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

Area of Science:

• Environmental Engineering
• Artificial Intelligence in Water Management
• Machine Learning for Wastewater Treatment

Background:

• Wastewater treatment plants (WWTPs) face challenges in efficient operation due to complex processes and data collection limitations.
• Machine learning adoption in WWTPs is hindered by laborious, expert-dependent data acquisition for tasks like water quality prediction and fault detection.

Purpose of the Study:

• To provide a comprehensive review of transfer learning (TL) applications in WWTPs.
• To address the lack of a dedicated review on TL for intelligent WWTP strategies.
• To inform the development and implementation of TL-based solutions for WWTP optimization.

Main Methods:

• Summarizing the framework of TL applications in WWTPs.
• Examining pre-trained models, data sources, sensing/control modalities, computing platforms, and TL strategies used in previous studies.
• Identifying challenges and future research directions for TL in WWTPs.

Main Results:

• TL enhances WWTP processes by transferring learned features for hydraulic patterns, pollutant indicators, and sludge characteristics.
• TL improves prediction accuracy in data-scarce environments, aiding nutrient concentration estimation, sensor calibration, and anomaly detection.
• Recent studies show TL's effectiveness in identifying sensor fouling and equipment faults.

Conclusions:

• Transfer learning offers an efficient approach to overcome data limitations in WWTPs.
• Key challenges include data heterogeneity, lack of benchmark datasets, model generalization, and dynamic conditions.
• Further research is needed to integrate TL into next-generation intelligent WWTPs.