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Research Papers: Petroleum Engineering

Transient Optimization of Natural Gas Networks Using Intelligent Algorithms

[+] Author and Article Information
Esmaeel Khanmirza

Mechanical Engineering Department,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: khanmirza@iust.ac.ir

Reza Madoliat

Mechanical Engineering Department,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: r_madoliat@iust.ac.ir

Ali Pourfard

Mechanical Engineering Department,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: pourfard@mecheng.iust.ac.ir

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received April 28, 2017; final manuscript received April 16, 2018; published online September 14, 2018. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 141(3), 032901 (Sep 14, 2018) (11 pages) Paper No: JERT-17-1184; doi: 10.1115/1.4040073 History: Received April 28, 2017; Revised April 16, 2018

Compressor stations in natural gas networks should perform such that time-varying demands of customers are fulfilled while all of the system constraints are satisfied. Power consumption of compressor stations impose the most operational cost to a gas network so their optimal performance will lead to significant money saving. In this paper, the gas network transient optimization problem is addressed. The objective function is the sum of the compressor's power consumption that should be minimized where compressor speeds and the value status are decision variables. This objective function is nonlinear which is subjected to nonlinear and combinatorial constraints including both discrete and continuous variables. To handle this challenging optimization problem, a novel approach based on using two different structure intelligent algorithms, namely the particle swarm optimization (PSO) and cultural algorithm (CA), is utilized to find the optimum of the decision variables. This approach removes the necessity of finding an explicit expression for the power consumption of compressors as a function of decision variables as well as the calculation of objective function derivatives. The objective function and constraints are evaluated in the transient condition by a fully implicit finite difference numerical method. The proposed approach is applied on a real gas network where simulation results confirm its accuracy and efficiency.

