Multivariate SPC of a sequencing batch reactor for wastewater treatment

Data from a sequencing batch reactor (SBR) operated for enhanced biological phosphorus removal from wastewater have been analysed in order to propose an efficient MSPC scheme of the process. Different multivariate bilinear approaches have been applied and compared in terms of their capabilities for on-line and off-line fault detection and diagnosis. The typical three-way data structure from a batch process was unfolded batch-wise and variable-wise. In the latter case, two models were built: with (AT) and without (WKFH) removing the main non-linear behaviour of the process data. Since the process consists of several stages, the monitoring strategies tested include: one model for all stages and also individual modelling of each process stage. The approaches that removed the non-linear process trajectory yielded good results when each process stage was described with one model as well as when one model was used for the whole batch. However, when the non-linear process behaviour was not eliminated it was necessary to model individually each stage in order to achieve good performance. From the results obtained, an integrated system for on-line and off-line monitoring and diagnosis of the SBR process has been proposed. The monitoring scheme proposed includes two levels. This allows monitoring new batches as they are evolving and also the overall process evolution associated with finished batches. Moreover, the off-line level can be used to detect process drifts (due to seasonal fluctuations, new operating conditions, etc.) and therefore to determine when the covariance structure of the model needs to be updated. This is of great importance for monitoring processes that exhibit a non-stationary behaviour in order to provide reliable monitoring charts and avoid many false alarms.