Computer control of pH and DO in a laboratory fermenter using a neural network technique
Abstract In this contribution, the advantages of the artificial neural network approach to the identification and control of a laboratory-scale biochemical reactor are demonstrated. It is very important to be able to maintain the levels of two process variables, pH and dissolved oxygen (DO) concentr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mészáros, A. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2004 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2004 |
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| Übergeordnetes Werk: |
Enthalten in: Bioprocess and biosystems engineering - Springer-Verlag, 2001, 26(2004), 5 vom: 06. Aug., Seite 331-340 |
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| Übergeordnetes Werk: |
volume:26 ; year:2004 ; number:5 ; day:06 ; month:08 ; pages:331-340 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00449-004-0374-0 |
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| 520 | |a Abstract In this contribution, the advantages of the artificial neural network approach to the identification and control of a laboratory-scale biochemical reactor are demonstrated. It is very important to be able to maintain the levels of two process variables, pH and dissolved oxygen (DO) concentration, over the course of fermentation in biosystems control. A PC-supported, fully automated, multi-task control system has been designed and built by the authors. Forward and inverse neural process models are used to identify and control both the pH and the DO concentration in a fermenter containing a Saccharomyces cerevisiae based-culture. The models are trained off-line, using a modified back-propagation algorithm based on conjugate gradients. The inverse neural controller is augmented by a new adaptive term that results in a system with robust performance. Experimental results have confirmed that the regulatory and tracking performances of the control system proposed are good. | ||
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10.1007/s00449-004-0374-0 doi (DE-627)OLC2106609310 (DE-He213)s00449-004-0374-0-p DE-627 ger DE-627 rakwb eng 660 VZ 570 690 540 VZ 12 ssgn Mészáros, A. verfasserin aut Computer control of pH and DO in a laboratory fermenter using a neural network technique 2004 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2004 Abstract In this contribution, the advantages of the artificial neural network approach to the identification and control of a laboratory-scale biochemical reactor are demonstrated. It is very important to be able to maintain the levels of two process variables, pH and dissolved oxygen (DO) concentration, over the course of fermentation in biosystems control. A PC-supported, fully automated, multi-task control system has been designed and built by the authors. Forward and inverse neural process models are used to identify and control both the pH and the DO concentration in a fermenter containing a Saccharomyces cerevisiae based-culture. The models are trained off-line, using a modified back-propagation algorithm based on conjugate gradients. The inverse neural controller is augmented by a new adaptive term that results in a system with robust performance. Experimental results have confirmed that the regulatory and tracking performances of the control system proposed are good. Laboratory fermenter Control system Neural networks Andrášik, A. aut Mizsey, P. aut Fonyó, Z. aut Illeová, V. aut Enthalten in Bioprocess and biosystems engineering Springer-Verlag, 2001 26(2004), 5 vom: 06. Aug., Seite 331-340 (DE-627)333469763 (DE-600)2056063-1 (DE-576)094533709 1615-7591 nnns volume:26 year:2004 number:5 day:06 month:08 pages:331-340 https://doi.org/10.1007/s00449-004-0374-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-CHE GBV_ILN_11 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 AR 26 2004 5 06 08 331-340 |
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computer control of ph and do in a laboratory fermenter using a neural network technique |
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Computer control of pH and DO in a laboratory fermenter using a neural network technique |
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Abstract In this contribution, the advantages of the artificial neural network approach to the identification and control of a laboratory-scale biochemical reactor are demonstrated. It is very important to be able to maintain the levels of two process variables, pH and dissolved oxygen (DO) concentration, over the course of fermentation in biosystems control. A PC-supported, fully automated, multi-task control system has been designed and built by the authors. Forward and inverse neural process models are used to identify and control both the pH and the DO concentration in a fermenter containing a Saccharomyces cerevisiae based-culture. The models are trained off-line, using a modified back-propagation algorithm based on conjugate gradients. The inverse neural controller is augmented by a new adaptive term that results in a system with robust performance. Experimental results have confirmed that the regulatory and tracking performances of the control system proposed are good. © Springer-Verlag 2004 |
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Abstract In this contribution, the advantages of the artificial neural network approach to the identification and control of a laboratory-scale biochemical reactor are demonstrated. It is very important to be able to maintain the levels of two process variables, pH and dissolved oxygen (DO) concentration, over the course of fermentation in biosystems control. A PC-supported, fully automated, multi-task control system has been designed and built by the authors. Forward and inverse neural process models are used to identify and control both the pH and the DO concentration in a fermenter containing a Saccharomyces cerevisiae based-culture. The models are trained off-line, using a modified back-propagation algorithm based on conjugate gradients. The inverse neural controller is augmented by a new adaptive term that results in a system with robust performance. Experimental results have confirmed that the regulatory and tracking performances of the control system proposed are good. © Springer-Verlag 2004 |
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Abstract In this contribution, the advantages of the artificial neural network approach to the identification and control of a laboratory-scale biochemical reactor are demonstrated. It is very important to be able to maintain the levels of two process variables, pH and dissolved oxygen (DO) concentration, over the course of fermentation in biosystems control. A PC-supported, fully automated, multi-task control system has been designed and built by the authors. Forward and inverse neural process models are used to identify and control both the pH and the DO concentration in a fermenter containing a Saccharomyces cerevisiae based-culture. The models are trained off-line, using a modified back-propagation algorithm based on conjugate gradients. The inverse neural controller is augmented by a new adaptive term that results in a system with robust performance. Experimental results have confirmed that the regulatory and tracking performances of the control system proposed are good. © Springer-Verlag 2004 |
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