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

A.A. Onogu, M.I. Oseni, A. Ashwe. Published in Networks

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: A.A. Onogu, M.I. Oseni, A. Ashwe
10.5120/ijais2016451556
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  1. A A Onogu, M I Oseni and 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):23-27, May 2016. URL, DOI BibTeX

    @article{10.5120/ijais2016451556,
    	author = "A.A. Onogu and M.I. Oseni and 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 = 5,
    	url = "http://www.ijais.org/archives/volume10/number10/897-2016451556",
    	doi = "10.5120/ijais2016451556",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, 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.

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Keywords

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