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.
The work focuses on the development of an artificial neural network (ANN) based model that can describe the adsorption of benzalkonium chloride from aqueous solutions onto commercially available kitchen paper. Various ANN architectures were tested in order to find the most suitable one in terms of overlapping between calculated and measured output data (coefficient of determination and mean absolute percentage error), as well as correctly interpolating outputs when using inputs form inside the experimental training range. The networks all had 4 inputs and 1 output, as well as a single hidden layer. Optimal ANN design was sought by varying both the number of neurons in the hidden layer and the type of transfer function towards it. The best find was employed in assessing the relative importance of input parameter values in the output, as well as the model’s suitability for predictions outside the training range.
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