As the world progresses toward a digitally connected and sustainable future, the integration of semi-supervised anomaly detection in wastewater treatment processes (WWTPs) promises to become an essential tool in preserving water resources and assuring the continuous effectiveness of plants. When these complex and dynamic systems are coupled with limited historical anomaly data or complex anomalies, it is crucial to have powerful tools capable of detecting subtle deviations from normal behavior to enable the early detection of equipment malfunctions. To address this challenge, in this study, we analyzed five semi-supervised machine learning techniques (SSLs) such as Isolation Forest (IF), Local Outlier Factor (LOF), One-Class Support Vector Machine (OCSVM), Multilayer Perceptron Autoencoder (MLP-AE), and Convolutional Autoencoder (Conv-AE) for detecting different anomalies (complete, concurrent, and complex) of the Dissolved Oxygen (DO) sensor and aeration valve in the WWTP. The best results are obtained in the case of Conv-AE algorithm, with an accuracy of 98.36 for complete faults, 97.81% for concurrent faults, and 98.64% for complex faults (a combination of incipient and concurrent faults). Additionally, we developed an anomaly detection system for the most effective semi-supervised technique, which can provide the detection of delay time and generate a fault alarm for each considered anomaly.

The efficiency of wastewater treatment plants (WWTPs) highly depends on correctly detecting the anomalies hindering their processes. This study investigates the use of hybrid Artificial Neural Networks (A-NNs) for detecting mechanical faults injected in the Dissolved Oxygen (DO) sensor of a WWTP. The hybrid networks are obtained by combining Radial Basis Function Neural Network (RBF-NN) with the specific architectures of Feedforward Neural Network (FF-NN), Long Short-Term Memory Neural Network (LSTM-NN) and Convolutional Neural Network (C-NN), respectively. Each hybrid model is tested on several simulated anomaly scenarios containing both normal and faulty operating conditions of the DO sensor, and evaluated using a comprehensive set of classification metrics, including accuracy (A), precision (P), recall (R), F1-score (F1-S), balanced accuracy (BA), Cohen’s Kappa (CK), Matthew’s Correlation Coefficient (MCC), and the areas under the Receiver Operating Characteristic curve (ROC-AUC) and the Precision–Recall curve (PR-AUC). The results show that the LSTM-NN + RBF hybrid consistently outperforms the other two hybrids, achieving accuracy of 96.04%, precision of 96.78%, recall of 89.51%, F1-score of 92.89%, ROC-AUC of 96.01%, PR-AUC of 94.93%, MCC of 90.25%, CK of 90.03% and BA of 94.16%. These results suggest that the proposed LSTM-NN + RBF hybrid is a promising tool for efficiently detecting mechanical faults in a WWTP.