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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Bioresource technology Ročník 395; s. 130361
Hlavní autori: Ye, Gang, Wan, Jinquan, Deng, Zhicheng, Wang, Yan, Chen, Jian, Zhu, Bin, Ji, Shiming
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 01.03.2024
Predmet:
algorithms
Bayesian algorithm
Bayesian theory
energy
Model prediction
prediction
process control
Random seed
system optimization
total nitrogen
wastewater treatment
water quality
WWTPs
Model prediction
Random seed
Bayesian algorithm
WWTPs
ISSN:0960-8524, 1873-2976, 1873-2976
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:[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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0960-8524
1873-2976
1873-2976
DOI:10.1016/j.biortech.2024.130361