Sigmafine Analysis and Data Reconciliation
The key to the Sigmafine analysis suite is the concept of data reconciliation. Data reconciliation is the method that Sigmafine uses to resolve inconsistencies between plant measurement data and known heat, volume, density and mass balances. The method is based on a "Kalman" filtering algorithm, a fast, stable solver that uses a least squares formulation to quantify the error in the Model measurements. The algorithm applied in different scenarios can analyze physical temperature and pressure measurements, as well as the properties and material compositions of the flows in the process.
Considering the number of operations being performed in a typical process plant, obtaining the precise measurements for all the streams associated with various processes is extremely difficult. Since many plants have hundreds of streams and use a variety of instrument types to monitor them, verifying the accuracy of the reported data becomes a monumental task.
Factors that contribute to erroneous data include the following.
- Faulty instruments
- Incomplete instrumentation
- Incorrectly installed instruments
- Out-of-service instruments
- Interference
- Incorrect instrument calibration
- Unaccounted or unmeasured flows into or out of the plant
Sigmafine's data reconciliation capability enables you to reduce the time required to verify questionable data and identify error sources that require correction.