Optimisation of additively manufactured coiled flow inverters for continuous viral inactivation processes

This article reports the development and utilisation of an adaptive design workflow methodology for use as a platform technology for the printing, testing, and optimisation of biopharmaceutical processing reactors. This design strategy was developed by application to the complex structure of the coiled flow inverter (CFI). In this way, the many possible physical parameters of the reactor were optimised, via a combination of experimental results, computational fluid dynamics and machine learning approaches, to find the CFI setups that provide the optimal flow properties for a particular application.
Additively manufactured reactors are seeing increasing interest in the field of biopharmaceutical production. This is because the desired output volumes are typically small and there is an increasing move towards adopting continuous production, to replace traditional batch production. This approach allows for the tailoring of reactors for a specific reaction, i.e. attempting to maximise the desired aspects of the reaction through refinement of the physical parameters of the reactor, so creating a large possible parameter space to explore.
Consequently, the holistic optimisation of CFI reactors and 3D printing is established as providing better plug flow mixing relative to traditional tube coiled reactors. In addition, a trained metamodel in combination with multilayer equations is demonstrated to predict reactor performance quickly and accurately.