Estimation of biodiesel properties from chemical composition – an artificial neural network (ANN) approach

Article

Jahirul, M. I., Senadeera, W., Brown, R. J. and Moghaddam, L.. 2014. "Estimation of biodiesel properties from chemical composition – an artificial neural network (ANN) approach." International Scientific Journal of Environmental Science. 3 (3), pp. 1-7.
Article Title

Estimation of biodiesel properties from chemical composition – an artificial neural network (ANN) approach

Article CategoryArticle
AuthorsJahirul, M. I. (Author), Senadeera, W. (Author), Brown, R. J. (Author) and Moghaddam, L. (Author)
Journal TitleInternational Scientific Journal of Environmental Science
Journal Citation3 (3), pp. 1-7
Number of Pages7
Year2014
Place of PublicationBelize, Central America
Web Address (URL)http://environment.scientific-journal.com/articles/3/31.pdf
Abstract

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. 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 and learning algorithm were 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 coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Keywordsbiodiesel; kinematic viscosity; Artificial Neural Network (ANN); prediction model
ANZSRC Field of Research 2020401703. Energy generation, conversion and storage (excl. chemical and electrical)
Byline AffiliationsQueensland University of Technology
Institution of OriginUniversity of Southern Queensland
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Karunasena, H. C. P., Gu, Y. T., Brown, R. J. and Senadeera, W.. 2015. "Numerical investigation of plant tissue porosity and its influence on cellular level shrinkage during drying." Biosystems Engineering. 132, pp. 71-87. https://doi.org/10.1016/j.biosystemseng.2015.02.002
Influence of drying conditions on the moisture diffusion during single stage and two stage fluidized bed drying of bovine intestine for pet food
Alves-Filho, O., Senadeera, W. and Eikevik, T.. 2013. "Influence of drying conditions on the moisture diffusion during single stage and two stage fluidized bed drying of bovine intestine for pet food." Goncharova-Alves, S., Alves-Filho, O. and Eikevik, T. M. (ed.) 6th Nordic Drying Conference (NDC2013). Copenhagen, Denmark 05 - 07 Jun 2013 Copenhagen, Denmark.
Drying condition effects on fluidization quality and fluidization velocity at initial and final stages of cubical particulates for pet food
Alves-Filho, O., Senadeera, W. and Eikevik, T.. 2013. "Drying condition effects on fluidization quality and fluidization velocity at initial and final stages of cubical particulates for pet food." Goncharova-Alves, S., Alves-Filho, O. and Eikevik, T. M. (ed.) 6th Nordic Drying Conference (NDC2013). Copenhagen, Denmark 05 - 07 Jun 2013 Copenhagen, Denmark.
Application of meshfree methods to numerically simulate microscale deformations of different plant food materials during drying
Karunasena, H. C. P., Brown, R. J., Gu, Y. T. and Senadeera, W.. 2015. "Application of meshfree methods to numerically simulate microscale deformations of different plant food materials during drying." Journal of Food Engineering. 146, pp. 209-226. https://doi.org/10.1016/j.jfoodeng.2014.09.011
The use of artificial neural networks for identifying sustainable biodiesel feedstocks
Jahirul, Mohammed I., Brown, Richard J., Senadeera, Wijitha, O'Hara, Ian M. and Ristovski, Zoran D.. 2013. "The use of artificial neural networks for identifying sustainable biodiesel feedstocks." Energies. 6 (8), pp. 3764-3806. https://doi.org/10.3390/en6083764
Scanning electron microscopic study of microstructure of gala apples during hot air drying
Karunasena, H. C. P., Hesami, P., Senadeera, W., Gu, Y. T., Brown, R. J. and Oloyede, A.. 2014. "Scanning electron microscopic study of microstructure of gala apples during hot air drying." Drying Technology: an international journal. 32 (4), pp. 455-468. https://doi.org/10.1080/07373937.2013.837479