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References

Lior, N. , 2016, “Exergy, Energy, and Gas Flow Analysis of Hydrofractured Shale Gas Extraction,” ASME J. Energy Resour. Technol., 138(6), p. 061601. [CrossRef]
Ying, Z. , Zhanghua, L. , Abdelal, G. F. , and Tiejun, L. , 2017, “Numerical and Experimental Investigation on Flow Capacity and Erosion Wear of Blooey Line in Gas Drilling,” ASME J. Energy Resour. Technol., 140(5), p. 054501. [CrossRef]
Tong, Z. , Zhao, G. , and Wei, S. , 2017, “A Novel Intermittent Gas Lifting and Monitoring System Toward Liquid Unloading for Deviated Wells in Mature Gas Field,” ASME J. Energy Resour. Technol., 140(5), p. 052906. [CrossRef]
Seales, M. B. , Ertekin, T. , and Yilin Wang, J. , 2017, “Recovery Efficiency in Hydraulically Fractured Shale Gas Reservoirs,” ASME J. Energy Resour. Technol., 139(4), p. 042901. [CrossRef]
Teng, B. , Cheng, L. , Huang, S. H. , and Li, H. , 2017, “Production Forecast for Shale Gas Reservoirs With Fast Marching-Succession of Steady States Method,” ASME J. Energy Resour. Technol., 140(3), p. 032913. [CrossRef]
Babu, B. V. , Angira, R. , Chakole, P. G. , and Syed Mubeen, J. H. , 2003, “Optimal Design of Gas Transmission Network Using Differential Evolution,” Birla Institute of Technology & Science, Rajasthan, India.
Sanaye, S. , and Mahmoudimehr, J. , 2013, “Optimal Design of a Natural Gas Transmission Network Layout,” Chem. Eng. Res. Des., 91(12), pp. 2465–2476. [CrossRef]
Wu, S. , 1998, “Steady-State Simulation and Fuel Cost Minimization of Gas Pipeline Networks,” Ph.D. thesis, University of Houston, Houston, TX.
Mahmoudimehr, J. , and Sanaye, S. , 2014, “Minimization of Fuel Consumption of Natural Gas Compressor Stations With Similar and Dissimilar Turbo-Compressor Units,” J. Energy Eng., 140(1), p. 04013001. [CrossRef]
Wong, P. , and Larson, R. , 1968, “Optimization of Natural-Gas Pipeline Systems Via Dynamic Programming,” IEEE Trans. Autom. Control, 13(5), pp. 475–481. [CrossRef]
Lall, H. S. , and Percell, P. B. , 1990, “A Dynamic Programming Based Gas Pipeline Optimizer,” Analysis and Optimization of Systes: Proceedings of the 9th International Conference Antibes, June 12–15, 1990, A. Bensoussan and J. L. Lions , eds., Springer Berlin, pp. 123–132.
Carter, R. G. , 1998, “Pipeline Optimization: Dynamic Programming after 30 Years,” PSIG Annual Meeting, Denver, CO, Oct. 28–30, Paper No. PSIG-9803.
Ríos-Mercado, R. Z. , Kim, S. , and Boyd, E. A. , 2006, “Efficient Operation of Natural Gas Transmission Systems: A Network-Based Heuristic for Cyclic Structures,” Comput. Oper. Res., 33(8), pp. 2323–2351. [CrossRef]
Percell, P. B. , and Ryan, M. J. , 1987, “Steady State Optimization of Gas Pipeline Network Operation,” PSIG Annual Meeting, Tulsa, OK, Oct. 22–23, Paper No. PSIG-8703.
Flores-Villarreal, H. J. , and Ríos-Mercado, R. Z. , 2003, “Computational Experience With a GRG Method for Minimizing Fuel Consumption on Cyclic Natural Gas Networks,” Computational Methods in Circuits and Systems Applications, WSEAS Press, pp. 90–94.
Tabkhi, F. , Pibouleau, L. , Azzaro-Pantel, C. , and Domenech, S. , 2009, “Total Cost Minimization of a High-Pressure Natural Gas Network,” ASME J. Energy Resour. Technol., 131(4), p. 043002. [CrossRef]
Nguyen, H. H. , and Chan, C. W. , 2006, “Applications of Artificial Intelligence for Optimization of Compressor Scheduling,” Eng. Appl. Artif. Intell., 19(2), pp. 113–126. [CrossRef]
MohamadiBaghmolaei, M. , Mahmoudy, M. , Jafari, D. , MohamadiBaghmolaei, R. , and Tabkhi, F. , 2014, “Assessing and Optimization of Pipeline System Performance Using Intelligent Systems,” J. Nat. Gas Sci. Eng., 18, pp. 64–76. [CrossRef]
Sanaye, S. , and Mahmoudimehr, J. , 2012, “Minimization of Fuel Consumption in Cyclic and Non-Cyclic Natural Gas Transmission Networks: Assessment of Genetic Algorithm Optimization Method as an Alternative to Non-Sequential Dynamic Programing,” J. Taiwan Inst. Chem. Eng., 43(6), pp. 904–917. [CrossRef]
Chebouba, A. , Yalaoui, F. , Smati, A. , Amodeo, L. , Younsi, K. , and Tairi, A. , 2009, “Optimization of Natural Gas Pipeline Transportation Using Ant Colony Optimization,” Comput. Oper. Res., 36(6), pp. 1916–1923. [CrossRef]
Zheng, Z. , and Wu, C. , 2012, “Power Optimization of Gas Pipelines Via an Improved Particle Swarm Optimization Algorithm,” Pet. Sci., 9(1), pp. 89–92. [CrossRef]
Wu, X. , Li, C. , Jia, W. , and He, Y. , 2014, “Optimal Operation of Trunk Natural Gas Pipelines Via an Inertia-Adaptive Particle Swarm Optimization Algorithm,” J. Nat. Gas Sci. Eng., 21, pp. 10–18. [CrossRef]
Furey, B. , 1993, “A Sequential Quadratic Programming-Based Algorithm for Optimization of Gas Networks,” Automatica, 29(6), pp. 1439–1450. [CrossRef]
Osiadacz Andrzej, J. , 1998, “Hierarchical Control of Transient Flow in Natural Gas Pipeline Systems,” Int. Trans. Oper. Res., 5(4), pp. 285–302. [CrossRef]
Carter, R. G. , and Rachford, H. H., Jr. , 2003, “Optimizing Line-Pack Management to Hedge against Future Load Uncertainty,” PSIG Annual Meeting, Bern, Switzerland, Oct. 15–17, Paper No. PSIG-0306.
