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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.

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Article

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

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


American Journal of Energy Research. 2014, Vol. 2 No. 4, 74-80
DOI: 10.12691/ajer-2-4-1
Copyright © 2014 Science and Education Publishing

Cite this paper:
Bekir Cirak, 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.

Correspondence to: Bekir  Cirak, Siirt University, Faculty of Engineering &Architecture, Department of Mechanical Engineering, Kezer Campus, Siirt / TURKEY. Email: bekircirak@mynet.com

Abstract

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.

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