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

2. Asadzadeh, Tolson, Mckillop
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
WDSA 2010, BWCN, Tucson, Arizona, Sept., 12-15

3. Stage 0: Pre-Calibration
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
Tank levels remain constant for a couple of hours
Modify these data points by linear Interpolation
Sometimes pumps work while based on rules they should not
Modify pumping control rule threshold based on SCADA
Initial pumping status (on/off) is not specified
Set the initial pumping status based on SCADA
note that above includes findings of Stage 0 (which is what we want to do - good)
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 SCADA vs. FFT data
Relative Absolute Error in SCADA: Tank Level Pump Flow
Suggest results of each Stage come right after Methodology described for each stage.
Relative Absolute Error in FFT: Static Pressure Residual Pressure Flow

5. Stage 1: Results 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. Stage 2: DPM Calibration
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. Asadzadeh, Tolson, Mckillop
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
Measurement
MRAE (%)
Static pressure
3.6
FFT flow
1.5
FFT pressure
1.3
Tank level
1.0*
Pumping rate
4.2

8. Optimization Algorithms
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)
DDS and PADDS are the tools we used and algorithm desccriptions are really another presentation entirely. Let’s tell this audience what we want them to know about DDS and PADDS:
1) give references
2) how many decision variables were optimized and how many EPANET evaluations were required (with computation time)
3) no algorithm parameter tuning conducted – all results generated under default algorithm parameter
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. Asadzadeh, Tolson, Mckillop
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
Optimization can guide us towards finding inconsistencies in the data and model inputs 9

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