Special Issue on “Recent Advances in Population Balance Modeling”

Population Balance Modeling (PBM) is a powerful modeling framework that allows the prediction of the dynamics of distributed properties of a population of individuals at the mesoscale. This is of particular interest when such a property is a critical quality attribute of a production system (e.g., particle size distribution and particle composition). The framework found its roots in the chemical engineering field in the 1960s and boomed in the late 1990s with increasing computational power. It is now gaining ground in other application fields, such as pharmaceutical engineering and biotechnology.
Population balance models come in different forms. They can be formulated taking into account different continuous and discrete mechanisms such as nucleation, growth, aggregation and breakage. For these mechanisms, process rates or kernels need to be defined. Calibration and validation of these kernels based on experimental data is of particular interest to secure the model’s predictive power and, hence, successful use in scenario analysis for process operational and design optimization.
Moreover, PBM can include one or more distributed properties and either be embedded in a Computational Fluid Dynamics framework or spatial compartments to include the effect of spatial heterogeneities. Recently, it has been integrated with stochastic and machine learning-based approaches as well. Specific numerical and computational burden challenges arise when doing so.
The latest research in this intriguing field of research was discussed at the 6th International Conference on Population Balance Modeling (PBM2018) held in Ghent, Belgium on 7–9 May 2018 organized by Ghent University in collaboration with the Technical University of Denmark and supported by the European Federation of Chemical Engineering (EFCE). The past five International Conferences on Population Balance Modeling, held in Kona (Hawaii, 2000), Valencia (Spain, 2004), Quebec (Canada, 2007), Berlin (Germany, 2010) and Bangalore (India, 2013) have stimulated the increasing interest in the development and application of the PBM framework.
The issue is a reflection of high-quality and invited papers presented at PBM2018, a conference with international participants and several keynote speakers. This Special Issue on “Population Balance Modeling” aims to show the most recent advances in applications, parameter estimation/model calibration, numerical methods and stochastic methods of population balance modeling. As summarized below, this Special Issue provides a collection of twelve papers on original advances in population balance modeling.