Modeling of Oil-Water Emulsion Separation in Ultrasound Standing Wavefield by Neural Network

A new approach has been introduced for separation of oil-in-water emulsion by using ultrasound standing wavefield. A neural networks model was used to simulate changes in the size of droplet during treatment. Model outputs were then validated and generalization capability was evaluated. For each net...

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Bibliographic Details
Published inJournal of dispersion science and technology Vol. 34; no. 4; pp. 490 - 495
Main Authors Nasiri, H. Ghafourian, Mosavian, M. T. Hamed, Kadkhodaee, R., Sargolzae, J.
Format Journal Article
LanguageEnglish
Published Philadelphia, PA Taylor & Francis Group 01.04.2013
Taylor & Francis
Taylor & Francis Ltd
Subjects
Chemistry
Colloidal state and disperse state
Emulsions. Microemulsions. Foams
Exact sciences and technology
General and physical chemistry
Neural networks
o/w emulsion
RBF
separation
Studies
Ultrasonic imaging
ultrasound
Separation
Oil water emulsion
Neural networks
RBF
Neural network
o/w emulsion
Modeling
Ultrasound
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Summary:A new approach has been introduced for separation of oil-in-water emulsion by using ultrasound standing wavefield. A neural networks model was used to simulate changes in the size of droplet during treatment. Model outputs were then validated and generalization capability was evaluated. For each network, the optimum values of isotropic spread were obtained by minimizing the root mean square error and maximizing the corresponding coefficient. It was found that the predicted values were in good agreements with experimental results. Also, increasing voice speed was demonstrated to predict size of emulsion particles more efficiently and accurately.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0193-2691
1532-2351
DOI:10.1080/01932691.2012.681997