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Sustainability 2016, 8(1), 72; doi:10.3390/su8010072

Modeling and Multi-Objective Optimization of Engine Performance and Hydrocarbon Emissions via the Use of a Computer Aided Engineering Code and the NSGA-II Genetic Algorithm

1
School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
2
Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
3
Mechanical Engineering Department, Ho Polytechnic, P.O. Box HP 217, Ho 036, Ghana
4
Automotive and Tractors Engineering Department, Faculty of Engineering, Minia University, El-Minia 61111, Egypt
*
Author to whom correspondence should be addressed.
Academic Editors: Muge Mukaddes Darwish and Marc A. Rosen
Received: 6 November 2015 / Revised: 22 December 2015 / Accepted: 8 January 2016 / Published: 13 January 2016
(This article belongs to the Section Sustainable Engineering and Science)
View Full-Text   |   Download PDF [3826 KB, uploaded 18 January 2016]   |  

Abstract

It is feared that the increasing population of vehicles in the world and the depletion of fossil-based fuel reserves could render transportation and other activities that rely on fossil fuels unsustainable in the long term. Concerns over environmental pollution issues, the high cost of fossil-based fuels and the increasing demand for fossil fuels has led to the search for environmentally friendly, cheaper and efficient fuels. In the search for these alternatives, liquefied petroleum gas (LPG) has been identified as one of the viable alternatives that could be used in place of gasoline in spark-ignition engines. The objective of the study was to present the modeling and multi-objective optimization of brake mean effective pressure and hydrocarbon emissions for a spark-ignition engine retrofitted to run on LPG. The use of a one-dimensional (1D) GT-Power™ model, together with Group Method of Data Handling (GMDH) neural networks, has been presented. The multi-objective optimization was implemented in MATLAB® using the non-dominated sorting genetic algorithm (NSGA-II). The modeling process generally achieved low mean squared errors (0.0000032 in the case of the hydrocarbon emissions model) for the models developed and was attributed to the collection of a larger training sample data using the 1D engine model. The multi-objective optimization and subsequent decisions for optimal performance have also been presented. View Full-Text
Keywords: engine modeling; NSGA-II genetic algorithm; optimization; emissions engine modeling; NSGA-II genetic algorithm; optimization; emissions
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Turkson, R.F.; Yan, F.; Ali, M.K.A.; Liu, B.; Hu, J. Modeling and Multi-Objective Optimization of Engine Performance and Hydrocarbon Emissions via the Use of a Computer Aided Engineering Code and the NSGA-II Genetic Algorithm. Sustainability 2016, 8, 72.

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