Minimizing the effort associated with the pilot and laboratory-scale
experiments needed for a successful scale-up of a process from laboratory
to production scale is a significant challenge in process development.
Efficient scale-up is becoming increasingly important in process development
due to the growing pressure to reduce costs and timelines while achieving
a first-time-right approach. This article describes innovative technologies
that enable direct and efficient process scale-up from the laboratory
to production scale, while concurrently optimizing scale-dependent
parameters through in-depth process understanding. Those technologies
include a dynamic process model (based on a digital twin) and a laboratory-scale
imitation (Scale-Down-Reactor) of a specific production-scale reactor
(4000 L). The core component of the Scale-Down-Reactor is a 3D-printed
metallic insert (H/C-Finger), designed to replicate the heat transfer
behavior of the production reactor by maintaining a similar heat transfer
coefficient and surface-to-volume ratio. In order to maintain comparable
gas–liquid mass transfer between the scales, the Scale-Down-Reactor
was designed with geometric similarity to its large-scale counterpart.
Both mass transfer and heat transfer were experimentally evaluated
for the two scales, and the comparison demonstrated an excellent agreement.
To finally prove and validate the concept, a hydrogenation process
currently running at the production scale was conducted in the Scale-Down-Reactor.
As a second technology, a dynamic process model is described that
includes a kinetic model of the chemical reactions and a heat/mass
transfer model (digital twin) of the aforementioned production-scale
reactor. For the gas–liquid mass transfer model, an improved
mathematical description (equation) was developed. Moreover, the production-scale
hydrogenation process conditions were efficiently optimized using
the dynamic process model. The measured reaction mixture temperature
profile of the optimized production batch demonstrated excellent agreement
with the profile predicted by the dynamic process model. By enabling
direct and efficient process scale-up while concurrently optimizing
scale-dependent parameters, the technologies described within this
article offer a promising approach to reducing costs and timelines
while improving process understanding.