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.