الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Natural gas plays an important role in the world energy market due to the continuous expansion of natural gas applications whether as a fuel or as a feed stock for the fertilizers and petrochemical industries. Also, it consider the ideal fossil fuel for the future because of its manifold benefits of low greenhouse gas emissions compared to the other fossil fuel when burnt. Also, much safer to transport and store compared to other fuels.
The rapid growth in natural gas production and consumption requires more construction of natural gas pipeline transportation system to transport the natural gas from the resource location to the customers, this involves large capital expenditures as operation and maintenance costs, these high costs force the need of a good compromise of technical and economic considerations in pipeline industry, optimization has an important role in achieving this compromise.
Several researchers have attempted to apply optimization techniques for pipeline problems included in a literature survey highlights in this thesis.
One of the most problem in natural gas transportation is the pressure and energy DROP through the gas pipeline networks due to friction between the natural gas; this energy and pressure DROP is restored using compressor stations which using the gas from the pipeline as fuel to make up; this consuming huge amount of fuel to provide the required power for the compressors.
In this thesis, two types of gas pipeline networks introduced and multi objective optimization applied using fuzzy approach technique to reach the optimum discharge pressure from the compressors which achieving minimum power and minimum fuel consumption for the compressors in the same time achieving maximum gas volume line pack in the network which consider safety stocks.
The results of the thesis matching the optimization concept that allows for much cheaper gas transmission network management without affecting the gas security and customers demand.