scholarly journals Practical Solutions for Specific Growth Rate Control Systems in Industrial Bioreactors

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 693 ◽  
Author(s):  
Galvanauskas ◽  
Simutis ◽  
Levišauskas ◽  
Urniežius
Keyword(s):  
Growth Rate ◽  
Automatic Control ◽  
Specific Growth Rate ◽  
Control Systems ◽  
Rate Control ◽  
Specific Growth ◽  
Physiological State ◽  
Automatic Control Systems ◽  
Biotechnological Processes ◽  
Growth Rate Control

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.

scholarly journals Specific Growth Rate control in Fed-Batch Bakers' Yeast Fermentation

1993 ◽  
Vol 26 (2) ◽  
pp. 185-188
Author(s):  
M. Keulers ◽  
L. Ariaans ◽  
M. Giuseppin ◽  
R. Soeterboek
Keyword(s):  
Growth Rate ◽  
Specific Growth Rate ◽  
Rate Control ◽  
Specific Growth ◽  
Yeast Fermentation ◽  
Fed Batch ◽  
Growth Rate Control

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
Keyword(s):  
Growth Rate ◽  
Specific Growth Rate ◽  
Rate Control ◽  
Specific Growth ◽  
Growth Rate Control

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.

scholarly journals Robust Adaptive Specific Growth Rate Control in Biotechnological Fed-Batch Processes

2004 ◽  
Vol 37 (3) ◽  
pp. 499-504 ◽  
Author(s):  
E. Picó-Marco ◽  
J. Picó ◽  
H. DeBattista ◽  
J.L. Navarro
Keyword(s):  
Growth Rate ◽  
Specific Growth Rate ◽  
Rate Control ◽  
Specific Growth ◽  
Batch Processes ◽  
Fed Batch ◽  
Robust Adaptive ◽  
Growth Rate Control

Estimating Bacterial Growth Parameters by Means of Detection Times

1999 ◽  
Vol 65 (2) ◽  
pp. 732-736 ◽  
Author(s):  
József Baranyi ◽  
Carmen Pin
Keyword(s):  
Growth Rate ◽  
Specific Growth Rate ◽  
Bacterial Growth ◽  
Specific Growth ◽  
Growth Parameters ◽  
Physiological State ◽  
Cell Number ◽  
Maximum Specific Growth Rate ◽  
Specific Rate ◽  
The Individual

ABSTRACT We developed a new numerical method to estimate bacterial growth parameters by means of detection times generated by different initial counts. The observed detection times are subjected to a transformation involving the (unknown) maximum specific growth rate and the (known) ratios between the different inoculum sizes and the constant detectable level of counts. We present an analysis of variance (ANOVA) protocol based on a theoretical result according to which, if the specific rate used for the transformation is correct, the transformed values are scattered around the same mean irrespective of the original inoculum sizes. That mean, termed the physiological state of the inoculum,α̂, and the maximum specific growth rate, μ, can be estimated by minimizing the variance ratio of the ANOVA procedure. The lag time of the population can be calculated as λ = −ln α̂/μ; i.e. the lag is inversely proportional to the maximum specific growth rate and depends on the initial physiological state of the population. The more accurately the cell number at the detection level is known, the better the estimate for the variance of the lag times of the individual cells.

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