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The present work investigates continuous pan granulation using dolomite primary particles and sprayed solution of sodium lignosulphonate binder. Individual changes of operating parameters were investigated with respect to their influences on process behavior and product quality. The latter is characterized in terms of the granules’ characteristic size, compressive strength, angle of repose, bulk density, and humidity. The experimental results show clear trends between operation parameters and product granules’ characteristic properties. It is shown that all investigated operation parameters have a significant effect on the particle size distribution and the average diameter of the granules: the average particle size increases with increasing binder flow rate to feed rate ratio, increasing binder concentration and inclination angle of the pan. For the angle of repose and the bulk density the ratio of binder flow rate to raw material flow rate and humidity is shown to be the most significant influence factor.

A large group of biopharmaceuticals is produced in cell lines. The yield of such products can be increased by genetic engineering of the corresponding cell lines. The prediction of promising genetic modifications by mathematical modeling is a valuable tool to facilitate experimental screening. Besides information on the intracellular kinetics and genetic modifications the mathematical model has to account for ubiquitous cell-to-cell variability. In this contribution, we establish a novel model-based methodology for influenza vaccine production in cell lines with overexpressed genes. The manipulation of the expression level of genes coding for host cell factors relevant for virus replication is achieved by lentiviral transduction. Since lentiviral transduction causes increased cell-to-cell variability due to different copy numbers and integration sites of the gene constructs we use a population balance modeling approach to account for this heterogeneity in terms of intracellular viral components and distributed kinetic parameters. The latter are estimated from experimental data of intracellular viral RNA levels and virus titers of infection experiments using cells overexpressing a single host cell gene. For experiments with cells overexpressing multiple host cell genes, only final virus titers were measured and thus, no direct estimation of the parameter distributions was possible. Instead, we evaluate four different computational strategies to infer these from single gene parameter sets. Finally, the best computational strategy is used to predict the most promising candidates for future modifications that show the highest potential for an increased virus yield in a combinatorial study. As expected, there is a trend to higher yields the more modifications are included., Author summary In the present work, we use a sophisticated simulation-based methodology to account for the impact of genetic modifications in producer cell lines on the yield of biomanufacturing processes. Furthermore, our approach opens the possibility to predict the most promising genetic modifications instead of identifying them in costly and time-consuming screening experiments. As an example, we apply our methodology to cell culture-based influenza vaccine production, a process that is of tremendous importance for the maintenance of public health. Here, we consider cell lines in which genes coding for one or more cellular factors are up-regulated by genetic engineering to increase the virus yield. However, the gene editing procedure increases the heterogeneity in the producer cell population because genetic modifications do not occur equally in each cell. This cell-to-cell variability is taken into account in a population balance modeling framework, thus providing a more accurate prediction of the virus yield in the heterogeneous population. Finally, we use our approach and a concise experimental data set from cell lines with one gene modification to predict the virus yield of cell lines with multiple genetic modifications. Thereby, we facilitate the experimental screening of potential candidates. We suggest that this methodology is transferable to a wide range of biomanufacturing processes and constitutes a valuable contribution to experimental design.

Porous structures are naturally involved in electrochemical processes. The specific architectures of the available porous materials, as well as their physical properties, crucially affect their applications, e.g., their use in fuel cells, batteries, or electrolysers. A key point is the correlation of transport properties (mass, heat, and charges) in the spatially&mdash, and in certain cases also temporally&mdash, distributed pore structure. In this paper, we use mathematical modeling to investigate the impact of the pore structure on the distribution of wetting and non-wetting phases in porous transport layers used in water electrolysis. We present and discuss the potential of pore network models and an upscaling strategy for the simulation of the saturation of the pore space with liquid and gas, as well as the computation of the relative permeabilities and oxygen dissolution and diffusion. It is studied how a change of structure, i.e., the spatial grading of the pore size distribution and porosity, change the transport properties. Several situations are investigated, including a vertical gradient ranging from small to large pore sizes and vice versa, as well as a dual-porosity network. The simulation results indicate that the specific porous structure has a significant impact on the spatial distribution of species and their respective relative permeabilities. In more detail, it is found that the continuous increase of pore sizes from the catalyst layer side towards the water inlet interface yields the best transport properties among the investigated pore networks. This outcome could be useful for the development of grading strategies, specifically for material optimization for improved transport kinetics in water electrolyser applications and for electrochemical processes in general.