Articles & Issues

Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received September 19, 2017
Accepted December 19, 2017

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

All issues

An artificial neural network approach to determine the rheological behavior of pickering-type diesel-in-water emulsion prepared with the use of β-cyclodextrin

Alireza Monazzami Farzaneh Vahabzadeh^† Abdolreza Aroujalian Azadeh Mogharei

Chemical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

far@aut.ac.ir

Korean Journal of Chemical Engineering, April 2018, 35(4), 847-852(6), 10.1007/s11814-017-0351-3

Download PDF

Abstract

With the use of β-cyclodextrin (β-CD), Pickering-type diesel-in-water emulsions were prepared based on the inclusion complex formed between diesel and β-CD which acted as an emulsifier. By using the artificial neural network (ANN), the rheological behavior of the emulsions was characterized using three input variables: diesel-to-water ratio, β-CD concentration, and shear rate and one-output variable as shear stress. Gradient descent (GD), conjugate gradient (CG), and quasi Newton (QN) were used as three different methods in the feed-forward back-propagation algorithm for network training. Hyperbolic tangent sigmoid and pure linear were the transfer functions used for transforming information between input and output through one hidden layer containing ten neurons. By dividing the experimental data into three sets of training, validation, and testing, the QN method in predicting shear stress was found to have performed better than the other two network learning techniques (R2=0.994 and MSE=0.006).

Keywords

β-Cyclodextrin Diesel-in-water Pickering-type Emulsion Rheological Behavior of Emulsion Artificial Neural Network Feed-forward Back-propagation Learning Algorithm

References

Rosen J, Surfactants and Interfacial Phenomena, 3rd Ed., New York, Wiley (2004).
McClements DJ, Food Emulsions, Principles, Practices, and Techniques, 2nd Ed., Florida, USA, CRC Press (2005).
Frelichowska J, Bolzinger MA, Chevalier Y, J. Colloid Interface Sci., 351(2), 348 (2010)
Inoue M, Hashizaki K, Taguchi H, Saito Y, J. Dispersion Sci. Technol., 31, 1648 (2010)
Fennema OR, Food Chemistry, 2nd Ed., New York, Marcel Dekker (1985).
Inoue M, Hashizaki K, Taguchi H, Saito Y, Chem. Pharm. Bull., 56, 1335 (2008)
Hashizaki K, Kageyama T, Inoue M, Taguchi H, Saito Y, J. Dispersion Sci. Technol., 30, 852 (2009)
Davarpanah L, Vahabzadeh F, Starch, 64, 898 (2012)
Saenger W, Angew. Chem.-Int. Edit., 19, 344 (1980)
Monazzami A, Vahabzadeh F, Aroujalian A, Chem. Eng. Trans., 53, 265 (2016)
Basheer IA, Hajmeer M, J. Microbiol. Methods, 43, 3 (2000)
Monazzami A, Vahabzadeh F, Aroujalian A, J. Dispersion Sci. Technol., Accepted for Publication (2017).
Vining GG, Statistical Methods for Engineers, Belmont, CA, U.S.A., Duxbury Press (1998).
Sisko AW, Research and Development Department, Standard Oil Co., 50, 1789 (1958).
Sharma K, Mulvaney J, Rizvi SH, Food Process Engineering: Theory and Laboratory Experiments, New York, Wiley (2000).