Novel dynamic model for aerated shaking bioreactors

Tabrizi, Hamed Osouli; Amoabediny, Ghassem; Moshiri, Behzad; Abbas, Mahdi Pesaran Haji; Pouran, Behdad; Imenipour, Emad; Rashedi, Hamid; Büchs, Jochen

doi:10.1002/bab.18

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…Oxygen transfer capacity between the gas and liquid phases is an essential factor for judging bioreactor performance . It is described by the volumetric mass transfer coefficient ( k L a ) and serves as the limiting step for cell growth . The k L a consists of two parts: the specific gas–liquid interface area ( a ) and the mass transfer coefficient ( k L ) .…”

Section: Resultsmentioning

confidence: 99%

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CFD studies on hydrodynamic characteristics of shaking bioreactors with wide conical bottom

Wang

Gao

et al. 2017

J of Chemical Tech & Biotech

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BACKGROUND Disposable bioreactors based on orbital shaking technology have been employed extensively for mammalian cell culture, which proved to be less expensive and more flexible compared with mechanical stirred‐tank bioreactors. However, it is difficult to evaluate flow parameters quantitatively since the flow field in bioreactors is complicated. RESULTS The flow characteristics of shaking bioreactors with a wide conical bottom under different shaking frequencies and filling volumes were simulated by CFD method. The validity of the simulation model was investigated by comparing simulated free‐surface shapes and liquid levels with experimental results, and the CFD simulation results were shown to be in good agreement with experiment data. CONCLUSION The results show that: turbulence parameters (k and ϵ) increase with shaking frequency, while they decrease with filling volume proportionally; shaking frequency has no significant effect on specific gas–liquid interface area (a) but a positive effect on mass transfer coefficient (kL); the volumetric mass transfer coefficient (kLa) decreases with filling volume because of the combined effect of a and kL; the average shear strain rate (SSR) increases significantly with frequency, while it decreases with filling volume. Results also show that the SSR of shaking bioreactors is mainly distributed in low range which is acceptable for cell cultivation. © 2017 Society of Chemical Industry

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Section: Resultsmentioning

confidence: 99%

“…20 It is described by the volumetric mass transfer coefficient (k L a) and serves as the limiting step for cell growth. [21][22][23] The k L a consists of two parts: the specific gas-liquid interface area (a) and the mass transfer coefficient (k L ). 23,24 In shaking bioreactors, a can be determined as:…”

Section: Oxygen Transfer Capacitymentioning

confidence: 99%

CFD studies on hydrodynamic characteristics of shaking bioreactors with wide conical bottom

Wang

Gao

et al. 2017

J of Chemical Tech & Biotech

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“…In bioprocess engineering, models are used for process design, monitoring, control and optimization [61][62][63][64]. In bioprocess engineering, models are used for process design, monitoring, control and optimization [61][62][63][64].…”

Section: Consistent Modeling For Efficient Product Developmentmentioning

confidence: 99%

“…The new regulations of the PAT initiative of the Food and Drug Administration and the EMA underline the importance that process monitoring and control gain in the pharmaceutical and generally in the biotechnological industry (http://www.fda.gov/ downloads/Drugs-/GuidanceComplianceRegulatoryInformat ion/Guidances/UCM070336.pdf). In bioprocess engineering, models are used for process design, monitoring, control and optimization [61][62][63][64]. Bioprocess complexity and increasing restrictions have driven design and control to demand accurate and robust models [65], triggering a rapid development over the last years [66].…”

Section: Consistent Modeling For Efficient Product Developmentmentioning

confidence: 99%

Consistent development of bioprocesses from microliter cultures to the industrial scale

Neubauer

Cruz

Glauche

et al. 2013

Engineering in Life Sciences

103

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Bioprocess development today is slow and expensive compared to chemical process development. A drastic paradigm shift is necessary and possible by the consistent application of engineering strategies that are typically used in the process development phase already in the early product development. Aside from providing a consistent pathway, strategies such as statistical‐based design of experiments, fed‐batch, minibioreactors, new on‐line sensors, process modeling, and control tools in combination with automation of manual steps offer a higher success rate and the opportunity to find the optimum parameters and operation point. This also directly benefits the early phases of biomolecular screening and initial production of small amounts of the target molecule. The paper reviews the bioprocess developmental phases from a business perspective and the available systems and technologies.

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“… Figure 7 illustrates the comparison between the model and experimental results for a model microorganism, which suggest that a minor discrepancy between their model and the results exist [139]. …”

Section: Mathematical Modeling Of Engineering Parametersmentioning

confidence: 99%

Engineering Parameters in Bioreactor’s Design: A Critical Aspect in Tissue Engineering

Salehi

Amoabediny

Pouran

et al. 2013

BioMed Research International

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Bioreactors are important inevitable part of any tissue engineering (TE) strategy as they aid the construction of three-dimensional functional tissues. Since the ultimate aim of a bioreactor is to create a biological product, the engineering parameters, for example, internal and external mass transfer, fluid velocity, shear stress, electrical current distribution, and so forth, are worth to be thoroughly investigated. The effects of such engineering parameters on biological cultures have been addressed in only a few preceding studies. Furthermore, it would be highly inefficient to determine the optimal engineering parameters by trial and error method. A solution is provided by emerging modeling and computational tools and by analyzing oxygen, carbon dioxide, and nutrient and metabolism waste material transports, which can simulate and predict the experimental results. Discovering the optimal engineering parameters is crucial not only to reduce the cost and time of experiments, but also to enhance efficacy and functionality of the tissue construct. This review intends to provide an inclusive package of the engineering parameters together with their calculation procedure in addition to the modeling techniques in TE bioreactors.

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Novel dynamic model for aerated shaking bioreactors

Cited by 7 publications

References 35 publications

CFD studies on hydrodynamic characteristics of shaking bioreactors with wide conical bottom

CFD studies on hydrodynamic characteristics of shaking bioreactors with wide conical bottom

Consistent development of bioprocesses from microliter cultures to the industrial scale

Engineering Parameters in Bioreactor’s Design: A Critical Aspect in Tissue Engineering

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