Microbial specific growth rate control via MRAC method

1993 ◽  
Vol 24 (11) ◽  
pp. 1973-1985 ◽  
Author(s):  
F. Y. ZENG ◽  
B. DAHHOU ◽  
M. T. NIHTILÄ ◽  
G. GOMA
1993 ◽  
Vol 26 (2) ◽  
pp. 185-188
Author(s):  
M. Keulers ◽  
L. Ariaans ◽  
M. Giuseppin ◽  
R. Soeterboek

2004 ◽  
Vol 37 (3) ◽  
pp. 499-504 ◽  
Author(s):  
E. Picó-Marco ◽  
J. Picó ◽  
H. DeBattista ◽  
J.L. Navarro

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 693 ◽  
Author(s):  
Galvanauskas ◽  
Simutis ◽  
Levišauskas ◽  
Urniežius

This contribution discusses the main challenges related to successful application of automatic control systems used to control specific growth rate in industrial biotechnological processes. It is emphasized that, after the implementation of basic automatic control systems, primary attention shall be paid to the specific growth rate control systems because this process variable critically affects the physiological state of microbial cultures and the formation of the desired product. Therefore, control of the specific growth rate enables improvement of the quality and reproducibility of the biotechnological processes. The main requirements have been formulated that shall be met to successfully implement the specific growth rate control systems in industrial bioreactors. The relatively easy-to-implement schemes of specific growth rate control systems have been reviewed and discussed. The recommendations for selection of particular control systems for specific biotechnological processes have been provided.


2001 ◽  
Vol 67 (3) ◽  
pp. 1292-1299 ◽  
Author(s):  
Martin Ostrowski ◽  
Ricardo Cavicchioli ◽  
Maarten Blaauw ◽  
Jan C. Gottschal

ABSTRACT The marine oligotrophic ultramicrobacterium Sphingomonas alaskensis RB2256 has a physiology that is distinctly different from that of typical copiotrophic marine bacteria, such as Vibrio angustum S14. This includes a high level of inherent stress resistance and the absence of starvation-induced stress resistance to hydrogen peroxide. In addition to periods of starvation in the ocean, slow, nutrient-limited growth is likely to be encountered by oligotrophic bacteria for substantial periods of time. In this study we examined the effects of growth rate on the resistance of S. alaskensis RB2256 to hydrogen peroxide under carbon or nitrogen limitation conditions in nutrient-limited chemostats. Glucose-limited cultures of S. alaskensis RB2256 at a specific growth rate of 0.02 to 0.13 h−1 exhibited 10,000-fold-greater viability following 60 min of exposure to 25 mM hydrogen peroxide than cells growing at a rate of 0.14 h−1 or higher. Growth rate control of stress resistance was found to be specific to carbon and energy limitation in this organism. In contrast, V. angustum S14 did not exhibit growth rate-dependent stress resistance. The dramatic switch in stress resistance that was observed under carbon and energy limitation conditions has not been described previously in bacteria and thus may be a characteristic of the oligotrophic ultramicrobacterium. Catalase activity varied marginally and did not correlate with the growth rate, indicating that hydrogen peroxide breakdown was not the primary mechanism of resistance. More than 1,000 spots were resolved on silver-stained protein gels for cultures growing at rates of 0.026, 0.076, and 0.18 h−1. Twelve protein spots had intensities that varied by more than twofold between growth rates and hence are likely to be important for growth rate-dependent stress resistance. These studies demonstrated the crucial role that nutrient limitation plays in the physiology of S. alaskensis RB2256, especially under oxidative stress conditions.


Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 810 ◽  
Author(s):  
Vytautas Galvanauskas ◽  
Rimvydas Simutis ◽  
Vygandas Vaitkus

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.


Sign in / Sign up

Export Citation Format

Share Document