An artificial neural network approach to determine the rheological behavior of pickering-type diesel-in-water emulsion prepared with the use of β-cyclodextrin
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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).
Keywordsβ-Cyclodextrin Diesel-in-water Pickering-type Emulsion Rheological Behavior of Emulsion Artificial Neural Network Feed-forward Back-propagation Learning Algorithm
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- 2.D. J. McClements, Food Emulsions, Principles, Practices, and Techniques, 2nd Ed., Florida, USA, CRC Press (2005).Google Scholar
- 5.O.R. Fennema, Food Chemistry, 2nd Ed., New York, Marcel Dekker (1985).Google Scholar
- 10.A. Monazzami, F. Vahabzadeh and A. Aroujalian, Chem. Eng. Trans., 53, 265 (2016).Google Scholar
- 12.A. Monazzami, F. Vahabzadeh and A. Aroujalian, J. Dispersion Sci. Technol., Accepted for Publication (2017).Google Scholar
- 13.G. G. Vining, Statistical Methods for Engineers, Belmont, CA, U.S.A., Duxbury Press (1998).Google Scholar
- 14.A.W. Sisko, Research and Development Department, Standard Oil Co., 50, 1789 (1958).Google Scholar
- 15.K. Sharma, J. Mulvaney and S. H. Rizvi, Food Process Engineering: Theory and Laboratory Experiments, New York, Wiley (2000).Google Scholar