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Assessing the Impact of Alternative Pipe Groupings on Multi-Objective Water Distribution Network

Assessing the Impact of Alternative Pipe Groupings on Multi-Objective Water Distribution Network. Masoud Asadzadeh Bryan Tolson. Outline. Objectives Problem Description Calibration Problem Setup Optimization Algorithms Numerical Experiment Results and Discussion Main Findings.

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Assessing the Impact of Alternative Pipe Groupings on Multi-Objective Water Distribution Network

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  1. Assessing the Impact of Alternative Pipe Groupings on Multi-Objective Water Distribution Network Masoud Asadzadeh Bryan Tolson

  2. Outline • Objectives • Problem Description • Calibration Problem Setup • Optimization Algorithms • Numerical Experiment • Results and Discussion • Main Findings WDSA2012, Adelaide, South Australia, Sept. 24-27

  3. Objectives • Assessing the impact of reducing the number of decision variables on the quality of model calibration results • Comparing PA-DDS and ε-NSGAII across a range of computational budgets WDSA2012, Adelaide, South Australia, Sept. 24-27

  4. Problem Description DMA2 DMA5 DMA1 DMA4 DMA3 • The problem is simplified from the Battle 2010 and needs the Calibration of: • Roughness Coefficients of 429 pipes (there could be some partially closed pipes in DMA2) • Demand Pattern Multipliers of 5 DMAs WDSA2012, Adelaide, South Australia, Sept. 24-27

  5. Calibration Problem Setup • The perfect EPANET2 input file and perfect observed data are taken from Ostfeld et al. (in press). • Pipes and DPMs are grouped: • Full Calibration Model with 32 decision variables: • Pipes in each DMA are grouped based on their size + 3 groups of pipes to make the partially closed pipes identifiable  27 Decision Variables • Each DMA has its own DPM  5 Decision Variables • Reduced Calibration Model with 7 decision variables: • Pipes are grouped based on their age 3 groups of pipes to make the partially closed pipes identifiable  6 Decision Variable • All DMAs has the same DPM  1 Decision Variable WDSA2012, Adelaide, South Australia, Sept. 24-27

  6. Calibration Problem Setup (cont’d) • EPANET2 toolkit and a MATLAB code are linked to: • Modify the EPANET2 input file by changing pipe roughness coefficients and demand pattern multipliers based on decision variable values of each solution • Simulate the modified input file • Return the desired simulated data points for: • Tank levels and Pumping Station flows at the end of Hour1 • Static Pressure • Fire Flow Test (FFT) Pressure • FFT flow • Objective functions are : • SSRE Hour1 • SSRE FFT WDSA2012, Adelaide, South Australia, Sept. 24-27

  7. Optimization Algorithm: Pareto Archive Dynamically Dimensioned Search (Asadzadeh and Tolson 2009) Initialize starting solutions Perturb current ND solution Update ND solutions Create ND-solution set Pick a ND solution Pick the New solution New solution is ND? Y N Y STOP N Continue?

  8. Optimization Algorithm: ε-NSGAII(Kollat and Reed 2005) • A variant of NSGAII with a modified solution archiving scheme: • Discretize the objective space into grid cells with the size of epsilon (ε) • Archives at most a single solution in each grid cell • Archives a new solution only if it: • Dominates a previously archived solutions or • Corresponds to a vacant grid cell

  9. Numerical Experiment & Results Comparison • Apply PA-DDS and ε-NSGAII to both full and reduced calibration problems: • With limited (1,000) and large (10,000) computational Budgets • In multiple independent trials (10) • Compare the aggregated results of reduced and full models • Compare PA-DDS and ε-NSGAII in both limited and large computational budgets WDSA2012, Adelaide, South Australia, Sept. 24-27

  10. Comparing Final Calibration Results of Full and Reduced Models (aggregated tradeoffs after 220,000 solution evaluaitons) WDSA2012, Adelaide, South Australia, Sept. 24-27

  11. Comparing Final Calibration Results of Full and Reduced Models(Detailed Evaluation of Selected Solutions) WDSA2012, Adelaide, South Australia, Sept. 24-27

  12. Comparing Final Calibration Results of Full and Reduced Models(Detailed Evaluation of Selected Solutions cont’d) WDSA2012, Adelaide, South Australia, Sept. 24-27

  13. Comparing Calibration Problem Difficulty WDSA2012, Adelaide, South Australia, Sept. 24-27

  14. Comparing MO Algorithm Performance WDSA2012, Adelaide, South Australia, Sept. 24-27

  15. Main Findings • Reducing the number of decision variables would: • Make the calibration problem easier to solve • Reduce the quality of calibrated model significantly • Both optimization algorithms found high quality results of the full model even with limited budget, but PA-DDS performed slightly better • It is recommended to not reduce the number of decision variables only due to the limited computational budget. • Also, if a stochastic optimization algorithm is used, it is recommended to have multiple optimization trials and aggregate the results. WDSA2012, Adelaide, South Australia, Sept. 24-27

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