This paper presents application of Genetic Algorithm (GA) methodologies to multi-objective optimization of two complex gas pipeline networks to achieve specific operational objectives. The first network contains 10 compressor stations resulting in 20 decision variables and an optimization space of 6.3 × 1029 cases. The second system contains 25 compressor stations resulting in 54 decision variables and an optimization space of 1.85 × 1078 cases. Compressor stations generally included multiple unit sites, where the compressor characteristics of each unit is taken into account constraining the solution by the surge and stonewall limits, maximum and minimum speeds and maximum power available. A key challenge to the optimization of such large systems is the number of constraints and associated penalty functions, selection of the GA operators such as crossover, mutation, selection criteria and elitism, as well as the population size and number of generations. The paper discusses the approach taken to arrive at optimal values for these parameters for large gas pipeline networks. Examples for two-objective optimizations, referred to as Pareto fronts, include maximum throughput and minimum fuel, as well as, minimum linepack and maximum throughput in typical linepack/throughput/fuel envelopes.
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2004 International Pipeline Conference
October 4–8, 2004
Calgary, Alberta, Canada
Conference Sponsors:
- International Petroleum Technology Institute
ISBN:
0-7918-4176-6
PROCEEDINGS PAPER
Multi-Objective Optimization of Large Pipeline Networks Using Genetic Algorithm Available to Purchase
K. K. Botros,
K. K. Botros
NOVA Research & Technology Corporation, Calgary, AB, Canada
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D. Sennhauser,
D. Sennhauser
NOVA Research & Technology Corporation, Calgary, AB, Canada
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K. J. Jungowski,
K. J. Jungowski
TransCanada PipeLines Limited, Calgary, AB, Canada
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G. Poissant,
G. Poissant
TransCanada PipeLines Limited, Calgary, AB, Canada
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H. Golshan,
H. Golshan
TransCanada PipeLines Limited, Calgary, AB, Canada
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J. Stoffregen
J. Stoffregen
TransCanada PipeLines Limited, Calgary, AB, Canada
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K. K. Botros
NOVA Research & Technology Corporation, Calgary, AB, Canada
D. Sennhauser
NOVA Research & Technology Corporation, Calgary, AB, Canada
K. J. Jungowski
TransCanada PipeLines Limited, Calgary, AB, Canada
G. Poissant
TransCanada PipeLines Limited, Calgary, AB, Canada
H. Golshan
TransCanada PipeLines Limited, Calgary, AB, Canada
J. Stoffregen
TransCanada PipeLines Limited, Calgary, AB, Canada
Paper No:
IPC2004-0378, pp. 2005-2015; 11 pages
Published Online:
December 4, 2008
Citation
Botros, KK, Sennhauser, D, Jungowski, KJ, Poissant, G, Golshan, H, & Stoffregen, J. "Multi-Objective Optimization of Large Pipeline Networks Using Genetic Algorithm." Proceedings of the 2004 International Pipeline Conference. 2004 International Pipeline Conference, Volumes 1, 2, and 3. Calgary, Alberta, Canada. October 4–8, 2004. pp. 2005-2015. ASME. https://doi.org/10.1115/IPC2004-0378
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