Functional state modeling and fuzzy control of fed-batch aerobic baker's yeast process

Zhang, Xia-Chang; Visala, Arto; Halme, Aarne; Linko, P.

doi:10.1016/0168-1656(94)90196-1

Cited by 25 publications

(13 citation statements)

References 10 publications

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“…However, there is no consensus among researchers as to the number of stages or their exact timing during the process. For example, researchers divided yeast cell culture process into four, six, or even seven stages (Zhang et al, 1994). In this study the number of stages was determined according to the given data and the desired model, in an automatic manner, by cluster/ model validity methods.…”

Section: Discussionmentioning

confidence: 99%

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Hybrid model building methodology using unsupervised fuzzy clustering and supervised neural networks

Ronen

Shabtai

Guterman

2002

Biotech & Bioengineering

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This paper suggests a model building methodology for dealing with new processes. The methodology, called Hybrid Fuzzy Neural Networks (HFNN), combines unsupervised fuzzy clustering and supervised neural networks in order to create simple and flexible models. Fuzzy clustering was used to define relevant domains on the input space. Then, sets of multilayer perceptrons (MLP) were trained (one for each domain) to map input-output relations, creating, in the process, a set of specified sub-models. The estimated output of the model was obtained by fusing the different sub-model outputs weighted by their predicted possibilities. On-line reinforcement learning enabled improvement of the model. The determination of the optimal number of clusters is fundamental to the success of the HFNN approach. The effectiveness of several validity measures was compared to the generalization capability of the model and information criteria. The validity measures were tested with fermentation simulations and real fermentations of a yeast-like fungus, Aureobasidium pullulans. The results outline the criteria limitations. The learning capability of the HFNN was tested with the fermentation data. The results underline the advantages of HFNN over a single neural network.

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Section: Discussionmentioning

confidence: 99%

“…Fuzzy logic inference has been used to estimate the model parameters (Aynsley et al, 1993;Konstantinov et al, 1993, Kosko, 1992Simutis et al, 1992Simutis et al, , 1993Sugeno, 1985). Fuzzy rules have also been combined with a parametric model, where the data is partitioned into several domains using expert knowledge (Zhang et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

Hybrid model building methodology using unsupervised fuzzy clustering and supervised neural networks

Ronen

Shabtai

Guterman

2002

Biotech & Bioengineering

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“…Maintaining a given metabolic state Zhang et al (1994) of rules for 3 states dissolved oxygen Turbidity probe, off-gas, ethanol…”

Section: S Cerevisiaementioning

confidence: 99%

Real-time monitoring and control of microbial bioprocesses with focus on the specific growth rate: current state and perspectives

Schuler

Marison

2012

Appl Microbiol Biotechnol

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Understanding the growth characteristics of microorganisms is an essential step in bioprocessing, not only because product formation may be growth-associated but also because they might influence cell physiology and thereby product quality. The specific growth rate, a key variable of many bioprocesses, cannot be measured directly and relies on the estimation through other measurable variables such as biomass, substrate, or product concentrations. Techniques for real-time estimation of the specific growth rate in microbial fed-batch cultures are discussed in the present paper. The advantages and limitations of different models and various monitoring techniques are discussed, highlighting the importance of the specific growth rate in the development of fast, reliable, and robust processes for the production of high-value products such as recombinant proteins.

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“…The estimation scheme is developed based on the fundamental process model and employed to estimate the unmeasured states and unknown parameters using the secondary variables obtained through online measurement. Moreover, the ANN model is solely used to infer a process state variable, although the neural networks have greatest promise in the realm of estimation problems [1][2][3][4][5][6][7][8][9][10]. The proposed DSP algorithms can be used when there is a difficulty in measuring the key process variables.…”

Section: Introductionmentioning

confidence: 98%

A comparison of three sets of DSP algorithms for monitoring the production of ethanol in a fed-batch baker’s yeast fermenter

Mangesh

Jana

2008

Measurement

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Functional state modeling and fuzzy control of fed-batch aerobic baker's yeast process

Cited by 25 publications

References 10 publications

Hybrid model building methodology using unsupervised fuzzy clustering and supervised neural networks

Hybrid model building methodology using unsupervised fuzzy clustering and supervised neural networks

Real-time monitoring and control of microbial bioprocesses with focus on the specific growth rate: current state and perspectives

A comparison of three sets of DSP algorithms for monitoring the production of ethanol in a fed-batch baker’s yeast fermenter

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