Enhancing gas pipeline network efficiency through VIKOR method
DOI:
https://doi.org/10.31181/dmame622023868Keywords:
Gas pipeline optimization, multi-criteria decision making, branched and branched-cyclic topologies, Line pack optimization, Energy consumption, VIKOR methodAbstract
The optimization of gas pipeline networks is critical for efficient and cost-effective transportation of natural gas. This study develops a mathematical model capable of analyzing different network configurations, including branched and branched-cyclic topologies, to explore the optimization of gas pipeline network conditions. The research provides valuable insights into the gas pipeline network optimization process, empowering industry stakeholders to make informed decisions and enhance performance in terms of efficiency, reliability, and cost-effectiveness. To attain these objectives, this study utilizes advanced simulation tools, state-of-the-art optimization algorithms, and sophisticated mathematical models that accurately represent the network's behavior. The optimization process aims to minimize the network's power requirements while simultaneously maximizing gas flow rate and optimizing line pack, ensuring optimal utilization of the pipeline infrastructure. The VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method is identifying the most optimal network configuration and operating conditions. Our analysis applies this approach to three case studies, demonstrating its effectiveness in identifying the best network configurations. Additionally, the calculations of total cost and fuel consumption coincide with relative closeness, which confirms the accuracy of our proposed method, whereas optimal scenarios of the three cases have the minimum total cost among all scenarios. In conclusion, this research successfully develops a mathematical model and optimization approach to tackle the complexities of gas pipeline network optimization. The application of The VIKOR method and the analysis of case studies offer substantial evidence of its effectiveness.
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Ali, Y., Ahmad, M., Sabir, M., & Shah, S. A. (2021). Regional development through energy infrastructure: a comparison and optimization of Iran-Pakistan-India (IPI) & Turkmenistan-Afghanistan-Pakistan-India (TAPI) gas pipelines. Operational Research in Engineering Sciences: Theory and Applications, 4(3), 82–106. https://doi.org/10.31181/oresta091221082a
Arya, A. K., & Honwad, S. (2018). Multiobjective optimization of a gas pipeline network: an ant colony approach. Journal of Petroleum Exploration and Production Technology, 8(4), 1389–1400. https://doi.org/10.1007/s13202-017-0410-7
Brodny, J., & Tutak, M. (2021). Assessing sustainable energy development in the central and eastern European countries and analyzing its diversity. Science of the Total Environment, 801, 149745. https://doi.org/10.1016/j.scitotenv.2021.149745
Brodny, J., & Tutak, M. (2023). Assessing the energy security of European Union countries from two perspectives–A new integrated approach based on MCDM methods. Applied Energy, 347, 121443. https://doi.org/10.1016/j.apenergy.2023.121443
Coelho, P. M., & Pinho, C. (2007). Considerations about equations for steady state flow in natural gas pipelines. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 29(3), 262–273. https://doi.org/10.1590/S1678-58782007000300005
Demissie, A., Zhu, W., & Belachew, C. T. (2017). A multi-objective optimization model for gas pipeline operations. Computers & Chemical Engineering, 100, 94–103. https://doi.org/10.1016/j.compchemeng.2017.02.017
Edgar, T. F., Himmelblau, D. M., & Lasdon, L. S. (2001). Optimization of chemical processes. http://dx.doi.org/10.4236/wjet.2014.24032
