Elaboration of a biased random walk model with a high spatial resolution for the simulation of the microorganisms exposure to gradient stress in scale-down reactors

The success of a bioprocess scaling-up operation is dependent of the mixing operation. Indeed, mixing is important in order to provide favourable extracellular conditions for microorganisms growth or metabolites production. However, the increase of the volume of the bioreactor leads to mixing efficiency drop and thus to fluctuations at the level of the extracellular conditions. In the present work, this phenomenon has been studied by a biased random walk model (BRWM) able to reproduce the displacement of microorganisms inside the non-mixed part of a scale-down reactor (SDR). Considering its stochastic nature and the possibility to increase to a great extent its spatial resolution, the model is able to simulate the great diversity of circulation paths taken by the individual cells of a microbial population crossing the reactor. The model has been validated and is able to reproduce experimental residence time distributions (RTD). In a second time, the simulated circulation paths have been superimposed to dye mixing experimental data in order to obtain the concentration profile experienced by the microorganisms. This method of superimposition has been previously used to characterize the dissolved oxygen and substrate gradients experienced by a single microbial cell in large-scale reactor [D. Vlaev, R. Mann, V. Lossev, S.D. Vlaev, J. Zahradnik, P. Seichter, Macro-mixing and Streptomyces fradiae: modelling oxygen and nutrient segregation in an industrial bioreactor, Trans. IChemE 78 (2000) 354–362]. The chemical engineering and physiological constraints have been discussed in order to fix an optimal spatial resolution for the model. Spectral analysis has highlighted two supplemental periodic components at the level of the simulated concentration profile when the spatial resolution of the model increases. However, it has been shown that there are a lot of biological implications that need to be considered in a future work.