Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 156-165 |
| Seitenumfang | 10 |
| Fachzeitschrift | Biotechnology and Bioprocess Engineering |
| Jahrgang | 9 |
| Ausgabenummer | 3 |
| Publikationsstatus | Veröffentlicht - Juni 2004 |
Abstract
Concentrations of substrates, glucose, and ammionia in biological processes have been on-line monitored by using glucose-flow injection (FIA) and ammonia-FIA systems. Based on the on-line monitored data the concentrations of substrates have been controlled by an on-off controller, a PID controller, and a neural network (NN) based controller. A simulation program has been developed to test the control quality of each controller and to estimate the control parameters. The on-off controller often produced high oscillations at the set point due to its low robustness. The control quality of a PID controller could have been improved by a high analysis frequency and by a short residence time of sample in a FIA system. A NN-based controller with 3 layers has been developed, and a 3(input)-2(hidden)-1(output) network structure has been found to be optimal for the NN-based controller. The performance of the three controllers has been tested in a simulated process as well as in a cultivation process of Saccharomyces cerevisiae, and the performance has also been compared to simulation results. The NN-based controller with the 3-2-1 network structure was robust and stable against some disturbances, such as a sudden injection of distilled water into a biological process.
ASJC Scopus Sachgebiete
- Biochemie, Genetik und Molekularbiologie (insg.)
- Biotechnologie
- Chemische Verfahrenstechnik (insg.)
- Bioengineering
- Immunologie und Mikrobiologie (insg.)
- Angewandte Mikrobiologie und Biotechnologie
- Ingenieurwesen (insg.)
- Biomedizintechnik
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in: Biotechnology and Bioprocess Engineering, Jahrgang 9, Nr. 3, 06.2004, S. 156-165.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - On-line Monitoring and Control of Substrate Concentrations in Biological Processes by Flow Injection Analysis Systems
AU - Rhee, Jong Il
AU - Ritzka, Adnan
AU - Scheper, Thomas
PY - 2004/6
Y1 - 2004/6
N2 - Concentrations of substrates, glucose, and ammionia in biological processes have been on-line monitored by using glucose-flow injection (FIA) and ammonia-FIA systems. Based on the on-line monitored data the concentrations of substrates have been controlled by an on-off controller, a PID controller, and a neural network (NN) based controller. A simulation program has been developed to test the control quality of each controller and to estimate the control parameters. The on-off controller often produced high oscillations at the set point due to its low robustness. The control quality of a PID controller could have been improved by a high analysis frequency and by a short residence time of sample in a FIA system. A NN-based controller with 3 layers has been developed, and a 3(input)-2(hidden)-1(output) network structure has been found to be optimal for the NN-based controller. The performance of the three controllers has been tested in a simulated process as well as in a cultivation process of Saccharomyces cerevisiae, and the performance has also been compared to simulation results. The NN-based controller with the 3-2-1 network structure was robust and stable against some disturbances, such as a sudden injection of distilled water into a biological process.
AB - Concentrations of substrates, glucose, and ammionia in biological processes have been on-line monitored by using glucose-flow injection (FIA) and ammonia-FIA systems. Based on the on-line monitored data the concentrations of substrates have been controlled by an on-off controller, a PID controller, and a neural network (NN) based controller. A simulation program has been developed to test the control quality of each controller and to estimate the control parameters. The on-off controller often produced high oscillations at the set point due to its low robustness. The control quality of a PID controller could have been improved by a high analysis frequency and by a short residence time of sample in a FIA system. A NN-based controller with 3 layers has been developed, and a 3(input)-2(hidden)-1(output) network structure has been found to be optimal for the NN-based controller. The performance of the three controllers has been tested in a simulated process as well as in a cultivation process of Saccharomyces cerevisiae, and the performance has also been compared to simulation results. The NN-based controller with the 3-2-1 network structure was robust and stable against some disturbances, such as a sudden injection of distilled water into a biological process.
KW - Bioprocess monitoring and control
KW - Controller
KW - Flow injection analysis
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=24344498420&partnerID=8YFLogxK
U2 - 10.1007/BF02942286
DO - 10.1007/BF02942286
M3 - Article
AN - SCOPUS:24344498420
VL - 9
SP - 156
EP - 165
JO - Biotechnology and Bioprocess Engineering
JF - Biotechnology and Bioprocess Engineering
SN - 1226-8372
IS - 3
ER -