Spatial Variation of Pressure in the Lyophilization Product Chamber Part 1: Computational Modeling

Abstract

The flow physics in the product chamber of a freeze dryer involves coupled heat and mass transfer at different length and time scales. The low-pressure environment and the relatively small flow velocities make it difficult to quantify the flow structure experimentally. The current work presents the three-dimensional computational fluid dynamics (CFD) modeling for vapor flow in a laboratory scale freeze dryer validated with experimental data and theory. The model accounts for the presence of a non-condensable gas such as nitrogen or air using a continuum multi-species model. The flow structure at different sublimation rates, chamber pressures, and shelf-gaps are systematically investigated. Emphasis has been placed on accurately predicting the pressure variation across the subliming front. At a chamber set pressure of 115 mtorr and a sublimation rate of 1.3 kg/h/m, the pressure variation reaches about 9 mtorr. The pressure variation increased linearly with sublimation rate in the range of 0.5 to 1.3 kg/h/m. The dependence of pressure variation on the shelf-gap was also studied both computationally and experimentally. The CFD modeling results are found to agree within 10% with the experimental measurements. The computational model was also compared to analytical solution valid for small shelf-gaps. Thus, the current work presents validation study motivating broader use of CFD in optimizing freeze-drying process and equipment design.

Primary Drying Optimization in Pharmaceutical Freeze-Drying: A Multivial Stochastic Modeling Framework

“…As proven by Trelea et al, this assumption works reasonably well when the condenser is separated from the drying chamber by a pipe, as for the equipment used for this study. Moreover, as confirmed by previous studies based on computational fluid dynamics simulations, , it is assumed that the variation of total pressure between the chamber and the condenser is negligible compared to the variation of water vapor partial pressure and that there are no significant pressure leaks in the chamber.…”

An Experimental-Based Approach to Construct the Process Design Space of a Freeze-Drying Process: An Effective Tool to Design an Optimum and Robust Freeze-Drying Process for Pharmaceuticals

“…Conversely, the minimum chamber pressure is limited by the capability of the freeze-drying equipment. [31][32][33] In this sense, a PDS offers a method to identify the product and equipment limitations and to select the most optimized and robust process parameters for a given formulation.…”

Determining Maximum Sublimation Rate for a Production Lyophilizer: Computational Modeling and Comparison With Ice Slab Tests

“…The differences in freeze dryers can be observed by looking at the shelf temperature nonuniformity, refrigeration system, and duct resistance, which was investigated by Rambhatla et al 13 Computational modeling of the vapor flow in the freeze dryer can give not only the desired design parameters but also reveal the vapor flow patterns, the distribution of pressure as well as the relative concentration of water vapor, and noncondensable gases in the freeze dryer product chamber. Computational fluid dynamics (CFD) computations can explain the pressure variation in the chamber as investigated by Barresi et al 14 for pilot-scale freeze dryers and confirmed by comparison of CFD and experimental measurements for laboratory scale by Ganguly et al 15 Input from CFD can be used to design a process as done by Rasetto et al 16 Ganguly et al 17 investigated the amount of radiation, convection, and conduction for different vial arrangements and concluded that convection contribution to vial heat transfer can be significant. CFD can also be coupled with 1-D heat transfer equations to generated unsteady-state calculators for process design.…”