Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods
[Display omitted] •The same input features enables prediction of effluent TN and TEC in the WWTPs.•The moving average method and Bayesian optimization to improve model prediction performance.•The impact of different random seeds on model generalization ability and robustness. The control of effluent...
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| Published in: | Bioresource technology Vol. 395; p. 130361 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Elsevier Ltd
01.03.2024
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| ISSN: | 0960-8524, 1873-2976, 1873-2976 |
| Online Access: | Get full text |
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| Abstract | [Display omitted]
•The same input features enables prediction of effluent TN and TEC in the WWTPs.•The moving average method and Bayesian optimization to improve model prediction performance.•The impact of different random seeds on model generalization ability and robustness.
The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants. In this study, effluent TN and TEC predictive models were established by selecting influent water quality and process control indicators as input features. The prediction performance of machine learning methods under different random seeds was explored, the moving average method was used for data amplification, and the Bayesian algorithm was used for hyperparameter optimization. The results showed that compared with the traditional hyperparameter optimization method for effluent TN prediction, the coefficient of determination (R2) increased by 0.092 and 0.067, reaching 0.725, and the root mean square error (RMSE) decreased by 0.262 and 0.215 mg/L, reaching 1.673 mg/L, respectively, after Bayesian optimization and data amplification. During TEC prediction, R2 increased by 0.068 and 0.042, reaching 0.884, and the RMSE decreased by 232.444 and 197.065 kWh, reaching 1305.829 kWh, respectively. |
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| AbstractList | The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants (WWTPs). In this study, effluent TN and TEC predictive models were established by selecting influent water quality and process control indicators as input features. The prediction performance of machine learning methods under different random seeds was explored, the moving average method was used for data amplification, and the Bayesian algorithm was used for hyperparameter optimization. The results showed that compared with the traditional hyperparameter optimization method for effluent TN prediction, the coefficient of determination (R
) increased by 0.092 and 0.067, reaching 0.725, and the root mean square error (RMSE) decreased by 0.262 and 0.215 mg/L, reaching 1.673 mg/L, respectively, after Bayesian optimization and data amplification. During TEC prediction, R
increased by 0.068 and 0.042, reaching 0.884, and the RMSE decreased by 232.444 and 197.065 kWh, reaching 1305.829 kWh, respectively. The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants. In this study, effluent TN and TEC predictive models were established by selecting influent water quality and process control indicators as input features. The prediction performance of machine learning methods under different random seeds was explored, the moving average method was used for data amplification, and the Bayesian algorithm was used for hyperparameter optimization. The results showed that compared with the traditional hyperparameter optimization method for effluent TN prediction, the coefficient of determination (R²) increased by 0.092 and 0.067, reaching 0.725, and the root mean square error (RMSE) decreased by 0.262 and 0.215 mg/L, reaching 1.673 mg/L, respectively, after Bayesian optimization and data amplification. During TEC prediction, R² increased by 0.068 and 0.042, reaching 0.884, and the RMSE decreased by 232.444 and 197.065 kWh, reaching 1305.829 kWh, respectively. The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants. In this study, effluent TN and TEC predictive models were established by selecting influent water quality and process control indicators as input features. The prediction performance of machine learning methods under different random seeds was explored, the moving average method was used for data amplification, and the Bayesian algorithm was used for hyperparameter optimization. The results showed that compared with the traditional hyperparameter optimization method for effluent TN prediction, the coefficient of determination (R2) increased by 0.092 and 0.067, reaching 0.725, and the root mean square error (RMSE) decreased by 0.262 and 0.215 mg/L, reaching 1.673 mg/L, respectively, after Bayesian optimization and data amplification. During TEC prediction, R2 increased by 0.068 and 0.042, reaching 0.884, and the RMSE decreased by 232.444 and 197.065 kWh, reaching 1305.829 kWh, respectively.The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants. In this study, effluent TN and TEC predictive models were established by selecting influent water quality and process control indicators as input features. The prediction performance of machine learning methods under different random seeds was explored, the moving average method was used for data amplification, and the Bayesian algorithm was used for hyperparameter optimization. The results showed that compared with the traditional hyperparameter optimization method for effluent TN prediction, the coefficient of determination (R2) increased by 0.092 and 0.067, reaching 0.725, and the root mean square error (RMSE) decreased by 0.262 and 0.215 mg/L, reaching 1.673 mg/L, respectively, after Bayesian optimization and data amplification. During TEC prediction, R2 increased by 0.068 and 0.042, reaching 0.884, and the RMSE decreased by 232.444 and 197.065 kWh, reaching 1305.829 kWh, respectively. [Display omitted] •The same input features enables prediction of effluent TN and TEC in the WWTPs.•The moving average method and Bayesian optimization to improve model prediction performance.•The impact of different random seeds on model generalization ability and robustness. The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants. In this study, effluent TN and TEC predictive models were established by selecting influent water quality and process control indicators as input features. The prediction performance of machine learning methods under different random seeds was explored, the moving average method was used for data amplification, and the Bayesian algorithm was used for hyperparameter optimization. The results showed that compared with the traditional hyperparameter optimization method for effluent TN prediction, the coefficient of determination (R2) increased by 0.092 and 0.067, reaching 0.725, and the root mean square error (RMSE) decreased by 0.262 and 0.215 mg/L, reaching 1.673 mg/L, respectively, after Bayesian optimization and data amplification. During TEC prediction, R2 increased by 0.068 and 0.042, reaching 0.884, and the RMSE decreased by 232.444 and 197.065 kWh, reaching 1305.829 kWh, respectively. |
| ArticleNumber | 130361 |
| Author | Wan, Jinquan Ye, Gang Deng, Zhicheng Zhu, Bin Chen, Jian Ji, Shiming Wang, Yan |
| Author_xml | – sequence: 1 givenname: Gang surname: Ye fullname: Ye, Gang organization: College of Environment and Energy, South China University of Technology, Guangzhou 510006, China – sequence: 2 givenname: Jinquan surname: Wan fullname: Wan, Jinquan email: ppjqwan@scut.edu.cn organization: College of Environment and Energy, South China University of Technology, Guangzhou 510006, China – sequence: 3 givenname: Zhicheng surname: Deng fullname: Deng, Zhicheng organization: College of Environment and Energy, South China University of Technology, Guangzhou 510006, China – sequence: 4 givenname: Yan surname: Wang fullname: Wang, Yan organization: College of Environment and Energy, South China University of Technology, Guangzhou 510006, China – sequence: 5 givenname: Jian surname: Chen fullname: Chen, Jian organization: College of Environment and Energy, South China University of Technology, Guangzhou 510006, China – sequence: 6 givenname: Bin surname: Zhu fullname: Zhu, Bin organization: Guangdong Shunkong Zihua Technology Co, Ltd, Foshan 528300, China – sequence: 7 givenname: Shiming surname: Ji fullname: Ji, Shiming organization: Guangdong Shunkong Zihua Technology Co, Ltd, Foshan 528300, China |
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•The same input features enables prediction of effluent TN and TEC in the WWTPs.•The moving average method and Bayesian optimization to... The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants (WWTPs). In this study,... The control of effluent total nitrogen (TN) and total energy consumption (TEC) is a key issue in managing wastewater treatment plants. In this study, effluent... |
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| SubjectTerms | algorithms Bayesian algorithm Bayesian theory energy Model prediction prediction process control Random seed system optimization total nitrogen wastewater treatment water quality WWTPs |
| Title | Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants: Bayesian optimization machine learning methods |
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