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Optimization problems in gas pipeline transportation involve numerous variables, multiple objectives and many complex linear-nonlinear equality and inequality constraints. The optimization techniques used for solving multiobjective gas transportation problems are essentially different as those for single-objective optimization. Discovering the Pareto front and non-dominated set of solutions for the nonlinear multiobjective pipeline problem obliges noteworthy registering exertion. In the present paper, for solving multiobjective gas pipeline transportation problem, a multiobjective ant colony optimization technique for pipeline optimization has been developed. The multiobjective problem considered is about minimizing fuel consumption in compressors and maximizing throughput. For validation of the technique used, it has been applied on some test problems reported in the literature. After validation, the technique has then been implemented in the gas pipeline transportation problem. An eighteen-node gas pipeline network has been taken for analysis. The result obtained supports the industrial practice of maximizing throughput at the cost of an increase in fuel consumption in compressors. The technique employed and the results obtained may be used by the pipeline operators and managers to develop strategies for improving the operating conditions of gas pipeline network.

Transportation of natural gas from gathering station to consumption centers is done through complex gas pipeline network system. The huge cost involved in transporting natural gas has made pipeline optimization of increased interest in natural gas pipeline industries. In the present work a lesser known application of Ant Colony in pipeline optimization, has been implemented in a real gas pipeline network. The objective chosen is to minimize the fuel consumption in a gas pipeline network consisting of seven compressors. Pressures at forty-five nodes are chosen as the decision variables. Results of Ant Colony Optimization (ACO) have been compared with those of GAMS that utilizes ‘Generalized gradient principles’ for optimization. Our results utilizing ACO show significant improvement in fuel consumption reductions. Similar procedures can be adopted by researchers and pipeline managers to help pipeline operators in fixing up the pressures at different nodes so as the fuel consumption in compressors gets minimized.

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