Recursive on-line estimation of the specific growth rate from off-gas analysis for the adaptive control of fed-batch processes

1995 ◽  
Vol 12 (4) ◽  
pp. 205-206
Author(s):  
M. U. Estler
Keyword(s):  
Growth Rate ◽  
Adaptive Control ◽  
Specific Growth Rate ◽  
Specific Growth ◽  
Gas Analysis ◽  
Batch Processes ◽  
Fed Batch ◽  
On Line

Automated fed-batch culture of Kluyveromyces fragilis based on a novel method for on-line estimation of cell specific growth rate

2001 ◽  
Vol 9 (3) ◽  
pp. 221-231 ◽  
Author(s):  
Zairossani M. Nor ◽  
Melih I. Tamer ◽  
Jeno M. Scharer ◽  
Murray Moo-Young ◽  
Eric J. Jervis
Keyword(s):  
Growth Rate ◽  
Specific Growth Rate ◽  
Batch Culture ◽  
Specific Growth ◽  
Fed Batch ◽  
Kluyveromyces Fragilis ◽  
On Line ◽  
Novel Method ◽  
Fed Batch Culture

scholarly journals Fuzzy Logic-Based Adaptive Control of Specific Growth Rate in Fed-Batch Biotechnological Processes. A Simulation Study

2020 ◽  
Vol 10 (19) ◽  
pp. 6818
Author(s):  
Mantas Butkus ◽  
Jolanta Repšytė ◽  
Vytautas Galvanauskas
Keyword(s):  
Growth Rate ◽  
Fuzzy Logic ◽  
Adaptive Control ◽  
Specific Growth Rate ◽  
Control Algorithm ◽  
Specific Growth ◽  
Expert Knowledge ◽  
Fed Batch ◽  
Biotechnological Process ◽  
Model Free

This article presents the development and application of a distinct adaptive control algorithm that is based on fuzzy logic and was used to control the specific growth rate (SGR) in a fed-batch biotechnological process. The developed control algorithm was compared with two adaptive control systems that were based on a model-free adaptive technique and gain scheduling technique. A typical mathematical model of recombinant Escherichia coli fed-batch cultivation process was selected to evaluate the performance of the fuzzy-based control algorithm. The investigated control techniques performed similarly when considering the whole process duration. The adaptive PI controller with fuzzy-based parameter adaptation demonstrated advantages over the previously mentioned algorithms—especially when compensating the deviations of the SGR. These deviations usually occur when the equipment malfunctions or process disturbances take place. The fuzzy-based control system was stable within the investigated ranges. It was determined that, regarding control quality, the investigated control algorithms are suited to control the SGR in a fed-batch biotechnological process. However, substrate feeding rate manipulation and limitation needs to be used. Taking into account the time needed to design and tune the controller, the developed controller is suitable for practical applications when expert knowledge is available. The proposed algorithm can be further adapted and developed to control the SGR in other cell cultivations while running the process under substrate limitation conditions.

Constant specific growth rate in fed-batch cultivation of Bordetella pertussis using adaptive control

2006 ◽  
Vol 125 (2) ◽  
pp. 252-268 ◽  
Author(s):  
Z.I.T.A. Soons ◽  
J.A. Voogt ◽  
G. van Straten ◽  
A.J.B. van Boxtel
Keyword(s):  
Growth Rate ◽  
Adaptive Control ◽  
Specific Growth Rate ◽  
Bordetella Pertussis ◽  
Specific Growth ◽  
Batch Cultivation ◽  
Fed Batch ◽  
Fed Batch Cultivation

scholarly journals Adaptive Control of Biomass Specific Growth Rate in Fed-Batch Biotechnological Processes. A Comparative Study

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 810 ◽  
Author(s):  
Vytautas Galvanauskas ◽  
Rimvydas Simutis ◽  
Vygandas Vaitkus
Keyword(s):  
Growth Rate ◽  
Adaptive Control ◽  
Specific Growth Rate ◽  
Specific Growth ◽  
Adaptive Controller ◽  
Fed Batch ◽  
Model Free ◽  
Substrate Limitation ◽  
Biotechnological Processes ◽  
Growth Rate Control

This article presents a comparative study on the development and application of two distinct adaptive control algorithms for biomass specific growth rate control in fed-batch biotechnological processes. A typical fed-batch process using Escherichia coli for recombinant protein production was selected for this research. Numerical simulation results show that both developed controllers, an adaptive PI controller based on the gain scheduling technique and a model-free adaptive controller based on the artificial neural network, delivered a comparable control performance and are suitable for application when using the substrate limitation approach and substrate feeding rate manipulation. The controller performance was tested within the realistic ranges of the feedback signal sampling intervals and measurement noise intensities. Considering the efforts for controller design and tuning, including development of the adaptation/learning algorithms, the model-free adaptive control algorithm proves to be more attractive for industrial applications, especially when only limited knowledge of the process and its mathematical model is available. The investigated model-free adaptive controller also tended to deliver better control quality under low specific growth rate conditions that prevail during the recombinant protein production phase. In the investigated simulation runs, the average tracking error did not exceed 0.01 (1/h). The temporary overshoots caused by the maximal disturbances stayed within the range of 0.025–0.11 (1/h). Application of the algorithm can be further extended to specific growth rate control in other bacterial and mammalian cell cultivations that run under substrate limitation conditions.

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