Performance and emission characteristics of a 4 stroke C.I. engine operated on honge methyl ester using artificial neural network

Shivakumar, P. Srinivas Pai, B. R. Shrinivasa Rao, B. S. Samaga

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

In wake of the present energy environment crises it has become essential to identify renewable and alternative clean burning fuels. One of the significant routes to tackle the problem of increasing prices and the pollution problems of petroleum fuels is by the use of vegetable oil fuels known as biodiesels. In the present work biodiesel was prepared from Honge oil (Pongamia) and used as a fuel in C.I engine. Performance studies were conducted on a single cylinder four-stroke water-cooled compression ignition engine connected to an eddy current dynamometer. Experiments were conducted for different percentage of blends of Honge oil with diesel at various compression ratios. Experimental investigation on the Performance parameters and Exhaust emissions from the engine were done. Artificial Neural Networks (ANNs) were used to predict the Engine performance and emission characteristics of the engine. To train the network compression ratio, blend percentage, percentage load were used as the input variables where as engine performance parameters together with engine exhaust emissions were used as the output variables. Experimental results were used to train the ANN. Back-propagation algorithm was used to train the network. ANN results showed good correlation between the ANN predicted values and the desired values for various engine performance values and the exhaust emissions. The R2 values were very close to 1 and the mean relative error values were less than 9 percent.

Original languageEnglish
Pages (from-to)83-94
Number of pages12
JournalJournal of Engineering and Applied Sciences
Volume5
Issue number6
Publication statusPublished - 01-06-2010
Externally publishedYes

Fingerprint

Esters
Engines
Neural networks
Compression ratio (machinery)
Exhaust systems (engine)
Backpropagation algorithms
Vegetable oils
Dynamometers
Fuel oils
Engine cylinders
Biodiesel
Eddy currents
Ignition
Pollution
Crude oil
Water
Experiments
Oils

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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abstract = "In wake of the present energy environment crises it has become essential to identify renewable and alternative clean burning fuels. One of the significant routes to tackle the problem of increasing prices and the pollution problems of petroleum fuels is by the use of vegetable oil fuels known as biodiesels. In the present work biodiesel was prepared from Honge oil (Pongamia) and used as a fuel in C.I engine. Performance studies were conducted on a single cylinder four-stroke water-cooled compression ignition engine connected to an eddy current dynamometer. Experiments were conducted for different percentage of blends of Honge oil with diesel at various compression ratios. Experimental investigation on the Performance parameters and Exhaust emissions from the engine were done. Artificial Neural Networks (ANNs) were used to predict the Engine performance and emission characteristics of the engine. To train the network compression ratio, blend percentage, percentage load were used as the input variables where as engine performance parameters together with engine exhaust emissions were used as the output variables. Experimental results were used to train the ANN. Back-propagation algorithm was used to train the network. ANN results showed good correlation between the ANN predicted values and the desired values for various engine performance values and the exhaust emissions. The R2 values were very close to 1 and the mean relative error values were less than 9 percent.",
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Performance and emission characteristics of a 4 stroke C.I. engine operated on honge methyl ester using artificial neural network. / Shivakumar; Srinivas Pai, P.; Shrinivasa Rao, B. R.; Samaga, B. S.

In: Journal of Engineering and Applied Sciences, Vol. 5, No. 6, 01.06.2010, p. 83-94.

Research output: Contribution to journalArticle

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