Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System
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 traditi...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2016/1548986 |
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| author | Stephan Birle Mohamed Ahmed Hussein Thomas Becker |
| author_facet | Stephan Birle Mohamed Ahmed Hussein Thomas Becker |
| author_sort | Stephan Birle |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7564fef65ea84f68bfdf47f534c1f9a7 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-7564fef65ea84f68bfdf47f534c1f9a72025-02-03T05:52:15ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2016-01-01201610.1155/2016/15489861548986Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy SystemStephan Birle0Mohamed Ahmed Hussein1Thomas Becker2Center of Life and Food Sciences Weihenstephan, Research Group of Bio-Process Analysis Technology, Technical University of Munich, Weihenstephaner Steig 20, 85354 Freising, GermanyCenter of Life and Food Sciences Weihenstephan, Research Group of Bio-Process Analysis Technology, Technical University of Munich, Weihenstephaner Steig 20, 85354 Freising, GermanyCenter of Life and Food Sciences Weihenstephan, Research Group of Bio-Process Analysis Technology, Technical University of Munich, Weihenstephaner Steig 20, 85354 Freising, GermanyIn 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.http://dx.doi.org/10.1155/2016/1548986 |
| spellingShingle | Stephan Birle Mohamed Ahmed Hussein Thomas Becker Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System Discrete Dynamics in Nature and Society |
| title | Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System |
| title_full | Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System |
| title_fullStr | Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System |
| title_full_unstemmed | Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System |
| title_short | Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System |
| title_sort | management of uncertainty by statistical process control and a genetic tuned fuzzy system |
| url | http://dx.doi.org/10.1155/2016/1548986 |
| work_keys_str_mv | AT stephanbirle managementofuncertaintybystatisticalprocesscontrolandagenetictunedfuzzysystem AT mohamedahmedhussein managementofuncertaintybystatisticalprocesscontrolandagenetictunedfuzzysystem AT thomasbecker managementofuncertaintybystatisticalprocesscontrolandagenetictunedfuzzysystem |