Abstract
The analysis of gas transportation networks is the backbone for further processes such as optimization and control. The static analysis is based on algebraic equations which are straightforward and easy to solve, but may result in solutions far from the optimum due to the dynamic nature of the network. Hence, the transient analysis is inevitable. It is based on a set of equations containing partial differential equations (PDEs) for each pipeleg (Navier–Stokes equations), algebraic equations of compressors, the initial conditions and the boundary values. Since the governing equations of each pipeleg are PDEs, the internal boundary values of the network should be considered according to the topography of the pipelegs in the network, which makes the traditional transient analysis complicated and time consuming. In this paper, a straightforward method based on metaheuristic algorithms is proposed for the transient analysis. Using the proposed technique, each pipeleg is analyzed separately which speeds up the analysis. The source flow rates are considered as the optimization variables and based on them, the demand pressures are calculated. The sum of the absolute differences between the real demand pressures (known as the boundary values) and the calculated ones is the error of the proposed modeling. To minimize the error, a powerful metaheuristic algorithm called Hybrid Taguchi Binary Genetic Algorithm is utilized. Numerical results confirm the efficiency and accuracy of the proposed method that leads to near-zero error.
References
[1] A. Gopalakrishnan and L. T. Biegler, Economic nonlinear model predictive control for periodic optimal operation of gas pipeline networks, Comput. Chem. Eng. 52 (2013), 90–99.10.1016/j.compchemeng.2012.11.011Search in Google Scholar
[2] Y. Xie, et al., Energy efficiency evaluation of a natural gas pipeline based on an analytic hierarchy process, Adv. Mech. Eng. 9(7) (2017), 1687814017711394.10.1177/1687814017711394Search in Google Scholar
[3] Z. Zhang and X. Liu, Study on optimal operation of natural gas pipeline network based on improved genetic algorithm, Adv. Mech. Eng. 9(8) (2017), 1687814017715981.10.1177/1687814017715981Search in Google Scholar
[4] A. J. Osiadacz and M. Chaczykowski, Dynamic control for gas pipeline systems, Arch. Min. Sci. 61(1) (2016), 69–82.10.1515/amsc-2016-0006Search in Google Scholar
[5] J. Liu, et al., Research on mixing law of natural gas pipeline replaced by nitrogen, Adv. Mech. Eng. 9(6) (2017), 1687814017713705.10.1177/1687814017713705Search in Google Scholar
[6] J. Szoplik, Improving the natural gas transporting based on the steady state simulation results, Energy 109 (2016), 105–116.10.1016/j.energy.2016.04.104Search in Google Scholar
[7] M. Abeysekera, et al., Steady state analysis of gas networks with distributed injection of alternative gas, Appl. Energy 164 (2016), 991–1002.10.1016/j.apenergy.2015.05.099Search in Google Scholar
[8] A. Bermúdez, et al., Simulation and optimization models of steady-state gas transmission networks, Energy Procedia 64 (2015), 130–139.10.1016/j.egypro.2015.01.016Search in Google Scholar
[9] E. Dyachenko, A. Sergey, et al., Operator splitting method for simulation of dynamic flows in natural gas pipeline networks, Physica D: Nonlinear Phenomena. 361 (2017), 1–11.10.1016/j.physd.2017.09.002Search in Google Scholar
[10] M. Taherinejad, S. M. Hosseinalipour and R. Madoliat, Dynamic simulation of gas pipeline networks with electrical analogy, J. Braz. Soc. Mech. Sci. Eng. 39(11) (2017), 4431–4441.10.1007/s40430-017-0821-xSearch in Google Scholar
[11] G. Guandalini, P. Colbertaldo and S. Campanari, Dynamic modeling of natural gas quality within transport pipelines in presence of hydrogen injections, Appl. Energy 185 (2017), 1712–1723.10.1016/j.apenergy.2016.03.006Search in Google Scholar
[12] T. Kiuchi, An implicit method for transient gas flows in pipe networks, Int. J. Heat Fluid Flow 15(5) (1994), 378–383.10.1016/0142-727X(94)90051-5Search in Google Scholar
