Biofuels or biodiesels are fuels that are in biodegradable and non-toxic. They are manufactured from waste cooking oils, vegetable oils and animal fats or tall oil (a by-product of the pulp and paper industry). These oils undergo a procedure called transesterification whereby they are subjected to a reaction with an alcohol usually ethanol or methanol by means of a catalyst such as sodium hydroxide. This results formation of ethyl or methyl ester called biodiesel and a by-product called glycerin. Pure biodiesel fuel is considerably not as much of flammable than petroleum diesel which burns at 50 degrees Celsius. Biodiesels are frequently used in blend with petroleum diesel and are named as biodiesel blends. These blends will contain a flashpoint and a gel point wherever between the two pure fuels depending on the mixture. Artificial neural networks (ANNs) are recently developed techniques which are in variably used in obtaining exact correlations which involves non-linear data. An ANN can be considered to be consisting of interconnected group of relatively simple processing elements or nodes, called neurons, where the global performance is resolute by the relations between the processing nodes and the network parameters. Neural networks, when trained properly are good at providing very fast, extremely close approximations of the correct output for nonlinear data. This study deals with artificial neural network modeling a diesel engine using palm oil methyl ester to predict brake power, brake thermal efficiency, specific fuel consumption and exhaust emission of engine. This property of biodiesel produced from palm oil was measured and the experimental results reveal that blends of palm oil with diesel fuel provide improved engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model program for the engine was developed. Different activation functions and several rules were used to assess the percentage error between the desired and predicted values. It was observed that the ANN model can predict the engine performance and exhaust emission quite well.
| Published in | American Journal of Modern Energy (Volume 2, Issue 4) |
| DOI | 10.11648/j.ajme.20160204.11 |
| Page(s) | 17-21 |
| 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), 2016. Published by Science Publishing Group |
Performance, Emission, Diesel Engine, POME, ANN
| [1] | G. Lakshmi Narayana rao et al, “Determination ofthe proportion of Blend of Biodiesel with Diesel for Optimal Engine Performance and Emission Characteristics, SAE International 2006, 1-7. |
| [2] | B. Ghobadian et al, “Diesel engine performance and emission analysis using waste cooking biodiesel fuel with an artificial neural network”, Renewable Energy 2009; 1-5. |
| [3] | D. Vinay Kumar et al, “Prediction of performance and emission of a biodiesel fueled Lanthanum Zirconate coated direct injection diesel engine using Artificial Neural Network”, Design and Manufacturing 2013; 993-1002. |
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| [5] | M. M. Deshmukh et al, “Application of ANN to Optimize the performance of CI engine fuelled with cotton seed oil”, International Journal of Engineering Technology and Advanced Engineering 2014, Vol 4, 351-358. |
| [6] | Dr. M. Rajendra et al, “Exhaust emission analysis using nathkamala oil biodiesel fuel in a C.I engine with ANN”, International journal of Research in Environment Science and Technology 2012, 48-53. |
| [7] | R. Manjunatha et al, “Application of Artificial Neural Networks for emission modeling of biodiesels for a C.I engine under varying operation conditions”, Modern applied science 2010, Vol 4, 77-89. |
| [8] | Yakup Sekmen et al, “Prediction of performance and smoke emission using artificial neural network in a diesel engine”, Mathematical and Computational Applications 2006, Vol 11, 205-214. |
| [9] | Venkata ramesh mamilla et al. “Performance analysis of IC engines with bio-diesel jatropha methyl ester (JME) blends” Academic Journals - Journal of Petroleum Technology and Alternatives Fuel, Vol. 4(5), pp. 90-93, May, 2013. |
| [10] | Venkata ramesh mamilla et al. “Effect of Injection Pressure on Performance, Emission and Combustion Characteristics of DI Diesel Engine Running on Blends of Jatropha methyl esters and Diesel Fuel”, CIIT International Journal of Artificial Intelligent Systems and Machine Learning, vol 5, no. 2, pp. 88- 94, February 2013. |
APA Style
Venkata Ramesh Mamilla, G. Lakshmi Narayana Rao. (2016). Optimal Performance and Emission Analysis of Diesel Engine Fuelled with Palm Oil Methyl Ester with an Artificial Neural Network. American Journal of Modern Energy, 2(4), 17-21. https://doi.org/10.11648/j.ajme.20160204.11
ACS Style
Venkata Ramesh Mamilla; G. Lakshmi Narayana Rao. Optimal Performance and Emission Analysis of Diesel Engine Fuelled with Palm Oil Methyl Ester with an Artificial Neural Network. Am. J. Mod. Energy 2016, 2(4), 17-21. doi: 10.11648/j.ajme.20160204.11
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Download@article{10.11648/j.ajme.20160204.11,
author = {Venkata Ramesh Mamilla and G. Lakshmi Narayana Rao},
