ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel

Samson Kolawole Fasogbon; Olusegun Oladapo Laosebikan; Chukwuemeka Uguba Owora

doi:doi:10.11648/j.ajme.20190502.16

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ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel

Samson Kolawole Fasogbon, Olusegun Oladapo Laosebikan, Chukwuemeka Uguba Owora

Published in American Journal of Modern Energy (Volume 5, Issue 2)

Received: 20 March 2019 Accepted: 27 April 2019 Published: 20 June 2019

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Abstract

In the present work, biodiesel prepared from Tropical almond oil (Terminalia Catappa) was used as fuel in C. I engine. Performance studies were conducted on a single cylinder four-stroke water-cooled compression ignition engine. Experiments were conducted for different percentage of blends of Tropical almond ester with diesel at different injection timings. Experimental investigations on the performance parameters from the engine were done. Artificial neural network (ANN) of back-propagation feed-forward Levenberg-Marquardt algorithm was used to predict the performance characteristics of the engine. An ANN model was developed for the performance parameters. To train the network, blend percentage, percentage load and injection timings were used as the input variables whereas engine performance parameters (brake thermal efficiency, exhaust gas temperature, and brake specific fuel consumption) were used as the output variables. The obtained experimental results were used to train the network structure. Results showed very good correlation between the ANN predicted values and the desired values for various engine performance values. Mean relative error values were less than 10 percent which by many standards is acceptable. The results show that ANN is an accurately reliable tool for the prediction of engine performance.

Published in	American Journal of Modern Energy (Volume 5, Issue 2)
DOI	10.11648/j.ajme.20190502.16
Page(s)	40-48
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), 2024. Published by Science Publishing Group

Keywords

Tropical Almond Ester, Injection Timing, Artificial Neural Network, Blend Percentage, Percentage Load, Brake Thermal Efficiency, Exhaust Temperature, Brake Specific Energy Consumption

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APA Style

Samson Kolawole Fasogbon, Olusegun Oladapo Laosebikan, Chukwuemeka Uguba Owora. (2019). ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel. American Journal of Modern Energy, 5(2), 40-48. https://doi.org/10.11648/j.ajme.20190502.16

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ACS Style

Samson Kolawole Fasogbon; Olusegun Oladapo Laosebikan; Chukwuemeka Uguba Owora. ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel. Am. J. Mod. Energy 2019, 5(2), 40-48. doi: 10.11648/j.ajme.20190502.16

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AMA Style

Samson Kolawole Fasogbon, Olusegun Oladapo Laosebikan, Chukwuemeka Uguba Owora. ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel. Am J Mod Energy. 2019;5(2):40-48. doi: 10.11648/j.ajme.20190502.16

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@article{10.11648/j.ajme.20190502.16,
  author = {Samson Kolawole Fasogbon and Olusegun Oladapo Laosebikan and Chukwuemeka Uguba Owora},
  title = {ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel},
  journal = {American Journal of Modern Energy},
  volume = {5},
  number = {2},
  pages = {40-48},
  doi = {10.11648/j.ajme.20190502.16},
  url = {https://doi.org/10.11648/j.ajme.20190502.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajme.20190502.16},
  abstract = {In the present work, biodiesel prepared from Tropical almond oil (Terminalia Catappa) was used as fuel in C. I engine. Performance studies were conducted on a single cylinder four-stroke water-cooled compression ignition engine. Experiments were conducted for different percentage of blends of Tropical almond ester with diesel at different injection timings. Experimental investigations on the performance parameters from the engine were done. Artificial neural network (ANN) of back-propagation feed-forward Levenberg-Marquardt algorithm was used to predict the performance characteristics of the engine. An ANN model was developed for the performance parameters. To train the network, blend percentage, percentage load and injection timings were used as the input variables whereas engine performance parameters (brake thermal efficiency, exhaust gas temperature, and brake specific fuel consumption) were used as the output variables. The obtained experimental results were used to train the network structure. Results showed very good correlation between the ANN predicted values and the desired values for various engine performance values. Mean relative error values were less than 10 percent which by many standards is acceptable. The results show that ANN is an accurately reliable tool for the prediction of engine performance.},
 year = {2019}
}

