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Optimization of Gas Transmission Networks under Energetic and Environmental Considerations
1Laboratoire de Génie Chimique, firstname.lastname@example.org
2Laboratoire de Génie Chimique, email@example.com
3Laboratoire de Génie Chimique, firstname.lastname@example.org
4Laboratoire de Génie Chimique, email@example.com
Citation Information: International Journal of Chemical Reactor Engineering. Volume 8, Issue 1, Pages –, ISSN (Online) 1542-6580, DOI: 10.2202/1542-6580.2083, September 2010
- Published Online:
The transport of large quantities of natural gas (NG) is carried out by pipeline network systems across long distances. Pipeline network systems include one or several compressor stations which compensate for pressure drops. A typical network today might consist of thousands of pipes, dozens of stations, and many other devices, such as valves and regulators. Inside each station, there can be several groups of compressor units of various vintages that were installed as the capacity of the system expanded. The compressor stations typically consume about 3 to 5% of the transported gas. It is estimated that the global optimization of operations can save considerably the fuel consumed by the stations. Hence, the problem of minimizing fuel cost is of great importance. This study presents a mathematical formulation for NG transport through pipelines and compressors by considering the mass and energy balance equations on the basic elements of a didactic network from the literature. First, a deterministic optimization procedure is implemented. The objective of this formulation is the fuel minimization problem in the compressor stations for a fixed gas mass flow delivery. A second example is devoted to the simultaneous consideration of gas mass flow delivery maximization and fuel consumption minimization. In that case, two procedures are compared: a genetic algorithm coupled with a Newton-Raphson procedure and the scalarization method of ?-constraint. In both monobjective and biobjective cases, a study of carbon dioxide (CO2) emissions is carried out. The Pareto front deduced from the biobjective optimization can be used either for identifying the minimum and maximum network capacity in terms of CO2 emissions and mass flow delivery or for a given mass flow delivery for determining the minimal CO2 emissions from an appropriate operating of the compressor stations.