Prediction of Kinematic Viscosity of Binary Mixture of Poly (Ethylene Glycol) in Water using Artificial Neural Networks
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.
[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.
[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.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:52956", author = "M. Mohagheghian and A. M. Ghaedi and A. Vafaei", title = "Prediction of Kinematic Viscosity of Binary Mixture of Poly (Ethylene Glycol) in Water using Artificial Neural Networks", 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)", volume = "5", number = "1", pages = "34-4", }
