Batch process monitoring in the original measurement's space

Quality control and safety related issues have become more and more important in industrial production of high added value products and chemical specialities during last years. In this regard, many successful applications of multivariate statistical process control (MSPC) for monitoring and diagnosis of batch processes have been presented. It is a common industrial practice to monitor the batch progress by exploiting the information contained in a historical database of successful batches using projection techniques such as principal components analysis (PCA), partial least squares (PLS) and independent component analysis (ICA). In this work, a new MSPC strategy for batch process monitoring is presented. Its distinctive feature is that it works in the space of the original variables. The technique uses only the T2-statistic for detection and identification purposes. The identification of the set of observations that signal the fault is accomplished by decomposing the T2-statistic as a unique sum of each variable contribution. Performance comparisons among the proposed strategy and the most popular PCA-based approaches are carried out by simulation of polymerization and penicillin cultivation batch processes. Results show that the new approach can be successfully applied to monitor this kind of processes since it works very well during both fault detection and identification stages.