What challenges does the process of scale-down face?
Recreating conditions as close to reality as possible in small reactors can be challenging due to the wide range of complex variables involved.
For instance, reproducing gradients that may occur in large-scale reactions due to the imperfect mix can be complex, as small-scale reactors are often well mixed, resulting in relatively homogeneous conditions. This links to another potential challenge faced by agitation at the time of down-scaling. Mixing and stirring in large-scale reactors often rely on the use of a large impeller, which will ensure adequate mixing, therefore ensuring appropriate mass and energy transfer rates. On the downside, these large impellers can exert shear stress on the cells, so balancing both values can prove difficult. Scale-down models, especially physical ones, can be time- and resource-consuming because of the time spent carefully controlling numerous parameters.
Additionally, the iterative nature also adds to the time and cost involved. However, one of the biggest challenges these tests can face is representing biological complexity, which is very difficult to capture into a scale-down model due to the highly variable nature of biological systems. Apart from biological complexity, the temporal complexity of the system can also be challenging to reproduce due to the often transient conditions changes.
BioXplorers can play a fundamental role in scale-down process analysis. Using H.E.L’s WinISO, BioXplorer 100 and 400 can be finely controlled to replicate the conditions found at a larger scale accurately. Multiple reactors can be used to increase the throughput of the process, positioning them as a potent tool for statistical scale-down processes. On the other hand, larger reactors, such as BioXplorer 5000, can be used to replicate complex conditions, such as physicochemical gradients, and therefore can be instrumental in physical scale-down processes. The application of scale-down processes using BioXplorer models results in significant time and cost reductions, underscoring their importance in efficient and economical bioprocess management.