ACM Home Page
Please provide us with feedback. Feedback
Decision of optimal scheduling scheme for gas field pipeline network based on hybrid genetic algorithm
Full text PdfPdf (1.94 MB)
Source
ACM/SIGEVO Summit on Genetic and Evolutionary Computation archive
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation table of contents
Shanghai, China
SESSION: Full papers table of contents
Pages 369-374  
Year of Publication: 2009
ISBN:978-1-60558-326-6
Authors
Wu Liu  School of Petroleum Engineering, Southwest Petroleum University, Chengdu, China
Min Li  School of Petroleum Engineering, Southwest Petroleum University, Chengdu, China
Yi Liu  Xi'an Changqing Technology Engineering Co. LTD, Xi'an , China
Yuan Xu  Graduate School, Southwest Petroleum University, Chengdu, China
Xinglan Yang  Graduate School, Southwest Petroleum University, Chengdu, China
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 11,   Downloads (12 Months): 28,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1543834.1543883
What is a DOI?

ABSTRACT

A mathematical model of optimal scheduling scheme for natural gas pipeline network is established, which takes minimal annual operating cost of compressor stations as objective function after comprehensively considering the resources of gas field, operating parameters of compressor stations and work conditions of pipeline system. In the light of the characteristics of the objective function such as nonliner, more optimal variables and complicated constraint conditions, based on the thought of modern heuristic evolutionary-algorithm, this paper presented a new hybrid genetic algorithm, which is featured by global search, fast convergence and strong robustness. It combined the reproduction strategy of differential evolution algorithm with the crossover and mutation of genetic algorithm. With the dynamic calibration of fitness and the elitism strategy of the optimal individual, this algorithm can improve the ability of searching and avoid the premature convergence effectively. The case study of a certain pipeline network system with 11 nodes, 11 pipelines,2 compressor stations demonstrates the effectiveness and application of the established model and algorithm. The optimal scheduling scheme could be adapted to daily operation and future retrofit of gas pipeline network.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
S. Wu. Steady-state simulation fuel cost minimization of gas pipeline networks. University of Houston, Houston, 1998.
 
2
F.X.Chu, C.C. Wu. Optimizing natural gas pipeline operation. Oil & Gas Storage and Transportation, 23(11): 3--6, 2004.
 
3
Q.G. Zheng, M. Zhao, Chang B.C. Program optimization of gas gathering-distributing network without compressor station. Natural Gas Industry, 13(3):72--76, 1993.
 
4
S. Kim. Minimum cost fuel consumption on natural gas transmission network problem. Texas A&M University, College Station, 1999.
 
5
Richard Carter.Optimizing gas transmission pipeline operation. Pipeline & Gas Journal, 20(10),2001.
 
6
C.K. Sun. An integrated expert system/operations research approach for the optimization of natural gas pipeline operations. Engineering Applications of Artificial Intelligence, 2000(13).
 
7
D. K. He, Y.Q.Li, F.L.Wang. Hybrid genetic algorithm based on the operator of pattern search. Information and Control, 30(3):276--278, 2001.
 
8
Y. Fan, R.H. Jin, J.P.Geng. A hybrid optimized algorithm based on differential evolution and genetic algorithm and its applications in pattern synthesis of antenna arrays. Chinese Journal of Electronics, 32(12):1997--2000, 2004.
 
9
Storn. System design by constraint adaptation and differential evolution. IEEE Transaction on Evolutionary Computation, 3(1):22--34, 1999.
 
10
G.F.Wang, Y.Sun, J.M.Wang. Hybrid GA and SA for solving nonlinear constrained optimization problems. Journal of Harbin Engineering University, 23(6):73--76, 2002.

Collaborative Colleagues:
Wu Liu: colleagues
Min Li: colleagues
Yi Liu: colleagues
Yuan Xu: colleagues
Xinglan Yang: colleagues