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Li Liu, E. Downey Brill, G. Mahinthakumar, James Uber, Emily M. Zechman, S. Ranjithan

Effects of Measurement Uncertainties on Adaptive Source Characterization in Water Distribution Networks. Li Liu, E. Downey Brill, G. Mahinthakumar, James Uber, Emily M. Zechman, S. Ranjithan North Carolina State University. Contaminant Source Determination. Rapid identification of …

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Li Liu, E. Downey Brill, G. Mahinthakumar, James Uber, Emily M. Zechman, S. Ranjithan

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  1. Effects of Measurement Uncertainties on Adaptive Source Characterization in Water Distribution Networks Li Liu, E. Downey Brill, G. Mahinthakumar, James Uber, Emily M. Zechman, S. Ranjithan North Carolina State University

  2. Contaminant Source Determination • Rapid identification of … • Contamination source location • Starting time • Mass loadings at different time • When to stop the search and make final decision • Necessary information for threat management in water distribution systems

  3. Challenges of Source Identification • Inverse Problem • Ill-posed/Non-uniqueness • Under dynamic environments • Dynamic system • Dynamically updated observations • Under noisy environments • Measurement error • Uncertain demands • Model error

  4. Csim Cobs Source characteristics t Water Quality Simulation EA-based Optimizer Hydraulic Simulation Observed Data Simulation-Optimization Method

  5. Adaptive Dynamic Optimization Technique (ADOPT) • An EA-based search • Solves as information becomes available over time • Multiple solutions to assess non-uniqueness

  6. Objective • Investigate the effects of sensor errors on source characteristics obtained using ADOPT

  7. Assumptions • Deterministic demand values • Conservative contaminant • Contamination occurs at any one location in the network • Only sensor errors are considered

  8. Scenarios with Sensor Error • Scenario 1: Sensor with continuous malfunction • Scenario 2: Sensor with intermittent malfunction • Scenario 3: Sensor activates after a lag time of first detection • Scenario 4: Sensor with systematic reading error

  9. Contamination Case A Mass Loading Profile

  10. Contamination Case A… Observed Conc. (mg/L) Node 115 Observed Conc. (mg/L) Time Step (10 mins) Node 197 Node 184 Node 211 Time Step (10 mins) Time Step (10 mins) Time Step (10 mins)

  11. Results for Case A with Perfect Data Observed Conc. (mg/L) Best solution Node 115 Prediction Error = 0.026 mg/L Time Step (10 mins) True source Best solution Observed Conc. (mg/L) Node 197 Node 184 Node 211

  12. Case A : scenario 1 Observed Conc. (mg/L) Node 115 True concentration Observed concentration

  13. Case A : scenario 1 Observed Conc. (mg/L) Node 115 True concentration Observed concentration Observed Conc. (mg/L) Best solution Node 184 Time Step (10 mins)

  14. Case A: scenario 2, 3 & 4 Scenario 2 Best solution Observed Conc. (mg/L) Node 115 Time Step (10 mins) Scenario 3 Scenario 4 Observed Conc. (mg/L) Node 115 Node 115 True concentration Observed concentration Time Step (10 mins) Time Step (10 mins)

  15. Contamination Case B True Source Mass Loading Profile

  16. Case B … Observed Conc. (mg/L) Node 197 Time Step (10 mins) Observed Conc. (mg/L) Node 184 Node 211 Time Step (10 mins) Time Step (10 mins)

  17. Results for Case B with Perfect Data

  18. Results for Case B with Perfect Data Node 197 Node 211 Node 184

  19. Case B: scenario 1 Observed Conc. (mg/L) Node 197 Time Step (10 mins) Observed Conc. (mg/L) Node 184 Node 211 Time Step (10 mins) Time Step (10 mins)

  20. Case B: scenario 2 Observed Conc. (mg/L) Node 197 Time Step (10 mins) Observed Conc. (mg/L) Node 184 Node 211 Time Step (10 mins) Time Step (10 mins)

  21. Case B: scenario 3 & 4 Scenario 4 Scenario 3

  22. Summary for results Number of alternative source locations Scenario #

  23. Summary for results… Mass Loading difference at true source location (g/min) Scenario #

  24. Final Remarks • Source characteristics identified by ADOPT are influenced by the type of sensor errors. • Investigate effects of demand uncertainty. • Update ADOPT to be robust under combined noisy conditions.

  25. Acknowledgements This work is supported by National Science Foundation (NSF) under Grant No. CMS-0540316 under the DDDAS program.

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