Applying multiple approaches to deepen understanding of mixing and mass transfer in large-scale aerobic fermentations
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FERMENTATION, CELL CULTURE AND BIOENGINEERING - MINI REVIEW
Applying multiple approaches to deepen understanding of mixing and mass transfer in large‑scale aerobic fermentations Navraj Hanspal1 · Ning Chai2 · Billy Allen3 · Dale Brown4 Received: 14 July 2020 / Accepted: 27 August 2020 © Society for Industrial Microbiology and Biotechnology 2020
Abstract Different methods are used at Corteva® Agriscience to improve our understanding of mixing in large-scale mechanically agitated fermentors. These include (a) use of classical empirical correlations, (b) use of small-scale models, and (c) computational fluid dynamics (CFD). Each of these approaches has its own inherent strengths and limitations. Classic empirical or semi-empirical correlations can provide insights into mass transfer, blending, shear, and other important factors but are dependent on the geometry and condition used to develop the correlations. Laboratory-scale modelling can be very useful to study mixing and model the effect of heterogeneity on the culture, but success is highly dependent on the methodology applied. CFD provides an effective means to accelerate the exploration of alternative design strategies through physics-based computer simulations that may not be adequately described by existing knowledge or correlations. However, considerable time and effort is needed to build and validate these models. In this paper, we review the various approaches used at Corteva Agriscience to deepen our understanding of mixing in large-scale fermentation processes. Keywords Aerobic fermentation · Large eddy simulation · Mixing and mass transfer · Scale-up · Scale-down List of symbols a (m−1 ) Total bubble surface area per volume of fluid 𝜇 (Pa ⋅ s) Dynamic viscosity 𝜌 (kg∕m3 ) Fluid density t (s) Time 𝜑 (–) Fractional gas hold-up Ci (mol∕m3 ) Concentration of the ith species * Dale Brown [email protected] Navraj Hanspal [email protected] Ning Chai [email protected] Billy Allen [email protected] 1
Corteva ® Agriscience, 3100 James Savage Rd, Midland, MI 48642, USA
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Corteva Agriscience, 901 Loveridge Rd, Pittsburg, CA 94565, USA
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Bioprocess Mixing Solutions, LLC, 6228 Deerwood Ct, Greenwood, IN, USA
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Corteva Agriscience, 9330 Zionsville Rd, Indianapolis, IN, USA
Cp (J∕(kg ⋅ K)) Specific heat at constant pressure Di (m2 ∕s) Diffusivity of the ith species D (m) Impeller diameter m (kg) Mass 𝜏 (kg∕(m ⋅ s2 )) Stress tensor vs (m∕s) Superficial gas velocity d3∕2 (m) Sauter mean diameter—an average of particle size T (K) Temperature k (W∕(m ⋅ K)) Thermal conductivity K (–) Proportionality constant in power law model kL a (s−1 ) Volumetric oxygen transfer coefficient kL (m∕s) Mass transfer coefficient for oxygen u⃗ (m∕s) Velocity vector Pungassed (W) Un-gassed power NP (–) Impeller power number ∇ (–) Gradient operator that operates on a scalar ∇⋅ (–) Divergence operator that operates on a vector P (Pa) Pressure Ri (mol∕(m3 ⋅ s)) Source/sink term accounting for species creation or removal
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