(587b) Applications Of Population Balance Equation Modeling To Food Emulsions | AIChE

(587b) Applications Of Population Balance Equation Modeling To Food Emulsions


Raikar, N. B. - Presenter, University of Massachusetts
Bhatia, S. R. - Presenter, University of Massachusetts Amherst
Malone, M. F. - Presenter, University of Massachusetts

A variety of products in the food industry are emulsions prepared via high pressure homogenization, whereby a coarse emulsion is forced through a small orifice under very high pressure. The homogenization process produces a distribution of emulsion drop sizes that depends on the formulation, the initial coarse distribution, and the homogenizer operating conditions. Many final properties of the emulsion, such as the rheological properties, are highly dependent on the drop size distribution as well as several other variables. In addition, the drop size distribution for many food products is quite complex and often multimodal in nature.

In this paper, we discuss the utility of a population balance equation (PBE) approach to describe the evolution of the drop size distribution in a high pressure homogenizer. Successful application of the PBE modeling approach requires knowledge of functions for droplet formation, aggregation, and breakup. In principle, once these functions are known, the PBE approach can be used in a predictive manner to aid in the selection of process and product variables that will lead to the desired drop size distribution.

We have applied the PBE modeling approach to droplet break-up in a high pressure homogenizer using several well-accepted breakage functions from the literature. We have compared our modeling results to experimental data that we have obtained on a model oil-in-water emulsion. We find that these approaches work fairly well for cases where the coarse emulsion and final product have unimodal drop size distributions. However, we find that the most commonly used forms of the breakage function are unable to accurately predict drop size distribution for bimodal and multimodal systems. We discuss strategies for improving predictions for these types of systems.