Fuzzy supervisory control of fed-batch baker's yeast process

Siimes, T.; Linko, P.

doi:10.1007/bf00159404

Cited by 4 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following this, all parameters are coded on a chromosome, where denotes the second input variablėY CC and represents the output variable. logic controllers and expert systems have been successfully used [26][27][28][47][48][49][50][51][52][53]. However, they show a deficiency in knowledge acquisition and their parameterization relies to a great extent on empirical and heuristic knowledge.…”

Section: Genetic Tuning Of Fuzzymentioning

confidence: 99%

Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System

Birle

Hussein

Becker

2016

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

In food industry, bioprocesses like fermentation often are a crucial part of the manufacturing process and decisive for the final product quality. In general, they are characterized by highly nonlinear dynamics and uncertainties that make it difficult to control these processes by the use of traditional control techniques. In this context, fuzzy logic controllers offer quite a straightforward way to control processes that are affected by nonlinear behavior and uncertain process knowledge. However, in order to maintain process safety and product quality it is necessary to specify the controller performance and to tune the controller parameters. In this work, an approach is presented to establish an intelligent control system for oxidoreductive yeast propagation as a representative process biased by the aforementioned uncertainties. The presented approach is based on statistical process control and fuzzy logic feedback control. As the cognitive uncertainty among different experts about the limits that define the control performance as still acceptable may differ a lot, a data-driven design method is performed. Based upon a historic data pool statistical process corridors are derived for the controller inputs control error and change in control error. This approach follows the hypothesis that if the control performance criteria stay within predefined statistical boundaries, the final process state meets the required quality definition. In order to keep the process on its optimal growth trajectory (model based reference trajectory) a fuzzy logic controller is used that alternates the process temperature. Additionally, in order to stay within the process corridors, a genetic algorithm was applied to tune the input and output fuzzy sets of a preliminarily parameterized fuzzy controller. The presented experimental results show that the genetic tuned fuzzy controller is able to keep the process within its allowed limits. The average absolute error to the reference growth trajectory is 5.2 × 106 cells/mL. The controller proves its robustness to keep the process on the desired growth profile.

show abstract

Section: Genetic Tuning Of Fuzzymentioning

confidence: 99%

Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System

Birle

Hussein

Becker

2016

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

show abstract

“…On account of the special conditions mentioned, monitoring and control strategies that are suggested in literature, cannot be applied without comprehensive adjustments, e.g. the feed control of baker's yeast production [26,[40][41][42][43]].…”

Section: Introductionmentioning

confidence: 99%

On‐line yeast propagation process monitoring and control using an intelligent automatic control system

Birle

Hussein

Becker

2014

Engineering in Life Sciences

View full text Add to dashboard Cite

The monitoring and control of bioprocesses is a challenging task. This applies particularly if the actions to the process have to be carried out in real-time. This work presents a system for on-line monitoring and control of batch yeast propagation under limiting conditions based on a virtual plant operator, which uses the concept of intelligent control algorithms by means of fuzzy logic theory. Process information is provided on-line using a sensor array comprising the measurement of OD, operating temperature, pressure, density, dissolved oxygen, and pH value. In this context practical problems arising through on-line sensing and signal processing are addressed. The preprocessed sensor data are fed to a neural network for on-line biomass estimation. The root mean squared error of prediction is 4 × 10 6 cells/mL. The proposed system then triggers temperature and aeration by usage of a temperature dependent metabolic growth model and sensor data. The deviation of the predicted biomass from that of the reference trajectory as modeled by the metabolic growth model and its temporal derivative are used as inputs for the fuzzy temperature controller. The inputs used by the fuzzy aeration controller are the deviation of measured extract from that of the reference trajectory, the predicted cell count, and the dissolved oxygen concentration. The fuzzy-based expert system allows to provide the desired yeast cell concentration of 100-120 × 10 6 cells/mL at a minimum residual extract limit of 6.0 g/100 g at the required point of time. Thus, a dynamic adjustment of the propagation process to the overall production schedule is possible in order to produce the required amount of biomass at the right time.

show abstract

Untitled

Beluhan

2000

Biotechnology Letters

View full text Add to dashboard Cite

Fuzzy supervisory control of fed-batch baker's yeast process

Cited by 4 publications

References 19 publications

Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System

Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System

On‐line yeast propagation process monitoring and control using an intelligent automatic control system

Untitled

Contact Info

Product

Resources

About