# A One-Class-Based Supervision System to Detect Unexpected Events in Wastewater Treatment Plants
## 1 Introduction
Multiple studies support the idea that optimizing WWTPs brings tangible benefits [19]. For example, in [20], an index is presented that allows evaluating the monitoring and diagnostic performance of fault detection methods which takes into account several characteristics such as false alarms, false acceptances, and undesirable changes from correct detection to non-detection during a fault event. Considering that many wastewater treatment facilities are publicly owned, the need for cost optimization becomes even more pronounced [21]. Publicly funded facilities must operate efficiently to ensure responsible resource allocation and effective waste management [22]. Moreover, the significance of treated water as a valuable resource is emphasized, particularly in regions facing frequent droughts [5]. This highlights the dual benefit of wastewater treatment optimization, not only in terms of cost reduction but also in the sustainable production of a valuable water resource, addressing challenges posed by water scarcity in drought-prone areas. In [23], a novel technique is proposed for real-time monitoring of foam presence in WWTP tanks using texture segmentation models trained with centralized and federated approaches. The proposed methodology integrates into an image processing chain that involves capturing images with a professional camera, ensuring the absence of anomalies in the captured images, and implementing a real-time communication method for event notifications to plant operators. While the studies mentioned earlier [4; 16; 17; 18; 19; 20; 21; 22] have made valuable contributions to the field by investigating strategies like enhancing energy efficiency, determining the optimal retention time of solids, and refining the ozonation process, there remains a need for further research to address specific limitations. For instance, the study's exclusive focus on Italy's largest WWTP might restrict the applicability of the findings to diverse geographical and operational settings. Additionally, the optimization strategies proposed in [18] may not comprehensively consider the myriad challenges encountered by WWTPs, including the identification of anomalies in sensor measurements within the plant. In light of these considerations, this work presents a novel method for optimizing WWTPs, offering a unique approach to address the complex and dynamic nature of wastewater treatment. Unlike the studies mentioned above, the proposed approach emphasizes the importance of continuous monitoring of a wide set of parameters, diagnosing and detecting possible faults in the sensors' measurements. This control not only minimizes the economic investment associated with the exhaustive review of measurements by an operator but also guarantees that the sensors measure correctly, a crucial point for regulatory compliance [24; 25]. This paper presents an approach to optimize WWTP through fault detection. During plant operation, numerous parameters are monitored with the aim of applying different techniques to detect anomalies that may occur in certain plant areas. The approach is based on one-class classifiers trained with data registered during normal operation and tested with synthetic anomalies involving critical parameter deviations. The document is structured as follows: after this introduction, the materials and methods are presented, then the experiments and results are described. Finally, conclusions are drawn, and future work is proposed.