Guo, B., & Ghalambor, A. (2014). Natural gas engineering handbook. Elsevier. https://doi.org/10.1016/b978-1-933762-41-8.50016-2
Guo, B., Song, S., Ghalambor, A., & Lin, T. R. (2013). Offshore pipelines: design, installation, and maintenance. Gulf Professional Publishing. https://doi.org/10.1016/b978-0-12-397949-0.00011-x
Habibvand, G., & Behbahani, R. M. (2012). Using genetic algorithm for fuel consumption optimization of a natural gas transmission compressor station. International Journal of Computer Applications, 43(1), 1–6. https://doi.org/10.5120/6064-8180
Hu, Y., Bie, Z., Ding, T., & Lin, Y. (2016). An NSGA-II based multi-objective optimization for combined gas and electricity network expansion planning. Applied Energy, 167, 280–293. https://doi.org/10.1016/j.apenergy.2015.10.148
Jahan, A., Mustapha, F., Ismail, M. Y., Sapuan, S. M., & Bahraminasab, M. (2011). A comprehensive VIKOR method for material selection. Materials & Design, 32(3), 1215–1221. https://doi.org/https://doi.org/10.1016/j.matdes.2010.10.015
Jiao, K., Wang, P., Wang, Y., Yu, B., Bai, B., Shao, Q., & Wang, X. (2021). Study on the multi-objective optimization of reliability and operating cost for natural gas pipeline network. Oil & Gas Science and Technology–Revue d’IFP Energies Nouvelles, 76, 42. https://doi.org/10.2516/ogst/2021020
Kashani, A. H. A., & Molaei, R. (2014). Techno-economical and environmental optimization of natural gas network operation. Chemical Engineering Research and Design, 92(11), 2106–2122. https://doi.org/10.1016/j.cherd.2014.02.006
Li, F., Zhang, J., & Li, X. (2023). Energy security dilemma and energy transition policy in the context of climate change: A perspective from China. Energy Policy, 181, 113624. https://doi.org/10.1016/j.enpol.2023.113624
Li, H., Wang, W., Fan, L., Li, Q., & Chen, X. (2020). A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR. Applied Soft Computing, 91, 106207. https://doi.org/10.1016/j.asoc.2020.106207
Li, L., Liu, Z., & Du, X. (2021). Improvement of analytic hierarchy process based on grey correlation model and its engineering application. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(2), 4021007. https://doi.org/10.1061/ajrua6.0001126
Manojlović, V., Arsenović, M., & Pajović, V. (1994). Optimized design of a gas-distribution pipeline network. Applied Energy, 48(3), 217–224. https://doi.org/10.1016/0306-2619(94)90011-6
Menon, E. S. (2005). Gas pipeline hydraulics. Crc Press. https://doi.org/10.1201/9781420038224
Mohitpour, M., Golshan, H., & Murray, M. A. (2003). Pipeline design & construction: a practical approach. Amer Society of Mechanical. https://doi.org/10.1115/1.802574.ch7
Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/s0377-2217(03)00020-1
Osiadacz, A. J., & Isoli, N. (2020). Multi-objective optimization of gas pipeline networks. Energies, 13(19), 5141. https://doi.org/10.3390/en13195141
Pambour, K. A., Bolado-Lavin, R., & Dijkema, G. P. J. (2016). An integrated transient model for simulating the operation of natural gas transport systems. Journal of Natural Gas Science and Engineering, 28, 672–690. https://doi.org/10.1016/j.jngse.2015.11.036
Paradowski, B., Shekhovtsov, A., Bączkiewicz, A., Kizielewicz, B., & Sałabun, W. (2021). Similarity analysis of methods for objective determination of weights in multi-criteria decision support systems. Symmetry, 13(10), 1874. https://doi.org/10.3390/sym13101874
Ríos-Mercado, R. Z., Wu, S., Scott, L. R., & Boyd, E. A. (2002). A reduction technique for natural gas transmission network optimization problems. Annals of Operations Research, 117(1), 217–234. https://doi.org/10.1023/a:1021529709006
Tabkhi, F., Pibouleau, L., Azzaro-Pantel, C., & Domenech, S. (2009). Total cost minimization of a high-pressure natural gas network. Journal of Energy Resources Technology, 131(4). https://doi.org/10.1115/1.4000325
