Anomaly Monitoring of Dynamic Wastewater Regeneration Process Based on Recursive Broad Learning System

In the wastewater regeneration process, a condition monitoring system must have dynamic adaptability to new operating conditions while maintaining a lasting adaptability to existing conditions to ensure efficient and stable operation. This paper proposes a Slow Feature Recursive Broad Learning System (SF-RBLS) to achieve incremental learning of new knowledge and sustained adaptation to condition characteristics. The model takes advantage of the incremental learning mechanism of the broad learning system, enabling online learning of unknown features as new operating conditions emerge. An innovative coupling mechanism between the slow feature space and the recursive structure is constructed. Through the slow feature analysis mechanism, temporal constraints are imposed on the dynamic evolution process of the recursive window, enabling the feature extraction process to simultaneously generate long-term steady state representations and short-term dynamic responses, achieving enduring adaptability to various conditions. Experimental results demonstrate that SF-RBLS exhibits significant advantages over state-of-the-art methods in both accuracy and stability for real-time monitoring and anomaly detection, confirming its effectiveness and potential in intelligent wastewater reclamation monitoring. Note to Practitioners—Monitoring wastewater regeneration processes is challenging due to dynamic environmental conditions, sudden pollution events, and changing operational states. Traditional models struggle to adapt to these variations, leading to reduced accuracy in fault detection. To address this, we propose a Slow Feature Recursive Broad Learning System (SF-RBLS) that combines slow feature analysis with a recursive mechanism to capture both long-term trends and short-term fluctuations. In addition, an incremental learning strategy allows the model to update in real time without retraining from scratch. The experimental results show that SF-RBLS outperforms existing methods in detecting anomalies such as toxicity shocks, habitat destruction, and sludge bulking. This approach offers a more adaptive and efficient solution for intelligent wastewater monitoring, with potential applications in other industrial process monitoring tasks.