0

Full Content is available to subscribers

Subscribe/Learn More  >

Large Pipeline Network Optimization: Summary and Conclusions of TransCanada Research Effort

[+] Author Affiliations
K. K. Botros, D. Sennhauser

NOVA Chemicals Research & Technology Corporation, Calgary, AB, Canada

J. Stoffregen, K. J. Jungowski, H. Golshan

TransCanada PipeLines Limited, Calgary, AB, Canada

Paper No. IPC2006-10007, pp. 657-670; 14 pages
doi:10.1115/IPC2006-10007
From:
  • 2006 International Pipeline Conference
  • Volume 3: Materials and Joining; Pipeline Automation and Measurement; Risk and Reliability, Parts A and B
  • Calgary, Alberta, Canada, September 25–29, 2006
  • Conference Sponsors: Pipeline Division
  • ISBN: 0-7918-4263-0
  • Copyright © 2006 by ASME

abstract

Operation of large gas pipeline networks calls for fulfilling variation in contractual volume obligations, and maintaining a certain range of linepack with minimum fuel consumptions to drive compressor units. This is often achieved with either operational experience or by utilization of optimization tools, which results in reduced hydraulic analysis time as well as improved pipeline operation as a whole. The main objective is to accurately identify the optimum set points for all compressor stations, control and block valves in the network, subject to several system and operational constraints. This implies multi-objective optimization of a highly constrained network with a large number of decision variables. Over the past three years, TransCanada has devoted a research effort in developing/integrating an optimization tool based on stochastic methods. It was found that it offers greater stability and is more suited for multi-objective optimizations of large networks with inherently large number of decision variables, than any gradient-based method. This paper describes the nature of the pipeline system under optimization, and discusses the basis for a Genetic-Algorithm-based tool employed. It summarizes the results of the past three years of research efforts outlining the selection criteria for the optimization parameters, integration with a robust steady-state thermal hydraulic simulator of the pipeline network and the notion that dynamic penalty parameters can affect convergence. The methodology is applied to a large gas pipeline network containing 22 compressor stations resulting in 54 decision variables and an optimization space of 1.85×1078 cases. Comparison of genetic algorithm optimization with traditional and manual optimization is demonstrated. Extensive effort has been devoted to reduce the computation time, which includes techniques to utilize various hybrid surrogate methods such as Kriging, Neural Networks, Response Surface, as well as exploitation of parallel processing.

Copyright © 2006 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In