1. Evaluation of Non-Uniqueness in Contaminant Source Characterization based on Sensors with Event Detection Methods Jitendra Kumar 1, E. M. Zechman 1, E. D. Brill 1, G. Mahinthakumar 1, S. Ranjithan 1, J. Uber 2 1 North Carolina State University, Raleigh, NC 2 University of Cincinnati, Cincinnati, OH

2. Outline Introduction Objective Methodology Case Study Observations Ongoing/future work

3. Introduction Water distribution systems are vulnerable to accidental or intentional contamination Contaminant can spread very fast and threaten public health Accurate characterisation of source is required for taking any control measures

4. Source characterization methods typically assume ideal sensor information Quality of information depends on the sensor –E.g., sensitivity, specificity, reliability Filtered sensor information –Event detection based on water quality parameters (chlorine, pH, etc.) –Binary contamination signal based on measurement sensitivity Affects quality of sensor information available for source characterization

5. Objectives of the study Effect of filtered sensor information on source characterization –Accuracy of predictions –Degree of non-uniqueness

6. EPANET SENSOR Binary Observation OPTIMIZATION FRAMEWORK Simulation Model Filtering of information

7. Simulation-optimization approach Mathematical Formulation Find –Source location [L(x,y)] –Contaminant loading profile [M t, T s ] Minimize prediction error

8. Optimization Procedure Niched-coevolution-based evolutionary algorithm Uses multiple subpopulations –One subpopulation identifies the solution that best fits the observations –Other subpopulations identify different (non-unique) solutions, if any Reference: Zechman, Emily M., and Ranjithan, S., (2004), “An Evolutionary Algorithm to Generate Alternatives (EAGA) for Engineering Optimization Problems.” Engineering Optimization, 36(5), pp. 539-553.

9. Example network 97 nodes 117 pipes Hydraulic simulations at 1 hour Quality simulations at 5 minutes Network simulated for 24 hours Example network included in EPANET distribution

10. Assumptions Contaminant specific sensor Threshold-based sensor with binary output signal Placement of sensors in the network was based on experience with the network Only non-reactive conservative pollutants are considered

11. True Source Trial contamination source

12. Ideal observations at the sensors

13. What’s the actual information available from a binary sensor ? Ideal information at a sensor

14. Sensor sensitivity value = 0.01 mg/l

15. 0.01 mg/l 0.1 mg/l0.2 mg/l Effect of sensor sensitivity on quality of observation 0.5 mg/l

16. Observations Filtered information might be available from the sensors Sensitivity of sensor has major effect on the quality of filtered data

17. Sensors with a sensitivity of 0.5 mg/l Predicted Source

18. contd… Predicted Source

19. Sensors with a sensitivity of 0.1 mg/l 3 non-unique solutions Predicted Source

20. Sensors with a sensitivity of 0.01 mg/l Perfectly matched the sensor signals at all sensors during simulation time

21. Observations More than one solutions possible at same location Different non-unique sources can match the observations Non-uniqueness increases with decrease in sensor sensitivity

22. Different scenarios of contaminant loading Several different contamination loading profiles were studied –Constant, increasing, decreasing, intermittent A sensor sensitivity of 0.1 mg/l was used in this analysis

23. Predicted Source Decreasing mass loading of contaminant The true source and two other possible sources were identified Increasing mass loading of contaminant

24. The true source and two other possible sources were identified Intermittent mass loading of contaminants Predicted Source

25. Both solutions were predicted at true source location Intermittent mass loading of contaminants Predicted Source

26. The true source and one other possible source was identified Intermittent mass loading of contaminants Predicted Source

27. Observations Identified the correct and the non-unique solutions for different scenarios of contaminant loadings Varying degree of non-uniqueness in different scenarios

28. Noise in observation data Measurement and machine errors –Error in the concentration measurement at the probes –Error in the trigger mechanism Random noise introduced –Normal distribution –± 10 %, ± 50 % errorlevel

29. Error level = 10 % –2 non-unique solutions Error level = 50 % –1 solution Higher prediction errors due to low quality data Predicted Source

30. Summary Effects of sensor sensitivity on contaminant source characterization was studied –Accuracy of predictions –Uncertainty of predictions Applicability of the contaminant source characterization method for different scenarios was illustrated Efficient scalable simulation-optimization framework –Parallel EPANET (using MPI) for UNIX environment –Grid enabled –Presented results were obtained using 4 processors on Neptune (Opteron cluster)

31. Ongoing work Extend study to include –Additional sensor types –Event detection procedures Multiple indicators Effects of measurement errors in sensors with filtered output signals

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

33. Thanks & Questions ??