Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter February 9, 2018

Optimal Operation of a Multi Source Multi Delivery Natural Gas Transmission Pipeline Network

Dr. Adarsh Kumar Arya and Dr. Shrihari Honwad

Abstract

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.

References

[1] Guo B, Ghalambor A. Natural gas engineering handbook. 1st ed. Houston, Texas: Gulf Publishing Company, 2005.10.1016/B978-1-933762-41-8.50008-3Search in Google Scholar

[2] BP. BP. Statistical review of world energy. Technical Report British Petroleum (BP), London, 2013.Search in Google Scholar

[3] Arnold K, Stewart M. Surface production operations - design of oil handling systems & facilities, vol. 1, 3rd ed. Houston, Texas: Gulf Professional Publishing Company, 2008.Search in Google Scholar

[4] Menon ES. Pipeline planning and construction field manual, vol. 1, 2nd ed. Elseviour, USA: Gulf Publishing, 2011.Search in Google Scholar

[5] Carter RG. Compressor station optimization: computational accuracy & speed. 28th Annual Meeting of Pipeline Simulation Interest Group, San Francisco, C.A, 1996.Search in Google Scholar

[6] Elshiekh TM. Optimization of fuel consumption in compressor stations. Oil and Gas facilities. Houston, TX: Society of Petroleum Engineers, 2014.10.2118/173888-PASearch in Google Scholar

[7] Gilmour BJ, Luongo CA, Schroeder DW. Optimization in natural gas transmission networks: A tool to improve operational efficiency. Technical report, Stoner Associates Inc. Presented at the Third SIAM Conference on Optimization, 1989.Search in Google Scholar

[8] Ríos-Mercado RZ, Borraz-Sánchez C. Optimization problems in natural gas transportation systems: A state-of-the-art review. Appl Energy. 2015;147:536–55.10.1016/j.apenergy.2015.03.017Search in Google Scholar

[9] Wong PJ, Larson RE. Optimization of natural-gas pipeline systems via dynamic programming. IEEE Trans Automat Contr. 1968;AC-13(5):475–81.10.1109/TAC.1968.1098990Search in Google Scholar

[10] Baumrucker BT, Biegler LT. MPEC strategies for cost optimization of pipeline operations. Comput Chem Eng. 2010;34(6):900–13.10.1016/j.compchemeng.2009.07.012Search in Google Scholar

[11] Alfred S, Fasullo J, Pfister J, Daniels A. Capacity determination using state finding and gas transient optimization. In the 44th Annual Meeting of Pipeline Simulation Interest Group, Prague, 2013.Search in Google Scholar

[12] Moritz S. A mixed integer approach for the transient case of gas network optimization. Ph.D. dissertation, Darmstadt, Germany: TU Darmstadt, 2007.Search in Google Scholar

[13] Carter RG. Pipeline optimization: dynamic programming after 30 years. In the 30th Annual Meeting of the Pipeline Simulation Interest Group, Colorado, 1998.Search in Google Scholar

[14] Wu S, Rios- Mercado RZ, Boyd EA, Scott LR. Model relaxations for the fuel cost minimization of steady-state gas pipeline networks. Math Comput Modell. 2000;31(2):197–220.10.1016/S0895-7177(99)00232-0Search in Google Scholar

[15] Tabkhi F. Optimization of gas transmission networks. PhD Thesis, France 2007.Search in Google Scholar

[16] Osiadacz AJ. Dynamic optimization of high pressure gas networks using hierarchical systems theory. 26th annual meeting of Pipeline Simulation Interest Group, Sandiego, CA, 1994.Search in Google Scholar

[17] Adeyanju OA, Oyekunle LO. Optimization of natural gas transportation in pipelines. Petroleum and gas engineering program. Nigeria: Univ. of Logos, 2004.Search in Google Scholar

[18] De Wolf D, Smeers Y. The gas transmission problem solved by an extension of the simplex algorithm. Manage Sci. 2000;46(11):1454–146510.1287/mnsc.46.11.1454.12087Search in Google Scholar

[19] Edgar TF, Himmelblau DM. Optimal design of gas transmission networks. Spe J. 1988;18(2):96–104. SPE-6034-PA.10.2118/6034-PASearch in Google Scholar

[20] Montoya SJ, Jovel WA, Hernandez JA, Gonzalez C. Genetic algorithms applied to the optimum design of gas transmission networks. SPE International Petroleum Conference and Exhibition, Mexico, 2000.Search in Google Scholar

[21] Molaei R, Ebrahimi M, Sadeghian S, Fahimnia B. Genetic algorithm optimization of fuel consumption in compressor stations. 3rd WSEAS International Conference on Applied and Theoretical Mechanics, Spain, 2007.Search in Google Scholar

[22] Hawryluk A, KK, Botros Golshan H, Huynh B. Multi-Objective Optimization of Natural Gas Compression Power Train with Genetic Algorithms. Presented at the 8th International Pipeline Conference,Volume 3, Calgary, IPC 2010-31017, Calgary, Canada.; 2010. DOI: 10.1115/IPC2010-31017.Search in Google Scholar

[23] Qin AK, Huang VL, Suganthan PN. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput. 2009;13:398–417.10.1109/TEVC.2008.927706Search in Google Scholar

