Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends

Youssef Kassem; Hüseyin Çamur; Kamal Elmokhtar Bennur

doi:doi:10.11648/j.ajcst.20180101.12

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Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends

Youssef Kassem, Hüseyin Çamur, Kamal Elmokhtar Bennur

Published in American Journal of Computer Science and Technology (Volume 1, Issue 1)

Received: 1 November 2017 Accepted: 13 November 2017 Published: 24 December 2017

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Abstract

Biodiesel is considered as an alternative source of energy obtained from renewable materials. In the present paper, the investigation of the applicability of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling the biodiesel blends property including kinematic viscosity and density at various temperatures and the volume fractions of biodiesel. An experimental database of kinematic viscosity and density of biodiesel blends (biodiesel blend with diesel fuel) were used for developing of models, where the input variables in the network were the temperature and volume fractions of biodiesel. The model results were compared with experimental ones for determining the accuracy of the models. The developed models produced idealized results and were found to be useful for predicting the kinematic viscosity and density of biodiesel blends with a limited number of available data. Moreover, the results suggest that the ANFIS approach can be used successfully for predicting the kinematic viscosity and density of biodiesel blends at various volume fractions and temperature compared to another models.

Published in	American Journal of Computer Science and Technology (Volume 1, Issue 1)
DOI	10.11648/j.ajcst.20180101.12
Page(s)	8-18
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

ANFIS, ANN, Biodiesel, Density, Kinematic Viscosity

References

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Youssef Kassem, Hüseyin Çamur, Kamal Elmokhtar Bennur. (2017). Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends. American Journal of Computer Science and Technology, 1(1), 8-18. https://doi.org/10.11648/j.ajcst.20180101.12

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ACS Style

Youssef Kassem; Hüseyin Çamur; Kamal Elmokhtar Bennur. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends. Am. J. Comput. Sci. Technol. 2017, 1(1), 8-18. doi: 10.11648/j.ajcst.20180101.12

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AMA Style

Youssef Kassem, Hüseyin Çamur, Kamal Elmokhtar Bennur. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends. Am J Comput Sci Technol. 2017;1(1):8-18. doi: 10.11648/j.ajcst.20180101.12

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@article{10.11648/j.ajcst.20180101.12,
  author = {Youssef Kassem and Hüseyin Çamur and Kamal Elmokhtar Bennur},
  title = {Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends},
  journal = {American Journal of Computer Science and Technology},
  volume = {1},
  number = {1},
  pages = {8-18},
  doi = {10.11648/j.ajcst.20180101.12},
  url = {https://doi.org/10.11648/j.ajcst.20180101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20180101.12},
  abstract = {Biodiesel is considered as an alternative source of energy obtained from renewable materials. In the present paper, the investigation of the applicability of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling the biodiesel blends property including kinematic viscosity and density at various temperatures and the volume fractions of biodiesel. An experimental database of kinematic viscosity and density of biodiesel blends (biodiesel blend with diesel fuel) were used for developing of models, where the input variables in the network were the temperature and volume fractions of biodiesel. The model results were compared with experimental ones for determining the accuracy of the models. The developed models produced idealized results and were found to be useful for predicting the kinematic viscosity and density of biodiesel blends with a limited number of available data. Moreover, the results suggest that the ANFIS approach can be used successfully for predicting the kinematic viscosity and density of biodiesel blends at various volume fractions and temperature compared to another models.},
 year = {2017}
}

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TY  - JOUR
T1  - Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends
AU  - Youssef Kassem
AU  - Hüseyin Çamur
AU  - Kamal Elmokhtar Bennur
Y1  - 2017/12/24
PY  - 2017
N1  - https://doi.org/10.11648/j.ajcst.20180101.12
DO  - 10.11648/j.ajcst.20180101.12
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 8
EP  - 18
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20180101.12
AB  - Biodiesel is considered as an alternative source of energy obtained from renewable materials. In the present paper, the investigation of the applicability of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling the biodiesel blends property including kinematic viscosity and density at various temperatures and the volume fractions of biodiesel. An experimental database of kinematic viscosity and density of biodiesel blends (biodiesel blend with diesel fuel) were used for developing of models, where the input variables in the network were the temperature and volume fractions of biodiesel. The model results were compared with experimental ones for determining the accuracy of the models. The developed models produced idealized results and were found to be useful for predicting the kinematic viscosity and density of biodiesel blends with a limited number of available data. Moreover, the results suggest that the ANFIS approach can be used successfully for predicting the kinematic viscosity and density of biodiesel blends at various volume fractions and temperature compared to another models.
VL  - 1
IS  - 1
ER  -

