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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.

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Sensor failures are common events in wastewater treatment plant (WWTP) operations, resulting in ineffective monitoring and inappropriate plant management. Efficient aeration control is typically achieved by the dissolved oxygen (DO) control, and its associated sensor becomes critical to the whole WWTP’s reliable and economical operation. This study presents the Fisher discriminant analysis (FDA) used for fault diagnosis of the DO sensor of a currently operating municipal WWTP. Identification of the bias, drift, wrong gain, loss of accuracy, fixed value, complete failure minimum and maximum types of DO sensor fault was investigated. The FDA-proposed methodology proved efficiency and promptitude in obtaining the diagnosis decision. The consolidated fault identification showed an accuracy of 87.5% correct identification of the seven faulty and normal considered classes. Depending on the fault type, the results of the diagnosing time varied from 2.5 h to 16.5 h during the very first day of the fault appearance and were only based on observation data not included in the training data set. The latter aspect reveals the potential of the methodology to learn from incomplete data describing the faults. The rank of the fault type detection promptitude was: bias, fixed value, complete failure minimum, complete failure maximum, drift, wrong gain and loss of accuracy. Greenhouse gases (GHGs) such as nitrous oxide (N2O) and carbon dioxide (CO2) emitted during wastewater treatment, electrical energy quantity in association with costs spent in the WWTP water line and clean water effluent quality were ranked and assessed for the normal operation and for each of the DO sensor faulty regimes. Both for CO2 and N2O, the on-site emissions showed the most significant GHG contribution, accounting for about three-quarters of the total emissions. The complete failure maximum, fixed value and loss of accuracy were the DO sensor faults with the highest detrimental impact on GHG-released emissions. The environmental and economic study reveals the incentives of the proposed DO sensor faults identification for the WWTP efficient and environmentally friendly operation.