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Reseach Article

Artificial Neural Network Prediction of Viscosity Index and Pour Point of Some Bio Lubricants from Selected Oil Plants

by A.A. Onogu, M.I. Oseni, A. Ashwe
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 10
Year of Publication: 2016
Authors: A.A. Onogu, M.I. Oseni, A. Ashwe
10.5120/ijais2016451556

A.A. Onogu, M.I. Oseni, A. Ashwe . Artificial Neural Network Prediction of Viscosity Index and Pour Point of Some Bio Lubricants from Selected Oil Plants. International Journal of Applied Information Systems. 10, 10 ( May 2016), 23-27. DOI=10.5120/ijais2016451556

@article{ 10.5120/ijais2016451556,
author = { A.A. Onogu, M.I. Oseni, A. Ashwe },
title = { Artificial Neural Network Prediction of Viscosity Index and Pour Point of Some Bio Lubricants from Selected Oil Plants },
journal = { International Journal of Applied Information Systems },
issue_date = { May 2016 },
volume = { 10 },
number = { 10 },
month = { May },
year = { 2016 },
issn = { 2249-0868 },
pages = { 23-27 },
numpages = {9},
url = { https://www.ijais.org/archives/volume10/number10/897-2016451556/ },
doi = { 10.5120/ijais2016451556 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:03:15.472321+05:30
%A A.A. Onogu
%A M.I. Oseni
%A A. Ashwe
%T Artificial Neural Network Prediction of Viscosity Index and Pour Point of Some Bio Lubricants from Selected Oil Plants
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 10
%N 10
%P 23-27
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial neural network as a modeling tool was utilized in predicting Viscosity index and Pour point of some bio lubricants from selected bio plants. In the laboratory the bio lubricant were extracted by Soxhlet processes and blended with additives from botanical plants after which their characterization were carried out. The neural model of Viscosity index and Pour point was developed based on parameters from Products extraction {Three (3) parameters}, Products blending {Four (4) parameters}, Products characterization {Two (2) parameters}. The nine (9) parameters were used as inputs into the network architecture of 9 (5)1 2 in predicting the Viscosity index and Pour point, after series of network architectures were trained using different training algorithm such as Levenberg-Marquardt, Bayesian regulation, Resilient back propagation etc. Using MATLAB 7.9.0 (r20096), the prediction of the neural network exhibited reasonable correlation with the targeted (real) Viscosity index and Pour point and predicted Viscosity index and Pour point with the network errors being reasonable.

References
  1. Lansdown, A. 2004. Lubrication and Lubricant Selection, a practical guide ASME Press
  2. Mang, T. and Dresel, W. 2005. Lubricants and Lubrication. Weinhein Wiley –VCH.
  3. Hafizpour, H.R., Sanjari, M., and Simchi, A. 2009. “Analysis of the effect of reinforcement particles on compressibility of AL-SIC Composites powders using neural network model” Materials Design Vol 33 pp1518-1523
  4. Birbilis, N. Cavanaugh, M.K. Sudholz, A.D. Zhu, S.M. Ecston, M.A. and Gibson M.A. 2011. “A Combined neural network and mechanistic approach for the prediction of corrosion rate and yield strength of magnesium – rare earth alloys” Corrosion Science Vol 53 pp 701-782.
  5. Kamrunnahar, M. and Urquidi- Macdonald, M. 2011. “Prediction of Corrosion behavior using neural network as a data mining tool” Corrosion Science Vol. 53 pp 961-967.
  6. Elaine, D.K. Ramon, S.C.P., Luiz, A. L., Yuri, C.S., Gabriel, P.D., Jose, L. 2009. “Artificial neural network corrosion modeling for metals in an equatorial climate”. Corrosion Science vol 51 pp 2256-2278.
  7. Cottis, R.A. Qing, L.I. Owen, G. Gartland, S.J. Heliwell, I.A. and Turega, M. 1999. “Neural network methods for corrosion data reduction” Materials and design Vol. 20 pp169-178.
  8. Zhang, Z. Barkoula, N.M. Karger-Kocsisk, K. Friedrick, F. 2003. “Artificial neural network prediction on erosive wear of Polymers.” Tribology international Vol 255 pp708-713
  9. Velten, K., Reinicke, R. and Friedrick, F. 2000. “Wear volume prediction with artificial neural network”. Tribology International Vol 33 pp 731-736.
  10. Farng, L.O. 2003. Ashless Antiwear and Extreme Pressure Additives in lubricant additives chemistry and Applications (ed L.R. Rudnick) Marcel Dekker Inc. New York.
  11. Dragan, A. 2010. “Neural network prediction of brake friction material wear” Wear Vol. 268 pp 117-125.
  12. Randles, S.J. 1994 Formulation of Environmentally Acceptable Lubricants 49TH STLE Annual meeting Pittsburgh.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial neural network Bio lubricants Oil plants Viscosity index and Pour point