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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2019

19.06.2017 | Original Article

Black box modeling and multiobjective optimization of electrochemical ozone production process

verfasst von: Seyed Reza Nabavi, Mahmoud Abbasi

Erschienen in: Neural Computing and Applications | Sonderheft 2/2019

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Abstract

In this paper, simultaneous maximization of generated ozone concentration and minimization of electrical energy consumption is investigated in a laboratory-scale electrochemical ozone production system (EOP). Neural network simulation of EOP was carried out for generated ozone concentration prediction by Abbasi et al. (Chem Eng Res Des 92(11):2618–2625, 2014). In this study, neural network models (as black box models) were developed to predict both generated ozone concentration and electrical energy consumption. The models then were used for optimization. Altruistic non-dominated sorting genetic algorithm with jumping gene variant and termination criterion was used for MOO. Generational distance and spread were used in the termination criterion in order to stop algorithm after the right number of generations. Moreover, several optimal solutions from the Pareto-optimal set are chosen and then validated experimentally.
Vorheriger Artikel Optimization of friction stir welding process using NSGA-II and DEMO
Nächster Artikel Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network

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Metadaten
Titel
Black box modeling and multiobjective optimization of electrochemical ozone production process
verfasst von
Seyed Reza Nabavi
Mahmoud Abbasi
Publikationsdatum
19.06.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 2/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3057-x

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