Experimental analysis of hydrodynamics in a radially agitated tank

Validation of computational fluid dynamics of shake flask experiments at moderate viscosity by liquid distributions and volumetric power inputs

Computational fluid dynamics (CFD) has recently become a pivotal tool in the design and scale-up of bioprocesses. While CFD has been extensively utilized for stirred tank reactors (STRs), there exists a relatively limited body of literature focusing on CFD applications for shake flasks, almost exclusively concentrated on fluids at waterlike viscosity. The importance of CFD model validation cannot be overstated. While techniques to elucidate the internal flow field are necessary for model validation in STRs, the liquid distribution, caused by the orbital shaking motion of shake flasks, can be exploited for model validation. An OpenFOAM CFD model for shake flasks has been established. Calculated liquid distributions were compared to suitable, previously published experimental data. Across a broad range of shaking conditions, at waterlike and moderate viscosity (16.7 mPa∙s), the CFD model's liquid distributions align excellently with the experimental data, in terms of overall shape and position of the liquid relative to the direction of the centrifugal force. Additionally, the CFD model was used to calculate the volumetric power input, based on the energy dissipation. Depending on the shaking conditions, the computed volumetric power inputs range from 0.1 to 7 kW/m3 and differed on average by 0.01 kW/m3 from measured literature data.

Applying multiple approaches to deepen understanding of mixing and mass transfer in large-scale aerobic fermentations

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.

The Process of the Intensification of Coal Fly Ash Flotation Using a Stirred Tank

Pulp preconditioning using a stirred tank as a pretreatment process is vital to the flotation system, which can be used to improve the flotation efficiency of mineral particles. The kinetic energy that is dissipated in the stirred tank could strengthen the interaction process between mineral particles and flotation reagents to improve the flotation efficiency in the presence of the preconditioning. In this paper, the effect of the conditioning speed on the coal fly ash flotation was investigated using numerical simulations and conditioning-flotation tests. The large eddy simulation coupled with the Smagorinsky-Lilly subgrid model was employed to simulate the turbulence flow field in the stirred tank, which was equipped with a six blade Rushton turbine. The impeller rotation was modelled using the sliding mesh. The simulation results showed that the large eddy simulation (LES) well matched the previous experimental data. The turbulence characteristics, such as the mean velocity, turbulent kinetic energy, power consumption and instantaneous structures of trailing vortices were analysed in detail. The turbulent length scale (η) decreased as the rotation speed increased, and the minimum value of η was almost unchanged when the rotation speed was more than 1200 rpm. The conditioning-flotation tests of coal fly ash were conducted using different conditioning speeds. The results showed that the removal of unburned carbon was greatly improved due to the strengthened turbulence in the stirred tank, and the optimal results were obtained with an LOI of 3.32%, a yield of 78.69% and an RUC of 80.89% when the conditioning speed was 1200 rpm.

Characterization of hydromechanical stress in aerated stirred tanks up to 40 m(3) scale by measurement of maximum stable drop size

BACKGROUND

Turbulence intensity, or hydromechanical stress, is a parameter that influences a broad range of processes in the fields of chemical engineering and biotechnology. Fermentation processes are often characterized by high agitation and aeration intensity resulting in high gas void fractions of up to 20% in large scale reactors. Very little experimental data on hydromechanical stress for such operating conditions exists because of the problems associated with measuring hydromechanical stress under aeration and intense agitation.

RESULTS

An indirect method to quantify hydromechanical stress for aerated operating conditions by the measurement of maximum stable drop size in a break-up controlled dispersion was applied to characterize hydromechanical stress in reactor scales of 50 L, 3 m(3) and 40 m(3) volume with a broad range of operating conditions and impeller geometries (Rushton turbines). Results for impellers within each scale for the ratio of maximum to specific energy dissipation rate ϕ based on measured values of maximum stable drop size for aerated operating conditions are qualitatively in agreement with results from literature correlations for unaerated operating conditions. Comparison of data in the different scales shows that there is a scale effect that results in higher values for ϕ in larger reactors. This behavior is not covered by the classic theory of turbulent drop dispersion but is in good agreement with the theory of turbulence intermittency. The data for all impeller configurations and all aeration rates for the three scales can be correlated within ±20% when calculated values for ϕ based on the measured values for dmax are used to calculate the maximum local energy dissipation rate. A correlation of the data for all scales and all impeller configurations in the form ϕ = 2.3∙(ϕunaerated)(0.34)∙(DR)(0.543) is suggested that successfully models the influence of scale and impeller geometry on ϕ for aerated operating conditions.

CONCLUSIONS

The results show that besides the impeller geometry, also aeration and scale strongly influence hydromechanical stress. Incorporating these effects is beneficial for a successful scale up or scale down of this parameter. This can be done by applying the suggested correlation or by measuring hydromechanical stress with the experimental method used in this study.