Data Driven Detection of Different Dissolved Oxygen Sensor Faults for Improving Operation of the WWTP Control System

Abstract

Sensor faults frequently occur in wastewater treatment plant (WWTP) operation, leading to incomplete monitoring or poor control of the plant. Reliable operation of the WWTP considerably depends on the aeration control system, which is essentially assisted by the dissolved oxygen (DO) sensor. Results on the detection of different DO sensor faults, such as bias, drift, wrong gain, loss of accuracy, fixed value, or complete failure, were investigated based on Principal Components Analysis (PCA). The PCA was considered together with two statistical approaches, i.e., the Hotelling’s T2 and the Squared Prediction Error (SPE). Data used in the study were generated using the previously calibrated first-principle Activated Sludge Model no.1 for the Anaerobic-Anoxic-Oxic (A2O) reactors configuration. The equation-based model was complemented with control loops for DO concentration control in the aerobic reactor and nitrates concentration control in the anoxic reactor. The PCA data-driven model was successfully used for the detection of the six investigated DO sensor faults. The statistical detection approaches were compared in terms of promptness, effectiveness, and accuracy. The obtained results revealed the way faults originating from DO sensor malfunction can be detected and the efficiency of the detection approaches for the automatically controlled WWTP.

Enhanced data-driven monitoring of wastewater treatment plants using the Kolmogorov–Smirnov test

“…In ref. 21, a data-driven approach based on PCA is employed to detect various dissolved oxygen (DO) sensor faults in WWTPs. The study focuses on biases, drifts, gains, accuracy losses, fixed values, and complete failures of DO sensors.…”

Semi-Supervised Anomaly Detection of Dissolved Oxygen Sensor in Wastewater Treatment Plants

“…It can be used in many different fields where detecting abnormalities or outliers is essential for maintaining the system's health, sustaining security, or optimizing processes. There are various examples of problems involving fault detection, such as frauds [1], network intrusions [2], manufacturing defects [3], anomaly detection in time series data [4], cybersecurity [5,6], microfluidics [7][8][9][10], and most importantly, anomaly detection in wastewater treatment plants [11][12][13].…”

Data-Driven Drift Detection in Real Process Tanks: Bridging the Gap between Academia and Practice

“…Kazemi et al [16] showed that incremental PCA was able to distinguish between time varying events and faults in simulated data, while Kazemi et al [17] investigated a number of technics including Support Vector Machine, Ensemble Neural Network and Extreme Learning and found that they performed better than a PCA based method after testing on simulated data. Luca et al [18] applied PCA and statistic for fault detection in DO sensors in simulated data and stated that the method was successful in detecting the faults. Mali and Laskar [19] proposed an optimized Monte Carlo deep neural network and were able to detect faults of low magnitude in simulated data.…”