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Computational modelling approach for the optimisation of a pulsed electric field system for liquid foods
被引:3
作者:
Araujo, Eduardo J.
[1
]
Lopes, Ivan J. S.
[2
]
Ramirez, Jaime A.
[2
]
机构:
[1] Univ Fed Minas Gerais, Grad Program Elect Engn, Ave Antonio Carlos 6627, BR-31270901 Belo Horizonte, MG, Brazil
[2] Univ Fed Minas Gerais, Dept Elect Engn, Ave Antonio Carlos 6627, BR-31270901 Belo Horizonte, MG, Brazil
关键词:
electric fields;
genetic algorithms;
Pareto optimisation;
microorganisms;
food technology;
pulsed electric field system;
liquid foods;
pulsed electric field treatment system;
electric field distribution;
multiobjective optimisation;
computational methodology;
Pareto optimal solutions;
electrical-thermal model;
multiobjective algorithm NSGA-II;
Pareto curves;
computational modelling;
E;
coli;
S;
aureus;
MatLab;
COMSOL;
THERMO-PHYSICAL PROPERTIES;
TREATMENT CHAMBERS;
JUICE;
STERILIZATION;
INACTIVATION;
PRESERVATION;
SIMULATIONS;
KINETICS;
DESIGN;
D O I:
10.1049/iet-smt.2018.5311
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
The microbial inactivation and specific energy of a pulsed electric field treatment system are dependent on the electric field distribution and treatment time since they have opposite behaviour in relation to these parameters, featuring a problem of multi-objective optimisation. This study proposes a computational methodology capable of providing Pareto optimal solutions for these two objectives, using a coupled electrical-thermal model, solved by COMSOL, which has been integrated to a multi-objective algorithm NSGA-II implemented in MatLab. The simulations were run for a computational design of experiment with the following variables: applied voltage, treatment time and the internal electrode radius (three levels for each one). In the post-processing analysis, the Pareto curves were plotted for two typical microorganisms of grape juice: E. coli and S. aureus, providing a set of solutions in terms of the log of the survival rate versus the specific energy. The methodology enables the decision maker to select the best solution from the Pareto curves as a function of a required microbial inactivation and energy features.
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页码:337 / 345
页数:9
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