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.

References

References
1.
U.S. EIA
,
2013
, “
Annual Energy Outlook 2013—Oil/Liquids From Executive Summary
,” U.S. Energy Information Administration, Washington, DC, Report No.
DOE/EIA-0383(2013)
.
2.
Reiter
,
A. J.
, and
Kong
,
S.-C.
,
2008
, “
Demonstration of Compression-Ignition Engine Combustion Using Ammonia in Reducing Greenhouse Gas Emissions
,”
Energy Fuels
,
22
(
5
), pp.
2963
2971
.
3.
Sukuraman
,
S.
, and
Kong
,
S.-C.
,
2010
, “
Numerical Study on Mixture Formation Characteristics in a Direct-Injection Hydrogen Engine
,”
Int. J. Hydrogen Energy
,
35
(
15
), pp.
7991
8007
.
4.
Dev
,
S.
,
Divekar
,
P.
,
Xie
,
K.
,
Han
,
X.
,
Chen
,
X.
, and
Zheng
,
M.
,
2015
, “
A Study of Combustion Inefficiency in Diesel Low Temperature Combustion and Gasoline–Diesel RCCI Via Detailed Emission Measurement
,”
ASME J. Eng. Gas Turbines Power
,
137
(
12
), p.
121501
.
5.
Kong
,
S.-C.
,
Sun
,
Y.
, and
Reitz
,
R. D.
,
2005
, “
Modeling Diesel Spray Flame Liftoff, Sooting Tendency, and NOx Emissions Using Detailed Chemistry With Phenomenological Soot Model
,”
ASME J. Eng. Gas Turbines Power
,
129
(
1
), pp.
245
251
.
6.
Kim
,
J.
,
Reitz
,
R. D.
,
Park
,
S. W.
, and
Sung
,
K.
,
2010
, “
Reduction in NOx and CO Emissions in Stoichiometric Diesel Combustion Using a Three-Way Catalyst
,”
ASME J. Eng. Gas Turbines Power
,
132
(
7
), p.
072803
.
7.
Cui
,
Y.
,
Hu
,
Z.
,
Deng
,
K. Y.
, and
Wang
,
Q. F.
,
2013
, “
Miller-Cycle Regulatable, Two-Stage Turbocharging System Design for Marine Diesel Engines
,”
ASME J. Eng. Gas Turbines Power
,
136
(
2
), p.
022201
.
8.
Engelmayer
,
M.
,
Wimmer
,
A.
,
Taucher
,
G.
,
Hirschl
,
G.
, and
Kammerdiener
,
T.
,
2015
, “
Impact of Very High Injection Pressure on Soot Emissions of Medium Speed Large Diesel Engines
,”
ASME J. Eng. Gas Turbines Power
,
137
(
10
), p.
101509
.
9.
Zamboni
,
G.
,
Moggia
,
S.
, and
Capobianco
,
M.
,
2016
, “
Hybrid EGR and Turbocharging Systems Control for Low NOx and Fuel Consumption in an Automotive Diesel Engine
,”
Appl. Energy
,
165
, pp.
839
848
.
10.
Karra
,
P. K.
, and
Kong
,
S.-C.
,
2008
, “
Diesel Emission Characteristics Using High Injection Pressure With Converging Nozzles in a Medium-Duty Engine
,”
SAE
Technical Paper No. 2008-01-1085.
11.
Lee
,
T.
, and
Reitz
,
R. D.
,
2003
, “
Response Surface Method Optimization of a High-Speed Direct-Injection Diesel Engine Equipped With a Common Rail Injection System
,”
ASME J. Eng. Gas Turbines Power
,
125
(
2
), pp.
541
546
.
12.
Bertram
,
A. M.
,
Zhang
,
Q.
, and
Kong
,
S.-C.
,
2016
, “
Novel Particle Swarm and Genetic Algorithm Hybrid Method for Diesel Engine Performance Optimization
,”
Int. J. Engine Res.
,
17
(
7
), pp.
732
747
.
13.
Shi
,
Y.
, and
Reitz
,
R. D.
,
2010
, “
Optimization of a Heavy-Duty Compression-Ignition Engine Fueled With Diesel and Gasoline-Like Fuels
,”
Fuel
,
89
(
11
), pp.
3416
3430
.
14.
Karra
,
P. K.
, and
Kong
,
S.-C.
,
2010
, “
Diesel Engine Emissions Reduction Using Particle Swarm Optimization
,”
Combust. Sci. Technol.
,
182
(
7
), pp.
879
903
.
15.
Deb
,
M.
,
Banerjee
,
R.
,
Majumder
,
A.
