Performance and emission optimization of diesel engine by single and multi-objective genetic algorithms
Küçük Resim Yok
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, single and also multi-objective (MO) genetic algorithms (GAs) were used for optimisation of performance and emissions of a diesel engine. Population space and initial population of both GAs were obtained by Artificial Neural Network (ANN). Specific fuel consumption (Sfc), NO x, power (P), torque (Tq) and air-flow rate (Afr) were reduced to %7.7, %8.51, %30, %4 and %7.4 respectively whereas HC increased at the rate of %10.5 by traditional single objective GA. HC, CO2, P and Sfc were reduced to %17.6, %30.05, %31.8 and %14.5 respectively whereas NOx increased at the rate of %13 by using multiobjective GA with Nondominated Sorting Genetic Algorithm II (NSGA II). %14.5 fuel reduction against %31 power reduction have never been obtained in the previous studies. This shows the effective usage of MOGA with NSGA II in optimisation of fuel diesel engine performance parameters. Copyright © 2010 ACM.
Açıklama
11th International Conference on Computer Systems and Technologies, CompSysTech'10 -- 17 June 2010 through 18 June 2010 -- Sofia -- 81605
Anahtar Kelimeler
ANN, Diesel engine performance, Multi-objective genetic algorithm, NSGA II, Optimization
Kaynak
ACM International Conference Proceeding Series
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
471
