DESIGN OPTIMIZATION OF GAS TRANSMISSION SYSTEM WITH DIFFERENTIAL EVOLUTION ALGORITHM

DOI: https://doi.org/10.30598/barekengvol20iss2pp1089–1098
Keywords: Differential evolution algorithm, Gas distribution, Minimum cost, Optimal solution, Pipeline network

Abstract

Gas distribution through a pipeline network is a highly complex process and requires significant financial investment. This network system consists of a source, pipe, the compressor and sink (consumer). The source is the node where the producer has the gas pressure that will be distributed, the pipe is used to connect the producer and the consumer. Between the pipes there is a compressor which functions to increase the pressure. This network system was created at a significant cost, so it is necessary to search for minimal costs, but consumer demand is still met.  This research discusses the search for an optimal gas network with minimum costs. This minimum cost depends on several parameters i.e. the length and diameter of pipe, also the pressure on the compressors entry and exit points. There are many optimization methods, but one of the simple and easy to implement methods is the Differential Evolution Algorithm, so this method is used to determine the optimal solution to this problem. Researchers used seven DE variants based on mutation strategies, namely DE/rand/1, DE/best/1, DE/rand/2, DE/best/2, DE/current-to-best/1, DE/current-to- rand/1, and DE/rand-to-best/1. The seven variants have never been used in gas distribution networks by previous researchers. Therefore, the seven variants were compared, and the minimum solution was determined. The results show that the DE/best/2 variant is the variant that produces the minimum total costs compared to the other variants. DE/best/2 achieved the lowest annual operating cost at USD 13.99 million.

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References

R. R. Singh and P. K. S. Nain, “OPTIMIZATION OF NATURAL GAS PIPELINE DESIGN AND ITS TOTAL COST USING GA,” Int. J. Sci. Res. Publ., vol. 2, no. 8, pp. 1–10, 2012, [Online]. Available: http://www.ijsrp.org/research-paper-0812/ijsrp-p0842.pdf

S. Mokhatab and W. A. Poe, “CHAPTER 1 - NATURAL GAS FUNDAMENTALS,” in Handbook of Natural Gas Transmission and Processing (Second Edition), Second Edi., S. Mokhatab and W. A. Poe, Eds., Boston: Gulf Professional Publishing, 2012, pp. 1–42. doi: https://doi.org/10.1016/B978-0-12-386914-2.00001-7.

B. V Babu, R. Angira, P. G. Chakole, and J. H. Syed Mubeen, “OPTIMAL DESIGN OF GAS TRANSMISSION NETWORK USING DIFFERENTIAL EVOLUTION,” in Proceedings of the second international conference on computational intelligence, robotics, and autonomous systems (CIRAS-2003), Singapore, 2003.

S. S. Rao, “IntroDUCTION TO OPTIMIZATION,” in Engineering Optimization Theory and Practice, John Wiley & Sons, Ltd, 2019, ch. 1, pp. 1–56. doi: https://doi.org/10.1002/9781119454816.ch1.

M. Dehghani, E. Trojovská, and P. Trojovský, “A NEW HUMAN-BASED METAHEURISTIC ALGORITHM FOR SOLVING OPTIMIZATION PROBLEMS ON THE BASE OF SIMULATION OF DRIVING TRAINING PROCESS,” Sci. Rep., vol. 12, no. 1, p. 9924, 2022, doi: https://doi.org/10.1038/s41598-022-14225-7.

M. Georgioudakis and V. Plevris, “A COMPARATIVE STUDY OF DIFFERENTIAL EVOLUTION VARIANTS IN CONSTRAINED STRUCTURAL OPTIMIZATION,” Front. Built Environ., vol. 6, no. July, pp. 1–14, 2020, doi: https://doi.org/10.3389/fbuil.2020.00102.

P. A. Vikhar, “EVOLUTIONARY ALGORITHMS: A CRITICAL REVIEW AND ITS FUTURE PROSPECTS,” in 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2016, pp. 261–265. doi: https://doi.org/10.1109/ICGTSPICC.2016.7955308.

K. V Price, “AN INTRODUCTION TO DIFFERENTIAL EVOLUTION,” in New Ideas in Optimization, GBR: McGraw-Hill Ltd., UK, 1999, pp. 79–108.

L. Cui et al, ENHANCE DIFFERENTIAL EVOLUTION ALGORITHM BASED ON NOVEL MUTATION STRATEGY AND PARAMETER CONTROL METHOD. 2015. doi: https://doi.org/10.1007/978-3-319-26532-2_70.

C. Ni et al, “SIMULATION AND OPTIMIZATION OF DISTRIBUTED ENERGY SYSTEM BASED ON DIFFERENTIAL EVOLUTION ALGORITHM,” IOP Conf. Ser. Earth Environ. Sci., vol. 702, no. 1, p. 12033, Mar. 2021, doi: https://doi.org/10.1088/1755-1315/702/1/012033

M. Bilal, M. Pant, M. Stanko, and L. Sales, “DIFFERENTIAL EVOLUTION FOR EARLY-PHASE OFFSHORE OILFIELD DESIGN CONSIDERING UNCERTAINTIES IN INITIAL OIL-IN-PLACE AND WELL PRODUCTIVITY,” Upstream Oil Gas Technol., vol. 7, p. 100055, 2021, doi: https://doi.org/10.1016/j.upstre.2021.100055.

