Application of artificial neural network to predict brake specific fuel consumption of retrofitted cng engine

Jahirul, M.I. and Saidur, Rahman and Masjuki, Haji Hassan (2009) Application of artificial neural network to predict brake specific fuel consumption of retrofitted cng engine. International Journal of Mechanical and Materials Engineering, 4 (3). pp. 249-255. ISSN 1823-0334,

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In this paper the applicability of artificial neural networks (ANN) is investigated for a retrofitted compressed natural gas (CNG) fueled spark ignition (SI) internal combustion engine (ICE). A four cylinder carbureted petrol engine is converted to run with NG and used throughout the work. The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. An optimal design is completed for the 3 to 12 hidden neurons on single hidden layer with six different algorithms: batch gradient descent (GD), resilient back-propagation (RP), levenberg-marquardt (LM), batch gradient descent with momentum (GDM), variable learning rate (GDX), scaled conjugate gradient (SCG) in the back-propagation neural network model. The training data for ANN is obtained from experimental measurements. Engine speed (rpm), throttle position, fuel-air equivalence ratio (φ) and torque (N-m) were used in input layer while break specific fuel consumption (gm/kWh) was used as output layer. Statistical analysis in terms of Root-Mean-Squared (RMS), absolute fraction of variance (R2), as well as mean percentage error is used to investigate the prediction performance of ANN. LM algorithm with 10 neurons on single hidden layer in back-propagation of ANN model has shown best result in the present study. The degree of accuracy of the ANN model in prediction is proven acceptable in all statistical analysis and shown in results. So, it can be concluded that ANN provides a feasible method in predicting specific fuel consumption of CNG driven SI engine.

Item Type: Article
Additional Information: Cited By (since 1996): 2 Export Date: 6 December 2012 Source: Scopus Language of Original Document: English Correspondence Address: Jahirul, M. I.; Department of Mechanical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia; email: References: Aslam, M.U., Masjuki, H.H., Kalam, M.A., Abdesselam, H., Mahlia, T.M.I., Amalina, M.A., An experimental investigation of CNG as an alternative fuel for a retrofitted gasoline vehicle (2006) Fuel, 85, pp. 717-724; Aslam, M.U., Masjuki, H.H., Maleque, M.A., Kalam, M.A., Mahlia, T.M.I., Zainon, Z., (2003) Introduction of natural gas fueled automotive in Malaysia. Proc. TECHPOS'03, 160. , UM, Malaysia; Arcakhoglu, E., Cavusoglu, A., Erisen, A., Thermodynamic analyses of refrigerant mixtures using artificial neural-networks (2004) Applied Energy, 78, pp. 219-230; International Energy Outlook, 2008. International Energy Outlook Energy information administration. Washington, DC: Department of Energy;, Dater 15/08/2008Kreider, J.F., Wang, X.A., Artificial neural networks demonstrations for automated generation of energy use predictors for commercial buildings (1992) ASHRAE Transactions, 97 (1), pp. 775-779; Lucas, A., Duran, M., Carmona, M., Lapuerta, M., Modeling diesel particulate emissions with neural networks (2001) Fuel, 4, pp. 548-593; Nylund, N.O., Laurikko, J., Ikonen, M., Pathways for natural gas into advanced vehicles (2002) IANGV (International Association for Natural Gas Vehicle) Edited Draft Report; Shayler, P.J., Goodman, M., Ma, T., The exploitation of neural networks in automotive engine management systems (2000) Eng. Appl. Artif. Intell, 13, pp. 147-151; Sozen, A., Arcakhoglu, E., Prediction of solar potential in Turkey (2005) Appl Energ, 80, pp. 35-45; Tan, Y., Saif, M., Neural-networks-based nonlinear dynamic modeling for automotive engines (2000) Neurocomputing, 30, pp. 129-142; Yuanwang, D., Meilin, Z., Dong, X., Xiaobei, C., An analysis for effect of cetane number on exhaust emissions from engine with the neural network (2003) Fuel, 81, pp. 1963-1970
Uncontrolled Keywords: Internal combustion engine (ICE); Compressed natural gas (CNG); Artificial neural network (ANN) and Specific fuel consumption (SFC)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 08 Jul 2013 01:02
Last Modified: 18 Oct 2018 04:42

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