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Oil Production:
The virtual sensor plot on the right shows the estimated (blue) and actual (red) production from an oil platform.  It uses a variety of surface measures, mostly casing, tubing and flow line pressures, to estimate total liquid production.  Data shown is "out-of-sample" (actual vs. estimated production on data not used to build the virtual sensor) and reflects true performance (98.83% accurate).  This is not the most accurate model possible, but one built in about 10 minutes work using the Process Modeler.  This virtual sensor, now built, does not rely on any flow measure hence forward.

Distillation:
This virtual sensor shows the estimated (blue) and actual (red) distillation product property downstream from a distillation column.  A model of this type is in use 24 hours a day, 7 days a week estimating product performance between sensor readings and while the sensor is removed.  Data shown is "out-of-sample" (actual vs. estimated production on data not used to build the virtual sensor) and reflects true performance (99.59% accurate).

Fluidized Bed Drying: Moisture from Lab Results
This virtual sensor shows the estimated (blue) and actual (red) percent moisture (water) in product coming out of a fluidized bed dryer.  The remarkable thing about this virtual sensor is that it uses NO WATER INFORMATION as an input, but just screw feed and conveyor amps, dryer temperatures and pressures.  Data shown is "out-of-sample" (actual vs. estimated moisture on data not used to build the virtual sensor) and reflects true performance (98.1% accurate).  The available data is limited in quantity which is why you only see 12 data points in the out of sample results.

NOx Emissions
This virtual sensor shows the estimated (blue) and actual (red) NOx emissions per million BTUs for a gas and oil fired power plant.  Data shown is "out-of-sample" (actual vs. estimated moisture on data not used to build the virtual sensor) and reflects true performance (93% accurate).  Now granted the customer has an NOx emissions sensor in the stack and can calculate the NOx Per MM BTU, but this virtual sensor can back up the physical sensor in case of fouling or failure and the difference between the virtual sensor and the physical sensor can be monitored for abnormalcy, leading to alerts for investigation or repair.

How Do You Build and Implement Virtual Sensors?
You gather data that relate to the predicted result.  You then build and validate a mathematical model of the relationships using Process Modeler, our Virtual Sensor builder (request a free evaluation here).  You then place the model on-line using Process Intellect.  Process Intellect supports unlimited sensor implementations and can estimate properties sub-second, up to the performance limitations of the computer.

Note: All data shown here is REAL, but has been modified so as to not reveal any customer information.