Monitoring of Bioreactor using Statistical Techniques

Abstract

Present study addresses the monitoring of a continuous bioreactor operation. New methodologies; based on clustering time series data and moving window based pattern matching have been proposed for the detection of fault in the chosen bioreactor process. A modified k-means clustering algorithm using similarity measure as a convergence criterion has been adopted for discriminating among time series data pertaining to various operating conditions. The proposed distance and PCA based combined similarity along with the moving window approach were used to discriminate among the normal operating conditions as well as detection of fault for the process taken up.