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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.

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Figures

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Fig. 1

Flowchart of the improved ABC algorithm

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Fig. 2

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

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Fig. 3

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

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Fig. 4

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

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Fig. 5

Fitness values for 65 engine runs over eight generations

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Fig. 6

Global best fitness values versus engine runs

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Fig. 7

Two-dimensional set of Pareto frontier representing NOx versus PM

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Fig. 8

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

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Fig. 9

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

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Fig. 10

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

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Fig. 11

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

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Fig. 12

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

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Fig. 13

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

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Fig. 14

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

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Fig. 15

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

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Fig. 16

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

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