Multivariate analysis and monitoring of sequencing batch reactor using multiway independent component analysis

This contribution describes the monitoring on a pilot-scale sequencing batch reactor (SBR) using a batchwise multiway independent component analysis method (MICA) which can extract meaningful hidden information from non-Gaussian data. Given that independent component analysis (ICA) is superior to principal component analysis (PCA) to extract features from non-Gaussian data sets, the use of ICA may improve monitoring performance. The monitoring results of a pilot-scale SBR for biological wastewater treatment showed the power and advantages of MICA monitoring in comparison to conventional monitoring methods.