The order of micromixing and segregation effects on the biological growth process in a stirred-tank reactor

Atiqullah, Muhammad; Rahman, A.K.M.S.; Beg, S.A.; Hassan, M.M.

doi:10.1016/0300-9467(93)80020-o

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…Different methods have been introduced for the transfer of an optimized microbial process from laboratory scale to production scale. Tests with different pilot fermentors on a larger scale may provide data for the production process via regime analysis (Moser, 1988;Schilling et al, 1999;Sweere, 1987), or the scale-up behavior may be described by sophisticated mathematical models (Atiqullah et al, 1993;Mayr et al, 1994;Nagy et al, 1994Nagy et al, , 1995Oosterhuis and Kossen, 1984;Reuss and Bajpai, 1991;Zhu et al, 1996). Less cost-and time-consuming procedures include simulations of relevant process-limiting factors via scaledown using tailor-made model reactors (Schilling et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

A special reactor design for investigations of mixing time effects in a scaled-down industrialL-lysine fed-batch fermentation process

Schilling¹,

Pfefferle²,

Bachmann³

et al. 1999

Biotechnol. Bioeng.

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A specially designed model reactor based on a 42‐L laboratory fermentor was equipped with six stirrers (Rushton turbines) and five cylindrical disks. In this model reactor, the mixing time, Θ90, turned out to be 13 times longer compared with the 42‐L standard laboratory fermentor fitted with two Rushton turbines and four wall‐fixed longitudinal baffles. To prove the suitability of the model reactor for scaledown studies of mixing‐time‐dependent processes, parallel exponential fed‐batch cultivations were carried out with the leucine‐auxotrophic strain, Corynebacterium glutamicum DSM 5715, serving as a microbial test system. L‐Leucine, the process‐limiting substrate, was fed onto the liquid surface of both reactors. Cultivations were conducted using the same inoculum material and equal oxygen supply. The model reactor showed reduced sugar consumption (−14%), reduced ammonium consumption (−19%), and reduced biomass formation (−7%), which resulted in a decrease in L‐lysine formation (−12%). These findings were reflected in less specific enzyme activity, which was determined for citrate synthase (CS), phosphoenolpyruvate carboxylase (PEP‐C), and aspartate kinase (AK). The reduced specific activity of CS correlated with lower CO2 evolution (−36%) during cultivation. The model reactor represents a valuable tool to simulate the conditions of poor mixing and inhomogeneous substrate distribution in bioreactors of industrial scale. © 1999 John Wiley & Sons, Inc. Biotechnol Bioeng 64: 599–606, 1999.

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Section: Introductionmentioning

confidence: 99%

A special reactor design for investigations of mixing time effects in a scaled-down industrialL-lysine fed-batch fermentation process

Schilling¹,

Pfefferle²,

Bachmann³

et al. 1999

Biotechnol. Bioeng.

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“…In forward chaining, the objective is to determine all logical consequences of a particular assertion about a process. Because biological processes are complex, and in a bioreactor they interact with nonbiological features of the flow and mixing [9], in most applications both kinds of reasoning are required.…”

Section: Ai and Expert Systemsmentioning

confidence: 99%

Artificial intelligence as a tool for automatic state estimation and control of bioreactors

Patnaik

1997

Laboratory Robotics and Automation

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“…This phenomenon is so widely known that it is not necessary to consider it more carefully here. However, it should be pointed out that it is exactly this response on which mathematical treatments are based, in which, depending on the apparatus construction and operating regime, different proportions of the ideal mixing and complete segregation models are combined [8][9][10][11][12][13][14][15][16]. These groups of responses can have an effect as macromixing (concentration, temperature and pressure gradients throughout the reactor volume) as well as micromixing (oscillation of the same factors in local elementary volumes).…”

Section: Changes Of Biomass Growthmentioning

confidence: 99%

“…However, it is shown that the calculated sugar concentration gradients are higher than the experimentally observed ones, and more extensive studies are necessary for specifying the model. Assuming that cell growth is regarded as a result of many elementary reactions, a theoretical continuous process model has been developed [10], which allows the expected results (i.e. biomass growth and substrate conversion depending on the culture medium flow rate and mixing character in the reactor) to be predicted.…”

Section: Introductionmentioning

confidence: 99%

Influence of Micromixing on Microorganisms and Products

et al. 2001

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The influence of mixing intensity as well as physical and chemical parameters on the cells of different microorganisms and the biosynthesis process is examined in this paper. Some reactions of cells effecting mixing intensity are described, such as retarded biomass growth, changes in aggregation and mutual arrangement of cells, morphological changes of cells and decreasing of biological activity, caused by an increased intensity of turbulence (turbohypobiosis). Several methods for investigating the local energy in reactors are compared. It is concluded that conventional methods of hydrodynamic analysis do not always allow valid results for the optimization of the mixing regime to be obtained; practically any system requires its own optimization to achieve the maximum yield. To prevent possible adverse effects of micromixing, while simultaneously employing the positive effects, the developers of a new biotechnological process should perform a whole range of experiments (for process optimization) varying temperature, pH and other factors, as well as aeration and mixing intensity simultaneously with medium composition.

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The order of micromixing and segregation effects on the biological growth process in a stirred-tank reactor

Cited by 10 publications

References 15 publications

A special reactor design for investigations of mixing time effects in a scaled-down industrialL-lysine fed-batch fermentation process

A special reactor design for investigations of mixing time effects in a scaled-down industrialL-lysine fed-batch fermentation process

Artificial intelligence as a tool for automatic state estimation and control of bioreactors

Influence of Micromixing on Microorganisms and Products

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