[24] Kennedy J. Particle swarm optimization. Encyclopedia of Machine Learning.Boston, MA: Springer US, 2010:760–66.Search in Google Scholar

[25] Arya AK, Honwad S. Modeling, simulation and optimization of a high pressure cross- country natural gas pipeline: application of ant colony optimization technique. J Pipeline Syst Eng Pract. 2015;7.10.1061/(ASCE)PS.1949-1204.0000206Search in Google Scholar

[26] Dorigo M. Optimization, learning and natural algorithms. Ph.D. Thesis, Italy: Politecnico di Milano, 1992.Search in Google Scholar

[27] Sun KC, Chan CW, Tontwiachwuthikul P. A fuzzy expert system for optimizing pipeline operation. Canada,IEEE Xplore, 1997 DOI:10.1109/CCECE.1997.608357.10.1109/CCECE.1997.608357Search in Google Scholar

[28] Sancez BC, Mercado RZ. Improving the operation of pipeline systems on cyclic structures by tabu search. Comput Chem Eng. 2009;33:58–64.10.1016/j.compchemeng.2008.07.009Search in Google Scholar

[29] Mohajeri I, Taffazzoli R. Optimization of tree-structured gas distribution network using ant colony optimization: a case study. IJE Transactions A: Basics.25, 2012:141–56.10.5829/idosi.ije.2012.25.02a.04Search in Google Scholar

[30] Chebouba A, Yalaoui F, Amodeo L, Smati A, Tairi A. A new method to minimize fuel consumption of gas pipeline using ant colony optimization algorithms. Proc., 2006 Int. Conf. on Service Systems and Service Management, IEEE, New York, 2006.10.1109/ICSSSM.2006.320759Search in Google Scholar

[31] Menon ES. Gas pipeline hydraulics. Boca Raton, Florida: CRC Press, Taylor & Francis Group, 2005.10.1201/9781420038224Search in Google Scholar

[32] Mohring J, Hoffmann J, Hoffmann T, Zemitis A, Basso G, Lagoni P. Automated model reduction of complex gas pipeline networks. In Proceedings of the 36th Annual Meeting of Pipeline Simulation Interest Group, California: Palm Springs, 2004.Search in Google Scholar

[33] Coelho PM, Pinho C. Considerations about equations for steady state flow in natural gas pipelines. J Braz Soc Mech Sci Eng. 2007;29:262–73.10.1590/S1678-58782007000300005Search in Google Scholar

[34] Woldeyohannes AD, Majid MA. Simulation model for natural gas transmission pipeline network system. Simulation Modell Pract Theory. 2011;19(1):196–212.10.1016/j.simpat.2010.06.006Search in Google Scholar

[35] Smith J, Van Ness H. Introduction to chemical engineering thermodynamics. 4th ed. Singapore: McGraw-Hill Book Company, 1998.Search in Google Scholar

[36] Odom FM. Tutorials on modelling of gas turbine driven centrifugal compressors, 22nd annual meeting of pipeline simulation interest group, Baltimore, Maryland, USA, 1990.Search in Google Scholar

[37] Socha K, Blum C. Ant colony optimization. In: Alba E, Mart´I R, editors. Metaheuristic procedures for training neural networks, computer science interfaces series. Berlin, Germany: Springer-Verlag, 2006:153–80.10.1007/0-387-33416-5_8Search in Google Scholar

[38] Stutzle T, Hoos HH. MAX-MIN ant system. Future Generation Comput Syst. 2000;16(8):889–914.10.1016/S0167-739X(00)00043-1Search in Google Scholar

[39] Maniezzo V, Colorni A, Dorigo M. The ant system applied to the quadratic assignment problem, Technical Report IRIDIA/94-28, IRIDIA, Universit´e Libre de Bruxelles, and Belgium, 1994.10.1109/69.806935Search in Google Scholar

[40] Iredi S, Merkle D, Middendorf M.Bi-criterion optimization with multicolony ant algorithms. In: Zitzler E, et al., editors. Proceedings of the Evolutionary Multi-Criterion optimization, First International Conference (EMO’01) Vol. 1993 of LNCS. Berlin, Germany: Springer-Verlag, 2001:359–72.10.1007/3-540-44719-9_25Search in Google Scholar

[41] Schlueter M. Nonlinear mixed inter based optimization technique for space application. PhD. Thesis, England: Univ. of Birmingham, 2012.Search in Google Scholar

[42] Cengel AY, Michael BA. Thermodynamics: an engineering approach. New York: McGraw-Hill, 2016.Search in Google Scholar

[43] Nasr GG. Natural gas engineering and safety challenges: downstream process, analysis, utilization and safety.Cham, Switzerland: Springer International Publishing, 2014.10.1007/978-3-319-08948-5Search in Google Scholar

[44] Arya AK, Honwad S. Multiobjective optimization of a gas pipeline network: an ant colony approach. Journal of Petroleum Exploration and Production Technology 2017.10.1007/s13202-017-0410-7.Search in Google Scholar

Received: 2017-07-21
Revised: 2017-12-13
Accepted: 2018-01-15
Published Online: 2018-02-09

© 2018 Walter de Gruyter GmbH, Berlin/Boston