Bridging to the large-scale fermentation vessels.
In large-scale fermentation processes, microbes experience and respond to heterogenous conditions when they move around the bioreactor, which may impact process performance.
A fermentation digital twin would be able to simulate and predict the microbial response, allowing ultimately for monitoring, control and optimization of the fermentation process. Currently, a prototype of such a digital twin is available, by combining first-principal spatially resolved hydrodynamic models and dynamic metabolic models.
The prototype is however computationally very intensive and a real-time online application, which should also include process dynamics (such as changes in volume, aeration, and rheology, etc.), is infeasible. Recent studies show that data-driven approaches, such as Physics-Informed Neural Networks, incorporating physical constraints (Navier-Stokes equation), is able to reconstruct flow fields and can be employed even in direct flow modelling, allowing for substitution of the computationally intensive fluid dynamics calculation. However, these promising approaches have not been extended to industrially-relevant geometry (3D bioreactor with stirrers) and process conditions (e.g., gas and liquid phase) and to include microbial response.
From industrial fermentor to CFD-guided downscaling: what have we learned?
WEBINAR: Towards smart biomanufacturing | advanced measurements, big data and digital twinsSummary
Industry 4.0 is about smart manufacturing, also and especially in biomanufacturing. Smart manufacturing relies on the ability to collect and interpret large data sets.Read more
Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization
The goal of the project is to evaluate and develop data-driven approaches, including AI methods, to speed-up computation by at least 100 times, towards a real-time fermentation digital twin, able to simulate microbial performance indicators (Titer – Rate - Yield (TRY)), under relevant industrial conditions in large-scale bioreactors of 50-500 m3. The proposed algorithm will be evaluated in a laboratory or simulated (literature-based) process with publishable data, and eventually translated to a real industrial application at DSM.