, and
Sastry
,
G. R. K.
,
2014
, “
Multi Objective Optimization of Performance Parameters of a Single Cylinder Diesel Engine With Hydrogen as a Dual Fuel Using Pareto-Based Genetic Algorithm
,”
Int. J. Hydrogen Energy
,
39
(
15
), pp.
8063
8077
.
16.
Shi
,
Y.
, and
Reitz
,
R. D.
,
2010
, “
Assessment of Multiobjective Genetic Algorithms With Different Niching Strategies and Regression Methods for Engine Optimization and Design
,”
ASME J. Eng. Gas Turbines Power
,
132
(
5
), p.
052801
.
17.
Lee
,
C.-H.
,
Ge
,
H.-W.
,
Reitz
,
R. D.
,
Kurtz
,
E.
, and
Willems
,
W.
,
2012
, “
Computational Optimization of a Down-Scaled Diesel Engine Operating in the Conventional Diffusion Combustion Regime Using a Multi-Objective Genetic Algorithm
,”
Combust. Sci. Technol.
,
184
(
1
), pp.
78
96
.
18.
Hardy
,
W. L.
, and
Reitz
,
R. D.
,
2006
, “
An Experimental Investigation of Partially Premixed Combustion Strategies Using Multiple Injections in a Heavy-Duty Diesel Engine
,”
SAE
Technical Paper No. 2006-01-0917.
19.
Archer
,
J. R.
,
Fang
,
T. G.
,
Ferguson
,
S.
, and
Buckner
,
G. D.
,
2014
, “
Multi-Objective Design Optimization of a Variable Geometry Spray Fuel Injector
,”
ASME J. Eng. Gas Turbines Power
,
136
(
4
), p.
044501
.
20.
Ge
,
H.-W.
,
Shi
,
Y.
,
Reitz
,
R. D.
,
Wickman
,
D. D.
, and
Willems
,
W.
,
2010
, “
Engine Development Using Multi-Dimensional CFD and Computer Optimization
,”
SAE
Technical Paper No. 2010-01-03600.
21.
Zhang
,
Q.
,
Ogren
,
R. M.
, and
Kong
,
S.-C.
,
2016
, “
A Comparative Study of Biodiesel Engine Performance Optimization Using Enhanced Hybrid PSO-GA and Basic GA
,”
Appl. Energy
,
165
, pp.
676
684
.
22.
Şahin
,
A. Ş.
,
Kiliç
,
B.
, and
Kilicç
,
U.
,
2011
, “
Design and Economic Optimization of Shell and Tube Heat Exchangers Using Artificial Bee Colony (ABC) Algorithm
,”
Energy Convers. Manage.
,
52
(
11
), pp.
3356
3362
.
23.
Derakhshan
,
S.
,
Tavaziani
,
A.
, and
Kasaeian
,
N.
,
2015
, “
Numerical Shape Optimization of a Wind Turbine Blades Using Artificial Bee Colony Algorithm
,”
ASME J. Energy Resour. Technol.
,
137
(
5
), p.
051210
.
24.
Karaboga
,
D.
,
2005
, “
An Idea Based on Honey Bee Swarm for Numerical Optimization
,” Erciyes University, Kayseri, Turkey, Technical Report No. TR06.
25.
Karaboga
,
D.
, and
Akay
,
B.
,
2009
, “
A Comparative Study of Artificial Bee Colony Algorithm
,”
Appl. Math. Comput.
,
214
(
1
), pp.
108
132
.
26.
Gao
,
W.
, and
Liu
,
S.
,
2011
, “
Improved Artificial Bee Colony Algorithm for Global Optimization
,”
Inf. Process Lett.
,
111
(
17
), pp.
871
882
.
27.
Gao
,
W.
,
Liu
,
S.
, and
Huang
,
L.
,
2015
, “
Enhancing Artificial Bee Colony Algorithm Using More Information-Based Search Equations
,”
Inf. Sci.
,
270
, pp.
112
133
.
28.
Zhu
,
G.
, and
Kwong
,
S.
,
2010
, “
Gbest-Guided Artificial Bee Colony Algorithm for Numerical Function Optimization
,”
Appl. Math. Comput.
,
217
(
7
), pp.
3166
3173
.
29.
Environmental Protection Agency
,
2016
, “
Nonroad Compression-Ignition Engines—Exhaust Emission Standards
,” Environmental Protection Agency, Washington, DC, accessed June 3, 2013, https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P100OA05.txt
30.
Zhang
,
Q.
,
Ogren
,
R. M.
, and
Kong
,
S.-C.
,
2015
, “
Trade-Offs Between Emissions and Efficiency for Multiple Injections of Neat Biodiesel in a Turbocharged Diesel Engine Using an Enhanced PSO-GA Optimization Strategy
,”
SAE
Technical Paper No. 2016-01-0630.
You do not currently have access to this content.