An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

Jahirul, M.I., Senadeera, W., Brooks, P., Brown, R.J., Situ, R., 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: Proceedings of the 20th International Congress on Modelling and Simulation, pp. 1561-1567. From: MODSIM2013: 20th International Congress on Modelling and Simulation, 1-6 December 2013, Adelaide, SA, Australia.

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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.

Item ID: 31502
Item Type: Conference Item (Refereed Research Paper - E1)
Keywords: ANN model, kinematic viscosity, biodiesel, temperature dependence
Additional Information:

This publication is available on the conference website: http://www.mssanz.org.au/modsim2013/G6/jahirul.pdf

Jahirul, M.I., Senadeera, W., Brooks, P., Brown, R.J., Situ, R., 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 Piantadosi, J., Anderssen, R.S. and Boland J. (eds) MODSIM2013, 20th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2013, pp. 1561-1567. ISBN: 978-0-9872143-3-1. http://www.mssanz.org.au/modsim2013/G6/jahirul.pdf

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ISBN: 978-0-9872143-3-1
Funders: Queensland University of Technology, Australian Research Council (ARC)
Date Deposited: 04 Mar 2014 04:16
FoR Codes: 17 PSYCHOLOGY AND COGNITIVE SCIENCES > 1702 Cognitive Science > 170205 Neurocognitive Patterns and Neural Networks @ 50%
09 ENGINEERING > 0902 Automotive Engineering > 090201 Automotive Combustion and Fuel Engineering (incl Alternative/Renewable Fuels) @ 50%
SEO Codes: 85 ENERGY > 8505 Renewable Energy > 850599 Renewable Energy not elsewhere classified @ 100%
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