Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural network

2018 ◽  
Vol 117 ◽  
pp. 538-544 ◽  
Author(s):  
Samet Gürgen ◽  
Bedir Ünver ◽  
İsmail Altın
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Diesel Fuel ◽  
Cyclic Variability ◽  
Fuel Blends ◽  
Artificial Neural

scholarly journals Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network

2017 ◽  
Vol 26 (2) ◽  
pp. 74 ◽  
Author(s):  
Hasan Aydogan
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Diesel Fuel ◽  
Internal Combustion Engines ◽  
Back Propagation Algorithm ◽  
Performance And Emission ◽  
Fuel Blends ◽  
Artificial Neural

The changes in the performance, emission and combustion characteristics of bioethanol-safflower biodiesel and diesel fuel blends used in a common rail diesel engine were investigated in this experimental study. E20B20D60 (20% bioethanol, 20% biodiesel, 60% diesel fuel by volume), E30B20D50, E50B20D30 and diesel fuel (D) were used as fuel. Engine power, torque, brake specific fuel consumption, NOx and cylinder inner pressure values were measured during the experiment. With the help of the obtained experimental data, an artificial neural network was created in MATLAB 2013a software by using back-propagation algorithm. Using the experimental data, predictions were made in the created artificial neural network. As a result of the study, the correlation coefficient was found as 0.98. In conclusion, it was seen that artificial neural networks approach could be used for predicting performance and emission values in internal combustion engines.

Prediction of performance and exhaust emissions of diesel engine fuelled with adulterated diesel: An artificial neural network assisted fuzzy-based topology optimization

2018 ◽  
Vol 29 (8) ◽  
pp. 1413-1437 ◽  
Author(s):  
Subrata Bhowmik ◽  
Rajsekhar Panua ◽  
Subrata K Ghosh ◽  
Abhishek Paul ◽  
Durbadal Debroy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Network Model ◽  
Neural Network Model ◽  
Diesel Fuel ◽  
Artificial Neural Network Model ◽  
Specific Energy Consumption ◽  
Oxides Of Nitrogen ◽  
Artificial Neural

This study evaluates the effects of diesel fuel adulteration on the performance and exhaust emission characteristics of an existing diesel engine. Kerosene is added to diesel fuel in volumetric proportions of 5, 10, 15, and 20%. Adulterated fuel significantly reduced the oxides of nitrogen emissions of the engine. In view of the engine experimentations, artificial intelligence-based artificial neural network model has been developed to accurately predict the input–output relationships of the diesel engine under adulterated fuel. The investigation also attempts to explore the applicability of fuzzy logic in the selection of the network topology of artificial neural network model under adulterated fuel. A (2–7–5) topology is found to be optimal for predicting input parameters, namely load, diesel–kerosene blend and output parameters, namely brake thermal efficiency, brake-specific energy consumption, oxides of nitrogen, total hydrocarbon, carbon monoxide of the network. The developed artificial neural network model is enabled for predicting engine output responses with high accuracy. The regression coefficient (R) of 0.99887, mean square error of 1.5e-04 and mean absolute percentage error of 2.39% have been obtained from the plausible artificial neural network model.

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