Advances in transfer learning for smart wastewater treatment plants: Learning frameworks and emerging pathways

Wastewater treatment plants (WWTPs) must operate efficiently under varying influent conditions and rigorous regulations due to growing populations and industrial expansion. For complex processes such as nutrient removal, effluent quality, activated sludge behaviour, and aeration control, data collection is laborious, expensive, and expert-dependent, which slows machine learning and deep learning for water quality parameter prediction, fault detection, and process optimization. Transfer learning (TL) provides an efficient approach by allowing pre-trained models, developed on extensive generic or wastewater datasets, to be tailored for WWTP applications. TL enhances wastewater processes by transferring learned features that identify hydraulic patterns, pollutant indicators, reactor state transitions, and sludge characteristics, thus improving prediction accuracy in data-scarce environments. Recent studies indicate that TL improves the estimation of nutrient concentrations, sensor calibration, anomaly detection in aeration and settling units, and the early identification of sensor fouling and equipment faults. A comprehensive review focused on TL in WWTP is currently lacking, despite these benefits. Addressing this gap is crucial for informing the development and implementation of TL-based intelligent WWTP strategies. This review summarizes the framework of TL applications in WWTPs, examining previously explored pre-trained models, data sources, sensing and control modalities, computing platforms, and TL strategies. The primary challenges identified include data heterogeneity, the scarcity of benchmark datasets, model generalization, and dynamic operational conditions. Additionally, potential research directions for the integration of TL into next-generation WWTP are discussed.