Masoud Asadzadeh Bryan Tolson Robert McKillop

1. WDSA 2010, BWCN, Tucson, Arizona, Sept., 12-15 A Two Stage Optimization Approach for Calibrating Water Distribution Systems Masoud Asadzadeh Bryan Tolson Robert McKillop

2. WDSA 2010, BWCN, Tucson, Arizona, Sept., 12-15 Asadzadeh, Tolson, Mckillop Assumptions • SCADA may contain erroneous data • Originally supplied EPANET input file may be incorrect • Negative pressure in the network is infeasible • Zoning is based on the demand pattern Stages of Methodology 0) Pre-Calibration and data quality assessment 1) Roughness Coefficient (RC) Calibration 2) Demand Pattern Multiplier (DPM) Calibration 2

3. WDSA 2010, BWCN, Tucson, Arizona, Sept., 12-15 Asadzadeh, Tolson, Mckillop Stage 0: Pre-Calibration Data Quality Assessment, Decision Variable Grouping Data/Input file problem ‘Solution’ to problem Modify these data points by linear Interpolation Tank levels remain constant for a couple of hours Modify pumping control rule threshold based on SCADA Sometimes pumps work while based on rules they should not Initial pumping status (on/off) is not specified Set the initial pumping status based on SCADA Group nodal demands by DMA  5 groups of demand multipliers to calibrate per time step Group pipes by diameter, DMA age & water flow direction during FFTs 28 RCs to calibrate Assign plausible nominal (initial) decision variable values and range (Walski et al. 2003)

4. WDSA 2010, BWCN, Tucson, Arizona, Sept., 12-15 Asadzadeh, Tolson, Mckillop Stage 1: RC Calibration Stage 1a): Identify pipes that are potentially partially closed • Set RC lower bound of these partially closed pipe groupings to 1 Groups of pipes with RC lower than 40 may contain partially closed pipes (Walski et al. 2003) • Solve a sub-problem for each DMA to simulate FFT results corresponding to that DMA by calibrating RC of groups of pipes affecting that DMA, while other pipes in network are set to their nominal RC values Set the lower bound of RC for these pipes to 1 in stage 1b Stage 1b): Calibrate RC across network, minimize simulation errors of: Hour 1 of SCADAvs. FFT data Relative Absolute Error in SCADA: Tank Level Pump Flow Relative Absolute Error in FFT: Static Pressure Residual Pressure Flow

5. WDSA 2010, BWCN, Tucson, Arizona, Sept., 12-15 Asadzadeh, Tolson, Mckillop Stage 1: Results Default Dvs, Default initial pump status Nominal DVs, Default initial pump status Nominal DVs, Modified initial Pump status Calibrated results assuming no partially closed pipes Calibrated results assuming some partially closed pipes 5

6. WDSA 2010, BWCN, Tucson, Arizona, Sept., 12-15 Asadzadeh, Tolson, Mckillop Stage 2: DPM Calibration Model performance in each hour is independent from subsequent hours Calibrate DPM hour-by-hour according to the following: Minimize the following single objective function: volume error in tanks volume error through pumps

7. WDSA 2010, BWCN, Tucson, Arizona, Sept., 12-15 Asadzadeh, Tolson, Mckillop Stage 2: Results 2a) pick subset of solutions on tradeoff 2b) for each of these, calibrate DPM 2c) at end, pick one of these solutions

8. WDSA 2010, BWCN, Tucson, Arizona, Sept., 12-15 Asadzadeh, Tolson, Mckillop Optimization Algorithms • DDS (Tolson & Shoemaker 2007) • Single Objective • Single solution based heuristic Optimizer • Adjusts the search strategy to the user-defined computational budget Stage 2: 5 DPM per hour  5 decision variables 1000 EPANET simulations for each hourly calibration • Multi-Objective version of DDS (Asadzadeh & Tolson 2009) • Single solution based heuristic Optimizer • Only one algorithm parameter Stage 1: 28 RC + 5 DPM  33 decision variables 100,000 EPANET simulations Both algorithms applied at default algorithm parameter setting

9. WDSA 2010, BWCN, Tucson, Arizona, Sept., 12-15 Asadzadeh, Tolson, Mckillop Main Findings • WDN calibration is not simply an optimization problem, because a substantial amount of analyst/modeller time is required for Data/Input quality assessment and corresponding fixes/adjustments • 30% of total project time spent here responsible for much more than 50% of the overall reduction in simulation errors • Analyst/modeller time best reserved for pre-optimization analysis rather than optimization algorithm testing and tuning (why we selected DDS and PADDS optimization algorithms) • With access to the real network and engineers familiar with the network, the assumptions, data and input file fixes/adjustments we made could be confirmed or refuted with near certainty 9 Optimization can guide us towards finding inconsistencies in the data and model inputs

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