Tabkhi, F., Pibouleau, L., Hernandez‐Rodriguez, G., Azzaro‐Pantel, C., & Domenech, S. (2010). Improving the performance of natural gas pipeline networks fuel consumption minimization problems. AIChE Journal, 56(4), 946–964. https://doi.org/10.1002/aic.12011
Tavana, M., Kiani Mavi, R., Santos-Arteaga, F. J., & Rasti Doust, E. (2016). An extended VIKOR method using stochastic data and subjective judgments. Computers & Industrial Engineering, 97, 240–247. https://doi.org/https://doi.org/10.1016/j.cie.2016.05.013
Üster, H., & Dilaveroğlu, Ş. (2014). Optimization for design and operation of natural gas transmission networks. Applied Energy, 133, 56–69. https://doi.org/10.1016/j.apenergy.2014.06.042
Vetter, C. P., Kuebel, L. A., Natarajan, D., & Mentzer, R. A. (2019). Review of failure trends in the US natural gas pipeline industry: An in-depth analysis of transmission and distribution system incidents. Journal of Loss Prevention in the Process Industries, 60, 317–333. https://doi.org/10.1016/j.jlp.2019.04.014
Wang, C.-N., Nguyen, N.-A.-T., Dang, T.-T., & Lu, C.-M. (2021). A compromised decision-making approach to third-party logistics selection in sustainable supply chain using fuzzy AHP and fuzzy VIKOR methods. Mathematics, 9(8), 886. https://doi.org/10.3390/math9080886
Wang, L., Zhang, H., Wang, J., & Li, L. (2018). Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project. Applied Soft Computing, 64, 216–226. https://doi.org/https://doi.org/10.1016/j.asoc.2017.12.014
Wang, R., Li, X., & Li, C. (2021). Optimal selection of sustainable battery supplier for battery swapping station based on Triangular fuzzy entropy-MULTIMOORA method. Journal of Energy Storage, 34, 102013. https://doi.org/10.1016/j.est.2020.102013
Wen, K., Lu, Y., Lu, M., Zhang, W., Zhu, M., Qiao, D., Meng, F., Zhang, J., Gong, J., & Hong, B. (2022). Multi-period optimal infrastructure planning of natural gas pipeline network system integrating flowrate allocation. Energy, 257, 124745. https://doi.org/10.1016/j.energy.2022.124745
Wu, S., Rios-Mercado, R. Z., Boyd, E. A., & Scott, L. R. (2000). Model relaxations for the fuel cost minimization of steady-state gas pipeline networks. Mathematical and Computer Modelling, 31(2–3), 197–220. https://doi.org/10.1016/s0895-7177(99)00232-0
Wu, X., Li, C., He, Y., & Jia, W. (2018). Operation optimization of natural gas transmission pipelines based on stochastic optimization algorithms: a review. Mathematical Problems in Engineering, 2018. https://doi.org/10.1155/2018/1267045
Wu, Y., Lai, K. K., & Liu, Y. (2007). Deterministic global optimization approach to steady-state distribution gas pipeline networks. Optimization and Engineering, 8(3), 259–275. https://doi.org/10.1007/s11081-007-9018-y
Yang, K., Duan, T., Feng, J., & Mishra, A. R. (2022). Internet of things challenges of sustainable supply chain management in the manufacturing sector using an integrated q-Rung Orthopair Fuzzy-CRITIC-VIKOR method. Journal of Enterprise Information Management, 35(4/5), 1011–1039. https://doi.org/10.1108/jeim-06-2021-0261
Zhou, D., Jia, X., Ma, S., Shao, T., Huang, D., Hao, J., & Li, T. (2022). Dynamic simulation of natural gas pipeline network based on interpretable machine learning model. Energy, 253, 124068. https://doi.org/10.1016/j.energy.2022.124068
Zhou, J., Peng, J., Liang, G., Chen, C., Zhou, X., & Qin, Y. (2021). Technical and economic optimization of natural gas transmission network operation to balance node delivery flow rate and operation cost. Journal of Intelligent & Fuzzy Systems, 40(3), 4345–4366. https://doi.org/10.3233/jifs-201072
Zou, C., Zhao, Q., Zhang, G., & Xiong, B. (2016). Energy revolution: From a fossil energy era to a new energy era. Natural Gas Industry B, 3(1), 1–11. https://doi.org/10.1016/j.ngib.2016.02.001
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