An Analysis of Uncertainty Propagation Methods Applied to Breakage Population Balance

Satyajeet Bhonsale, Dries Telen, Bard Stokbroekx, Jan Van Impe
2018 Processes  
In data-driven empirical or hybrid modeling, the experimental data influences the model parameters and thus also the model predictions. The experimental data has some variability due to measurement noise and due to the intrinsic stochastic nature of certain pharmaceutical processes such as aggregation or breakage. To use predictive models, it is imperative that the accuracy of the predictions is known. To this extent, various uncertainty propagation techniques applied to a predictive breakage
more » ... edictive breakage population balance model are studied. Three uncertainty propagation techniques are studied: linearization, sigma point, and polynomial chaos. These are compared to the uncertainty obtained from Monte Carlo simulations. Linearization performs the worst in the given scenario, while sigma point and polynomial chaos methods have similar performance in terms of accuracy.
doi:10.3390/pr6120255 fatcat:cmbqrpuzsng2jg722xbwlzygrm