Numerical Simulation of Biomass Growth in OKTOP®9000 Reactor at Industrial Scale

Abstract

Computational fluid dynamics is a powerful method for scale-up of reactors although it is still challenging to fully embrace hydrodynamics and biological complexities. In this article, an aerobic fermentation of Pichia pastoris cells is modeled in a batch OKTOP®9000 reactor. The 800 m3 industrial scale reactor is equipped with a radial impeller, designed by Outotec Oy for gas dispersion in the draft tube reactor. Measured Np of the impeller is used in hydrodynamics validation. The resolved energy dissipation rate is compensated, and its influence on mass transfer is analyzed and discussed. Gas–liquid drag force is modified to simulate effects of liquid turbulence and bubble swarms. Resolved steady state multiphase hydrodynamics is used to simulate the fermentation process. Temporal evolution of species concentrations is compared to experimental data measured in a small copy of the reactor at lab scale (14 L). The effect of oxygenation on the P. pastoris cells cultivation is considered.

Auto-Aspirated DAF Sparger Study on Flow Hydrodynamics, Bubble Generation and Aeration Efficiency

“…The pressure drop measured using an in-line pressure gauge was used to find power consumption of the pump. The aeration feasibility of the system was compared with the available gas-liquid contactors of a similar scale that have been studied previously in our lab; namely, a flat-bottomed stirred tank reactor [22], air-lift reactor [23], and the draft-tube stirred tank reactor [24]. More details are available in the stipulated references.…”

Modeling multiphase fluid flow, mass transfer, and chemical reactions in bioreactors using large‐eddy simulation

“…They used CFD to evaluate the dynamic behavior of yeast cells and the effect of glucose gradients on the metabolic response with simplified glucose uptake kinetics. Although numerous other CFD basedreaction engineering studies have been reported in the literature [4][5][6], a majority of them have significantly relied on the utilization of conventional RANS and MRF type approaches coupled with reaction kinetics. Furthermore, a majority of conventional and commercial CFD solvers present limited compatibility with high-performance computing architectures such as Graphics Processing Units (GPUs) [7] thereby, severely restricting their applicability to bioreactor simulations involving transient computations with prolonged time-scales.…”