Resolving conflicting parameter estimates in multivariate population balance models

We present an extended methodology for parametric inference in complex population balance models. The aim is twofold. Firstly, it is assumed that the parameter distribution of the model is a multimodal Gaussian rather than a unimodal Gaussian. After projection of experimental data through a response surface approximation, estimates for the parameters and their uncertainties along with the associated weights of each mode are established. Secondly, the methodology is used to ask the following question—if n professors each have a ‘best’ estimate of a particular parameter, which of these estimates is more likely to be correct? A toy example is used to show the applicability of the methodology, aiding in the discrimination between a bimodal and trimodal parameter distribution. The identification of the ‘best’ model parameter among two conflicting estimates is demonstrated in an example from granulation modelling.