Abstract
Due to the heterogeneous nature of large-scale fermentation processes they cannot be modelled as ideally mixed reactors, and therefore flow models are necessary to accurately represent the processes. Computational fluid dynamics (CFD) is used more and more to derive flow fields for the modelling of bioprocesses, but the computational demands associated with simulation of multiphase systems with biokinetics still limits their wide applicability. Hence, a demand for simpler flow models persists. In this study, an approach to develop data-based flow models in the form of compartment models is presented, which utilizes axial-flow rates obtained from flow-following sensor devices in combination with a proposed procedure for automatic zoning of volume. The approach requires little experimental effort and eliminates the necessity for computational determination of inter-compartmental flow rates and manual zoning. The concept has been demonstrated in a 580 L stirred vessel, of which models have been developed for two types of impellers with varying agitation intensities. The sensor device measurements were corroborated by CFD simulations, and the performance of the developed compartment models was evaluated by comparing predicted mixing times with experimentally determined mixing times. The data-based compartment models predicted the mixing times for all examined conditions with relative errors in the range of 3–27%. The deviations were ascribed to limitations in the flow-following behavior of the sensor devices, whose sizes were relatively large compared to the examined system. The approach provides a versatile and automated flow modelling platform which can be applied to large-scale bioreactors.
Highlights
In the biotechnology industry models serve as an important tool to improve process efficiency and to provide a quantitative basis for process optimization, design, and control [1].The environment of fermentation broths in industrial bioreactors is heterogeneous [2], and models of microbial kinetics must be accompanied by liquid flow models to provide an accurate representation of the system [3]
The validity of the fundamental assumption for determining the flow rates between the compartments is corroborated by results from Computational fluid dynamics (CFD) simulations, and compartment model predictions of mixing times are compared against experimental data
The results obtained by the sensor devices have beencompared comparedagainst againstCFD
Summary
In the biotechnology industry models serve as an important tool to improve process efficiency and to provide a quantitative basis for process optimization, design, and control [1]. The environment of fermentation broths in industrial bioreactors is heterogeneous [2], and models of microbial kinetics must be accompanied by liquid flow models to provide an accurate representation of the system [3] These flow models are often developed based on the compartment model approach or with the use of computational fluid dynamics [4]. The traditional approach to compartment models is to support these decisions by knowledge from gross flow patterns, which have been extensively studied [11], and calculate the flow rates between the compartments with empirical correlations and experimentally determined global quantities, such as dimensionless flow numbers Models of this type have been developed for both stirred tank bioreactors and for bubble-column bioreactors [4,8,12,13] and have been shown to provide reasonable predictions for mixing in stirred bioreactors with and without aeration [4,8]. The validity of the fundamental assumption for determining the flow rates between the compartments is corroborated by results from CFD simulations, and compartment model predictions of mixing times are compared against experimental data
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