Hydrodynamics and Mass Transfer Analysis in BioFlow® Bioreactor Systems

Kordas, Marian; Konopacki, Maciej; Grygorcewicz, Bartłomiej; Augustyniak, Adrian; Musik, Daniel; Wójcik, Krzysztof; Jędrzejczak-Silicka, Magdalena; Rakoczy, Rafał

doi:10.3390/pr8101311

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…The agitator was powered by an electrical engine controlled by a bioreactor central unit that could be decoupled to connect the impeller shaft (7) with other equipment. On the agitator shaft, arranged on a central axis, two Rushton-type turbines (8) with six rectangular blades each (one near the vessel bottom, just above the gas distributor, and the second one at the middle of the vessel height) were mounted. The bioreactor system was also equipped with control probes-pH and dissolved oxygen (DO)-and peristaltic pumps that could add some substances to the bioreactor vessel through a designated injection port (9) placed in the top cover.…”

Section: Methodsmentioning

confidence: 99%

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The Influence of Hydrodynamic Conditions in a Laboratory-Scale Bioreactor on Pseudomonas aeruginosa Metabolite Production

et al. 2022

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Hydrodynamic conditions are critical in bioprocessing because they influence oxygen availability for cultured cells. Processes in typical laboratory bioreactors need optimization of these conditions using mixing and aeration control to obtain high production of the desired bioproduct. It could be done by experiments supported by computational fluid dynamics (CFD) modeling. In this work, we characterized parameters such as mixing time, power consumption and mass transfer in a 2 L bioreactor. Based on the obtained results, we chose a set of nine process parameters to test the hydrodynamic impact on a selected bioprocess (mixing in the range of 0–160 rpm and aeration in the range of 0–250 ccm). Therefore, we conducted experiments with P. aeruginosa culture and assessed how various hydrodynamic conditions influenced biomass, pyocyanin and rhamnolipid production. We found that a relatively high mass transfer of oxygen (kLa = 0.0013 s−1) connected with intensive mixing (160 rpm) leads to the highest output of pyocyanin production. In contrast, rhamnolipid production reached maximal efficiency under moderate oxygen mass transfer (kLa = 0.0005 s−1) and less intense mixing (in the range of 0–60 rpm). The results indicate that manipulating hydrodynamics inside the bioreactor allows control of the process and may lead to a change in the metabolites produced by bacterial cells.

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Section: Methodsmentioning

confidence: 99%

“…Therefore, increasing k L a provides higher oxygen availability to the growing biomass. It can be achieved by altering bioreactor construction and operating parameters [ 8 ]. Parameters such as the geometry of the bioreactor, impeller/turbine type, aeration, and stirring rate also influence k L a [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Hydrodynamic Conditions in a Laboratory-Scale Bioreactor on Pseudomonas aeruginosa Metabolite Production

et al. 2022

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“…A conventional approach for estimating the occurrence of gradients is the comparison of critical timescales τ for substrate supply τ s supply versus substrate consumption τ s cons . Whereas the first may be approximated by the mixing time τ mix or circulation time τ circulation , the latter resembles the quotient of average substrate concentration divided by volumetric substrate consumption rates (26). Regarding dO 2 , scenarios 1 and 2 anticipate the occurrence of gradients because τ o cons,1 and τ o cons,2 showing ~28 s and ~30 s are smaller than τ circulation ≈ 47 s (τ mix = 186 s).…”

Section: Oxygen Gradientmentioning

confidence: 99%

“…It is worth mentioning that shear gradients may have a significant effect on shear sensitive hosts. Simulating shear fields and calculating the frequencies of cellular exposure may be a highly valuable tool for future applications to investigate this particular large-scale impact [26].…”

Section: Introductionmentioning

confidence: 99%

Predicting By-Product Gradients of Baker’s Yeast Production at Industrial Scale: A Practical Simulation Approach

2020

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Scaling up bioprocesses is one of the most crucial steps in the commercialization of bioproducts. While it is known that concentration and shear rate gradients occur at larger scales, it is often too risky, if feasible at all, to conduct validation experiments at such scales. Using computational fluid dynamics equipped with mechanistic biochemical engineering knowledge of the process, it is possible to simulate such gradients. In this work, concentration profiles for the by-products of baker’s yeast production are investigated. By applying a mechanistic black-box model, concentration heterogeneities for oxygen, glucose, ethanol, and carbon dioxide are evaluated. The results suggest that, although at low concentrations, ethanol is consumed in more than 90% of the tank volume, which prevents cell starvation, even when glucose is virtually depleted. Moreover, long exposure to high dissolved carbon dioxide levels is predicted. Two biomass concentrations, i.e., 10 and 25 g/L, are considered where, in the former, ethanol production is solely because of overflow metabolism while, in the latter, 10% of the ethanol formation is due to dissolved oxygen limitation. This method facilitates the prediction of the living conditions of the microorganism and its utilization to address the limitations via change of strain or bioreactor design or operation conditions. The outcome can also be of value to design a representative scale-down reactor to facilitate strain studies.

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“…In bioprocesses, studies on modeling, simulation, and optimization are still limited, despite being relevant for the evaluation of the viability and understanding of these systems [12,13]. Recent scaling studies assisted by computational fluid dynamics and powerful stimulus-response metabolic models have been developed [14]. These allow for better prediction and evaluation of processes and faster scaling-up with minimal losses [3].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Model for Scaling up Bioprocesses Using Experiment Design Combined with Buckingham Pi Theorem

et al. 2021

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Scaling up bioprocesses from the experimental to the pilot or industrial scale involves heuristics and scale relationships that are far from the specific phenomena and are usually not connected to the experimental data. In complex systems, the scaling-up methodology must connect the experimental data with the tools of engineering design. In this work, a two-stage gold bioleaching process was used as a case study to develop a mathematical model of bioprocess scaling that combines the design of experiments with dimensional analysis using the Buckingham Pi theorem to formulate a predictive model that allows scaling up bioprocesses. It was found that the C/N, C/K, and T/C ratios are dimensionless factors that can explain the behavior of a system. Using the Pearson Product–Moment bivariate analysis, it was found that the dimensionless factors C/N and C/K were correlated with the leaching potential of the fermented broth at 1060 cm−1. With these results, a non-linear logarithmic model based on dimensionless parameters was proposed to explain the behavior of the system with a correlation coefficient of R2 = 0.9889, showing that the optimal conditions to produce fermented broth comprised a C/N ratio close to 50 and a C/K ratio close to 800, which allows predicting the scaling of the bioprocess.

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Hydrodynamics and Mass Transfer Analysis in BioFlow® Bioreactor Systems

Cited by 6 publications

References 25 publications

The Influence of Hydrodynamic Conditions in a Laboratory-Scale Bioreactor on Pseudomonas aeruginosa Metabolite Production

The Influence of Hydrodynamic Conditions in a Laboratory-Scale Bioreactor on Pseudomonas aeruginosa Metabolite Production

Predicting By-Product Gradients of Baker’s Yeast Production at Industrial Scale: A Practical Simulation Approach

Mathematical Model for Scaling up Bioprocesses Using Experiment Design Combined with Buckingham Pi Theorem

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