Predictability of artificial neural network (ANN) in performance prediction of a retrofitted CNG engine

Jahirul, M.I. and Saidur, Rahman and Masjuki, Haji Hassan (2010) Predictability of artificial neural network (ANN) in performance prediction of a retrofitted CNG engine. International Journal of Mechanical and Materials Engineering, 5 (2). pp. 268-275. ISSN 1823-0334,

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Compressed natural gas (CNG) is a potential alternative of liquid petroleum fuel in automotive application. The combustion process of CNG in engine is a complex thermodynamic process and highly sensitive with operating conditions. Additionally, the experimental investigations of engine performance are time consuming and quite expensive. Present study utilized artificial neural networks (ANN) modeling technique to evaluate the performance of a retrofitted automotive CNG engine. Back propagation (BP) neural network with single hidden-layer and logistic sigmoid transfer function was used to optimize prediction model performance. The neural networks toolbox of MatLab 7 was used to train and test the prediction models. Engine speed (rpm), throttle position () and operation time (min) were used as the input layers, while engine thermal efficiency (η, ), brake power (bp, kW), break specific fuel consumption (bsfc, kg/kWh) and exhaust temperature (Tex, °C) were used in output layers. For each performance parameter two prediction models, trained with 12 and 24 set of experimental data, were developed in order to investigate the prediction ability of ANN in different number of training samples. After successful model development, CNG performance parameters were simulated with new set of input parameter. Simulation results then compared with experimental results and prediction performance of ANN were evaluated statistically. The results of this study show that ANN is an appropriate modeling technique to estimate performance of the engine used in the experiments. Moreover the prediction ability of ANN models was significantly improved with increasing number of training sample.

Item Type: Article
Additional Information: Export Date: 6 December 2012 Source: Scopus Language of Original Document: English Correspondence Address: Jahirul, M. I.; Department of Mechanical Engineering, Faculty of Engineering, 50603 Kuala Lumpur, Malaysia; email: References: 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 (2006) Applied Thermal Engineering, 26 (8-9), pp. 824-828. , DOI 10.1016/j.applthermaleng.2005.10.006, PII S1359431105003339; Arcakloglu, E., Cavusoglu, A., Erisen, A., Thermodynamic analyses of refrigerant mixtures using artificial neural networks (2004) Applied Energy, 78 (2), pp. 219-230. , DOI 10.1016/j.apenergy.2003.08.001, PII S030626190300165X; 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 (5-6), pp. 717-724. , DOI 10.1016/j.fuel.2005.09.004, PII S0016236105003303; Canakci, M., Ozsezen, A.N., Arcaklioglu, E., Erdil, A., Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil (2009) Expert Systems with Applications, 6 (5), pp. 9268-9280; Hafner, M., Schuler, M., Nelles, O., Isermann, R., Fast neural networks for diesel engine control design (2000) Control Engng Pract, 8 (11), pp. 1211-1221; Jahirul, M.I., Masjuki, H.H., Saidur, R., Jayed, M.H., Kalam, M.A., Wazed, M.A., Comparative Engine performance and Emission Analysis of CNG and Gasoline in a Retrofitted Car Engine (2010) Applied Thermal Engineering, (30), pp. 2219-2226; Jahirul, M.I., R Saidur, R., Masjuki, H.H., Specific fuel consumption of natural gas engine: A predictive model (2009) International Journal of Mechanical and Materials Engineering, 4, pp. 249-255; Jahirul, M.I., Saidur, R., Masjuki, H.H., Kalam, M.A., Rashid, M.M., Application of artificial neural networks (ANN) for prediction the performance of a dual fuel internal combustion engine (2009) HKIE Transactions, 16 (1), pp. 14-20; Jahirul, M.I., Saidur, R., Hasanuzzaman, M., Masjuki, H.H., Kalam, M.A., A comparison of the air pollution of gasoline and CNG driven car for Malaysia (2007) International Journal of Mechanical and Materials Engineering, 2 (2), pp. 130-138; Kalogirou, S.A., Bojic, M., Artificial neural networks for the prediction of the energy consumption of a passive solar building (2000) Energy (Oxford), 25 (5), pp. 479-491. , DOI 10.1016/S0360-5442(99)00086-9; De Lucas, A., Duran, A., Carmona, M., Lapuerta, M., Modeling diesel particulate emissions with neural networks (2001) Fuel, 80 (4), pp. 539-548. , DOI 10.1016/S0016-2361(00)00111-3; Nasr, G.E., Badr, E.A., Joun, C., Backpropagation Neural Networks for Modelling gasoline Consumption, Energy Convers (2003) Manage, 44, pp. 893-905; 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; Oǧuz, H., Santas, S., Baydan, H.E., Prediction of diesel engine performance using biofuels with artificial neural network (2010) Expert Systems with Applications, 37, pp. 6579-6586; Saidur, R., Jahirul, M.I., Hasanuzzaman, M., Masjuki, H.H., Analysis of exhaust emissions of natural gas engine by using response surface methodology (2008) Journal of Applied Sciences, 8 (19), pp. 3328-3339; 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 modelling 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. 963-1970
Uncontrolled Keywords: ANN; CNG; Engine performance
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: 05 Jul 2013 08:37
Last Modified: 19 Oct 2018 01:16

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