CFD modelling of two-phase stirred bioreaction systems by segregated solution of the Euler–Euler model

An advanced study of a bioreactor system involving a Navier–Stokes based model has been accomplished. The model allows a more realistic impeller induced flow image to be combined with the Monod bioreaction kinetics reported previously. The time-course of gluconic acid production by Aspergillus niger strain is simulated at kinetic conditions proposed in the literature. The simulation is based on (1) a stepwise solution strategy resolving first the fluid flow field, further imposing oxygen mass transfer and bioreaction with subsequent analysis of flow interactions, and (2) a segregated solution of the model replacing the multiple iterations per grid cell with single iterations. The numerical results are compared with experimental data for the bioreaction dynamics and show satisfactory agreement. The model is used for assessment of the viscosity effect upon the bioreactor performance. A 10-fold viscosity rise results in 2-fold decrease of KLa and 25% decrease of the specific gluconic acid production rate. The model allows better understanding of the mechanism of the important bioprocess

Computational Fluid Dynamics Analysis of Two-Phase Chemical and Biochemical Reactors

In this work, the numerical analysis of turbulent two-phase processes in stirred tanks and bioreactors is performed with a computational fluid dynamics (CFD) approach. The modelling of the turbulent two-phase phenomena is achieved in the context of the Reynolds Averaged Navier-Stokes (RANS) equations and the Two-Fluid Model (TFM). Different modelling strategies are studied, tested and developed to improve the prediction of mixing phenomena, interphase interactions and bio-chemical reactions in chemical and process equipment. The systems studied in this work are a dilute immiscible liquid-liquid dispersion and dense solid-liquid suspensions, both in stirred tanks of standard geometry, a gas-liquid system consisting of a dual impeller vortex ingesting fermenter for the production of biohydrogen, analyzed in two different configurations of the supports for the attached growth of biomass, and two different bioreactors, of different scale and configuration, subject to substrate concentration segregation. Purposely collected experimental data and data from the literature were extensively used to validate the numerical results and either confirmed the goodness of the models and the modelling techniques, helped the definition of the limits and the uncertainties of the model formulations or guided the development of new models. In all cases, particular attention was devoted to the precision of the numerical solution, and to the validation with experimental data to quantify the appropriateness of the models and the accuracy of the CFD predictions

Simulation of a Bioreactor with an Improved Fermentation Kinetics – Fluid Flow Model

Ethanolic fermentation experiments were carried out using a stirred tank equipped with a Rushton turbine. The data were used to estimate kinetic parameters based on a newly developed kinetics model originated from Herbert’s microbial kinetics model. This newly developed model took into account the effects of aeration rate (AR) and stirrer speed (SS). Experiment data i.e. glucose, ethanol and biomass concentrations obtained from different experiment sets were used for kinetics prediction. Assuming a perfectly-stirred condition, the kinetic parameters were initially estimated through solving Herbert’s model equations. These estimated kinetic parameters were then incorporated in a Computational Fluid Dynamics (CFD) model but the simulation results did not agree well with the experiment findings. Based on the proposed CFD model, the kinetic parameters were corrected. The correction factors were expressed as functions of AR and SS. This analysis highlighted the need to estimate kinetic parameters based on CFD simulation because it is able to account for the spatial variation in a reactor. A sensitivity analysis of the kinetic parameters using the coupled CFD-fermentation kinetic model was carried out to further understand the influence of each set of kinetic parameters on the model prediction. It was found that the sensitivities of the kinetic parameters varied with the concentrations of glucose, ethanol and biomass

An assessment of methods of moments for the simulation of population dynamics in large-scale bioreactors

A predictive modelling for the simulation of bioreactors must account for both the biological and hydrodynamics complexities. Population balance models (PBM) are the best approach to conjointly describe these complexities, by accounting for the adaptation of inner metabolism for microorganisms that travel in a large-scale heterogeneous bioreactor. While being accurate for solving the PBM, the Class and Monte-Carlo methods are expensive in terms of calculation and memory use. Here, we apply Methods of Moments to solve a population balance equation describing the dynamic adaptation of a biological population to its environment. The use of quadrature methods (Maximum Entropy, QMOM or EQMOM) is required for a good integration of the metabolic behavior over the population. We then compare the accuracy provided by these methods against the class method which serves as a reference. We found that the use of 5 moments to describe a distribution of growth-rate over the population gives satisfactory accuracy against a simulation with a hundred classes. Thus, all methods of moments allow a significant decrease of memory usage in simulations. In terms of stability, QMOM and EQMOM performed far better than the Maximum Entropy method. The much lower memory impact of the methods of moments offers promising perspectives for the coupling of biological models with a fine hydrodynamics depiction

Modelling of industrial-scale bioreactors using the particle lifeline approach

Data Availability: Data have been made available in a repository, details are in the data availability statement.Supplementary material is available online at https://www.sciencedirect.com/science/article/pii/S1369703X23001845?via%3Dihub#sec0030 .Copyright © 2023 The Author(s). A key factor in improving the performance of large-scale bioreactors is understanding the conditions experienced by the cells inside the reactor. This can be challenging due to the practical difficulties involved, hence there is increasing use of simulation to quantify the environmental conditions found in large-scale bioreactors. In this work we have used the particle lifeline approach to quantify the effect of the reactor design on the conditions experienced by two very commonly used industrial organisms (Escherichia coli and Saccharomyces cerevisiae). It was found that the cells in the stirred tank reactor tended to experience longer fluctuations of both starvation and overflow metabolism when compared with those in the bubble column, this behaviour being caused by differences in mixing between the two reactor designs. It was found that a significant (60%) fraction of the population in the stirred tank reactors experienced starvation conditions for a large fraction (>70%) of the time, with exposure to such conditions being likely to affect the cellular metabolism. Results from this work provide a detailed insight into the conditions experienced inside industrial-scale bioreactors operated at realistic conditions. Such data can be leveraged to optimise large-scale reactor designs as well as for the development of scale-down systems.Technical University of Denmark and Novozymes A/S