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TY - JOUR
T1 - ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel
AU - Samson Kolawole Fasogbon
AU - Olusegun Oladapo Laosebikan
AU - Chukwuemeka Uguba Owora
Y1 - 2019/06/20
PY - 2019
N1 - https://doi.org/10.11648/j.ajme.20190502.16
DO - 10.11648/j.ajme.20190502.16
T2 - American Journal of Modern Energy
JF - American Journal of Modern Energy
JO - American Journal of Modern Energy
SP - 40
EP - 48
PB - Science Publishing Group
SN - 2575-3797
UR - https://doi.org/10.11648/j.ajme.20190502.16
AB - In the present work, biodiesel prepared from Tropical almond oil (Terminalia Catappa) was used as fuel in C. I engine. Performance studies were conducted on a single cylinder four-stroke water-cooled compression ignition engine. Experiments were conducted for different percentage of blends of Tropical almond ester with diesel at different injection timings. Experimental investigations on the performance parameters from the engine were done. Artificial neural network (ANN) of back-propagation feed-forward Levenberg-Marquardt algorithm was used to predict the performance characteristics of the engine. An ANN model was developed for the performance parameters. To train the network, blend percentage, percentage load and injection timings were used as the input variables whereas engine performance parameters (brake thermal efficiency, exhaust gas temperature, and brake specific fuel consumption) were used as the output variables. The obtained experimental results were used to train the network structure. Results showed very good correlation between the ANN predicted values and the desired values for various engine performance values. Mean relative error values were less than 10 percent which by many standards is acceptable. The results show that ANN is an accurately reliable tool for the prediction of engine performance.
VL - 5
IS - 2
ER -

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Author Information

Samson Kolawole Fasogbon

Department of Mechanical Engineering, University of Ibadan, Ibadan, Nigeria
Olusegun Oladapo Laosebikan

Department of Mechanical Engineering, University of Ibadan, Ibadan, Nigeria
Chukwuemeka Uguba Owora

Department of Mechanical Engineering, University of Ibadan, Ibadan, Nigeria

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APA Style

Samson Kolawole Fasogbon, Olusegun Oladapo Laosebikan, Chukwuemeka Uguba Owora. (2019). ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel. American Journal of Modern Energy, 5(2), 40-48. https://doi.org/10.11648/j.ajme.20190502.16

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ACS Style

Samson Kolawole Fasogbon; Olusegun Oladapo Laosebikan; Chukwuemeka Uguba Owora. ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel. Am. J. Mod. Energy 2019, 5(2), 40-48. doi: 10.11648/j.ajme.20190502.16

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AMA Style

Samson Kolawole Fasogbon, Olusegun Oladapo Laosebikan, Chukwuemeka Uguba Owora. ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel. Am J Mod Energy. 2019;5(2):40-48. doi: 10.11648/j.ajme.20190502.16

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@article{10.11648/j.ajme.20190502.16,
  author = {Samson Kolawole Fasogbon and Olusegun Oladapo Laosebikan and Chukwuemeka Uguba Owora},
  title = {ANN Analysis of Injection Timing on Performance Characteristics of Compression Ignition Engines Running on the Blends of Tropical Almond Based Biodiesel},
  journal = {American Journal of Modern Energy},
  volume = {5},
  number = {2},
  pages = {40-48},
  doi = {10.11648/j.ajme.20190502.16},
  url = {https://doi.org/10.11648/j.ajme.20190502.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajme.20190502.16},
  abstract = {In the present work, biodiesel prepared from Tropical almond oil (Terminalia Catappa) was used as fuel in C. I engine. Performance studies were conducted on a single cylinder four-stroke water-cooled compression ignition engine. Experiments were conducted for different percentage of blends of Tropical almond ester with diesel at different injection timings. Experimental investigations on the performance parameters from the engine were done. Artificial neural network (ANN) of back-propagation feed-forward Levenberg-Marquardt algorithm was used to predict the performance characteristics of the engine. An ANN model was developed for the performance parameters. To train the network, blend percentage, percentage load and injection timings were used as the input variables whereas engine performance parameters (brake thermal efficiency, exhaust gas temperature, and brake specific fuel consumption) were used as the output variables. The obtained experimental results were used to train the network structure. Results showed very good correlation between the ANN predicted values and the desired values for various engine performance values. Mean relative error values were less than 10 percent which by many standards is acceptable. The results show that ANN is an accurately reliable tool for the prediction of engine performance.},
 year = {2019}
}

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