Comparison of first principles model of beer microfiltration to experiments via systematic parameter identification

Abstract

A first principles microfiltration model based on shear-induced diffusion is compared to experiments performed on the clarification of beer. After performing an identifiability and sensitivity analysis, the model parameters are estimated using global minimization of the sum of least squares. The model is compared to different series of experiments, where either crossflow or permeate flux is varied. This study is concluded with a parameter study on the scaling of the filtration time with various model parameters. We have found that the filtration time primarily depends on two dimensionless numbers, namely the normalized critical distance for cake layer formation, and the dimensionless time required to plug all pores in the selective layer. We have found that there is an optimal setting of these parameters, rendering a maximal amount of filtrated beer in one cycle.