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Czasopismo
2013 | 11 | 7 | 1213-1224
Tytuł artykułu

Optimization methodology based on neural networks and genetic algorithms applied to electro-coagulation processes

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
An optimization methodology based on neural networks and genetic algorithms was developed and used to optimize a real world process - an electro-coagulation process involving three pollutants at different concentrations: kaolin (250–1000 mg L−1), Eriochrome Black T solutions (50–200 mg L−1), and oil/water emulsion (1500–4500 mg L−1). Feed-forward neural networks using heterogeneous combination of transfer functions were developed, leading to good results in the validation stage (relative error about 8%). The parameters of the process (concentration of pollutant, time, pH0, conductivity and current density) were optimized handling the genetic algorithm parameters, in order to obtain a maximum removal efficiency for each pollutant. Therefore, the optimization methodology combines neural networks as modeling tools with genetic algorithms as solving method. Validation of the optimization results using supplementary experimental data reveals errors under 11%. [...]
Wydawca

Czasopismo
Rocznik
Tom
11
Numer
7
Strony
1213-1224
Opis fizyczny
Daty
wydano
2013-07-01
online
2013-04-26
Twórcy
  • Department of Chemical Engineering, “Gheorghe Asachi” Technical University of Iasi, 700050, Iasi, Romania
  • Department of Chemical Engineering, “Gheorghe Asachi” Technical University of Iasi, 700050, Iasi, Romania, silvia_curteanu@yahoo.com
  • Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004, Ciudad Real, Spain
  • Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004, Ciudad Real, Spain
  • Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004, Ciudad Real, Spain
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.-psjd-doi-10_2478_s11532-013-0253-0
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