Multivariate Forecasting of Batch Evolution for Monitoring and Fault Detection

To monitor a batch process, which is dynamic in nature, it is necessary to consider the time varying relationship of its variables throughout the entire run. MPCA models built with batch wise unfolded data have been used extensively for batch process monitoring, these methods will not only consider the known samples to asses the ongoing batch run, but will also consider a dynamic forecast of the future unknown samples. Such forecast, implicit in the methodology, is uncovered and analyzed in this work; and proven to be a powerful feature of a batch-monitoring scheme built with MPCA and the batch-wise unfolded matrix of batch data.