scholarly journals A structured mathematical model of PHA biopolymer production process

2013 ◽  
Vol 23 (3) ◽  
pp. 351-360 ◽  
Author(s):  
Jana Finkeová ◽  
Pavel Hrnčiřík ◽  
Jan Mareš ◽  
Jaroslav Vovsík ◽  
Ondřej Hudeček ◽  
...  
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Production Process ◽  
Octanoic Acid ◽  
Batch Cultivation ◽  
Model Parameters ◽  
Mass Balances ◽  
Fed Batch ◽  
Fed Batch Cultivation ◽  
Good Agreement

Abstract The paper describes a mathematical model of PHA biopolymer production process by Pseudomonas putida KT2442 where the octanoic acid is used as a substrate. The process is modeled using mass balances for fed-batch cultivation. Proper fitting to experimental data is obtained by identification of the model parameters. The model exhibits good agreement with experiments and its possible application for control is considered in the paper.

Genetic Algorithms Quality Assessment Implementing Intuitionistic Fuzzy Logic

2015 ◽  
pp. 1125-1152
Author(s):  
Tania Pencheva ◽  
Maria Angelova ◽  
Krassimir Atanassov
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Batch Cultivation ◽  
Generation Gap ◽  
Model Parameters ◽  
Fed Batch ◽  
Intuitionistic Fuzzy ◽  
Before And After ◽  
Intuitionistic Fuzzy Logic ◽  
Fed Batch Cultivation

Intuitionistic fuzzy logic has been implemented in this investigation aiming to derive intuitionistic fuzzy estimations of model parameters of yeast fed-batch cultivation. Considered here are standard simple and multi-population genetic algorithms as well as their modifications differ from each other in execution order of main genetic operators (selection, crossover, and mutation). All are applied for the purpose of parameter identification of S. cerevisiae fed-batch cultivation. Performances of the examined algorithms have been assessed before and after the application of a procedure for narrowing the range of model parameters variation. Behavior of standard simple genetic algorithm has been also examined for different values of proof as the most sensitive genetic algorithms parameter toward convergence time, namely, generation gap (GGAP). Results obtained after the intuitionistic fuzzy logic implementation for assessment of genetic algorithms performance have been compared. As a result, the most reliable algorithm/value of GGAP ensuring the fastest and the most valuable solution is distinguished.

Segregated mathematical model for the fed-batch cultivation of a high-producing strain of Penicillium chrysogenum

1994 ◽  
Vol 34 (2) ◽  
pp. 119-131 ◽  
Author(s):  
Volker Tiller ◽  
Juliane Meyerhoff ◽  
Ditmar Sziele ◽  
Karl Schügerl ◽  
Karl-Heinz Bellgardt
Keyword(s):  
Mathematical Model ◽  
Penicillium Chrysogenum ◽  
Batch Cultivation ◽  
Fed Batch ◽  
Fed Batch Cultivation

scholarly journals From Physics to Bioengineering: Microbial Cultivation Process Design and Feeding Rate Control Based on Relative Entropy Using Nuisance Time

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 779 ◽  
Author(s):  
Renaldas Urniezius ◽  
Vytautas Galvanauskas ◽  
Arnas Survyla ◽  
Rimvydas Simutis ◽  
Donatas Levisauskas
Keyword(s):  
Convex Optimization ◽  
Relative Entropy ◽  
Feeding Rate ◽  
Batch Cultivation ◽  
Box Model ◽  
Model Parameters ◽  
Fed Batch ◽  
Cultivation Process ◽  
Microbial Cultivation ◽  
Fed Batch Cultivation

