33 research outputs found
Development of dynamic compartment models for industrial aerobic fed-batch fermentation processes
Inhomogeneities in key cultivation variables (e.g., substrate and oxygen concentrations) have been shown to affect key process metrics in large-scale bioreactors. Being able to understand these gradients is hence of key interest from both an industrial and academic perspective. One of the main shortcomings of current modelling approaches is that volume change is not considered. Volume increase is a key feature of fed-batch fermentation processes. Existing models are restricted to simulating snapshots (hundreds of seconds) of industrial processes, which can last several weeks. This study presents a novel methodology that overcomes this limitation by constructing dynamic compartment models for the simulation of fed-batch fermentation processes. This strategy is applied to an industrial aerobic fed-batch fermentation process (40–90 m3) with Saccharomyces cerevisiae. First, it has been validated numerically that the compartmentalization strategy used captures the mixing performance and fluid dynamics. This was done by comparing the mixing times and the local concentration profiles of snapshot fermentation process simulations calculated with both CFD and compartment models. Subsequently, simulations of the entire process have been performed using the dynamic compartment model with kinetics. The simulation allows the spatio-temporal characterization of all process variables (e.g., glucose and DO concentrations), as well as the quantification of the metabolic regimes that the cells experience over time. This strategy enables the rapid characterization and assessment of the impact of gradients on process performance in industrial (aerobic) fed-batch fermentation processes and can be readily generalized to any type of bioreactor and microorganism.Technical University of Denmark; Novozymes A/S
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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
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Computational fluid dynamics modelling of hydrodynamics, mixing and oxygen transfer in industrial bioreactors with Newtonian broths
© 2021 The Author(s). Industrial aerobic fermentation processes are performed in large-scale bioreactors (> 20 m3). Understanding the local values of the velocity field, the eddy dissipation rate and the gas volume fraction is of interest, as these parameters affect mixing and mass transfer and hence fermentation process performance and profitability. Despite the industrial and academic importance of these flow variables in large-scale bioreactors, there is scarce literature addressing it. This article provides a numerical comparison using Computational Fluid Dynamics (CFD) of different industrially relevant reactor types (bubble columns and stirred tanks with different impeller configurations) operated within a realistic range of industrial conditions (40 – 90 m3, 0.3 – 6 kW m-3, 0.5 – 1 vvm). Local flow variables and mixing times are evaluated for all cases studied. The collection of these data allows the prediction of the typical values of mixing time (10 – 206 s) and oxygen transfer rate (1 – 8 kg m-3 h-1) in industrial bioreactors, and serves as basis for the comparison between different reactor types.Technical University of Denmark; Novozymes A/S