Prediction of Kinematic Viscosity of Binary Mixture of Poly (Ethylene Glycol) in Water using Artificial Neural Networks

Authors: M. Mohagheghian, A. M. Ghaedi, A. Vafaei

Abstract:

An artificial neural network (ANN) model is presented for the prediction of kinematic viscosity of binary mixtures of poly (ethylene glycol) (PEG) in water as a function of temperature, number-average molecular weight and mass fraction. Kinematic viscosities data of aqueous solutions for PEG (0.55419×10-6 – 9.875×10-6 m2/s) were obtained from the literature for a wide range of temperatures (277.15 - 338.15 K), number-average molecular weight (200 -10000), and mass fraction (0.0 – 1.0). A three layer feed-forward artificial neural network was employed. This model predicts the kinematic viscosity with a mean square error (MSE) of 0.281 and the coefficient of determination (R2) of 0.983. The results show that the kinematic viscosity of binary mixture of PEG in water could be successfully predicted using an artificial neural network model.

Keywords: Artificial neural network, kinematic viscosity, poly ethylene glycol (PEG)

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331059

References:

[1] M. Mohsen-Nia, H. Modarress and H. Rasa, "Measurement and modeling of density, kinematic viscosity, and refractive index for poly (ethylene glycol) aqueous solution at different temperatures," J. Chem. Eng. Data, vol. 50, no.5, pp. 1662-1666, Aug. 2005.
[2] M. Rahbari-sisakht, M. Taghizadeh, and A. Eliassi, "Densities and viscosities of binary mixture poly ethylene glycol) and poly (propylene glycol) in water and ethanol in the 293.15-338.15 K temperature rang," J. Chem. Eng. Data, vol. 48, no. 5, pp. 1221-1224, June. 2003.
[3] M. S. Cruz, L. D. A. Chumpitaz, J. G. L. F. Alves and A. J. A. Meirelles, "Kinematic Viscosities of poly(ethylene glycols)," J. Chem. Eng. Data, vol. 45, no. 1, pp. 61-63, Dec. 2000.
[4] P. Gonzalez-Tello, F. Camacho and G. Blazquez, "Density and viscosity of concentrated aqueous solutions of polyethylene glycol," J. Chem. Eng. Data, vol. 39, no. 3, pp. 611-614, Jul. 1994.
[5] F. Han, J. Zhang, G. Chen and X. Wei, "Density, Viscosity, and Excess Properties for Aqueous poly(ethylene glycol) Solution from (298.18 to 323.15) K," J. Chem. Eng. Data., vol. 53, no. 11, pp. 2598-2601, Oct. 2008.
[6] N. G. Tesierkezos and I. E. Molinou, "Density and Viscosities of Ethylene Glycol Binary Mixtures at 293.15 K," J. Chem. Eng. Data., vol. 44, no. 5, pp. 955-958, Jul.1999.
[7] I. C. Wel and R. L. Rowley, "Binary Liquid Mixture viscosities and Densities," J. Chem. Eng. Data., vol.29, no. 3, pp.332-335, Jul.1984.
[8] R. J. Lee and A. S. Teja, "Viscosities of poly (ethylene glycols)," J. Chem. Eng. Data., vol. 35, no. 4, pp.385-387, Oct.1990.
[9] A. Pal and W. Singh, "Speeds of Sound and Viscosities in Aqueous poly(ethylene glycol) Solutions at 303.15 and 308.15 K," J. Chem. Eng. Data., vol. 42, no. 2, pp.234-237, Mar.1997.
[10] L. Ninni, H. Burd, W. H. Fung and J. A. Meirelles, "Kinematic Viscosities of poly(ethylene glycol)Aqueous Solutions," J. Chem. Eng. Data., vol. 48, no. 2, pp.324-329, Jan.2003.
[11] L. Li, J. Wang, M. Zhao, C. Cui, and Y. Jiang, "Artificial neural network for production antioxidant peptides derived from Bighead carp muscles with alcalase," Food Technol. Biotechnol., vol. 44, no. 3, pp. 441-448, Mar. 2006.
[12] S. Erenturk, and K. Erenturk, "Comparison of genetic algorithm and neural network approaches for the drying process of carrot," J. Food Eng., vol. 78, no. 3, pp. 905-912, Feb. 2007.
[13] H. Yang, Z. Ring, Y. Briker, N. Mclean, W. Friesen, and C. Fairbrige, "Neural network prediction of cetane number and density of diesel fuel from its chemical composition determined by LC and GC-MS," Fuel, vol. 81, no. 10, pp. 65-74, Jan. 2002.
[14] H. L. Mattar, L. A. Minim, J. S. R. Coimbra, V. P. R. Minim, S. H. Saraiva, and R. J. Telis, "Modeling thermal conductivity specific heat and density of milk a neural network approach," Int. J. Food Properties, vol. 7, no. 3, pp. 531-539, Dec. 2004.
[15] K. Saeki, K. Tanabe, T. Matsumoto, H. Uesaka, T. Amano, and K. Funatsu, "Prediction of poly (ethylene glycol) density by near infrared spectroscopy combined with neural network analysis," J. Comput. Chem. Jpn., vol. 2, no. 1, pp. 34-40, Jan. 2003.
[16] S. Kerdpiboon, W. L. Kerr, and S. Devahastin, "Neural network prediction of physical property changes of dried carrot as a function of fractal dimension and moisture content," Food Res. Int., vol. 30, no. 10, pp. 1110-1118, Dec. 2006.