Modeling the effects of substrate fluctuations on the maintenance rate in bioreactors with a probabilistic approach

Analyzing of hydrodynamic stress and mass transfer requirements of a fermentation process carried out in a coaxial bioreactor: a scale-up study

Fluid hydrodynamic stress has a deterministic effect on the morphology of filamentous fungi. Although the coaxial mixer has been recognized as a suitable gas dispersion system for minimizing inhomogeneities within a bioreactor, its performance for achieving enhanced oxygen transfer while operating at a reduced shear environment has not been investigated yet, specifically upon scale-up. Therefore, the influence of the impeller type, aeration rate, and central impeller retrofitting on the efficacy of an abiotic coaxial system containing a shear-thinning fluid was examined. The aim was to assess the hydrodynamic parameters, including stress, mass transfer, bubble size, and gas hold-up, upon conducting a scale-up study. The investigation was conducted through dynamic gassing-in, tomography, and computational fluid dynamics combined with population balance methods. It was observed that the coaxial bioreactor performance was strongly influenced by the agitator type. In addition, coaxial bioreactors are scalable in terms of shear environment and oxygen transfer rate.

Modeling large-scale bioreactors with diffusion equations. Part II: Characterizing substrate, oxygen, temperature, carbon dioxide, and pH profiles

Large-scale fermentation processes involve complex dynamic interactions between mixing, reaction, mass transfer, and the suspended biomass. Empirical correlations or case-specific computational simulations are usually used to predict and estimate the performance of large-scale bioreactors based on data acquired at bench scale. In this two-part-study, one-dimensional axial diffusion equations were studied as a general and predictive model of large-scale bioreactors. This second part focused on typical fed-batch operations where substrate gradients are known to occur, and characterized the profiles of substrate, pH, oxygen, carbon dioxide, and temperature. The physically grounded steady-state axial diffusion equations with first- and zeroth-order kinetics yielded analytical solutions to the relevant variables. The results were compared with large-scale Escherichia coli and Saccharomyces cerevisiae experiments and simulations from the literature, and good agreement was found in substrate profiles. The analytical profiles obtained for dissolved oxygen, temperature, pH, andCO 2 ${\text{CO}}_{2}$ were also consistent with the available data. Distribution functions for the substrate were defined, and efficiency factors for biomass growth and oxygen uptake rate were derived. In conclusion, this study demonstrated that axial diffusion equations can be used to model the effects of mixing and reaction on the relevant variables of typical large-scale fed-batch fermentations.

The closure issue related to liquid-cell mass transfer and substrate uptake dynamics in biological systems

An original dynamic model for substrate uptake under transient conditions is established and used to simulate a variety of biological responses to external perturbations. The actual uptake and growth rates, treated as cell properties, are part of the model variables as well as the substrate concentration at the cell-liquid interface. Several regulatory loops inspired by the structure of the glycolytic chain are considered to establish a set of ordinary differential equations. The uptake rate evolves so as to reach an equilibrium between the cell demand and the environmental supply. This model does not contain any of the usual algebraic closure laws relating to the instantaneous uptake, growth rates, and the substrate concentration, nor does it enforce the continuity of mass fluxes at the liquid-cell interface. However, these relationships are found in the steady-state solution. Previously unexplained experimental observations are well reproduced by this model. Also, the model structure is suitable for further coupling with flux-based metabolic models and fluid-flow equations.