Simultaneous multi-objective framework of natural gas pipeline network operations Original scientific paper

Main Article Content

Mostafa Hassanei Hussein Mohamed
https://orcid.org/0000-0002-8776-3099

Abstract

The optimization of gas transportation networks is essential as natural gas demand increases. Conflicting objectives, such as maximizing delivery flow rate, minimizing power consumption, and maximizing line pack, pose challenges in this context. To address these complexities, a novel multi-objective optimization method based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed. The method generates a diverse set of Pareto optimal solutions, empowering decision-makers to select the most suitable solution for gas transportation networks. Three case studies validate the approach's effectiveness, showcasing its advantages in yielding more economical networks and enhancing the cost-effectiveness of natural gas transmission networks. The proposed method's versatility allows application to various gas transportation network scenarios. Decision-makers benefit from a range of Pareto optimal solutions, providing valuable insights. Moreover, the seamless integration of the proposed method into existing gas transportation network optimization frameworks further enhances performance. In conclusion, the study presents a robust multi-objective optimization method based on TOPSIS for gas transportation network optimization. It offers cost-effective solutions and improves the efficiency of natural gas transmission networks. The provision of diverse Pareto optimal solutions enables well-informed decision-making, contributing to sustainable energy solutions in the face of increasing natural gas demand.

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How to Cite
Hussein Mohamed, M. H. (2024). Simultaneous multi-objective framework of natural gas pipeline network operations: Original scientific paper. Chemical Industry & Chemical Engineering Quarterly. https://doi.org/10.2298/CICEQ231203016M
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References

X. Wu, C. Li, Y. He, W. Jia, Math. Probl. Eng. (2018)1267045. https://doi.org/10.1155/2018/1267045

C.Li, W.Jia, E.Liu, X. Wu,Int. J. Ind. Eng.19 (6) (2012) 241-251. https://doi.org/10.23055/ijietap.2012.19.6.631

S. Mokhatab, W.Poe, J.Mak, Handbook of natural gas transmission and processing: principles and practices, Gulf Professional Publishing, (2018) ISBN: 0128016647, 9780128016640

A.Kashani, R. Molaei, Chem. Eng. Res. Des.92 (2014) 2106-2122. https://doi.org/10.1016/j.cherd.2014.02.006

X. Wu, C. Li, W. Jia, Y. He, J. Nat. Gas Sci. Eng. 21 (2014) 10-18. https://doi.org/10.1016/j.jngse.2014.07.028

H. Üster, Ş. Dilaveroğlu, Appl. Energy133 (2014)56-69. https://doi.org/10.1016/j.apenergy.2014.06.042

F.da Silva, J.de Souza, A.Costa, Comput. Chem. Eng.93(2016) 212–220. https://doi.org/10.1016/j.compchemeng.2016.06.006

H. Su, E. Zio, J. Zhang, X. Li, L. Chi, L. Fan, Z. Zhang, Comput. Chem. Eng.131 (2019) 106584. https://doi.org/10.1016/j.compchemeng.2019.106584

K. Liu, L.Biegler, B. Zhang, Q. Chen, Chem. Eng. Sci. 215 (2020) 115449. https://doi.org/10.1016/j.ces.2019.115449

Q. Chen, C. Wu, L. Zuo, M. Mehrtash, Y. Wang, Y. Bu, R. Sadiq, Y. Cao, Comput. Chem. Eng.147 (2020) 107260. https://doi.org/10.1016/j.compchemeng.2021.107260

X. Yin, K. Wen, Y. Wu, X. Han, Y. Mukhtar, J. Gong, J. Nat. Gas Sci. Eng.98 (2022) 104384. https://doi.org/10.1016/j.jngse.2021.104384

E. Menon, Gas pipeline hydraulics, Online Course, (2005). https://doi.org/10.1201/9781420038224

P. Coelho, C. Pinho, J. Brazilian Soc. Mech. Sci. Eng.29 (3) (2007) 262–273. https://doi.org/10.1590/S1678-58782007000300005

M. Mohitpour, H. Golshan, M. Murray, Pipeline Design & Construction: A Practical Approach, 3rdEd, American Society of Mechanical Engineer, (2007). https://doi.org/10.1115/1.802574

K. Pambour, R. Bolado-Lavin, G.Dijkema, J. Nat. Gas Sci. Eng. 28 (2016) 672–690. https://doi.org/10.1016/j.jngse.2015.11.036

A. Demissie, W. Zhu, C. Belachew, Comput. Chem. Eng.100 (2017) 94-103. https://doi.org/10.1016/j.compchemeng.2017.02.017

T.Edgar, D.Himmelblau, L.Lasdon, Optimization of chemical processes, McGraw Hill, New York,(2001). https://doi.org/10.1002/aic.690490128

C.Hwang,K. Yoon,Multiple Attribute Decision Making: Methods and Applications, 1st Ed,Springer, Berlin 186(1981) 58–191. https://doi.org/10.1007/978-3-642-48318-9

S. Wu, R. Rios, E. Boyd, L. Scott, Math. Comput. Model. 31 (2000) 197–220. https://doi.org/10.1016/S0895-7177(99)00232-0

F.Tabkhi, L. Pibouleau, C. Azzaro, S. Domenech, J. Energy Resour. Technol. 131 (4) (2009) 043002. https://doi.org/10.1115/1.4000325

D. Zhou, X. Jia, S. Ma, T. Shao, D. Huang, J. Hao,T. Li, Energy. 253 (2022).https://doi.org/10.1016/j.energy.2022.124068

J. Zhou, J. Peng, G. Liang, C. Chen, X. Zhou,Y. Qin, J. of Intell.& Fuzzy Systems. 40(3) (2021). 4345– 4366. https://doi.org/10.3233/jifs-201072

M.H.H.Mohamed, H.A.A. Gawad, Deci. Making: Applic. in Manag. and Eng. 7, (2024) 420–441 https://doi.org/10.31181/dmame712024983