Granularity determination of activated sludge through on-line profiles by means of case-based reasoning.

Aerobic granulation from floccular sludge is difficult to detect in first stages with the naked eye. This work proposes a combination of multi-way principal components and case-based reasoning to predict the granulation state of a sequencing batch reactor, based solely on the on-line registered profiles of common sensors (i.e. pH, dissolved oxygen and oxidation-reduction potential). The methodology is able to discriminate between two active sludge granularities (floccular and granular). Two different scenarios are presented: one in which both granularities are present, and another scenario for which the granular state is not initially available. Analysis reported pH as the key variable in the transition between both states according to its variation, and that, in general, the granularity of the process can be correctly predicted at the end of the anaerobic phase. This methodology improves process monitoring capabilities during granulation and is an on-line alternative to a microscope analysis before the batch release.

Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP.

The main idea of this paper is to develop a methodology for process monitoring, fault detection and predictive diagnosis of a WasteWater Treatment Plant (WWTP). To achieve this goal, a combination of Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) is proposed. First, MPCA is used to reduce the multi-dimensional nature of online process data, which summarises most of the variance of the process data in a few (new) variables. Next, the outputs of MPCA (t-scores, Q-statistic) are provided as inputs (descriptors) to the CBR method, which is employed to identify problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology is a promising tool for automatic diagnosis and real-time warning, which can be used for daily management of plant operation.