Dynamic model development for a continuous culture of <i>Saccharomyces cerevisiae</i>

O'Neil, Dominic; Lyberatos, G.

doi:10.1002/bit.260360502

Cited by 25 publications

(21 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first data set is for a single strain, yeast Candida tropicalis, cultivated in a chemostat and subjected to a sudden shift in the dilution rate. 46 The second one is taken from the work of Patarinska et al 14 and O'Neil and Lyberatos 47 who have been working on continuous cultures of Saccharomyces cerevisiae. The third set of data is issued from the works of Gaudy and coworkers 50,51 and concerns a continuous culture of heterogeneous microbial population.…”

Section: The Chemostat Simulationsmentioning

confidence: 99%

“…14,46 • The history of the culture or the initial conditions before the perturbation. 17,22,46 • The sign of the perturbation: O'Neil and Lyberatos 47 have identified different time constants for step-up and stepdown experiments performed on continuous cultures of Saccharomyces cerevisiae.…”

Section: Introductionmentioning

confidence: 98%

“…O'Neil and Lyberatos, 47 carried out experiments on continuous culture of Saccharomyces cerevisiae. They imposed step changes (positive and negative) for the concentration feed and dilution rate.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A structured model for the simulation of bioreactors under transient conditions

Morchain

Fonade

2009

AIChE Journal

View full text Add to dashboard Cite

Modeling the transient behavior of continuous culture is of primary importance for the scale-up of biological processes. Spatial heterogeneities increase with the reactor size and micro-organisms have to cope with a fluctuating environment along their trajectories within the bioreactor. In this article, a structured model for bioreactions expressed in terms of biological extensive variables is proposed. A biological variable is introduced to calculate the growth rate of the population. The value is updated on the basis of the difference between the composition in the liquid and biotic phase. The structured model is able to predict the transient behavior of different continuous cultures subject to various drastic perturbations. This performance is obtained with a minimum increase in the standard unstructured model complexity (one additional time constant). In the final part, the consequences of decoupling the growth rate from the substrate uptake rate are discussed. V

show abstract

Section: The Chemostat Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

A structured model for the simulation of bioreactors under transient conditions

Morchain

Fonade

2009

AIChE Journal

View full text Add to dashboard Cite

show abstract

“…[5][6][7][8] The time delays are incorporated into the model by introducing a time delay kernel in the state equations representing the past history influence on the present response of the system. Another similar approach was the cause-effect relation (CER) between the past inputs and current output of a dynamic plant.…”

Section: Introductionmentioning

confidence: 99%

Memory effects description by neural networks with delayed feedback connections

Koprinkova

Patarinska

Petrova

2004

Int. J. Intell. Syst.

View full text Add to dashboard Cite

For the purpose of dynamic systems modeling, it was proposed to include feedback connections or delay elements in the classical feed-forward neural network structure so that the present output of the neural network depends on its previous values. These delay elements can be connected to the hidden and/or output neurons of the main neural network. Each delay element gets a value of a state variable at a past time instant and keeps this value during a single sampling period. The groups of delay elements record the values of the state variables for a given time period in the past. Changing the number of the delay elements, which belongs to one group, a shorter or a longer time period in the past can be accounted for. Thus, the connection weights determine the influence of the past process states on the present state in a similar way as it is in the time delay kernel or cause-effect relation membership function (CER-MF) models. Specific feed-forward neural networks with time delay connections are used to solve the problem of neural network chemostat modeling as well as specific kinetic rates modeling. The weights of the feedback connections obtained during model training are discussed as the points of a time delay kernel or as the strength levels in a CER model (the points in the CER-MF). The corresponding changes in these weights with the changing time period in the past are shown.

show abstract

“…With the advent of sophisticated computer techniques~attention is nO\\I being turned to dynamic nlodeJling and quasi-unsteady-state behaviour (O'Neil and Lyberatos, 1990). Several studies of osciHatofY behaviour in continuous ferrnentation of S. cerellisiae have been reported.…”

Section: Continuous Fermentationmentioning

confidence: 99%