[13] M. Abbaspour and K. S. Chapman, Nonisothermal transient flow in natural gas pipeline, J. Appl. Mech. 75(3) (2008), 031018.10.1115/1.2840046Search in Google Scholar
[14] S. L. Ke and H. C. Ti, Transient analysis of isothermal gas flow in pipeline network, Chem. Eng. J. 76(2) (2000), 169–177.10.1016/S1385-8947(99)00122-9Search in Google Scholar
[15] W. Q. Tao and H. C. Ti, Transient analysis of gas pipeline network, Chem. Eng. J. 69(1) (1998), 47–52.10.1016/S1385-8947(97)00109-5Search in Google Scholar
[16] M. Behbahani-Nejad and A. Bagheri, A MATLAB simulink library for transient flow simulation of gas networks, Proc. World Acad. Sci. Eng. Tech. 33 (2008), 139–145.Search in Google Scholar
[17] M. Behbahani-Nejad and A. Bagheri, The accuracy and efficiency of a MATLAB-Simulink library for transient flow simulation of gas pipelines and networks, J. Pet. Sci. Eng. 70(3) (2010), 256–265.10.1016/j.petrol.2009.11.018Search in Google Scholar
[18] A. Osiadacz, Simulation of transient gas flows in networks, Int. J. Numer. Methods Fluids 4(1) (1984), 13–24.10.1002/fld.1650040103Search in Google Scholar
[19] A. D. Woldeyohannes and M. A. A. Majid, Simulation model for natural gas transmission pipeline network system, Simul. Modell. Pract. Theory. 19(1) (2011), 196–212.10.1016/j.simpat.2010.06.006Search in Google Scholar
[20] S. Wu, et al., Model relaxations for the fuel cost minimization of steady-state gas pipeline networks, Math. Comput. Model 31(2–3) (2000), 197–220.10.1016/S0895-7177(99)00232-0Search in Google Scholar
[21] E. S. Menon, Gas pipeline hydraulics, CRC Press, United States, 2005.10.1201/9781420038224Search in Google Scholar
[22] M. S. Phadke, Quality engineering using robust design, Prentice Hall PTR, United States, 1995.Search in Google Scholar
[23] K.-S. Tang, et al., Genetic algorithms and their applications, IEEE Signal Process. Mag. 13(6) (1996), 22–37.10.1109/79.543973Search in Google Scholar
[24] J.-T. Tsai, T.-K. Liu and J.-H. Chou, Hybrid Taguchi-genetic algorithm for global numerical optimization, IEEE Trans. Evol. Comput. 8(4) (2004), 365–377.10.1109/TEVC.2004.826895Search in Google Scholar
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Articles in the same Issue
- Frontmatter
- Original Research Articles
- Thermal Effect on Dynamics of Beam with Variable-Stiffness Nonlinear Energy Sink
- Nonlinear Dynamics Behavior of Tethered Submerged Buoy under Wave Loadings
- A Multistep Legendre Pseudo-Spectral Method for Nonlinear Volterra Integral Equations
- A Meshfree Numerical Technique Based on Radial Basis Function Pseudospectral Method for Fisher’s Equation
- Transient Simulation of Natural Gas Network by Hybrid Taguchi Binary Genetic Algorithm
- Numerical Analysis of TB32 Crash Tests for 4-cable Guardrail Barrier System Installed on the Horizontal Convex Curves of Road
- Nonlinear Vibration of Truncated Conical Shells: Donnell, Sanders and Nemeth Theories
- Singular Perturbed Vector Field (SPVF) Applied to Complex ODE System with Hidden Hierarchy Application to Turbocharger Engine Model
Articles in the same Issue
- Frontmatter
- Original Research Articles
- Thermal Effect on Dynamics of Beam with Variable-Stiffness Nonlinear Energy Sink
- Nonlinear Dynamics Behavior of Tethered Submerged Buoy under Wave Loadings
- A Multistep Legendre Pseudo-Spectral Method for Nonlinear Volterra Integral Equations
- A Meshfree Numerical Technique Based on Radial Basis Function Pseudospectral Method for Fisher’s Equation
- Transient Simulation of Natural Gas Network by Hybrid Taguchi Binary Genetic Algorithm
- Numerical Analysis of TB32 Crash Tests for 4-cable Guardrail Barrier System Installed on the Horizontal Convex Curves of Road
- Nonlinear Vibration of Truncated Conical Shells: Donnell, Sanders and Nemeth Theories
- Singular Perturbed Vector Field (SPVF) Applied to Complex ODE System with Hidden Hierarchy Application to Turbocharger Engine Model