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Author Information

Youssef Kassem

Department of Mechanical Engineering, Faculty of Engineering, Near East University, Nicosia, Cyprus
Hüseyin Çamur
Kamal Elmokhtar Bennur

Department of Mechanical Engineering, Faculty of Engineering, Near East University, Nicosia, Cyprus

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Youssef Kassem, Hüseyin Çamur, Kamal Elmokhtar Bennur. (2017). Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends. American Journal of Computer Science and Technology, 1(1), 8-18. https://doi.org/10.11648/j.ajcst.20180101.12

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ACS Style

Youssef Kassem; Hüseyin Çamur; Kamal Elmokhtar Bennur. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends. Am. J. Comput. Sci. Technol. 2017, 1(1), 8-18. doi: 10.11648/j.ajcst.20180101.12

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AMA Style

Youssef Kassem, Hüseyin Çamur, Kamal Elmokhtar Bennur. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends. Am J Comput Sci Technol. 2017;1(1):8-18. doi: 10.11648/j.ajcst.20180101.12

Copy | Download

@article{10.11648/j.ajcst.20180101.12,
  author = {Youssef Kassem and Hüseyin Çamur and Kamal Elmokhtar Bennur},
  title = {Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends},
  journal = {American Journal of Computer Science and Technology},
  volume = {1},
  number = {1},
  pages = {8-18},
  doi = {10.11648/j.ajcst.20180101.12},
  url = {https://doi.org/10.11648/j.ajcst.20180101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20180101.12},
  abstract = {Biodiesel is considered as an alternative source of energy obtained from renewable materials. In the present paper, the investigation of the applicability of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling the biodiesel blends property including kinematic viscosity and density at various temperatures and the volume fractions of biodiesel. An experimental database of kinematic viscosity and density of biodiesel blends (biodiesel blend with diesel fuel) were used for developing of models, where the input variables in the network were the temperature and volume fractions of biodiesel. The model results were compared with experimental ones for determining the accuracy of the models. The developed models produced idealized results and were found to be useful for predicting the kinematic viscosity and density of biodiesel blends with a limited number of available data. Moreover, the results suggest that the ANFIS approach can be used successfully for predicting the kinematic viscosity and density of biodiesel blends at various volume fractions and temperature compared to another models.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends
AU  - Youssef Kassem
AU  - Hüseyin Çamur
AU  - Kamal Elmokhtar Bennur
Y1  - 2017/12/24
PY  - 2017
N1  - https://doi.org/10.11648/j.ajcst.20180101.12
DO  - 10.11648/j.ajcst.20180101.12
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 8
EP  - 18
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20180101.12
AB  - Biodiesel is considered as an alternative source of energy obtained from renewable materials. In the present paper, the investigation of the applicability of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling the biodiesel blends property including kinematic viscosity and density at various temperatures and the volume fractions of biodiesel. An experimental database of kinematic viscosity and density of biodiesel blends (biodiesel blend with diesel fuel) were used for developing of models, where the input variables in the network were the temperature and volume fractions of biodiesel. The model results were compared with experimental ones for determining the accuracy of the models. The developed models produced idealized results and were found to be useful for predicting the kinematic viscosity and density of biodiesel blends with a limited number of available data. Moreover, the results suggest that the ANFIS approach can be used successfully for predicting the kinematic viscosity and density of biodiesel blends at various volume fractions and temperature compared to another models.
VL  - 1
IS  - 1
ER  -

Copy | Download