InterCriteria Analysis Approach to Parameter Identification of a Fermentation Process Model

2015 ◽  
pp. 385-397 ◽  
Author(s):  
Tania Pencheva ◽  
Maria Angelova ◽  
Peter Vassilev ◽  
Olympia Roeva
Keyword(s):  
Parameter Identification ◽  
Process Model ◽  
Fermentation Process ◽  
Analysis Approach

scholarly journals Genetic Algorithm and Cuckoo Search Hybrid Technique for Parameter Identification of Fermentation Process Model

2020 ◽  
Vol 24 (3) ◽  
pp. 277-288
Author(s):  
Maria Angelova ◽  
◽  
Peter Vassilev ◽  
Tania Pencheva ◽  
◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Process Model ◽  
Fermentation Process ◽  
Cuckoo Search ◽  
Hybrid Technique

scholarly journals Modeling and Parameter Identification Involving 3-Hydroxypropionaldehyde Inhibitory Effects in Glycerol Continuous Fermentation

2012 ◽  
Vol 2012 ◽  
pp. 1-18
Author(s):  
Zhaohua Gong ◽  
Chongyang Liu ◽  
Yongsheng Yu
Keyword(s):  
Parameter Identification ◽  
Fermentation Process ◽  
Continuous Fermentation ◽  
Robustness Analysis ◽  
Function Technique ◽  
Improved Genetic Algorithm ◽  
Proposed Model ◽  
Biotechnological Processes ◽  
Extracellular Substances ◽  
Identification Model

Mathematical modeling and parameter estimation are critical steps in the optimization of biotechnological processes. In the 1,3-propanediol (1,3-PD) production by glycerol fermentation process under anaerobic conditions, 3-hydroxypropionaldehyde (3-HPA) accumulation would arouse an irreversible cessation of the fermentation process. Considering 3-HPA inhibitions to cells growth and to activities of enzymes, we propose a novel mathematical model to describe glycerol continuous cultures. Some properties of the above model are discussed. On the basis of the concentrations of extracellular substances, a parameter identification model is established to determine the kinetic parameters in the presented system. Through the penalty function technique combined with an extension of the state space method, an improved genetic algorithm is then constructed to solve the parameter identification model. An illustrative numerical example shows the appropriateness of the proposed model and the validity of optimization algorithm. Since it is difficult to measure the concentrations of intracellular substances, a quantitative robustness analysis method is given to infer whether the model is plausible for the intracellular substances. Numerical results show that the proposed model is of good robustness.

Continuous alcoholic fermentation process: model considering loss of cell viability

1999 ◽  
Vol 20 (2) ◽  
pp. 157 ◽  
Author(s):  
S. C. Oliveira ◽  
T. C. B. Paiva ◽  
A. E. S. Visconti ◽  
R. Giudici
Keyword(s):  
Cell Viability ◽  
Process Model ◽  
Fermentation Process ◽  
Alcoholic Fermentation

Modeling and Parameter Identification of the Simultaneous Saccharification-Fermentation Process for Ethanol Production

2007 ◽  
Vol 23 (6) ◽  
pp. 1454-1462 ◽  
Author(s):  
S. Ochoa ◽  
A. Yoo ◽  
J.-U. Repke ◽  
G. Wozny ◽  
D.R. Yang
Keyword(s):  
Parameter Identification ◽  
Ethanol Production ◽  
Fermentation Process ◽  
Simultaneous Saccharification
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