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viscosity-prediction
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A novel correlation approach for viscosity prediction of water based nanofluids of Al2O3, TiO2, SiO2 and CuO
, Article Journal of the Taiwan Institute of Chemical Engineers ; Volume 58 , 2016 , Pages 19-27 ; 18761070 (ISSN) ; Daryasafar, A ; MoradiKoochi, M ; Moghadasi, J ; BabaeiMeybodi, R ; KhorramGhahfarokhi, A ; Sharif University of Technology
Taiwan Institute of Chemical Engineers
2016
Abstract
Nanofluids viscosity is one of the most important thermophysical properties in nanofluids usage especially in chemical and petroleum engineering applications. So it is highly desirable to predict the viscosity of nanofluids accurately. Experimental measurements are impossible in most situations and present models are not comprehensive and efficient especially for high temperature, high volume concentration and high viscosity values. In this study, a new correlation has been developed based on the comprehensive database of water based Al2O3, TiO2, SiO2 and CuO nanofluids viscosity data found in literature. The proposed correlation uses temperature, nanoparticle size, nanoparticle volumetric...
Toward reservoir oil viscosity correlation
, Article Chemical Engineering Science ; Volume 90 , 2013 , Pages 53-68 ; 00092509 (ISSN) ; Khishvand, M ; Naseri, A ; Mohammadi, A. H ; Sharif University of Technology
2013
Abstract
Oil viscosity plays a key role in reservoir simulation and production forecasting, as well as planning thermal enhanced oil recovery methods and these make its accurate determination necessary. In this communication, the most frequently used oil viscosity correlations are evaluated using a large databank of Iranian oil reservoirs which were measured using a Rolling Ball viscometer (Ruska, series 1602). To evaluate the performance and accuracy of these correlations, statistical and graphical error analyses have been used simultaneously. Three of the most accurate correlations for each region, including dead oil viscosity, viscosity below bubble point, viscosity at bubble point and the...
Viscosity prediction of ternary mixtures containing ILs using multi-layer perceptron artificial neural network
, Article Fluid Phase Equilibria ; Volume 326 , 2012 , Pages 15-20 ; 03783812 (ISSN) ; Hezave, A. Z ; Al Ajmi, A. M ; Ayatollahi, S ; Sharif University of Technology
Elsevier
2012
Abstract
Ionic liquids (ILs) have been considered as a good candidate to be replaced by the conventional solvent in recent years due to their potential consumptions and unique properties. In the present study, artificial neural network was used to predict the ternary viscosity of mixtures containing ILs. A collection of 729 experimental data points were gathered from the previously public shed literatures. Different topologies of a multilayer feed forward artificial neural network (MFFANN) were examined and optimum architecture was determined. Ternary viscosity data from the literature for 5 ILs with 547 data points have been used to train the network. In addition, to differentiate dissimilar...