Krishnaswami, P. , Chapman, K. S. , and Abbaspour, M. , 2004, “Compressor Station Optimization for Linepack Maintenance,” PSIG Annual Meeting, Palm Springs, CA, Oct. 20–22.
Abbaspour, M. , and Chapman, K. S. , 2008, “Nonisothermal Transient Flow in Natural Gas Pipeline,” ASME J. Appl. Mech., 75(3), p. 031018. [CrossRef]
Abbaspour, M. , Krishnaswami, P. , and Chapman, K. S. , 2007, “Transient Optimization in Natural Gas Compressor Stations for Linepack Operation,” ASME J. Energy Resour. Technol., 129(4), pp. 314–324. [CrossRef]
Domschke, P. , Geißler, B. , Kolb, O. , Lang, J. , Martin, A. , and Morsi, A. , 2011, “Combination of Nonlinear and Linear Optimization of Transient Gas Networks,” INFORMS J. Comput., 23(4), pp. 605–617. [CrossRef]
Zhang, X. , Wu, C. , and Zuo, L. , 2016, “Minimizing Fuel Consumption of a Gas Pipeline in Transient States by Dynamic Programming,” J. Nat. Gas Sci. Eng., 28, pp. 193–203. [CrossRef]
Kiuchi, T. , 1994, “An Implicit Method for Transient Gas Flows in Pipe Networks,” Int. J. Heat Fluid Flow, 15(5), pp. 378–383. [CrossRef]
Sanaye, S. , and Mahmoudimehr, J. , 2012, “Technical Assessment of Isothermal and Non-Isothermal Modelings of Natural Gas Pipeline Operational Conditions,” Oil Gas Sci. Technol., 67(3), pp. 435–449. [CrossRef]
Chaczykowski, M. , and Osiadacz, A. , 2001, “Simulation of Non-Isothermal Transient Gas Flow in a Pipeline,” Arch. Thermodyn., 22(1–2), pp. 51–70 http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-article-BGPK-0055-2063.
Madoliat, R. , Khanmirza, E. , and Pourfard, A. , 2017, “Application of PSO and Cultural Algorithms for Transient Analysis of Natural Gas Pipeline,” J. Pet. Sci. Eng., 149, pp. 504–514. [CrossRef]
Wu, S. , Ríos-Mercado, R. Z. , Boyd, E. A. , and Scott, L. R. , 2000, “Model Relaxations for the Fuel Cost Minimization of Steady-State Gas Pipeline Networks,” Math. Comput. Modell., 31(2–3), pp. 197–220. [CrossRef]
Woldeyohannes, A. D. , and Majid, M. A. A. , 2011, “Simulation Model for Natural Gas Transmission Pipeline Network System,” Simul. Modell. Pract. Theory, 19(1), pp. 196–212. [CrossRef]
Kennedy, J. , and Eberhart, R. , 1995, “Particle Swarm Optimization,” IEEE International Conference on Neural Networks, Perth, WA, Nov. 27–Dec. 1, pp. 1942–1948.
Poli, R. , Kennedy, J. , and Blackwell, T. , 2007, “Particle Swarm Optimization,” Swarm Intell., 1(1), pp. 33–57. [CrossRef]
Suresh, K. , Ghosh, S. , Kundu, D. , Sen, A. , Das, S. , and Abraham , A., 2008, “Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search,” Eighth International Conference on Intelligent Systems Design and Applications, Kaohsiung, Taiwan, Nov. 26–28, pp. 253–258.
Haji Agha Mohammad Zarbaf, S. E. , Norouzi, M. , Allemang, R. J. , Hunt, V. J. , and Helmicki, A. , 2017, “Stay Cable Tension Estimation of Cable-Stayed Bridges Using Genetic Algorithm and Particle Swarm Optimization,” J. Bridge Eng., 22(10), p. 05017008. [CrossRef]
Reynolds, R. G. , and Sverdlik, W. , 1994, “Problem Solving Using Cultural Algorithms,” First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, Orlando, FL, June 27–29, pp. 645–650.
Xidong, J. , and Reynolds, R. G. , 1999, “Using Knowledge-Based Evolutionary Computation to Solve Nonlinear Constraint Optimization Problems: A Cultural Algorithm Approach,” Congress on Evolutionary Computation-CEC99, Washington, DC, July 6–9, pp. 1672–1678.
Reynolds, R. G. , and Peng, B. , 2005, “Cultural Algorithms: Computational Modeling of How Cultures Learn to Solve Problems: An Engineering Example,” Cybern. Syst., 36(8), pp. 753–771. [CrossRef]

Figures

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Fig. 1

Feasible domain of the compressor

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Fig. 2

The CA algorithm pseudo-code

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Fig. 3

The schematic structure of the gas network

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Fig. 4

Flowchart of the proposed approach

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Fig. 5

Desired outlet flow rates over the 72 h period

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Fig. 6

The optimum speeds of the two compressor stations

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Fig. 7

The optimum status of the network valve

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Fig. 8

The optimum power of the two compressor stations

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Fig. 9

The calculated inlet flow rates

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Fig. 10

The passing flow rate over the network valve

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Fig. 11

Outlet pressures of the network

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Fig. 12

Computational time comparison of the PSO or CA algorithm at different time levels

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Fig. 13

Performance comparison of CA and PSO at different time levels

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