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

Richard Fiifi Turkson, Fuwu Yan, Mohamed Kamal Ahmed Ali, Bo Liu and Jie Hu
Additional contact information
Richard Fiifi Turkson: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Fuwu Yan: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Mohamed Kamal Ahmed Ali: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Bo Liu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Jie Hu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China

Sustainability, 2016, vol. 8, issue 1, 1-15

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.

Keywords: engine modeling; NSGA-II genetic algorithm; optimization; emissions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/8/1/72/pdf (application/pdf)
https://www.mdpi.com/2071-1050/8/1/72/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:8:y:2016:i:1:p:72-:d:62107

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().