Accurate and robust ammonia level forecasting of aeration tanks using long short-term memory ensembles: A comparative...

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.