1. Stochastic-Based Accuracy of Data Reconciliation Estimators for Linear Systems.
Nguyen Tanth, DuyQuang and Miguel Bagajewicz, Chemical, Biological and Materials Engineering, University of Oklahoma. Norman OK 73019
Purpose: Given a set of sensors measuring process variables (flows, temperature, pressure, etc.) and a data reconciliation protocol in place, we explore the expected value of accuracy.
We define accuracy as the sum of the precision (random errors) and the bias. Bias is in turn induced through data reconciliation, and originated in sensors as follows.
Types of biases emerging at random times having random size
Deterministic (not random ) bias
Sensor
output
Sensor
output
Concave
shape
Convex
shape
Sensor output
Asymptotic
shape
Bias size
Bias size
Time
Time
Time
Sudden fixed bias
Random drifts
Deterministic drifts
Snapshot of one Monte Carlo Simulation
THE RESULT IS THAT FOR EACH VARIABLE IN THE FLOWSHEET ONE GETS AN AVERAGE VALUE OF DEVIATION FROM THE TRUE VALUE OVER A PERIOD OF TIME.