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Integrated sensing and modeling on a sensor node

Integrated sensing and modeling on a sensor node. Yeonjeong Park and Tom Harmon UC Merced Environmental Systems program. Why do this? Moisture , specific conductivity , and temperature sensing in soils A closed-loop system demonstration pilot scale Demonstration at full scale. Outline.

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Integrated sensing and modeling on a sensor node

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  1. Integrated sensing and modeling on a sensor node Yeonjeong Park and Tom Harmon UC Merced Environmental Systems program

  2. Why do this? Moisture, specific conductivity, and temperature sensing in soils A closed-loop system demonstration pilot scale Demonstration at full scale Outline

  3. Several reasons for local, automated analysis Sensor system design (optimize numbers, locations of sensors while you are installing them) Feedback-control algorithms: observe, model, forecast, control, …, observe [emphasis of this presentation] Computations locally or remotely? If speed is not an issue, than remote computations may be important Motivation

  4. We notice that data analysis can become routine with arrays of individual sensors Energy balances Water balances Metabolism Mixing Let the sensor array behave as a more sophisticated “sensor” Creating higher order virtual sensors “Salinity flux” sensor

  5. Example: Irrigation in the Mojave Desert

  6. Typical sensor array for field testing • Sensors • Moisture • Temperature • Soil salinity • (also meteorology) Decagon 5TE

  7. These sensors are robust (much testing in agriculture) 25 cm blue 50 cm red 100 cm black Moisture (v/v)

  8. Irrigation control “sensor”

  9. Coupling sensors readings with models(compressing the timeframe for analysis) • Palmdale water reuse experimental site (not in the dairy site, but could be…) • Microclimate + soil pylons (moisture, temp, short-term nitrate and ammonium) • sensor feedback, model calibration, model forecast • After a reasonable amount of time, the model parameters become stable

  10. Pilot demonstration: Sensor-trained simulation model with a management model (feedback-control) • Receding horizon control • Optimize irrigation rate for current system state and future states • Execute the best estimate for the current state and move the management horizon forward, repeating…

  11. Step 1: observe and model Sample model fits (all at 5 cm depth, different management steps) Note: model is a coupled flow, mass and energy transport model (one-dimensional, 2 soil layers assumed)

  12. Receding Horizon Control Nonlinear optimization algorithm producing an array of future control actions (here, irrigation rates)

  13. Soil Moisture Control Variable Application Rate of Fixed Frequency and Duration

  14. Soil Moisture Control Fixed Application Rate of Variable Frequency and Duration

  15. …and trying feedback-control in the field • Center pivot irrigation system • Manually control by changing the rotational speed (not automated) • 3 speeds to simplify the objective space Park, Shamma, & Harmon (2009) Environ ModellSoftw, in press

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