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CLC number: TK41; TP39

On-line Access: 2013-06-02

Received: 2013-01-04

Revision Accepted: 2013-06-17

Crosschecked: 2013-08-20

Cited: 11

Clicked: 6747

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2013 Vol.14 No.9 P.657-670

http://doi.org/10.1631/jzus.A1300010


Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms


Author(s):  Jos D. Martnez-Morales1, Elvia R. Palacios-Hernndez2, Gerardo A. Velzquez-Carrillo3

Affiliation(s):  1. Faculty of Engineering, Autonomous University of San Luis Potosi, San Luis Potosi 78290, Mexico; more

Corresponding email(s):   jdaniel.martinez@alumnos.uaslp.edu.mx

Key Words:  Engine calibration, Multi-objective optimization, Neural networks, Multiple objective particle swarm optimization (MOPSO), Nondominated sorting genetic algorithm II (NSGA-II)


José D. Martínez-Morales, Elvia R. Palacios-Hernández, Gerardo A. Velázquez-Carrillo. Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms[J]. Journal of Zhejiang University Science A, 2013, 14(9): 657-670.

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author="José D. Martínez-Morales, Elvia R. Palacios-Hernández, Gerardo A. Velázquez-Carrillo",
journal="Journal of Zhejiang University Science A",
volume="14",
number="9",
pages="657-670",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1300010"
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%T Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms
%A José D. Martínez-Morales
%A Elvia R. Palacios-Hernández
%A Gerardo A. Velázquez-Carrillo
%J Journal of Zhejiang University SCIENCE A
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%N 9
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%@ 1673-565X
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1300010

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T1 - Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms
A1 - José D. Martínez-Morales
A1 - Elvia R. Palacios-Hernández
A1 - Gerardo A. Velázquez-Carrillo
J0 - Journal of Zhejiang University Science A
VL - 14
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SP - 657
EP - 670
%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A1300010


Abstract: 
In this paper, a multi-objective particle swarm optimization (MOPSO) algorithm and a nondominated sorting genetic algorithm II (NSGA-II) are used to optimize the operating parameters of a 1.6 L, spark ignition (SI) gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NO x ), which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks (ANNs) were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms: NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NO x , respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NO x , respectively.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

References

[1] Abido, M., 2009. Multiobjective particle swarm optimization for environmental/economic dispatch problem. Electric Power Systems Research, 79(7):1105-1113. 


[2] Alonso, J.M., Alvarruiz, F., Desantes, J.M., Hernandez, L., Hernandez, V., Molto, G., 2007. Combining neural networks and genetic algorithms to predict and reduce diesel engine emissions. IEEE Transactions on Evolutionary Computation, 11(1):46-55. 


[3] Atashkari, K., Nariman-Zadeh, N., Golcu, M., Khalkhali, A., Jamali, A., 2007. Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms. Energy Conversion and Management, 48(3):1029-1041. 


[4] Canakci, M., Ozsezen, A.N., Arcaklioglu, E., Erdil, A., 2009. Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil. Expert Systems with Applications, 36(5):9268-9280. 


[5] Coello, C., Lechuga, M., 2002. MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization. Proceedings of the Congress on Evolutionary Computation, Honolulu, HI, 2:1051-1056. 


[6] Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., 2002. A fast and elitist multi-objective genetic-algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197. 


[7] DErrico, G., Cerri, T., Pertusi, G., 2011. Multi-objective optimization of internal combustion engine by means of 1D fluid-dynamic models. Applied Energy, 88(3):767-777. 


[8] Ghobadian, B., Rahimi, H., Nikbakht, A.M., Najafi, G., Yusaf, T.F., 2009. Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renewable Energy, 34(4):976-982. 


[9] Guerrier, M., Cawsey, P., 2004. The development of model based methodologies for gasoline IC engine calibration. SAE Technical Paper, No 2004-01-1466,:


[10] Hafner, M., Schuler, M., Nelles, O., Isermann, R., 2000. Fast neural networks for diesel engine control design. Control Engineering Practice, 8(11):1211-1221. 


[11] Ismail, H.M., Ng, H.K., Queck, C.W., Gan, S., 2012. Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends. Applied Energy, 92:769-777. 


[12] Kennedy, J., Heberhart, R., 1995. Particle Swarm Optimization. IEEE International Conference on Neural Networks, Perth, WA, 4:1942-1948. 


[13] Kesgin, U., 2004. Genetic algorithm and artificial neural network for engine optimisation of efficiency and NO x emission. Fuel, 83(7-8):885-895. 


[14] Langouet, H., Mtivier, L., Sinoquet, D., Huy, Q., 2011. Engine calibration: multi-objective constrained optimization of engine maps. Optimization and Engineering, 12(3):407-424. 


[15] Mori, K., 1997. Worldwide trends in heavy-duty diesel engine exhaust emission legislation and compliance technologies. SAE Technical Paper, No 970753,:


[16] Nelles, O., 2001.  Nonlinear System Identification: from Classical Approaches to Neural Networks and Fuzzy Models. Springer,Berlin :365-389. 

[17] Saerens, B., Vandersteen, J., Persoons, T., Swevers, J., Diehl, M., Bulck, E., 2009. Minimization of the fuel consumption of a gasoline engine using dynamic optimization. Applied Energy, 86(9):1582-1588. 


[18] Shi, Y., Reitz, R., 2010. Optimization of a heavy duty compression ignition engine fueled with diesel and gasoline-like fuels. Fuel, 89(11):3416-3430. 


[19] Yap, W., Ho, T., Karri, V., 2012. Exhaust emissions control and engine parameters optimization using artificial neural network virtual sensors for a hydrogen-powered vehicle. International Journal of Hydrogen Energy, 37(10):8704-8715. 


[20] Zhao, B., Cao, Y.J., 2005. Multiple objective particle swarm optimization technique for economic load dispatch. Journal of Zhejiang University SCIENCE, 6(5):420-427. 



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