Viscosity prediction of hydrocarbon binary mixture using an artificial neural network-group contribution method
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Sara Nanvakenari
Abstract
In this study, the viscosity of hydrocarbon binary mixtures has been predicted with an artificial neural network and a group contribution method (ANN-GCM) by utilizing various training algorithm including Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), Resilient back Propagation (RP), and Gradient Descent with variable learning rate back propagation (GDX). Moreover, different transfer functions such as Tan-sigmoid (tansig), Log-sigmoid (logsig), and purelin were investigated in hidden and output layer and their effects on network precision were estimated. Accordingly, 796 experimental data points of viscosity of hydrocarbon binary mixture were collected from the literature for a wide range of operating parameters. The temperature, pressure, mole fraction, molecular weight, and structural group of the system were selected as the independent input parameters. The statistical analysis results with R 2 = 0.99 revealed a small value for Average absolute relative deviation (AARD) of 1.288 and Mean square error (MSE) of 0.001018 by comparing the ANN predicted data with experimental data. Neural network configuration was also optimized. Based on the results, the network with one hidden layer and 27 neurons with the Levenberg-Marquardt training algorithm and tansig transfer function for hidden layer along with purelin transfer function for output layer constituted the best network structure. Further, the weights and bias were optimized to minimize the error. Then, the obtained results of the present study were compared with the data from some previous methods. The results suggested that this work can predict the viscosity of hydrocarbon binary mixture with better AARD. In general, the results indicated that combining ANN and GCM model is capable to predict the viscosity of hydrocarbon binary mixtures with a good accuracy.
Funding source: Babol Noshirvani University of Technology
Award Identifier / Grant number: BNUT/370675/98
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: This research was supported by Babol Noshiravani University of Technology through Grant Program No. BNUT/370675/98.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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© 2020 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Viscosity prediction of hydrocarbon binary mixture using an artificial neural network-group contribution method
- Design of an environmentally friendly fuel based on a synthetic composite nano-catalyst through parameter estimation and process modeling
- Numerical study of coupled natural convection to surface radiation in an open cavity submitted to lateral or corner heating
- A comparative study of thermodynamic models to describe the VLE of the ternary electrolytic mixture H2O–NH3–CO2
- Murphree vapor efficiency prediction in SCC columns by computational fluid dynamics analysis
- Retrofitting recycled stripping gas in a glycol dehydration regeneration unit
Articles in the same Issue
- Frontmatter
- Research Articles
- Viscosity prediction of hydrocarbon binary mixture using an artificial neural network-group contribution method
- Design of an environmentally friendly fuel based on a synthetic composite nano-catalyst through parameter estimation and process modeling
- Numerical study of coupled natural convection to surface radiation in an open cavity submitted to lateral or corner heating
- A comparative study of thermodynamic models to describe the VLE of the ternary electrolytic mixture H2O–NH3–CO2
- Murphree vapor efficiency prediction in SCC columns by computational fluid dynamics analysis
- Retrofitting recycled stripping gas in a glycol dehydration regeneration unit
