Performance and Emission Analysis of Rapeseed Methyl Ester on DI Diesel Engine Using Artificial Neural Network

2020 ◽  
pp. 215-221
Author(s):  
V. Amosu ◽  
S. K. Bhatti ◽  
S. Jaikumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Methyl Ester ◽  
Emission Analysis ◽  
Performance And Emission ◽  
Di Diesel Engine ◽  
Rapeseed Methyl Ester ◽  
Artificial Neural

Performance & emission analysis of HHO enriched dual-fuelled diesel engine with artificial neural network prediction approaches

2020 ◽  
Vol 45 (49) ◽  
pp. 26357-26369 ◽  
Author(s):  
Raif Kenanoğlu ◽  
Mustafa Kaan Baltacıoğlu ◽  
Mehmet Hakan Demir ◽  
Merve Erkınay Özdemir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Emission Analysis ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Artificial Neural

Artificial Neural Network Prediction of Diesel Engine Performance and Emission Fueled With Diesel–Kerosene–Ethanol Blends: A Fuzzy-Based Optimization

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Subrata Bhowmik ◽  
Rajsekhar Panua ◽  
Durbadal Debroy ◽  
Abhishek Paul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Specific Energy Consumption ◽  
Ann Model ◽  
Volume Percentage ◽  
Acceptable Error ◽  
Performance And Emission ◽  
Artificial Neural ◽  
The Impact

The present study explores the impact of ethanol on the performance and emission characteristics of a single cylinder indirect injection (IDI) Diesel engine fueled with Diesel–kerosene blends. Five percent ethanol is added to Diesel–kerosene blends in volumetric proportion. Ethanol addition to Diesel–kerosene blends significantly improved the brake thermal efficiency (BTE), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), total hydrocarbon (THC), and carbon monoxide (CO) emission of the engine. Based on engine experimental data, an artificial neural network (ANN) model is formulated to accurately map the input (load, kerosene volume percentage, ethanol volume percentage) and output (BTE, BSEC, NOx, THC, CO) relationships. A (3-6-5) topology with Levenberg–Marquardt feed-forward back propagation (trainlm) is found to be optimal network than other training algorithms for predicting input and output relationship with acceptable error. The mean square error (MSE) of 0.000225, mean absolute percentage error (MAPE) of 2.88%, and regression coefficient (R) of 0.99893 are obtained from the developed model. The study also attempts to make clear the application of fuzzy-based analysis to optimize the network topology of ANN model.

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.

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