Batch Process Modeling and MSPC

Batch-wise manufacturing (applied in fermentation, cell culturing, and chemical synthesis), gives rise to three-way data arrays when several process variables are measured on the process at regular intervals. The same data structure results from in pharmacokinetics and metabonomics, when data profiles of are taken from individuals at specified intervals. The modeling approaches of three-way batch data for the purpose of understanding, fault detection, control, and prediction, fall in two broad categories, using summarizing variables such as discrete features from the trajectories – landmark points (e.g., peak temp., slopes, times in various phases, etc.), and then forming a batchwise X matrix from these and analyzing by regular PCA/PLS; unfolding the three way array of batch data into a two way matrix (can be done in several ways), followed by PCA/PLS of the two way array to extract an efficient feature set – i.e. latent variables. The established approaches of batch data analysis are reviewed and illustrated by three examples, of yeast production, nylon manufacturing, and of a drying process step.