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Gas Distribution Network Optimization with Genetic Algorithm

Gas Distribution Network Optimization with Genetic Algorithm. Kuntjoro Adji S. Lala Septem Riza Kusuma Chasanah Widita Febi Haryadi. Introduction. N atural gas plays an important role in providing clean energy for the community .

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Gas Distribution Network Optimization with Genetic Algorithm

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  1. Gas Distribution Network Optimization with Genetic Algorithm Kuntjoro Adji S. Lala Septem Riza Kusuma Chasanah Widita Febi Haryadi

  2. Introduction • Natural gas plays an important role in providing clean energy for the community. • Gas companies have planned to design and build a new gas pipeline network in many places. • To build pipeline network needs expensive cost in pipeline cost, investment cost, operasional cost, etc. • So, pipe diameter optimization process must be done to minimize the investment cost with considering to the pressure and flow rates that have been agreed in the contract

  3. Methodology • INPUT DATA: • Fix pressure on inlet and outlet node • Schematic of pipeline network • Geometries of pipe • Flow rate on each outlet • OUTPUT DATA: • ID, OD, t • Pressure Distribution • Cost (investment, coating, installation) • GENETIC ALGORITHM • To optimize inside diameter with minimize total cost • To determine pressure distribution on junction • With constrains: • Fix pressure on outlet node • Available pipe on market • Output: • Optimal inside diameter • Cost calculation • Pressure on each node • VALIDATION OF PRESSURE DISTRIBUTION • (Using Genetic Algorithm and Newton Method for checking/calculating of pressure distribution) • Input: • Optimal inside diameter • Pressure on inlet node • Output: • Pressure distribution on each node

  4. The problem formulation Minimize subject to is balancing equation at node i.

  5. The Genetic Optimization Minimize subject to • Pressure on each inlet is given. • Inside diameter which available on market, 64 kind of ID (3 inch – 16 inch). • flow rate on each outlet.

  6. The model of gas flow in pipe • The panhandle A: • The equation system is constructed based on kirchoff’s law: “ at any node, the sum of mass flow into that node is equal to the sum of mass flow out of that node”

  7. Continuity equations at the system:

  8. Continuity equation at node m: • QNmis the node flow (supply / demand rate) at node m

  9. Continuity equation at node 6: • Obtained: • N continuity equations

  10. The economic model • Investment cost: • Coating cost: • Installation cost: • Operational cost(rule of thumb): 4% * investment cost.

  11. Flow Chart of Genetic Algorithm

  12. The Computation Method:The Genetic Algorithm • To search the suitable pressure and index of inside diameter available on the market which give the best fitness. • The representation of population:

  13. The Computation Method:The Genetic Algorithm (con’t) • Fitness function: • We use usual the Selection, crossover and mutation operator.

  14. Case Study Input Data: 1). The schematic network, 2). There are 18 nodes: 1 inlet, 7 junctions, 10 outlet. And there are 17 pipe segments. 3). Pressure on inlet and on each outlet. To find: • Pressure on each junction • ID on each segments. • Cost calculation

  15. The software“OptDistNet”

  16. 1st simulation • To calculate the optimum diameter using GA • Input: pressure on inlet “S1” and pressure on each outlet, length of pipe. • Output: ID on 17 segment pipe and pressure on each junction.

  17. Result: Pressure Distribution on junction

  18. Result: Optimum Pipe Diameter

  19. Result: Cost Calculation

  20. 2nd Simulation • To validate pressure on each node using optimum inside diameter. • Input: • Inside diameter • Pressure on inlet • Flow rate on each outlet • Output: • Pressure on each node.

  21. Result: Pressure Distribution

  22. Conclusions • The simple Genetic Algorithm can be helpful in finding an optimal inside diameter.

  23. Thank You

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