This paper presents a method for optimizing the fuel consumption of large and complex natural gas pipeline systems. The optimization method uses a biologically-inspired computational model, namely Particle Swarm Systems. The main objective is to identify the set of operating conditions that minimizes the use of fuel in compressor stations while maintaining the desired throughput and satisfying given system constraints. Solving this fuel optimization problem is non-trivial given the large number of decision variables and constraints in large networks, the nature of the fuel function and the minimum response time imposed by the frequent changes in flow nominations. The experimental evaluation tested on various subnetworks of TransCanada show that the proposed optimization approach meets TransCanada’s time requirements and reliably outperforms the interactive method that is the current state-of-the-art by providing solutions for which the fuel consumption is 12% less than state-of-the-art methods.

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