Genetic algorithm and artificial neural network for engine optimisation of efficiency and NOx emission

被引：88
作者：
Kesgin, U [1 ]
机构：
[1] Yildiz Tech Univ, Mech Engn Fac, Naval Architecture Dept, TR-34349 Istanbul, Turkey
来源：
FUEL | 2004年 / 83卷 / 7-8期
关键词：
engine efficiency; NOx emissions; genetic algorithm; neural network;
D O I：
10.1016/j.fuel.2003.10.025
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Genetic algorithm (GA) and neural network analysis are used to predict the effects of design and operational parameters on engine efficiency and NOx emissions of a natural gas engine. A computer program to calculate the amount of NOx emissions based on a reaction kinetic model is developed. The validity of this program is verified by measurements from a turbocharged, lean-burn, natural gas engine. Using the results from this program, the effects of operational and design parameters of the engine were investigated. Then a wide range of engine parameters are optimised using a simple GA regarding both efficiency and NOx emissions. Because of the large computation requirements especially for NOx level determination, an artificial neural network model based on results of these investigations is used to predict the engine efficiency and NOx emissions. The results show an increase in efficiency as well as the amount of NOx emissions being kept under the constraint value of 250 mg/Nm3 for stationary engines. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码：885 / 895
页数：11
相关论文
共 39 条
  • [1] *ABS, 1999, GUID NOT PREV AIR PO
  • [2] AKIMOTO S, 2000, B MARINE ENG SOC JAP, V28, P8
  • [3] ATHENSTAEDT G, 1993, MTZ, V54, P602
  • [4] Bartels T., 1994, MOTORTECH ZEITS, V55, P26
  • [5] Borman GL, 1998, Combustion Engineering
  • [6] Modeling diesel particulate emissions with neural networks
    de Lucas, A
    Durán, A
    Carmona, M
    Lapuerta, M
    [J]. FUEL, 2001, 80 (04) : 539 - 548
  • [7] DEJEAGER P, 1984, THESIS TU GRAZ
  • [8] Deng YW, 2002, FUEL, V81, P1963
  • [9] Simulation of atmospheric PAH emissions from diesel engines
    Durán, A
    de Lucas, A
    Carmona, M
    Ballesteros, R
    [J]. CHEMOSPHERE, 2001, 44 (05) : 921 - 924
  • [10] EYZAT P, 1968, 680124 SAE
1234