The Inverse Problem in Granulation Modeling—Two Different Statistical Approaches

Abstract

This article is concerned with parameter estimation for a multidimensional population balance model for granulation. Experimental results were obtained by running a laboratory mixer with sodium carbonate and aqueous polyethylene glycol solutions. Subsequently, a prescan of suitable parameter combinations utilising the experimental results is performed, and a local surrogate model constructed around the best combination. For the actual estimation of the parameters and their uncertainties two different approaches are applied—a projection method and a Bayesian approach. It is found that the model predictions with the parameters obtained through both methods are similar. Furthermore, the uncertainties in the model predictions increase as the experimental uncertainties are increased. Studies of the marginal densities of two-parameter combinations obtained through the Bayesian approach show a correlation between the collision and breakage rate constant, giving potential hints for further model development. Furthermore, a bimodal distribution of the compaction rate constant is observed.


Keywords: Bayesian parameter estimation, granulation, inverse problems, parameter estimation,

Associated Project: Particle Processes

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