Unsupervised learning bioreactor regimes

• Applies unsupervised learning techniques to identify regimes in bioreactors, ensuring spatial continuity and optimized cluster numbers. • Validates the method with case studies on a 202 m 3 Rushton impeller bioreactor and a 840 m 3 airlift reactor, capturing complex fluid dynamics and cellular activities efficiently. • Employs performance metrics like inertia, silhouette score, or in-house defined homogeneity score, with Pareto front analysis to identify optimal cluster numbers, balancing information retention and compartment/cluster clarity. • Enables better bioreactor design and scale-down experiments, with potential extensions to integrate metabolic states and other variables for more efficient industrial applications. Efficient operation of bioreactors is pivotal for the success of biomanufacturing. Traditional Computational Fluid Dynamics (CFD) simulations, while detailed, often suffer from long computation times and complexity, making them impractical for real-time applications. This study introduces a novel multivariate unsupervised learning algorithm for clustering bioreactors into coherent regions based on CFD-generated and real-world data. The resulting clusters can be used to determine regimes inside a reactor, or can work as an initial step for compartment models. Our method employs a custom k-means clustering algorithm that not only ensures spatial continuity within clusters but also optimizes the number of compartments based on clustering scores. This optimization process focuses on defining compartments clearly while retaining maximum information from the continuous body, as demonstrated by a Pareto front analysis. The efficacy and adaptability of this approach were validated through two case studies: a 202 m³ Rushton impeller bioreactor and an 840 m³ airlift reactor. The results underscore the benefits of 3-D compartmentalization in capturing the complex dynamics of fluid motion and cellular activities. By establishing a robust method for scaling down experiments and enhancing bioreactor design, this study paves the way for more efficient industrial applications.