Adaptive on-line simulation of bioreactors: Fermentation monitoring and modeling system

Fagervik, Kaj; Rydström, Mikael; Schalien, Randolf von; Saxén, Bjørn; Ringbom, Knut; Rothberg, Anette; Gros, Håkan

doi:10.1007/bf01569958

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…Using primary measurement variables, e.g., offgas composition, aeration rate, pH and base feed, state variables, such as X, S and P, are estimated every minute, i.e., Ar = 1 min. The estimations have been verified by HPLC analyses (Fagervik et al, 1995).…”

Section: Experimental Datamentioning

confidence: 80%

“…This study utilizes a state estimation model (Fagervik et al, 1995), which produces training and test data for the networks. Provided with measurement information about the in-and outcoming flows and some internal easily measurable state variables, e.g., pH and temperature, the model estimates state variables that are not measured, such as the concentrations of biomass, substrate and product (Appendix A).…”

Section: The Approachmentioning

confidence: 99%

“…In this work, the concept of combining a balance-based on-line simulation model (Fagervik et al, 1995) with neural networks for bioreaction kinetics (Saxtn and SaxCn, 1994) is exemplified for a batch process. The former model estimates the state of the bioreactor (the concentrations of biomass, substrate and product) on the basis of elemental balances and an electroneutrality equation, and utilizes conventional measurement variables, such as offgas composition, aeration rate, pH and base feed.…”

mentioning

confidence: 99%

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A neural‐network based model of bioreaction kinetics

Saxén¹,

Saxén²

1996

Can J Chem Eng

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Section: Experimental Datamentioning

confidence: 80%

Section: The Approachmentioning

confidence: 99%

mentioning

confidence: 99%

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A neural‐network based model of bioreaction kinetics

Saxén¹,

Saxén²

1996

Can J Chem Eng

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“…At the same time, the feed started and the glucose concentration was kept at 40 g l A1 until a total of 208 g had been consumed. The control was based on glucose estimates from an adaptive on-line simulation model, described in von Schalien et al [16] and Fagervik et al [17] None of the fed-batch strategies led to better results than those which were obtained with a batch run.…”

Section: Resultsmentioning

confidence: 99%

Optimization of an Aspergillus niger glucose oxidase production process

Rothberg

Weegar

Schalien

et al. 1999

Bioprocess Engineering

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The purpose of the present study was to ascertain the optimal concentration of dissolved oxygen in order to maximize the intracellular glucose oxidase formation in Aspergillus niger. Cultivations performed in a 3.5 l laboratory reactor showed that a dissolved oxygen concentration at 3% of saturation at a total pressure of 1.2 bar was optimal for maximizing intracellular glucose oxidase activity. Cultivations performed at higher dissolved oxygen concentrations did not produce as much glucose oxidase as those performed at 3%, although the formation rate was high. Experiments revealed that maximal intracellular glucose oxidase formation for the A. niger strain used, is accomplished by limiting the gluconic acid production rate by means of maintaining a low dissolved oxygen concentration. Several attempts to achieve higher intracellular glucose oxidase activity were also made by manipulating the glucose concentration at a 3% dissolved oxygen concentration. However, no enhancement in glucose oxidase activity was observed.

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“…Model based simulations matched experimental data very well and the validity of the modelling approach for the required brewery propagations could be proven. Therefore, an automatic adaptation of the parameters of the model, as it is proposed in other works 13 to reach a higher accuracy of the modelbased simulation, was not indicated in the required application. With only three variable parameters an easy manageable simulation tool is provided for the engineer.…”

Section: '32'097-327mentioning

confidence: 99%

A Model Based Simulation of Brewing Yeast Propagation

Kurz

Mieleitner

Becker

et al. 2002

Journal of the Institute of Brewing

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A comprehensive kinetic model of yeast propagation in breweries is introduced. It represents the basis for a control strategy aimed at the provision of optimal inoculum at the starting time of subsequent industrial beer fermentations. In the metabolic modelling approach presented, respiratory metabolism on sugars and ethanol and fermentative metabolism respectively, are included. Occurring limitations, due to specific nutritional data of the growth medium, were taken into account for sugar, nitrogen, ethanol and oxygen. Correspondingly, inhibitions of the metabolism by ethanol and high sugar concentrations were formulated. The model especially represents the Crabtree-effect. For model validation, literature data were used and selected experiments within the relevant range of manipulated variables (temperature, dissolved oxygen) were conducted. Model based simulations matched these data with a deviation of 5.6% and with standard deviations of 5.6% after an adaptation of three variable parameters. A relationship between the temperature and the variable parameters could be extracted, which allowed predictive simulations of the yeast propagation process using non-isothermal trajectories. It was shown, that with a precise adjustment of trajectories of both, temperature and dissolved oxygen, the crop time of the inoculum could be varied within a period of 7 to 50 h to maintain high fermentation activity for the subsequent anaerobic fermentation.

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Adaptive on-line simulation of bioreactors: Fermentation monitoring and modeling system

Cited by 7 publications

References 8 publications

A neural‐network based model of bioreaction kinetics

A neural‐network based model of bioreaction kinetics

Optimization of an Aspergillus niger glucose oxidase production process

A Model Based Simulation of Brewing Yeast Propagation

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