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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

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Citations:  Bibtex RefMan EndNote GB/T7714

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


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)

Jos D. Martnez-Morales, Elvia R. Palacios-Hernndez, Gerardo A. Velzquez-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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%T Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms
%A Jos D. Martnez-Morales
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T1 - Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms
A1 - Jos D. Martnez-Morales
A1 - Elvia R. Palacios-Hernndez
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J0 - Journal of Zhejiang University Science A
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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


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