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Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System
المؤلفون المشاركون
Birle, Stephan
Hussein, Mohamed Ahmed
Hussein, M. A.
المصدر
Discrete Dynamics in Nature and Society
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-07-12
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Birle, Stephan& Hussein, Mohamed Ahmed& Hussein, M. A.. 2016. Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System. Discrete Dynamics in Nature and Society،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1103330
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Birle, Stephan…[et al.]. Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System. Discrete Dynamics in Nature and Society No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1103330
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Birle, Stephan& Hussein, Mohamed Ahmed& Hussein, M. A.. Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System. Discrete Dynamics in Nature and Society. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1103330
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1103330
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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