Development of a fault detection algorithm for an alternate aerobic/anoxic cycle nitrogen removal process

This thesis presents a critical appraisal of several differing approaches to the design and testing of fault detection (FD) algorithms monitoring the instrumentation used in the alternated aerobic/anoxic cycles (AC) process for nitrogen removal. Several features are selected as the basis of the FD, involving the slope and the timing of the process measurements of the various nitrogen compounds. Two separate FD algorithms have been developed for the anoxic and the aerobic phases, requiring a separate tuning but sharing the same principles: first some low-level checks are performed on the raw signals, discriminating gross malfunctions like missing data and spikes, then more sophisticated methods are used to investigate the presence of more subtle anomalies that were not detected by the previous screening. The FD problem is treated either in terms of classification problem, testing different algorithms such as binary trees, support vector machines (SVM) and principal component analysis (PCA), or as a forecasting one, using the Bayesian theory to predict the faulty or normal state of the process based on the previous records. An operational data set obtained from a municipal plant was used to first train the algorithm. However, due to the fairly limited information which could be extracted, a more comprehensive data set was created building an AC model based on the standard Benchmark simulation model with improved nitrogen kinetics and seasonal temperature variations. Detailed sensor models were also included, so that the occurrence of faults could be totally controlled, both in kind and timing. The performances of the various methods on either the operational and the synthetic datasets have been assessed comparing the anomalies detected by the methods with those actually observed. While the great majority of the gross faults is successfully detected by the preliminary screening, differing performances of the subsequent finer detection are obtained, depending on both the quality of data set and the detection method used: poorer results are observed using the plant data, in part due to an insufficient characterization of the fault events and in part due to the limited number of signals monitored. The higher availability of measurements provided by the numerical model, instead, enhances the discrimination capabilities of the tested methods, especially the nonlinear SVM, while the PCA-based approach and the Bayesian predictor results less affected by a change in the combination of diagnostic parameters used.