Critical plant variables should be reliable and should be validated in real time.
Additional Information:
There are several methods of ensuring that critical variables are reliably presented to the operators. These methods should be used as appropriate to achieve a high data quality and veracity. Lack of data validation places the burden of identifying valid readings on the operator. One method of achieving this, would be to have an estimate of data quality and a data quality indicator associated with each critical variable, including derived synthetic variables. Other recommended methods include: range checks for failed instruments; comparison of redundant sensors; and analytical redundancy. Range checks for failed instruments can ensure that failed instruments are identified and that they are not averaged with other, valid readings, possibly masking the failed instrument. Comparing and possible averaging redundant instruments can improve the quality and reliability of data. Analytical redundancy refers to the intercomparison of measured variables, through the use of mathematical models based upon known physical relationships among variables to determine whether there are inconsistencies in the values of the measured variables. For example, 'reactor power,' 'reactor coolant temperature rise through the reactor core,' and 'reactor coolant flow rate' are interrelated variables based upon the physical principles of heat transfer. A measured value for coolant flow should be consistent with the analytically calculated value for coolant flow derived mathematically from the corresponding measured values of reactor power and coolant temperature rise.