An Analysis of Uncertainty Propagation Methods Applied to Breakage Population Balance

Abstract

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 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.

Dynamic Optimisation of Beer Fermentation under Parametric Uncertainty

“…For maximising ethanol, the variance is estimated with good accuracy, while for minimising batch time, the accuracy reduces. This is consistent with the results of Bhonsale et al [35], which demonstrate the failure of linearisation when a step change in input is applied to the process exciting its nonlinear dynamics.…”

“…When the model is highly nonlinear, the approach fails to provide a correct estimate of the variance [34,35].…”

Special Issue on “Recent Advances in Population Balance Modeling”

“…According to their study, in theory, any number of moments can be tracked with the new method, but the computational expense can be relatively large due to many scalar equations that may be included. Bhonsale et al [10] performed an analysis of uncertainty propagation methods applied to a breakage population balance model. This is because in data-driven or hybrid modeling, the variability in measured data influences the model parameters and the resulting model predictions.…”

Quantitative methods to predict the effect of climate change on microbial food safety: A needs analysis

“…Hence, uncertainty propagation is an important step in building reliable models. Uncertainty propagation can be per- the linear approximation method, polynomial chaos expansion method, and the sigma point method (Bhonsale et al, 2018). In the case of probabilistic models, uncertainty propagation can be also performed by Bayesian approaches.…”