title = {Optimal Performance and Emission Analysis of Diesel Engine Fuelled with Palm Oil Methyl Ester with an Artificial Neural Network},
journal = {American Journal of Modern Energy},
volume = {2},
number = {4},
pages = {17-21},
doi = {10.11648/j.ajme.20160204.11},
url = {https://doi.org/10.11648/j.ajme.20160204.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajme.20160204.11},
abstract = {Biofuels or biodiesels are fuels that are in biodegradable and non-toxic. They are manufactured from waste cooking oils, vegetable oils and animal fats or tall oil (a by-product of the pulp and paper industry). These oils undergo a procedure called transesterification whereby they are subjected to a reaction with an alcohol usually ethanol or methanol by means of a catalyst such as sodium hydroxide. This results formation of ethyl or methyl ester called biodiesel and a by-product called glycerin. Pure biodiesel fuel is considerably not as much of flammable than petroleum diesel which burns at 50 degrees Celsius. Biodiesels are frequently used in blend with petroleum diesel and are named as biodiesel blends. These blends will contain a flashpoint and a gel point wherever between the two pure fuels depending on the mixture. Artificial neural networks (ANNs) are recently developed techniques which are in variably used in obtaining exact correlations which involves non-linear data. An ANN can be considered to be consisting of interconnected group of relatively simple processing elements or nodes, called neurons, where the global performance is resolute by the relations between the processing nodes and the network parameters. Neural networks, when trained properly are good at providing very fast, extremely close approximations of the correct output for nonlinear data. This study deals with artificial neural network modeling a diesel engine using palm oil methyl ester to predict brake power, brake thermal efficiency, specific fuel consumption and exhaust emission of engine. This property of biodiesel produced from palm oil was measured and the experimental results reveal that blends of palm oil with diesel fuel provide improved engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model program for the engine was developed. Different activation functions and several rules were used to assess the percentage error between the desired and predicted values. It was observed that the ANN model can predict the engine performance and exhaust emission quite well.},
year = {2016}
}
TY - JOUR T1 - Optimal Performance and Emission Analysis of Diesel Engine Fuelled with Palm Oil Methyl Ester with an Artificial Neural Network AU - Venkata Ramesh Mamilla AU - G. Lakshmi Narayana Rao Y1 - 2016/10/17 PY - 2016 N1 - https://doi.org/10.11648/j.ajme.20160204.11 DO - 10.11648/j.ajme.20160204.11 T2 - American Journal of Modern Energy JF - American Journal of Modern Energy JO - American Journal of Modern Energy SP - 17 EP - 21 PB - Science Publishing Group SN - 2575-3797 UR - https://doi.org/10.11648/j.ajme.20160204.11 AB - Biofuels or biodiesels are fuels that are in biodegradable and non-toxic. They are manufactured from waste cooking oils, vegetable oils and animal fats or tall oil (a by-product of the pulp and paper industry). These oils undergo a procedure called transesterification whereby they are subjected to a reaction with an alcohol usually ethanol or methanol by means of a catalyst such as sodium hydroxide. This results formation of ethyl or methyl ester called biodiesel and a by-product called glycerin. Pure biodiesel fuel is considerably not as much of flammable than petroleum diesel which burns at 50 degrees Celsius. Biodiesels are frequently used in blend with petroleum diesel and are named as biodiesel blends. These blends will contain a flashpoint and a gel point wherever between the two pure fuels depending on the mixture. Artificial neural networks (ANNs) are recently developed techniques which are in variably used in obtaining exact correlations which involves non-linear data. An ANN can be considered to be consisting of interconnected group of relatively simple processing elements or nodes, called neurons, where the global performance is resolute by the relations between the processing nodes and the network parameters. Neural networks, when trained properly are good at providing very fast, extremely close approximations of the correct output for nonlinear data. This study deals with artificial neural network modeling a diesel engine using palm oil methyl ester to predict brake power, brake thermal efficiency, specific fuel consumption and exhaust emission of engine. This property of biodiesel produced from palm oil was measured and the experimental results reveal that blends of palm oil with diesel fuel provide improved engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model program for the engine was developed. Different activation functions and several rules were used to assess the percentage error between the desired and predicted values. It was observed that the ANN model can predict the engine performance and exhaust emission quite well. VL - 2 IS - 4 ER -
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