Pharmaceutical manufacturing is evolving from traditional batch processes to continuous ones. The new global competition focused on throughput and quality of drug products is certainly the driving force behind this transition which, thus, represents the new challenge of pharmaceutical manufacturing and hence of lyophilization as a downstream operation. In this direction, the present review deals with the most recent technologies, based on spray freeze-drying, that can achieve this objective. It provides a comprehensive overview of the physics behind this process and of the most recent equipment design.

In the present work fluid flow and solute transport through porous media are described by solving the governing equations at the pore scale with finite-volume discretization. Instead of solving the simplified Stokes equation (very often employed in this context) the full Navier-Stokes equation is used here. The realistic three-dimensional porous medium is created in this work by packing together, with standard ballistic physics, irregular and polydisperse objects. Emphasis is placed on numerical issues related to mesh generation and spatial discretization, which play an important role in determining the final accuracy of the finite-volume scheme and are often overlooked. The simulations performed are then analyzed in terms of velocity distributions and dispersion rates in a wider range of operating conditions, when compared with other works carried out by solving the Stokes equation. Results show that dispersion within the analyzed porous medium is adequately described by classical power laws obtained by analytic homogenization. Eventually the validity of Fickian diffusion to treat dispersion in porous media is also assessed.

The modeling of flow and transport in porous media is of the utmost importance in many chemical engineering applications, including catalytic reactors, batteries, and CO₂ storage. The aim of this study is to test the use of fully connected (FCNN) and convolutional neural networks (CNN) for the prediction of crucial properties in porous media systems: the permeability and the filtration rate. The data-driven models are trained on a dataset of computational fluid dynamics (CFD) simulations. To this end, the porous media geometries are created in silico by a discrete element method, and a rigorous setup of the CFD simulations is presented. The models trained have as input both geometrical and operating conditions features so that they could find application in multiscale modeling, optimization problems, and in-line control. The average error on the prediction of the permeability is lower than 2.5%, and that on the prediction of the filtration rate is lower than 5% in all the neural networks models. These results are achieved with at least a dataset of ∼100 CFD simulations.

In this work, the influence of pore space geometry on solute transport in porous media is investigated performing computational fluid dynamics pore-scale simulations of fluid flow and solute transport. The three-dimensional periodic domains are obtained from three different pore structure configurations, namely, face-centered-cubic (fcc), body-centered-cubic (bcc), and sphere-in-cube (sic) arrangements of spherical grains. Although transport simulations are performed with media having the same grain size and the same porosity (in fcc and bcc configurations), the resulting breakthrough curves present noteworthy differences, such as enhanced tailing. The cause of such differences is ascribed to the presence of recirculation zones, even at low Reynolds numbers. Various methods to readily identify recirculation zones and quantify their magnitude using pore-scale data are proposed. The information gained from this analysis is then used to define macroscale models able to provide an appropriate description of the observed anomalous transport. A mass transfer model is applied to estimate relevant macroscale parameters (hydrodynamic dispersion above all) and their spatial variation in the medium; a functional relation describing the spatial variation of such macroscale parameters is then proposed.