Numerical investigation of case hardening of plant tissue during drying and its influence on the cellular-level shrinkage
Karunasena, H. C. P., Gu, Y.T., Brown, R. J. and Senadeera, W.. 2015. "Numerical investigation of case hardening of plant tissue during drying and its influence on the cellular-level shrinkage." Drying Technology: an international journal. 33 (6), pp. 713-734. https://doi.org/10.1080/07373937.2014.982759
Biodiesel production from non-edible beauty leaf (Calophyllum inophyllum) oil: process pptimization using response surface methodology (RSM)
Jahirul, Mohammad I., Koh, Wenyong, Brown, Richard J., Senadeera, Wijitha, O'Hara, Ian and Moghaddam, Lalehvash. 2014. "Biodiesel production from non-edible beauty leaf (Calophyllum inophyllum) oil: process pptimization using response surface methodology (RSM)." Energies. 7 (8), pp. 5317-5331. https://doi.org/10.3390/en7085317
Numerical Modeling of Morphological Changes of Food Plant Materials during Drying
Karunasena, H. C. P. and Senadeera Wijitha. 2017. "Numerical Modeling of Morphological Changes of Food Plant Materials during Drying ." Zhang, Min, Bhandari, Bhesh and Fang, Zhongxiang (ed.) Handbook of Drying of Vegetables and Vegetable Products. United Kingdom. CRC Press. pp. 387-427
The Drying Constant and its Effect on the Shrinkage Constant of Different-Shaped Food Particulates
Senadeera, Wijitha. 2008. "The Drying Constant and its Effect on the Shrinkage Constant of Different-Shaped Food Particulates." International Journal of Food Engineering. 4 (8). https://doi.org/10.2202/1556-3758.1219
Drying constant and shrinkage constant relations of different shaped food particulates: empirical models
Senadeera, W.. 2008. "Drying constant and shrinkage constant relations of different shaped food particulates: empirical models." Thorat, B. N. and Mujumdar, A. S. (ed.) 16th International Drying Symposium (IDS 2008). Hyderabad, India 09 - 12 Nov 2008 https://doi.org/10.1080/07373930903221812
Fluidization characteristics of moist food particles
Senadeera, Wijitha, Wijesinghe, Bandu, Young, Gordon and Bhandari, Bhesh. 2006. "Fluidization characteristics of moist food particles." International Journal of Food Engineering. 2 (1), pp. 1-13. https://doi.org/10.2202/1556-3758.1047
Modeling dimensional shrinkage of shaped foods in fluidized bed drying
Senadeera, Wijitha, Bhandari, Bhesh R., Young, Gordon and Wijesinghe, Bandu. 2005. "Modeling dimensional shrinkage of shaped foods in fluidized bed drying." Journal of Food Processing and Preservation. 29 (2), pp. 109-119. https://doi.org/10.1111/j.1745-4549.2005.00017.x
Influence of shapes of selected vegetable materials on drying kinetics during fluidized bed drying
Senadeera, Wijitha, Bhandari, Bhesh R., Young, Gordon and Wijesinghe, Bandu. 2003. "Influence of shapes of selected vegetable materials on drying kinetics during fluidized bed drying." Journal of Food Engineering. 58 (3), pp. 277-283. https://doi.org/10.1016/S0260-8774(02)00386-2
Methods for effective fluidization of particulate food materials
Senadeera, Wijitha, Bhandari, Bhesh R., Young, Gordon and Wijesinghe, Bandu. 2000. "Methods for effective fluidization of particulate food materials." Drying Technology: an international journal. 18 (7), pp. 1537-1557. https://doi.org/10.1080/07373930008917792
Physical properties and fluidization behaviour of fresh green bean particulates during fluidized bed drying
Senadeera, W., Bhandari, B., Young, G. and Wijesinghe, B.. 2000. "Physical properties and fluidization behaviour of fresh green bean particulates during fluidized bed drying." Food and Bioproducts Processing. 78 (C1), pp. 43-47. https://doi.org/10.1205/096030800532707