Transient-State Natural Gas Transmission in Gunbarrel Pipeline Networks
Published Online:25 Feb 2020https://doi.org/10.1287/ijoc.2019.0904
References
- (2008) Nonisothermal transient flow in natural gas pipeline. J. Appl. Mech. 75(3):031018.Crossref, Google Scholar
- (1998) Hierarchical control of transient flow in natural gas pipeline systems. Internat. Trans. Oper. Res. 5(4):285–302.Crossref, Google Scholar
- (1961) Dynamic approach to gas-pipeline analysis. Oil Gas J. 59:65–78.Google Scholar
- (2001) Infinite-horizon policy-gradient estimation. J. Artificial Intelligence Res. 15:319–350.Crossref, Google Scholar
- (1996) Neuro-dynamic Programming (Athena Scientific, Belmont, MA).Google Scholar
- (2011) Minimizing fuel cost in gas transmission networks by dynamic programming and adaptive discretization. Comput. Indust. Engrg. 61(2):364–372.Crossref, Google Scholar
- (1998) Pipeline optimization: Dynamic programming after 30 years. Proc. 30th Annual Meeting Pipeline Simulation Interest Group (PSIG), October 28–30, Denver.Google Scholar
- (2019) A dynamic programming approach to power consumption minimization in gunbarrel natural gas networks with non-identical compressor units. INFORMS J. Comput. 31(3):593–611.Google Scholar
- (2011) Combination of nonlinear and linear optimization of transient gas networks. INFORMS J. Comput. 23(4):605–617.Link, Google Scholar
- (2005) Nonlinear optimization in gas networks. Bock HG, Kostina E, Phu HX, Rannacher R, eds. Modeling, Simulation and Optimization of Complex Processes (Springer, Berlin), 139–148.Google Scholar
- (2014) Oil and gas cooperation between china and central asia in an environment of political and resource competition. Petroleum Sci. 11(4):596–605.Crossref, Google Scholar
- (1994) An implicit method for transient gas flows in pipe networks. Internat. J. Heat Fluid Flow 15(5):378–383.Crossref, Google Scholar
- , eds. (2015) Evaluating Gas Network Capacities, vol. 21 (SIAM, New York).Google Scholar
- (2010) An implicit box scheme for subsonic compressible flow with dissipative source term. Numerical Algorithms 53(2–3):293–307.Crossref, Google Scholar
- (2011) Value function approximation in reinforcement learning using the Fourier basis. Proc. 25th AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 380–385.Google Scholar
- (2010) An approximate dynamic programming approach to benchmark practice-based heuristics for natural gas storage valuation. Oper. Res. 58(3):564–582.Link, Google Scholar
- (1990) A dynamic programming based gas pipeline optimizer. Bensoussan A, Lion JL, eds. Analysis and Optimization of Systems (Springer, Berlin), 123–132.Google Scholar
- (2007) A simulated annealing algorithm for transient optimization in gas networks. Math. Methods Oper. Res. 66(1):99–115.Crossref, Google Scholar
- (2010) A mixed integer approach for time-dependent gas network optimization. Optim. Methods Software 25(4):625–644.Crossref, Google Scholar
- (2010) Approximate dynamic programming for ambulance redeployment. INFORMS J. Comput. 22(3):266–281.Link, Google Scholar
- (2007) A mixed integer approach for the transient case of gas network optimization. PhD thesis, TU Darmstadt, Darmstadt, Germany.Google Scholar
- National Bureau of Statistics of China (2016) Length of natural gas pipelines. Technical report, National Bureau of Statistics of China, Beijing.Google Scholar
- (2011) World’s longest natural gas pipelines. Accessed May 1, 2016, http://www.forbes.com/sites/williampentland/2011/06/17/worlds-longest-natural-gas-pipelines/#b4fba4f31509.Google Scholar
- (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality, vol. 703 (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
- (2015) Optimization problems in natural gas transportation systems: A state-of-the-art review. Appl. Energy 147:536–555.Crossref, Google Scholar
- (2006) Efficient operation of natural gas transmission systems: A network-based heuristic for cyclic structures. Comput. Oper. Res. 33(8):2323–2351.Crossref, Google Scholar
- (1999) Policy gradient methods for reinforcement learning with function approximation. Proc. 12th Internat. Conf. Neural Inform. Processing Systems (MIT Press, Cambridge, MA), 1057–1063.Google Scholar
- (1987) Unsteady and transient flow of compressible fluids in pipelines—a review of theoretical and some experimental studies. Internat. J. Heat Fluid Flow 8(1):3–15.Crossref, Google Scholar
- (2006) Dynamic-programming approximations for stochastic time-staged integer multicommodity-flow problems. INFORMS J. Comput. 18(1):31–42.Link, Google Scholar
- U.S. Department of Transportation (2013) Annual report mileage for gas distribution systems. Technical report, U.S. Department of Transportation Pipeline and Hazardous Materials Safety Administration, Washington, DC.Google Scholar
- U.S. Energy Information Administration (2016) Natural gas consumption by end use. Technical report, U.S. Energy Information Administration, Washington, DC.Google Scholar
- (1968a) Optimization of tree-structured natural-gas transmission networks. J. Math. Anal. Appl. 24(3):613–626.Crossref, Google Scholar
- (1968b) Optimization of natural-gas pipeline systems via dynamic programming. IEEE Trans. Automatic Control 13(5):475–481.Crossref, Google Scholar
- (2000) Model relaxations for the fuel cost minimization of steady-state gas pipeline networks. Math. Comput. Model. 31(2):197–220.Crossref, Google Scholar
- (2009) Numerical methods of using BWRS equation of state in calculating compressibility factor of natural gas. Pipeline Technique Equipment 3:04.Google Scholar
- (2014) Stochastic optimal control model for natural gas networks. Comput. Chemical Engrg. 64:103–113.Crossref, Google Scholar

