Prediction of engine performance for an alternative fuel using artificial neural network

Abstract This study deals with artificial neural network (ANN) modeling to predict the brake specific fuel consumption, effective power and average effective pressure and exhaust gas temperature of the methanol engine. To obtain training and testing data, a number of experiments were performed with a four-cylinder, four-stroke test engine operated at different engine speeds and torques. Using some of the experimental data for training, an ANN model based on standard back propagation algorithm was developed. Then, the performance of the ANN predictions was measured by comparing the predictions with the experimental results. Engine speed, engine torque, fuel flow, intake manifold mean temperature and cooling water entrance temperature have been used as the input layer, while brake specific fuel consumption, effective power, average effective pressure and exhaust gas temperature have also been used separately as the output layer. After training, it was found that the R 2 values are close to 1 for both training and testing data. RMS values are smaller than 0.015 and mean errors are smaller than 3.8% for the testing data. This shows that the developed ANN model is a powerful one for predicting the brake specific fuel consumption, effective power and average effective pressure and exhaust gas temperature of internal combustion engines.

[1]  J. Míguez,et al.  Diesel engine condition monitoring using a multi-net neural network system with nonintrusive sensors , 2011 .

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

[3]  Habibollah Haron,et al.  Prediction of surface roughness in the end milling machining using Artificial Neural Network , 2010, Expert Syst. Appl..

[4]  M. Nalbant,et al.  The experimental investigation of the effects of uncoated, PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks , 2009 .

[5]  Sofiane Guessasma,et al.  Predictive analysis of combined burner parameter effects on oxy-fuel flames , 2011 .

[6]  Adnan Parlak,et al.  Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine , 2006 .

[7]  C. Sanjay,et al.  A study of surface roughness in drilling using mathematical analysis and neural networks , 2006 .

[8]  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 .

[9]  Erol Arcaklioğlu,et al.  A diesel engine's performance and exhaust emissions , 2005 .

[10]  Jun Li,et al.  Effect of injection and ignition timings on performance and emissions from a spark-ignition engine fueled with methanol , 2010 .

[11]  S. Setayeshi,et al.  Estimation of the radon concentration in soil related to the environmental parameters by a modified Adaline neural network. , 2003, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

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

[13]  Ali Mohebbi,et al.  Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers. , 2008, Journal of hazardous materials.

[14]  M. Çelik,et al.  The use of pure methanol as fuel at high compression ratio in a single cylinder gasoline engine , 2011 .

[15]  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 .

[16]  J. Paulo Davim,et al.  Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models , 2008 .

[17]  Ihsan Korkut,et al.  Application of regression and artificial neural network analysis in modelling of tool-chip interface temperature in machining , 2011, Expert Syst. Appl..

[18]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[19]  H. Raheman,et al.  Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA , 2009 .

[20]  Abdullah Kurt,et al.  Modelling of the cutting tool stresses in machining of Inconel 718 using artificial neural networks , 2009, Expert Syst. Appl..

[21]  Nikos Pasadakis,et al.  Prediction of the distillation profile and cold properties of diesel fuels using mid-IR spectroscopy and neural networks , 2006 .

[22]  Manoj Khandelwal,et al.  Prediction of macerals contents of Indian coals from proximate and ultimate analyses using artificial neural networks , 2010 .

[23]  Gholamhassan Najafi,et al.  Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network , 2009 .

[24]  ZainAzlan Mohd,et al.  Prediction of surface roughness in the end milling machining using Artificial Neural Network , 2010 .

[25]  Valentin Jijkoun,et al.  The University of Amsterdam at WiQA 2006 , 2006, CLEF.

[26]  Cenk Sayin,et al.  Engine performance and exhaust gas emissions of methanol and ethanol–diesel blends , 2010 .

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

[28]  Guozhu Liu,et al.  Artificial neural network approaches on composition-property relationships of jet fuels based on GC-MS , 2007 .

[29]  Wisley Falco Sales,et al.  Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network , 2005 .

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

[31]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[32]  S. C. Chelgani,et al.  Application of artificial neural networks to predict chemical desulfurization of Tabas coal , 2008 .