Paul Conrads SC Water Science Center GEER 2008 – Naples, FL July 30, 2008

1. Development of Inferential Sensors for Real-time Quality Control of Water-level Data for the EDEN Network Paul Conrads SC Water Science Center GEER 2008 – Naples, FL July 30, 2008

2. Presentation Outline • What is a “Inferential Sensor”? • Background • Industrial application • Homeland security • EDEN Network • Water-level Inferential Sensor • Challenges

3. Tough Environment to Monitor • Emissions regulations require measurements of effluent gases • Smoke stack burns up probes • Need alternative to “hard” sensors

4. Hard Sensor vs. Inferential Sensor • Virtual sensor replaces actual sensor • Temporary gage smoke stack • Operate industry to cover range of emissions • Develop model of emissions based on operations • Model becomes the “Inferential Sensor’

5. Industrial Application: Production Emission Monitoring System Production Control System Components. A Data Historian is a special database designed to hold massive amounts of process and laboratory time series data.

6. Production Emission Model Predicted Emissions History for a Manufactured Product.

7. Inferential Sensor Architecture PEMS Architecture

8. Industrial Benefits • Optimizes Manufacturing Processes • Documents Continuous Compliance • Lower Cost – In some situations, a proactive intelligent system can partially or fully replace passive back end controls to reduce capital and long-term operating expenses • Most Advantageous Permitting – because it actively prevents pollution • Works with Existing or New Production Controls

9. If it is good enough for Industry... • Use similar approach for real-time data • Develop models to predict real-time data • Use predictions as “soft-sensor” to: • QA/QC hard sensor • Provide accurate estimates for hard sensor • Provide redundant signal

10. Everglades

11. Water-depth and Ecosystems Differences of 1 ft can change vegetation communities

12. Everglade Depth Estimation Network(EDEN) • Grid up the Everglades (400 m) • Measure elevation of each grid • Create digital elevation model (DEM) • Create surface-water map • Water depths = Surface-Water Map – DEM • Goal: generate real-time water-surface and water-depth maps

13. EDEN – Model Integration

14. EDEN Gaging Network Agencies: USGS S. Florida Water Management District Big Cypress National Preserve Everglades National Park

15. EDEN Water-Surface Maps • Daily medians computed from hourly data • Water surface maps generated using radial basis function (RBF) in GIS

16. EDEN Water-Surface Map Bad values creates erroneous maps

17. Inferential Sensor application Problem Need to minimize missing and erroneous data Approach Develop “soft” sensors for redundant signal

18. Site B Site A Hypothetical Case Create model (Inferential Sensor) for Site B using Site A as an input Decide when to use Inferential Sensor instead of gage data Actual application would be for 253 stations

19. Possible Outliers Missing Data Hypothetical Case: Gage Data Gage Height, in feet

20. Hypothetical Case: Inferential Sensor How to decide when to use the inferential sensor? Gage Height, in feet 1. 95th confidence interval of model 2. Constant distance from inferential sensor R2=0.94

21. Hypothetical Case: When to use Inferential Sensor? Gage Height, in feet

22. Hypothetical Case: When to use Inferential Sensor? Gage Height, in feet

23. Hypothetical Case - comments • Issue of model accuracy • Immediate benefit for missing data • Made the assumption that Site A was correct • Example used daily data • Use inferential sensor on hourly data • Compare daily medians (used for EDEN maps) • What if data for Site A is missing? • Issues are magnified when dealing with a network of 253 gages

24. Initial Approach: Data Evaluation • Need to know what data is good • Set of filters to evaluate data quality • Robust series of thresholds • Differences with other gages • Time delays and moving window averages • Time derivatives • Rate of change over various time intervals • Create subset of good data

25. Initial Approach: Model Development • One Approach – Canned Models • Create multiple models for a gage • Set priority for model to use depending on available data • Large number of models • Not all combinations of gages would be addressed

26. Initial Approach: Model Development • Second Approach – Model on the Fly • Subset of good data, determine input data with the highest correlation • Develop models based on available data • Issue of evaluation of models • More complex programming than first approach

27. EDEN Inferential Sensor Architecture Data from NWIS – 253 sites Data Evaluation – robust filters Output Log File Inferential Sensor Models Data Fill and Replacement Data to EDEN server

28. Summary • Inferential Sensor may provide an approach for: • Real-time QA/QC • Redundant signal • Challenges • Identifying good data • Highly accurate models • Managing number of models • Develop prototype for EDEN • Stay tuned

29. Questions?