framework using Optuna to automate the calibration of the ASM2d model. Built on Python, the model integrates the Tree-structured P...
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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/s41545-025-00513-y |
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| author | Huarong Yu Yue Wang Tan Li Qibo Gan Dan Qu Fangshu Qu |
| author_facet | Huarong Yu Yue Wang Tan Li Qibo Gan Dan Qu Fangshu Qu |
| author_sort | Huarong Yu |
| collection | DOAJ |
| description | Abstract Traditional calibration of Activated Sludge Models (ASM) is often manual, expert-dependent, and inefficient. This study introduces a hyperparameter optimisation framework using Optuna to automate the calibration of the ASM2d model. Built on Python, the model integrates the Tree-structured Parzen Estimator (TPE) for single-objective and NSGA-II for multi-objective optimisation. A 50-day dataset from a full-scale wastewater treatment plant in Shenzhen, China, validates the approach. Compared to traditional methods, TPE reduced average relative errors for TN and COD from 4.587 and 24.846% to 0.798 and 15.291%, respectively, while decreasing iterations by 15–20%. NSGA-II lowered TN and COD errors to 4.72 and 15.17%, further improving to 0.095% and 8.43% with full-parameter tuning. Calibration efficiency increased by 65–75%. By effectively exploring parameter interdependencies, TPE and NSGA-II enhance calibration robustness and generalisation. This automated optimisation method significantly improves the accuracy and efficiency of ASM calibration, advancing intelligent wastewater process modelling. |
| format | Article |
| id | doaj-art-275a88dc0cb04113bdbb70f2479f855a |
| 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-275a88dc0cb04113bdbb70f2479f855a2025-08-24T11:05:43ZengNature Portfolionpj Clean Water2059-70372025-08-018111210.1038/s41545-025-00513-yCalibrating activated sludge models through hyperparameter optimization: a new framework for wastewater treatment plant simulationHuarong Yu0Yue Wang1Tan Li2Qibo Gan3Dan Qu4Fangshu Qu5School of Civil Engineering and Transportation, Guangzhou UniversitySchool of Civil Engineering and Transportation, Guangzhou UniversitySchool of Civil Engineering and Transportation, Guangzhou UniversitySchool of Civil Engineering and Transportation, Guangzhou UniversityCollege of Environmental Science and Engineering, Beijing Forestry UniversityKey Laboratory for Water Quality and Conservation of the Pearl River Delta, Guangzhou UniversityAbstract Traditional calibration of Activated Sludge Models (ASM) is often manual, expert-dependent, and inefficient. This study introduces a hyperparameter optimisation framework using Optuna to automate the calibration of the ASM2d model. Built on Python, the model integrates the Tree-structured Parzen Estimator (TPE) for single-objective and NSGA-II for multi-objective optimisation. A 50-day dataset from a full-scale wastewater treatment plant in Shenzhen, China, validates the approach. Compared to traditional methods, TPE reduced average relative errors for TN and COD from 4.587 and 24.846% to 0.798 and 15.291%, respectively, while decreasing iterations by 15–20%. NSGA-II lowered TN and COD errors to 4.72 and 15.17%, further improving to 0.095% and 8.43% with full-parameter tuning. Calibration efficiency increased by 65–75%. By effectively exploring parameter interdependencies, TPE and NSGA-II enhance calibration robustness and generalisation. This automated optimisation method significantly improves the accuracy and efficiency of ASM calibration, advancing intelligent wastewater process modelling.https://doi.org/10.1038/s41545-025-00513-y |
| spellingShingle | Huarong Yu Yue Wang Tan Li Qibo Gan Dan Qu Fangshu Qu Calibrating activated sludge models through hyperparameter optimization: a new framework for wastewater treatment plant simulation npj Clean Water |
| title | Calibrating activated sludge models through hyperparameter optimization: a new framework for wastewater treatment plant simulation |
| title_full | Calibrating activated sludge models through hyperparameter optimization: a new framework for wastewater treatment plant simulation |
| title_fullStr | Calibrating activated sludge models through hyperparameter optimization: a new framework for wastewater treatment plant simulation |
| title_full_unstemmed | Calibrating activated sludge models through hyperparameter optimization: a new framework for wastewater treatment plant simulation |
| title_short | Calibrating activated sludge models through hyperparameter optimization: a new framework for wastewater treatment plant simulation |
| title_sort | calibrating activated sludge models through hyperparameter optimization a new framework for wastewater treatment plant simulation |
| url | https://doi.org/10.1038/s41545-025-00513-y |
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