Filtration Properties of Mycelial Microbial Broths

Oolman, Timothy; Liu, Tuan Chi

doi:10.1021/bp00012a008

Cited by 17 publications

(14 citation statements)

References 10 publications

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“…In related studies, Oolman and Liu [7] and Liu and Yu [39] have shown that the compressibility index (n) increased when the cell morphology changed from predominantly filaments to predominantly pellets. Interestingly, they found a good correlation between cake compressibility and Kozeny constant, whereby increasing Kozeny constant was associated with increasing cake compressibility.…”

Section: Relating Cake Compressibility To Cell Morphologymentioning

confidence: 96%

“…An alternative approach was used by Shimuzu et al [6] who measured the dry cell concentration and assumed that the moisture content of a typical cell is 70 wt%. Oolman and Liu [7] devised a technique in which interstitial water of a wet filter cake was replaced by ethanol which was subsequently evaporated to leave pure biomass that was then weighed to yield the wet cell mass. Okamato et al [8] measured the wet cell concentration by filtering a known volume of suspension and leaving the deposited cake exposed to pressurised nitrogen overnight.…”

Section: A Note On the Measurement Of Wet Cell Concentrationsmentioning

confidence: 99%

“…Oolman and Liu [7] performed filtration in a custom-made glass apparatus in which the cake thicknesses of three different actinomycetes were measured and the cake porosity determined. They found that the Kozeny constant (at 0.26 × 10 5 Pa (0.26 bar)) varied between 2.2 and 9.0 depending on the organism and the stage of the fermentation.…”

Section: Data Interpretation Using the Carman-kozeny Equationmentioning

confidence: 99%

See 2 more Smart Citations

A review of factors affecting filter cake properties in dead-end microfiltration of microbial suspensions

Foley

2006

Journal of Membrane Science

129

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Section: Relating Cake Compressibility To Cell Morphologymentioning

confidence: 96%

Section: A Note On the Measurement Of Wet Cell Concentrationsmentioning

confidence: 99%

Section: Data Interpretation Using the Carman-kozeny Equationmentioning

confidence: 99%

See 1 more Smart Citation

A review of factors affecting filter cake properties in dead-end microfiltration of microbial suspensions

Foley

2006

Journal of Membrane Science

129

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“…The equations used to correlate specific resistance data are varied but most authors use the classic power-law equation [3], [4] and [5] or, in some cases (especially for microbial suspensions), a linear relationship between specific resistance and pressure is employed [6], [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

Dead-end filtration of yeast suspensions: Correlating specific resistance and flux data using artificial neural networks

Mhurchú

Foley

2006

Journal of Membrane Science

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The specific cake resistance in dead-end filtration is a complex function of suspension properties and operating conditions. In this study, the specific resistance of resuspended dried bakers yeast suspensions was measured in a series of 150 experiments covering a range of pressures, cell concentrations, pHs, ionic strengths and membrane resistances. The specific resistance was found to increase linearly with pressure and exhibited a complex dependence on pH and ionic strength. The specific resistance data were correlated using an artificial neural network containing a single hidden layer with nine neurons employing the sigmoidal activation function. The network was trained with 104 training points, 13 validation points and 33 test points. Excellent agreement was obtained between the neural network and the test data with average errors of less than 10%. In addition, a network was trained for prediction of the filtrate flux directly from the system inputs and this approach is easily extended to crossflow filtration by adding inputs such as the crossflow velocity and channel height. An attempt was made to interpret the network weights for both the specific resistance and flux networks. The effective contribution of each input to the system output was computed in each case and showed trends that were as expected. Although network weights, and consequently the computed effect of each parameter, is different each time a network is changed (depending on the initial weights used in the training process), the variation was low enough for information contained in the network to be interpreted in a meaningful way.Keywords: Dead-end filtration; Specific cake resistance; Artificial neural network; Bakers yeast

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“…Previous workers have examined such process interactions. Changes in the morphology of P. chrysogenum that occur during fermentation have been shown to result in very different filtration rates being achieved in subsequent downstream processing [27] while the conditions of bead milling S. cerevisiae impact the efficiency of subsequent depth filtration [24]. Here a clear compromise between the extent of milling to achieve good product release and the consequent blockage of the filter medium by fines was apparent.…”

Section: Introductionmentioning

confidence: 99%

A study of the use of computer simulations for the design of integrated downstream processes

Zhou

Holwill

Titchener-Hooker

1997

Bioprocess Engineering

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Simulation may be used as a powerful tool for accelerating bioprocess design. This paper demonstrates the use of simulations in exploring the nature and impact of the interactions that exist in a typical bioprocess for the recovery of an intracellular protein. The study shows that an integrated approach to design must be adopted in order to achieve acceptable process designs. Data from a fedbatch fermentation, with verified models for cell harvesting, cell disruption and cell debris removal have been integrated to demonstrate the consequence of process design and operating decisions on the resulting process performance. The trade-offs between product recovery and the extent of cell debris removal for a range of operating conditions have been represented through a series of windows of operation which show how process conditions must be altered in order for given process performance levels to be realised. The capacity to account for process performance including the impact of interactions is seen as a pre-requisite for rigorous bioprocess sequence design and optimisation. IntroductionDownstream processes for protein production and purification of biomolecules are often complex and may consist of many unit operations. The sequence for the recovery of a typical intracellular enzyme may require as many as eight steps to yield a crude enzyme [29]. Subsequent purification based on chromatography may necessitate a significant number of additional stages and hence the overall yield is often low. To increase the overall process efficiency it is desirable to operate each stage at a high yield, or to reduce the number of steps [12]. The latter approach may require substitution of new operations to enable such a truncation in the sequence without loss in performance. However in both cases a systematic approach to process design and operation is needed which takes account of any interactions existing between steps since these can severely influence subsequent performance. Previous workers have examined such process interactions. Changes in the morphology of P. chrysogenum that occur during fermentation have been shown to result in very different filtration rates being achieved in subsequent downstream processing [27] while the conditions of bead milling S. cerevisiae impact the efficiency of subsequent depth filtration [24]. Here a clear compromise between the extent of milling to achieve good product release and the consequent blockage of the filter medium by fines was apparent. These results suggest that it is necessary to analyse bioprocesses in an integrated fashion such that changes in the performance due to altered specifications in one step can be predicted. Without such a capacity the process chain cannot be operated to yield its potential since a high yield in one step may lead to lower recovery for the next step. A clear example of this is the trade-off between homogenisation conditions chosen for protein release and the issue of subsequent cell debris separation by centrifugation. Here a high yield at the homogenisa...

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Filtration Properties of Mycelial Microbial Broths

Cited by 17 publications

References 10 publications

A review of factors affecting filter cake properties in dead-end microfiltration of microbial suspensions

A review of factors affecting filter cake properties in dead-end microfiltration of microbial suspensions

Dead-end filtration of yeast suspensions: Correlating specific resistance and flux data using artificial neural networks

A study of the use of computer simulations for the design of integrated downstream processes

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