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IPA07-E-077

31 st IPA Convention: 14 – 16 May 2007. IPA07-E-077. A Compositional Gas Flow Model For Predicting Pressure And Heating Value Distribution In Complex Pipeline Network System . Mucharam, L., Sidarto, K.A., Riza, L.S., Mubassiran, Sophian, S. Background.

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IPA07-E-077

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  1. 31st IPA Convention: 14 – 16 May 2007 IPA07-E-077 A Compositional Gas Flow Model For Predicting Pressure And Heating Value Distribution In Complex Pipeline Network System Mucharam, L., Sidarto, K.A., Riza, L.S., Mubassiran, Sophian, S.

  2. Background • Gas operator companies have a responsibility to provide gas to consumers with certain rate, pressure, heating value described in the sales contract. • In a complex gas pipelines network system where several gas sources and outlets are encountered, different gas compositions and heating values may vary across the system. • Since the gas price is commonly determined by its heating value, therefore prediction of gas heating values distribution in pipeline network is very important.

  3. Objectives • Predicting/determining gas pressure distribution, flow direction, and flow rate on each segment . • Determining gas composition and heating value on each outlet.

  4. Pressure Dist. Work Flow Methodology User Input Gas Rate in each Segment Mole Rates Composition Determination Genetic Algorithm Flow Direction Heating Value • User Input : • Gas Properties • Flow Equation • Genetic Properties • Network Model • Node Properties • Pipe Properties • Inlet Composition Newton Raphson Output Display • Output Display : • Pressure Dist. • Flow Direction • Flow rate on each segment • Composition on each node • Heating Value on each Outlet

  5. Assumptions • Dry gas (no condensation) • Steady state condition • The fluid composition across the whole segment is uniform (i.e. non-reactive system, no leak and no chemical reaction).

  6. Methodology • Model development to determine pressure distribution • Problem representation using Kirchoff’s Law (mass balance). • Solving using combination of Genetic Algorithm and Newton’s Method. • Model development to determine composition and heating value. • Problem representation using linear equation system. • Solving using inverse matrix.

  7. Model Development To Determine Pressure Distribution • Gas flow correlation using Panhandle A • To represent a pipeline network system using Kirchoff’s Law (mass balance). • Thus non linear equation system is obtained.

  8. An Example: Non Linear Equation System of pipeline network system based on Kirchoff’s Law 2 1 3 Pipeline Network 6 4 5

  9. Solving of Non Linear Equation System • Genetic Algorithm to obtain the initial value • Newton’s Method to refine the initial value obtained from genetic algorithm as a solution of non linear equation system (final result of pressure distribution).

  10. 2.Model Development To Determine Composition and Heating Value • To determine composition of each node: Using linear equation system • Converting flow rate to mole rate on each segment : PV = znRT • Heating value : where yi : Composition of each component Lci : Heating value of component i (BTU/scf) Lc ideal : Heating Value (BTU/scf)

  11. Outlet Junction Inlet P = 643.14 P = 643.28 P = 650 P = 650 P = 505.12 P = 523.69 4 1 Q = - 50 Q = - 80 2 5 6 3 An Example of Model to Determine Composition on Each Node Linear equation system: where is concentration of the component j at node i. is amount of mole at segment from node i to j

  12. Study CaseGas Distribution Network of OFF TAKE SRPG and BTG Consist of • 2 nodes of supply • 36 nodes of delivery • 59 nodes of junction • 89 pipelines Input Data (in the paper) • Pressure at each inlet. • Pipe specifications. • Flow rate at each outlet. • Gas composition at each inlet. • Gas properties. • Network model.

  13. Study Case: Schematic of Network

  14. Result: Study Case Pressure Distribution, Flow direction and Flow rate on each segments

  15. Result Comparison OPPINET – TGNet Comparison of Pressure Distribution on each outlet Differentiation (%) < 6%

  16. Result: Study Case 1010.5 Btu/scf 1010.5 Btu/scf Heating Value 967.2 Btu/scf 967.2 Btu/scf 1067.3 Btu/scf P = 358.24 psia Q = 27.36 MMscfd Heating Value = 1067.3 Btu/scf P = 356.65 psia Q = 18.52 MMscfd Heating Value = 910.51 Btu/scf

  17. Conclusion • Genetic Algorithm and Newton’s is robustness method in solving non linear equation system for determining gas pressure distribution. • To calculate composition on each node, the system model could be built from system of linear simultaneous equation. • Based on the previous results, calculating the heating value has been performed • The model developed is viable to predict pressure distribution, flow rate, gas composition and heating value on each outlet.

  18. Thank You

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