O. Alves and C. Fontes, “MODELING AND OPTIMIZATION OF NATURAL GAS DISTRIBUTION NETWORKS FOR NEW SUPPLIER PROJECTS,” Energy Convers. Manag. X, vol. 15, no. May, p. 100240, 2022, doi: https://doi.org/10.1016/j.ecmx.2022.100240.

M. G. Drouven and I. E. Grossmann, “MIXED-INTEGER PROGRAMMING MODELS FOR LINE PRESSURE OPTIMIZATION IN SHALE GAS GATHERING SYSTEMS,” J. Pet. Sci. Eng., vol. 157, no. February 2014, pp. 1021–1032, 2017, doi: https://doi.org/10.1016/j.petrol.2017.07.026.

Z. E. Liu et al., “A NOVEL OPTIMIZATION FRAMEWORK FOR NATURAL GAS TRANSPORTATION PIPELINE NETWORKS BASED ON DEEP REINFORCEMENT LEARNING,” Energy AI, vol. 18, p. 100434, 2024, doi: https://doi.org/10.1016/j.egyai.2024.100434.

S. Burgumbayeva, D. Zhussupova, and B. Koshkarova, “MATHEMATICAL MODELLING OF THE PROCESS OF NATURAL GAS TRANSPORTATION VIA PIPE NETWORKS USING CROSSING-BRANCH METHOD,” J. Math. Mech. Comput. Sci., vol. 121, no. 1, pp. 12–26, 2024, doi: https://doi.org/10.26577/JMMCS202412112.

A. Sircar and K. Yadav, “OPTIMIZATION OF CITY GAS NETWORK: A CASE STUDY FROM GUJARAT, INDIA,” SN Appl. Sci., vol. 1, no. 7, p. 769, 2019, doi: https://doi.org/10.1007/s42452-019-0755-2.

M. Mikolajková-Alifov, F. Pettersson, M. Björklund-Sänkiaho, and H. Saxén, “A MODEL OF OPTIMAL GAS SUPPLY TO A SET OF DISTRIBUTED CONSUMERS,” Energies, vol. 12, no. 3, 2019, doi: https://doi.org/10.3390/en12030351.

“OPTIMAL DESIGN OF GAS PIPELINE TRANSMISSION NETWORK EJOMARIE, E.K. and Saturday, E.G. Department of Mechanical Engineering, Faculty of Engineering, University of Port Harcourt, P.M.B 5323 Port Harcourt, Rivers State, Nigeria .,” vol. 8, no. 5, pp. 918–933, 2020.

Q. P. Zheng, S. Rebennack, N. A. Iliadis, and P. M. Pardalos, “OPTIMIZATION MODELS IN THE NATURAL GAS INDUSTRY,” in Handbook of Power Systems I, P. M. Pardalos, S. Rebennack, M. V. F. Pereira, and N. A. Iliadis, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 121–148. doi: https://doi.org/10.1007/978-3-642-02493-1_6

A. J. Lee, “EVOLUTIONARY ALGORITHMS FOR OPTIMAL OPERATION OF GAS NETWORKS,” 2018. [Online]. Available: https://open.library.ubc.ca/collections/24/items/1.0363065

T. F. Edgar and D. M. Himmeblau, “OptIMIZATION OF CHEMICAL PROCESSES,” 1988, McGraw-Hill, New York.

H. B. Martch and N. J. McCall, “OPTIMIZATION OF THE DESIGN AND OPERATION OF NATURAL GAS PIPELINE SYSTEMS,” Oct. 08, 1972. doi: https://doi.org/10.2118/4006-MS.

P. G. and S. J. H. Babu B.V. Rakesh A., “OPTIMAL DESIGN OF GAS TRANSMISSION NETWORK USING DIFFERENTIAL EVOLUTION,” J. Multidiscip. Model. Mater. Struct., vol. 1, no. 4, pp. 315–328, 2005, [Online]. Available: https://doi.org/10.1163/157361105774501665

R. Storn and K. Price, “Differential Evolution - A SIMPLE AND EFFICIENT HEURISTIC FOR GLOBAL OPTIMIZATION OVER CONTINUOUS SPACES,” J. Glob. Optim., vol. 11, pp. 341–359, 1997, doi: https://doi.org/10.1023/A:1008202821328.

G. Jeyakumar and C. Shanmugavelayutham, “CONVERGENCE ANALYSIS OF DIFFERENTIAL EVOLUTION VARIANTS ON UNCONSTRAINED GLOBAL OPTIMIZATION FUNCTIONS,” Int. J. Artif. Intell. Appl., vol. 2, no. 2, pp. 116–127, 2011, doi: https://doi.org/10.5121/ijaia.2011.2209.

www.python.org, “Python.” [Online]. Available: https://www.python.org

Published
2026-01-26
How to Cite
[1]
A. Afdhal, T. Tasmi, A. Yunita, and R. G. Noegraha, “DESIGN OPTIMIZATION OF GAS TRANSMISSION SYSTEM WITH DIFFERENTIAL EVOLUTION ALGORITHM”, BAREKENG: J. Math. & App., vol. 20, no. 2, pp. 1089–1098, Jan. 2026.
Issue
Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
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Articles

Copyright (c) 2026 Ahmad Afdhal, Tasmi Tasmi, Ariana Yunita, Rangga Ganzar Noegraha

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