Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/13279
Title: Estimation of liquid viscosities of oils using associative neural networks
Authors: Neelamegam, P
Krishnaraj, S
Keywords: Andrade equation
Associative neural network
Oil
Regression
Viscosity
Issue Date: Nov-2011
Publisher: NISCAIR-CSIR, India
Abstract: Dynamic viscosities of a number of vegetable oils (castor oil, palm oil, sunflower oil and coconut oil) and lubricant oils (2T and 4T) have been determined at temperature range 30o - 90oC using Ubbelohde viscometer. An associative neural network is used to compute the viscosities of oils for unknown temperatures after training the neural network with type of oil, temperature as input and viscosity as output. Predicted results agree well with the experimental results. Simplified and modified form of Andrade equations that describe the temperature dependence of dynamic viscosities are fitted to the experimental data and correlations for the best fit are presented. The results obtained from associative neural network and best correlation equation show that both predict the viscosities very well with correlation coefficient R2 = 0.99.
Description: 463-468
URI: http://hdl.handle.net/123456789/13279
ISSN: 0975-0991 (Online); 0971-457X (Print)
Appears in Collections:IJCT Vol.18(6) [November 2011]

Files in This Item:
File Description SizeFormat 
IJCT 18(6) 463-468.pdf182.96 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.