For historic reasons, industrial knowledge of reproducibility and restrictions imposed by regulations, open-loop feeding control approaches dominate in industrial fed-batch cultivation processes. In this study, a generic gray box biomass modeling procedure uses relative entropy as a key to approach the posterior similarly to how prior distribution approaches the posterior distribution by the multivariate path of Lagrange multipliers, for which a description of a nuisance time is introduced. The ultimate purpose of this study was to develop a numerical semi-global convex optimization procedure that is dedicated to the calculation of feeding rate time profiles during the fed-batch cultivation processes. The proposed numerical semi-global convex optimization of relative entropy is neither restricted to the gray box model nor to the bioengineering application. From the bioengineering application perspective, the proposed bioprocess design technique has benefits for both the regular feed-forward control and the advanced adaptive control systems, in which the model for biomass growth prediction is compulsory. After identification of the gray box model parameters, the options and alternatives in controllable industrial biotechnological processes are described. The main aim of this work is to achieve high reproducibility, controllability, and desired process performance. Glucose concentration measurements, which were used for the development of the model, become unnecessary for the development of the desired microbial cultivation process.

Genetic Algorithms Quality Assessment Implementing Intuitionistic Fuzzy Logic

2014 ◽  
pp. 327-354 ◽  
Author(s):  
Tania Pencheva ◽  
Maria Angelova ◽  
Krassimir Atanassov
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Batch Cultivation ◽  
Generation Gap ◽  
Model Parameters ◽  
Fed Batch ◽  
Intuitionistic Fuzzy ◽  
Before And After ◽  
Intuitionistic Fuzzy Logic ◽  
Fed Batch Cultivation

Intuitionistic fuzzy logic has been implemented in this investigation aiming to derive intuitionistic fuzzy estimations of model parameters of yeast fed-batch cultivation. Considered here are standard simple and multi-population genetic algorithms as well as their modifications differ from each other in execution order of main genetic operators (selection, crossover, and mutation). All are applied for the purpose of parameter identification of S. cerevisiae fed-batch cultivation. Performances of the examined algorithms have been assessed before and after the application of a procedure for narrowing the range of model parameters variation. Behavior of standard simple genetic algorithm has been also examined for different values of proof as the most sensitive genetic algorithms parameter toward convergence time, namely, generation gap (GGAP). Results obtained after the intuitionistic fuzzy logic implementation for assessment of genetic algorithms performance have been compared. As a result, the most reliable algorithm/value of GGAP ensuring the fastest and the most valuable solution is distinguished.

scholarly journals Optimization of Synthetic Media Composition for Kluyveromyces marxianus Fed-Batch Cultivation

Fermentation ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 62
Author(s):  
Konstantins Dubencovs ◽  
Janis Liepins ◽  
Arturs Suleiko ◽  
Anastasija Suleiko ◽  
Reinis Vangravs ◽  
...  
Keyword(s):  
Kluyveromyces Marxianus ◽  
Reaction Medium ◽  
Value Added ◽  
Batch Cultivation ◽  
Batch Processes ◽  
Biomass Growth ◽  
High Cell ◽  
Media Composition ◽  
Fed Batch ◽  
Fed Batch Cultivation

The Kluyveromyces marxianus yeast recently has gained considerable attention due to its applicability in high-value-added product manufacturing. In order to intensify the biosynthesis rate of a target product, reaching high biomass concentrations in the reaction medium is mandatory. Fed-batch processes are an attractive and efficient way how to achieve high cell densities. However, depending on the physiology of the particular microbial strain, an optimal media composition should be used to avoid by-product synthesis and, subsequently, a decrease in overall process effi-ciency. Thus, the aim of the present study was to optimise the synthetic growth medium and feeding solution compositions (in terms of carbon, nitrogen, phosphorous, magnesium, and calcium concentrations) for high cell density K. marxianus fed‑batch cultivations. Additionally, the biomass yields from the vitamin mixture and other macro/microelements were identified. A model predictive control algorithm was successfully applied for a fed-batch cultivation control. Biomass growth and substrate consumption kinetics were compared with the mathematical model predictions. Finally, 2‑phenylethanol biosynthesis was induced and its productivity was estimated. The determined optimal macronutrient ratio for K. marxianus biomass growth was identified as C:N:P = 1:0.07:0.011. The maximal attained yeast biomass concentration was close to 70 g·L-1 and the 2-PE biosynthesis rate was 0.372 g·L−1·h−1, with a yield of 74% from 2-phenylalanine.

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