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.
- International Petroleum Technology Institute
Multi-Objective Optimization of Large Pipeline Networks Using Genetic Algorithm
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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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