Artificial neural network approach to study the effect of injection pressure and timing on diesel engine performance fueled with biodiesel

This study intends to predict the influence of injection pressure and injection timing on performance, emission and combustion characteristics of a diesel engine fuelled with waste cooking palm oil based biodiesel using the artificial neural network (ANN) model. To acquire data for training and testing in the proposed ANN, experiments were carried out in a single cylinder, four stroke direct injection diesel engine at a constant speed of 1500 rpm and at full load (100%) condition. From the experimental results, it was observed that waste cooking palm oil methyl ester provided better engine performance and improved emission and combustion characteristics at injection pressure of 280 bar and timing of 25.5° bTDC. An ANN model was developed using the data acquired from the experiments. Training of ANN was performed based on back propagation learning algorithm. Multilayer perceptron (MLP) network was used for non-linear mapping of the input and output parameters. Among the various networks tested the network with two hidden layers and 11 neurons gave better correlation coefficient for the prediction of engine performance, emission and combustion characteristics. The ANN model was validated with the test data which was not used for training and was found to be very well correlated.

[1]  M. Demirbas,et al.  Recent advances on the production and utilization trends of bio-fuels: A global perspective , 2006 .

[2]  G. Vicente,et al.  A Comparative Study of Vegetable Oils for Biodiesel Production in Spain , 2006 .

[3]  A. Ramesh,et al.  Parametric studies for improving the performance of a Jatropha oil-fuelled compression ignition engine , 2006 .

[4]  G. R. Kannan,et al.  Effect of metal based additive on performance emission and combustion characteristics of diesel engine fuelled with biodiesel , 2011 .

[5]  İsmail Şahin,et al.  The use of artificial neural network for prediction of grain size of 17-4 pH stainless steel powders , 2010 .

[6]  Peter Tritthart,et al.  Diesel fuel derived from vegetable oils, III. Emission tests using methyl esters of used frying oil , 1988 .

[7]  Deng Yuanwang,et al.  An analysis for effect of cetane number on exhaust emissions from engine with the neural network , 2002 .

[8]  M. Hosoz,et al.  Artificial neural network analysis of an automobile air conditioning system , 2006 .

[9]  Adnan Sözen,et al.  Performance prediction of a vapour-compression heat-pump , 2004 .

[10]  John B. Heywood,et al.  Internal combustion engine fundamentals , 1988 .

[11]  G. R. Kannan,et al.  Performance Emission and Combustion Characteristics of a Diesel Engine Fueled with Biodiesel Produced from Waste Cooking Oil , 2010 .

[12]  M. M Prieto,et al.  Power plant condenser performance forecasting using a non-fully connected artificial neural network , 2001 .

[13]  Havva Balat,et al.  A critical review of bio-diesel as a vehicular fuel. , 2008 .

[14]  N. S. Rathore,et al.  Experimental investigation of the effect of compression ratio and injection pressure in a direct injection diesel engine running on Jatropha methyl ester , 2010 .

[15]  Erol Arcaklioğlu,et al.  Performance maps of a diesel engine , 2005 .

[16]  G. Nagarajan,et al.  Influence of injection timing on performance, emission and combustion characteristics of a DI diesel engine running on waste plastic oil , 2009 .

[17]  Mustafa Canakci,et al.  Effect of Fuel Injection Timing on the Emissions of a Direct-Injection (DI) Diesel Engine Fueled with Canola Oil Methyl Ester−Diesel Fuel Blends , 2010 .

[18]  G. R. Kannan,et al.  Experimental investigation on diesel engine with diestrolwater micro emulsions , 2011 .

[19]  Naci Caglar,et al.  Neural network based approach for determining the shear strength of circular reinforced concrete columns , 2009 .

[20]  M. P. Dorado,et al.  KINETIC PARAMETERS AFFECTING THE ALKALI-CATALYZED TRANSESTERIFICATION PROCESS OF USED OLIVE OIL , 2004 .

[21]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[22]  Moh'd Sami Ashhab,et al.  Fuel economy and torque tracking in camless engines through optimization of neural networks , 2008 .

[23]  S. Jayaraj,et al.  Artificial neural networks used for the prediction of the cetane number of biodiesel , 2006 .

[24]  Gemma Vicente,et al.  Optimisation of FAME production from waste cooking oil for biodiesel use. , 2009 .

[25]  Gholamhassan Najafi,et al.  Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network , 2009 .

[26]  Nigel N. Clark,et al.  Translation of Distance-Specific Emissions Rates between Different Heavy Duty Vehicle Chassis Test Schedules , 2002 .

[27]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[28]  E. Arcaklioğlu,et al.  Artificial neural network analysis of heat pumps using refrigerant mixtures , 2004 .

[29]  Zoran Filipi,et al.  Cam-phasing Optimization Using Artificial Neural Networks as Surrogate Models-Fuel Consumption and NOx Emissions , 2006 .

[30]  S. Renganarayanan,et al.  Modelling of steam fired double effect vapour absorption chiller using neural network , 2006 .

[31]  Oliver Nelles,et al.  Neural net models for diesel engines: simulation and exhaust optimization , 1998 .

[32]  Cor M. van den Bleek,et al.  Prediction of NOx Emissions from a Transiently Operating Diesel Engine Using an Artificial Neural Network , 1999 .

[33]  Richard J. Atkinson,et al.  Neural Network-Based Diesel Engine Emissions Prediction Using In-Cylinder Combustion Pressure , 1999 .

[34]  G. R. Kannan,et al.  Effect of injection pressure and injection timing on DI diesel engine fuelled with biodiesel from waste cooking oil , 2012 .

[35]  Reyes García-Contreras,et al.  Effect of the alcohol type used in the production of waste cooking oil biodiesel on diesel performance and emissions , 2008 .

[36]  A. Demirbas,et al.  Biodiesel production via non-catalytic SCF method and biodiesel fuel characteristics. , 2006 .

[37]  Ján Cvengroš,et al.  Used frying oils and fats and their utilization in the production of methyl esters of higher fatty acids , 2004 .