American Journal of Energy Research
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American Journal of Energy Research. 2014, 2(4), 74-80
DOI: 10.12691/ajer-2-4-1
Open AccessArticle

An Application of Artificial Neural Network for Predicting Engine Torque in a Biodiesel Engine

Bekir Cirak1, and Selman Demirtas1

1Siirt University, Faculty of Engineering &Architecture, Department of Mechanical Engineering, Kezer Campus, Siirt / TURKEY

Pub. Date: August 12, 2014

Cite this paper:
Bekir Cirak and Selman Demirtas. An Application of Artificial Neural Network for Predicting Engine Torque in a Biodiesel Engine. American Journal of Energy Research. 2014; 2(4):74-80. doi: 10.12691/ajer-2-4-1


In this application study, an artificial neural network (ANN) model to predict the torque of a diesel engine. Using ANN performance of a diesel engine using biodiesel produced from canola and soybean oils through transesterification. To acquire data for training and testing of the proposed ANN. A four cylinder and four stroke test engine was fuelled with biodiesel and eurodiesel mixtured fuels with various percentages of biodiesel % amounts to half the CB with SB and operated at different loads engine speeds, coolant temperatures, biofuel mixtures and exhaust temperature. Levenberg Marquards algorithms for the engine was developed using some of the experimental data for training. As a nonlinear system has been accepted. The performance of the ANN was validated by comparing the prediction dataset with the experimental results. It was observed that the ANN model can predict the engine performance quite well with correlation coefficient R 0.98 for the engine torque respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values were obtained as 0.0002 by the model.

biofuel coolant temperature engine torque Levenberg Marquardt ANN

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[1]  Agarwal D, Kumar L, Agarwal AK. Performance evaluation of a vegetable oil fuelled compression ignition engine. Renewable Energy 2008; 33: 1147-1156.
[2]  Balat H. Prospects of biofuels for a sustainable energy future: A critical assessment. Energy Educ Sci Technol Part A 2010; 24: 85-111.
[3]  Canakci M, Van Gerpen J. Comparison of engine performance and emissions for petroleum diesel fuel, yellow grease biodiesel and soybean oil biodiesel. Transact ASAE 2001; 46: 937-944.
[4]  Demirbas A. Social, economic, environmental and policy aspects of biofuels. Energy Educ Sci Technol Part B 2010; 2: 75-109.
[5]  Dincer K. Lower emission from biodiesel combustion. Energy Source Part A 2008; 30: 963-968.
[6]  Kumar MS, Ramesh A, Nagalingam BA. Comparison of the different methods of using jatropha oil as fuel in a compression ignition engine. J Eng Gas Turb Power 2010; 132: 32801-32811.
[7]  Aydogan H, Altun A.A, Ozcelik A. E. Performance analysis of a turbocharged diesel engine using biodiesel with back propagation artificial neural network. Energy Education Science and Technology Part A: Energy Science and Research 2011; 28: 459-468.
[8]  Banapurmatha NR, Tewaria PG, Hosmath RS. Performance and emission characteristics of a compression ignition engine operated on honge, jatropha and sesame oil methyl esters. Renew Energy 2008; 33: 1982-1988.
[9]  Kalogirou SA. Artificial intelligence for the modeling and control of combustion processes: a review. Prog Energy Combus Sci 2003; 29: 515-566.
[10]  Sayin C. Ertunc H. Hosoz M. Kilicaslan I. Canakci M. Performance and exhaust emissions of a gasoline engine using artificial neural network. Appl Therm Eng 2007; 27: 46-54.
[11]  Golcu M. Sekmen Y. Erduranli P. Salman S. Artificial neural network based modeling of variable valve-timing in a spark ignition engine. Applied Energy 2005; 81: 187-197.
[12]  Yuanwang D. Meilin Z. Dong X. Xiaobei C. An analysis for effect of cetane number on exhaust emissions from engine with the neural network. Fuel 2003; 81: 1963-1970.
[13]  Ghobadian B. Rahimi H. Nikbakht AM. Najafi G. Yusaf T. Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renewable Energy 2009; 34: 976-982.
[14]  Parlak A. Islamoglu Y. Yasar H. Egrisogut A. Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a diesel engine. Appl Therm Eng 2006; 26: 82-828.
[15]  Hertz JA. Krogh AS. Palmer RG. Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Company, Red Wood City, California, 1991.
[16]  Kalogirou SA. Applications of artificial neural-networks for energy systems. Appl Energy 2000; 67: 17-35.
[17]  Ozgur T, Tuccar G, Ozcanli M, Aydin K. Prediction of emissions of a diesel engine fueled with soybean biodiesel using artificial neural Networks. Energy Educ Sci Technol Part A 2011; 27: 301-312.
[18]  Eyercioglu O, Kanca E, Pala M, Ozbay E. Prediction of martensite and austenite start temperatures of the Fe-based shape memory alloys by artificial neural networks. J Mater Process Tech 2008; 200: 146-52.
[19]  Togun N. K, Baysec S. Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural Networks Appl Energy 2010; 87: 349-355.
[20]  Almeida LB. Multilayer Perceptrons, Handbook of Neural Computation, IOP Publishing Ltd. and Oxford University Press, 1997.
[21]  Wang SW, Yu DL, Gomm JB, Page GF, Douglas SS. Adaptive neural network model based predictive control for air fuel ratio of SI engines. Eng Appl Artif Intel 2006; 19: 189-200.