Conference paper
An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions
Proceedings of the 20th International Congress on Modelling and Simulation, pp.1561-1567
International Congress on Modelling and Simulation (MODSIM), 20th (Adelaide, Australia, 01-Dec-2013–06-Dec-2013)
Modelling and Simulation Society of Australia and New Zealand
2013
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.
Details
- Title
- An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions
- Authors
- M I Jahirul (Author) - Queensland University of TechnologyWijitha Senadeera (Author) - Queensland University of TechnologyPeter R Brooks (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and EngineeringRichard J Brown (Author) - Queensland University of TechnologyR Situ (Author) - James Cook UniversityP X Pham (Author) - University of SydneyA R Masri (Author) - University of Sydney
- Contributors
- J Piantadosi (Editor)R S Anderssen (Editor)J Boland (Editor)
- Publication details
- Proceedings of the 20th International Congress on Modelling and Simulation, pp.1561-1567
- Conference details
- International Congress on Modelling and Simulation (MODSIM), 20th (Adelaide, Australia, 01-Dec-2013–06-Dec-2013)
- Publisher
- Modelling and Simulation Society of Australia and New Zealand
- Date published
- 2013
- ISBN
- 9780987214331
- Copyright note
- Copyright © 2013 The Author. Reproduced here with permission.
- Organisation Unit
- School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99449930402621
- Output Type
- Conference paper
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