An artificial neutral network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

Jahirul, M. L. and Senadeera, W. and Brooks, P. and Brown, R. J. and Situ, R. and Pham, P. X. and Masri, A. R. (2013) An artificial neutral network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions. In: 20th International Congress on Modelling and Simulation (MODSIM 2013): Adapting to Change: The Multiple Roles of Modelling , 1-6 Dec 2013, Adelaide, Australia.

Abstract

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and
physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright 2013 Modelling and Simulation Society of Australia and New Zealand Inc. Permanent restricted access to published version due to publisher copyright policy.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering
Date Deposited: 08 Feb 2017 02:50
Last Modified: 14 Feb 2017 04:52
Uncontrolled Keywords: ANN model, biodiesel, kinematic viscosity, temperature dependence
Fields of Research : 09 Engineering > 0913 Mechanical Engineering > 091305 Energy Generation, Conversion and Storage Engineering
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
URI: http://eprints.usq.edu.au/id/eprint/30553

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