0
Research Papers: Internal Combustion Engines

Application of Improved Artificial Bee Colony Algorithm to the Parameter Optimization of a Diesel Engine With Pilot Fuel Injections

[+] Author and Article Information
Qiang Zhang

School of Energy and Power Engineering,
Jiangsu University of Science and Technology,
2 Mengxi Road,
Zhenjiang 212003, China
e-mail: zhangqiangjust@163.com

Ryan M. Ogren

Department of Mechanical Engineering,
Iowa State University,
2529 Union Drive,
Ames, IA 50011
e-mail: rmogren@iastate.edu

Song-Charng Kong

Department of Mechanical Engineering,
Iowa State University,
2529 Union Drive,
Ames, IA 50011
e-mail: kong@iastate.edu

1Corresponding author.

Contributed by the IC Engine Division of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received August 25, 2016; final manuscript received May 5, 2017; published online June 6, 2017. Assoc. Editor: Stani Bohac.

J. Eng. Gas Turbines Power 139(11), 112801 (Jun 06, 2017) (9 pages) Paper No: GTP-16-1423; doi: 10.1115/1.4036766 History: Received August 25, 2016; Revised May 05, 2017

Modern diesel engines are charged with the difficult problem of balancing emissions and efficiency. For this work, a variant of the artificial bee colony (ABC) algorithm was applied for the first time to the experimental optimization of diesel engine combustion and emissions. In this study, the employed and onlooker bee phases were modified to balance both the exploration and exploitation of the algorithm. The improved algorithm was successfully trialed against particle swarm optimization (PSO), genetic algorithm (GA), and a recently proposed PSO-GA hybrid with three standard benchmark functions. For the engine experiments, six variables were changed throughout the optimization process, including exhaust gas recirculation (EGR) rate, intake temperature, quantity and timing of pilot fuel injections, main injection timing, and fuel pressure. Low sulfur diesel fuel was used for all the tests. In total, 65 engine runs were completed in order to reduce a five-dimensional objective function. In order to reduce nitrogen oxide (NOx) emissions while keeping particulate matter (PM) below 0.09 g/kW h, solutions call for 43% exhaust gas recirculation, with a late main fuel injection near top-dead center. Results show that early pilot injections can be used with high exhaust gas recirculation to improve the combustion process without a large nitrogen oxide penalty when main injection is timed near top-dead center. The emission reductions in this work show the improved ABC algorithm presented here to be an effective new tool in engine optimization.

Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

Flowchart of the improved ABC algorithm

Grahic Jump Location
Fig. 2

Convergence history of the 30th trial on Styblinski-Tang function using different algorithms

Grahic Jump Location
Fig. 3

Convergence history of the 30th trial on Rastrigin function using different algorithms

Grahic Jump Location
Fig. 4

Convergence history of the 30th trial on Ackley function using different algorithms

Grahic Jump Location
Fig. 5

Fitness values for 65 engine runs over eight generations

Grahic Jump Location
Fig. 6

Global best fitness values versus engine runs

Grahic Jump Location
Fig. 7

Two-dimensional set of Pareto frontier representing NOx versus PM

Grahic Jump Location
Fig. 8

Two-dimensional set of Pareto frontier representing BSNOx versus BSCO, BSHC, and BSFC

Grahic Jump Location
Fig. 9

Food source 2 parameter settings for EGR < 30% and corresponding overall, NOx and PM fitness

Grahic Jump Location
Fig. 10

Food source 3 parameter settings for EGR < 30% and corresponding overall, NOx and PM fitness

Grahic Jump Location
Fig. 11

Food source 4 parameter settings for EGR < 30% and corresponding overall, NOx and PM fitness

Grahic Jump Location
Fig. 12

Food source 1 parameter settings for EGR > 30% and corresponding overall, NOx and PM fitness

Grahic Jump Location
Fig. 13

Food source 2 parameter settings for EGR > 30% and corresponding overall, NOx and PM fitness

Grahic Jump Location
Fig. 14

Food source 3 parameter settings for EGR > 30% and corresponding overall, NOx and PM fitness

Grahic Jump Location
Fig. 15

Food source 4 parameter settings for EGR > 30% and corresponding overall, NOx and PM fitness

Grahic Jump Location
Fig. 16

Comparison of the in-cylinder pressure and heat release rate for the best three trials corresponding to Table 9

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In