Accurate and robust ammonia level forecasting of aeration tanks using long short-term memory ensembles: A comparative study of Adaboost and Bagging approaches

As wastewater treatment aeration systems are embracing innovative solutions to data management for operational sustainability, deep learning approaches like long short-term memory (LSTM) networks become imperative. However, how to enhance LSTMs to forecast aeration status through ensemble learning i...

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Veröffentlicht in:Journal of environmental management Jg. 371; S. 123173
Hauptverfasser: Shi, Hanxiao, Wei, Anlei, Zhu, Yaqi, Tang, Kangrong, Hu, Hao, Li, Nan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Elsevier Ltd 01.12.2024
Schlagworte:
aeration
Aeration control
Ammonia
comparative study
Deep learning
energy
information management
Long short-term memory
neural networks
prediction
Wastewater treatment
Deep learning
Aeration control
Long short-term memory
Ammonia
Wastewater treatment
ISSN:0301-4797, 1095-8630, 1095-8630
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Zusammenfassung:As wastewater treatment aeration systems are embracing innovative solutions to data management for operational sustainability, deep learning approaches like long short-term memory (LSTM) networks become imperative. However, how to enhance LSTMs to forecast aeration status through ensemble learning is still in its infancy. This study tackles this challenge by comprehensively comparing two ensemble learning algorithms, AdaBoost and Bagging. Both one-step and multi-step predictions were compared using performance metrics like Z-score derived from aeration set-points. The robustness of models was evaluated under quantified extreme events, such as sudden spikes in ammonia concentration. The results indicate that while AdaBoost-LSTM models slightly outperformed Bagging-LSTM models in one-step-ahead predictions, their true advantage lies in enabling precise decisions for switching aeration blowers on or off, which could avoid excess energy usage of aeration systems. This advantage was even more pronounced in multi-step forecasting. In 4-step-ahead prediction, the AdaBoost-LSTM model attained an optimal precision of 92.77%, marking an 8.88% improvement over the Bagging-LSTM model. Furthermore, AdaBoost-LSTM models showed greater resilience to fluctuations in ammonia levels, ensuring continued stable aeration. Therefore, AdaBoost-LSTM ensembles demonstrate greater suitability for accurate and robust ammonia forecasting of aeration tanks, leading to sustainable operation and target costs/energy savings. •Thorough comparison between AdaBoost and Bagging ensemble methods for LSTM.•Novel precision metric grounded in ammonia setpoints of WWTPs' aeration control.•AdaBoost outperforms Bagging for LSTM ensembles in both accuracy and precision.•AdaBoost-LSTMs provide superior performance over longer prediction horizons.•Deep learning with AdaBoost boosts sustainable wastewater treatment.
Bibliographie:ObjectType-Article-1
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ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2024.123173