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.
Highlights
In the pharmaceutical industry, regulatory authorities such as the Food and Drugs Agency (FDA) are encouraging the adoption of Quality by Design (QbD) enabling improved product quality through enhanced process control (Yu et al, 2014)
The addition of these terms in the control strategy significantly improved its performance; the confidence limits used for the constraints (Laurí et al, 2013; Nomikos and Macgregor, 1995a,b Ündey et al, 2003) were not clearly specified and assumed that the data could be approximated by normal and chisquared distributions for T2 and Square Prediction Error (SPE) respectively, which was only true in specific applications
An improved B2B optimisation strategy was successfully implemented on an industrial fed-batch penicillin simulation, greatly improving the yield from a nominal pre-optimised feed trajectory
Summary
Regulatory authorities such as the Food and Drugs Agency (FDA) are encouraging the adoption of Quality by Design (QbD) enabling improved product quality through enhanced process control (Yu et al, 2014). The same constraints with adaptations to a B2B optimisation strategy were applied in (Duran-Villalobos et al, 2016) for a B2B optimisation, which modified the control strategy presented by Wan et al (2012) and solved the QP in the real space, whilst including the effect of the projection of the future changes in the MVT to the ‘latent’ space The addition of these terms in the control strategy significantly improved its performance; the confidence limits used for the constraints (Laurí et al, 2013; Nomikos and Macgregor, 1995a,b Ündey et al, 2003) were not clearly specified and assumed that the data could be approximated by normal and chisquared distributions for T2 and SPE respectively, which was only true in specific applications.
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