Conference Agenda

The scale-up of processes with complex fluid flow presents significant challenges in process engineering, particularly in fermentation. As processes scale up, unfavourable fluid flow conditions lead to limitations in transport phenomena, resulting in spatial concentration gradients as well as mechanical stress gradients, negatively impacting the productivity and selectivity of the process. This leads to inefficiencies and suboptimal performance in large-scale operations. This challenge is particularly crucial in the biotechnology sector, where fermentation conditions can have seemingly unpredictable effects on product yield and quality.

Computational fluid dynamics (CFD) is a crucial tool for accurately modelling the hydrodynamic environment in bioreactors and understanding the effects of scale-up. This study utilizes Higher Order SVD (HOSVD)¹ the multidimensional extension of Singular Value Decomposition (SVD) to identify the dominant structures of fluid flow in CFD data of a fermentation processes. This method similarly to Proper Orthogonal Decomposition (POD), also based on SVD, can be used to model and identify the dominant structures of fluid flow in fermentation processes, with the added possibility of exploring additional parameter spaces.

We propose a novel application of HOSVD to investigate fluid flow patterns across different process scales, with scale being an explicit parametric component of the analysis. This approach enables us to identify the main coherent structures that emerge at various scales of fermentation and quantify the contribution of each structure to the total energy of the system. Our methodology includes CFD simulations of the fermentation process at multiple scales, utilizing operating parameters determined by traditional scale-up criteria. Through interpolation we bridged the different CFD meshes and constructed a snapshots tensor of the CFD results across all process scales, standardized on the same spatial reference grid.

As a first test case, we examined five scales of a reciprocally shaken flask bioreactor, ranging from 125 mL to 1 L, along with a hypothetical 10 L shake flask. Results indicated a common set of spatial modes across all scales, suggesting a degree of dynamic similarity with preserved main flow features. However, notable differences in the relative importance of these spatial modes were observed, particularly at the 10 L scale, where the main mode's contribution dropped to 12.5% of the total energy, compared to 21.5% for the 125 mL scale. This shift highlights how scaling affects flow dynamics, and ultimately the efficiency of the fermentation process, and the limitations of traditional scale-up methods.

These findings illustrate the impact of scale on fluid dynamics in a particular bioreactor flow, but also provide a proof of concept for this methodology which can give valuable insights for a more rational approach to bioreactor scale-up and optimization. The application of this method can easily be extended beyond fermentation scale-up, to the construction of parametric reduced models of CFD for other chemical and biochemical processes.

1. Lorente, L. S.; Vega, J. M.; Velazquez, A. Generation of aerodynamics databases using high-order singular value decomposition. Journal of Aircraft, 2008, 45.5: 1779-1788.

Miniaturised stirred bioreactors are crucial in high-throughput bioprocesses for their simplicity and cost-effectiveness. To accelerate process optimisation in chemical and bioprocess industries, models that integrate CFD-predicted flow fields with (bio)reaction kinetics are needed. However, conventional two-step coupling methods, which freeze flow fields after solving hydrodynamics and then address (bio)reaction transport, face numerical challenges in miniaturised systems due to unsteady radial flows, recirculation zones, and secondary vortices. These flow fluctuations prevent steady-state hydrodynamic convergence.

This study addresses these challenges by time-averaging the RANS solutions of the transitional SST model to achieve statistical hydrodynamic convergence. This method is particularly effective for internal flow problems at low to midrange Reynolds numbers (<10,000), typical of transitional regimes. Following this, photo-bioreaction transport models were solved using these converged fields, considering the bioreactor’s directional illumination and curvature.

Applied to a 0.7 L Schott bottle photobioreactor mechanically mixed by a magnetic stirrer (100-500 rpm), the model predictions were thoroughly validated against tracer dye experiments and Rhodopseudomonas palustris biomass growth data. It accurately predicted swirly vortex fields at 500 rpm with a 7% error margin and the biomass growth profiles align to literature datasets. However, parallel computing efficiency did not scale linearly from 16 to 32 processor cores (4G of RAM/core), making time-averaging computationally expensive for simulating scale-up bioreactors. Improved bioreactor mixing influenced cell light/dark cycles and enhanced biomass productivity, but stirring speeds above 300 rpm required increased light intensity (>100 W/m²) due to light limitation.

This model provides a framework for optimising stirring speeds and refining operational parameters, aiding in the scale-up and scale-down of bioprocesses.

The storage of chicory roots in commercial storage units requires maintaining optimal conditions to ensure product quality and minimize energy consumption. Traditional systems typically rely on a single measurement point to control storage room conditions, which may lead to suboptimal regulation and energy inefficiency. This study introduces a dynamic Computational Fluid Dynamics (CFD) model designed to move beyond single-point measurement by analyzing temperature and airflow gradients within the storage environment.

The model simulates the real-time thermal dynamics, accounting for spatial variations across the storage room. By studying these gradients, the approach aims to provide a more detailed understanding of how local conditions impact the overall system, allowing for more precise and energy-efficient control strategies. This gradient-based analysis represents a significant step forward in advancing storage technology, ensuring that environmental conditions are more uniformly optimized.

The results demonstrate the potential of using CFD to enhance current storage practices by providing insights into the thermal dynamics, which can inform improved control mechanisms. The long-term goal of this work is to transition from single-measurement systems to advanced, gradient-based control systems. Future work will explore the integration of machine learning algorithms further optimize storage room operations. This advancement has significant implications for both economic and environmental sustainability in post-harvest storage.