A neural network model for the prediction of compression ignition engine performance at different injection timings

Rapid depletion of fossil fuel and continuous increase in gasoline prices have stimulated the search of alternative fuels. This paper deals with the prediction of engine performance, emission and combustion characteristics of compression ignition engine fuelled with fish oil biodiesel using artificial neural network (ANN). Experimental investigations are carried out in a single cylinder constant speed direct injection diesel engine under variable load conditions at different injection timings−210, 240 and 270 bTDC. The performance, combustion and emission characteristics are measured using an exhaust gas analyser, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends and engine load conditions. For training the neural network, feed-forward back propagation algorithm is used. The developed ANN model predicts the performance, combustions and exhaust emissions with a correlation coefficients (R) of 0.97–0.99 and a mean relative error of 0.62–4.826%. The root mean square errors are found to be low. The developed model has found to predict accurately the engine performance, combustion and emission parameters at different injection timings.

[1]  Sedat Bayseç,et al.  Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks , 2010 .

[2]  O Obodeh,et al.  Evaluation of Artificial Neural Network Performance in Predicting Diesel Engine NOx Emissions , 2009 .

[3]  P. Srinivasa Pai,et al.  Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings , 2011 .

[4]  Vilmar Æsøy,et al.  Combustion and emissions characteristics of fish oil fuel in a heavy-duty diesel engine , 2013 .

[5]  Gholamhassan Najafi,et al.  Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends , 2010 .

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

[7]  Sumit Roy,et al.  Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network , 2014 .

[8]  Rasim Behçet,et al.  Performance and emission study of waste anchovy fish biodiesel in a diesel engine , 2011 .

[9]  M. Almeida,et al.  Biodiesel production using oil from fish canning industry wastes , 2013 .

[10]  O. Cortés,et al.  Optimization of operating conditions for compressor performance by means of neural network inverse , 2009 .

[11]  G. Nagarajan,et al.  Optimization of FOME (fish oil methyl esters) blend and EGR (exhaust gas recirculation) for simultaneous control of NOx and particulate matter emissions in diesel engines , 2013 .

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

[13]  M. Ilangkumaran,et al.  Performance and Exhaust Emissions of a Diesel Engine Using Hybrid Fuel with an Artificial Neural Network , 2011 .

[14]  Ismail Saritas,et al.  Prediction of diesel engine performance using biofuels with artificial neural network , 2010, Expert Syst. Appl..

[15]  Gholamhassan Najafi,et al.  Waste fish oil biodiesel as a source of renewable fuel in Iran , 2013 .

[16]  Mustafa Canakci,et al.  Performance and exhaust emissions of a gasoline engine using artificial neural network , 2007 .

[17]  Ming Jia,et al.  Neural network prediction of biodiesel kinematic viscosity at 313 K , 2014 .

[18]  Mustafa Canakci,et al.  Performance and exhaust emissions of a biodiesel engine , 2006 .

[19]  Cherng-Yuan Lin,et al.  Engine performance and emission characteristics of marine fish-oil biodiesel produced from the discarded parts of marine fish , 2009 .

[20]  Roman M. Balabin,et al.  Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy , 2011 .

[21]  B. F. Yousif,et al.  CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network , 2010 .

[22]  Murat Hosoz,et al.  Performance prediction of a CI engine using artificial neural network for various SME and diesel fuel blends , 2010 .

[23]  A. de Lucasa,et al.  Modeling diesel particulate emissions with neural networks , 2001 .

[24]  T. Kojima,et al.  Evaluation of Ozone Treated Fish Waste Oil as a Fuel for Transportation , 2004 .

[25]  Varun,et al.  Biodiesel as an Alternative Fuel for CI Engines: Environmental Effect , 2010 .

[26]  Christopher J. Rutland,et al.  Improvement of Neural Network Accuracy for Engine Simulations , 2003 .

[27]  Waldemar Rebizant,et al.  Application of Artificial Neural Networks , 2011 .

[28]  Sharanappa Godiganur,et al.  Performance and emission characteristics of a Kirloskar HA394 diesel engine operated on fish oil methyl esters , 2010 .

[29]  Mohand Tazerout,et al.  Effects of biofuel from fish oil industrial residue – Diesel blends in diesel engine , 2012 .

[30]  J. Sarangan,et al.  Performance and exhaust emission characteristics of a CI engine fueled with biodiesel (fish oil) with DEE as additive , 2012 .

[31]  Gholamreza Moradi,et al.  The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield , 2013 .