Performance Monitoring and Batch to Batch Control of Biotechnological Processes

Abstract

This Chapter describes two approaches to ensuring the production quality of batch biotechnological processes. The first makes use of the multivariate statistical data analysis and multivariate statistical process control (MSPC) or better termed multivariate statistical process performance monitoring (MSPM). An industrial application is described to the interrogation of data from a reaction vessel producing an active pharmaceutical ingredient (API) which enabled the realization of a better understanding of the factors causing the onset of an impurity formation to be obtained as well demonstrating the power of multivariate statistical data analysis techniques to provide an enhanced understanding of the process. In the second application, a simulation study of batch-to-batch iterative learning control strategy is presented where the batch control actions for the next batch are adjusted using the information obtained from current and previous batches. The control policy updating is calculated using a model linearized around a reference batch. In order to cope with process variations and disturbances, the reference batch can be taken as the immediate previous batch. After each batch, the newly obtained process operation data is added to the historical data base and an updated linearized model is re-identified. Since the control actions during different stages of a batch are usually correlated, it is proposed here that the linearized model can be identified from partial least square regression.