Multi-Objective Optimization of Large Pipeline Networks Using Genetic Algorithm

Abstract

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.

Energy efficient operation of gas export systems

“…This implies increasing system energy efficiency by minimising operational network costs and environmental emissions, while fulfilling contractual 3 Standard cubic metre, scm, defind as the volume under standard conditions -i.e, a temperature of 15°C and a pressure of 1.01325 bar. This implies increasing system energy efficiency by minimising operational network costs and environmental emissions, while fulfilling contractual 3 Standard cubic metre, scm, defind as the volume under standard conditions -i.e, a temperature of 15°C and a pressure of 1.01325 bar.…”

“…Botros et al [3] present an application of genetic algorithm methodologies to multi-objective optimisation of gas pipeline networks. Constraints are handled by means of penalty functions.…”

Study on the multi-objective optimization of reliability and operating cost for natural gas pipeline network

“…In recent years, multi-objective optimization has obtained more and more applications in oil and gas industry due to its comprehensive consideration of multiple motivations. For complex pipeline network structures, Botros et al [9] adopted a multiobjective optimization model to study simultaneous optimization of operating cost and line-pack of natural gas pipeline network. Kashani and Molaei [10] proposed a three-objective optimization model to optimize the operation parameters for low throughput, reduced operating costs and CO 2 emissions.…”

Deni̇ze Deşarj Yapilarinin Öğretme Ve Öğrenme Tabanli Algoritmayla Opti̇mum Tasarimi

“…Öğretme ve Öğrenme Tabanlı Optimizasyon (ÖÖTO) Algoritması çelik kafes sistem yapıların minimum ağırlıkla optimum tasarımı [4],yapıların boyut ve şekil optimizasyonu [5], akarsuda çözünmüş oksijen konsantrasyonunun modellenmesi [6] gibi birçok farklı amaçla literatürde kullanılmıştır. Boru hatlarının optimizasyonu ile ilgili yapılmış çalışmalar incelendiğinde ise çok amaçlı hibrit optimizasyon algoritması [7], genetik algoritma [8], oransal diferansiyel algoritma [9] gibi farklı algoritmaların kullanıldığı görülmektedir.…”