Multi-objective optimization of microfiltration of baker’s yeast using genetic algorithm
This paper presents a multi-objective optimization model by applying genetic algorithm in order to search for optimal operating parameters of microfiltration of baker’s yeast in the presence of static mixer as a turbulence promoter. The operating variables were the suspension concentrati...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Technology, Novi Sad
2017-01-01
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| Series: | Acta Periodica Technologica |
| Subjects: | |
| Online Access: | http://www.doiserbia.nb.rs/img/doi/1450-7188/2017/1450-71881748211L.pdf |
| Summary: | This paper presents a multi-objective optimization model by applying genetic
algorithm in order to search for optimal operating parameters of
microfiltration of baker’s yeast in the presence of static mixer as a
turbulence promoter. The operating variables were the suspension
concentration, transmembrane pressure, and feed flow rate. Two conflicting
objective functions, maximizing the permeate flux and maximizing the
reduction of energy consumption, were considered. This multi-objective
optimization problem was solved by using the elitist non-dominated sorting
genetic algorithm in the Matlab R2015b software. The Pareto fronts along with
the process decision variables correspondding to the optimal solutions were
obtained. It was found that lower suspension concentrations (2-4.5 g/L), feed
flow rate in the range 109-127 L/h, and transmembrane pressure of 1 bar were
the optimal process parameters which yielded maximum permeate flux (177-191
L/(m2h)) and maximum reduction of energy consumption (44-50%). Finally, the
results were compared with the previously published results obtained by
applying desirability function approach. Given that genetic algorithms have
generated multiple solutions in a single optimization run, the study proved
that genetic algorithms are preferable to classical optimization methods.
[Project of the Serbian Ministry of Education, Science and Technological
Development, Grant no. TR-31002] |
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| ISSN: | 1450-7188 2406-095X |