| Process measurements contain some degree of error that can be random or systematic. Random measurement noise is usually of high frequency, and results in high-frequency oscillations of manipulated variables that deteriorate the performance of the control system. Data reconciliation is a procedure that makes use of process models along with process measurements to give more precise and consistent estimates of process variables. Data reconciliation has been traditionally used to provide a more representative set of data to calculate steady-state inventories and process yields. For dynamic systems, the use of data reconciliation is relatively nascent. This work examined the potential use of data reconciliation in closed-loop control as a filter to attenuate the noise in measurements of the controlled variables so that the controllers can act on less variable, more accurate inputs. Data reconciliation filters were implemented in simulations of a PID control system for a binary distillation column.; The presence of measurement noise usually results in detuned controllers in order to prevent excessive high-frequency variations of manipulated variables. In this work, a Dynamic Data Reconciliation (DDR) algorithm, embedded within the structure of feedback control loops, was developed to reconcile noisy measurements. (Abstract shortened by UMI.)... |
