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

Hanxiao Shi; Anlei Wei; Yaqi Zhu; Kangrong Tang; Hao Hu; Nan Li

doi:10.1016/j.jenvman.2024.123173

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

J Environ Manage. 2024 Nov 4:371:123173. doi: 10.1016/j.jenvman.2024.123173. Online ahead of print.

Authors

Hanxiao Shi¹, Anlei Wei², Yaqi Zhu³, Kangrong Tang⁴, Hao Hu⁵, Nan Li⁶

Affiliations

¹ Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China; Institute of Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China. Electronic address: [email protected].
² Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China; Institute of Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China. Electronic address: [email protected].
³ Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China; Institute of Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China. Electronic address: [email protected].
⁴ Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China; Institute of Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China. Electronic address: [email protected].
⁵ Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China; Institute of Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China. Electronic address: [email protected].
⁶ Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China; Institute of Environmental Sciences, Northwest University, Xi'an, Shaanxi, 710127, China. Electronic address: [email protected].

PMID: 39500158
DOI: 10.1016/j.jenvman.2024.123173

Abstract

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.

Keywords: Aeration control; Ammonia; Deep learning; Long short-term memory; Wastewater treatment.