Multivariate statistical process control of an industrial-scale fed-batch simulator.

Abstract This article presents an improved batch-to-batch optimisation technique that is shown to be able to bring the yield closer to its set-point from one batch to the next. In addition, an innovative Model Predictive Control technique is proposed that over multiple batches, reduces the variability in yield that occurs as a result of random variations in raw material properties and in-batch process fluctuations. The proposed controller uses validity constraints to restrict the decisional space to that described by the identification dataset that was used to develop an adaptive multi-way partial least squares model of the process. A further contribution of this article is the formulation of a bootstrap calculation to determine confidence intervals within the hard constraints imposed on model validity. The proposed control strategy was applied to a realistic industrial-scale fed-batch penicillin simulator, where its performance was demonstrated to provide improved consistency and yield when compared with nominal operation.