Data reconciliation for dynamic processes

In a modern chemical plant, the implementation of a distributed control system leads to a large number of measurements that are available online for process monitoring, control, optimization and management decision making. Unfortunately, these measurements often contain errors that degrade the information quality obtained from the raw data. This thesis is dedicated to the development of dynamic data reconciliation (DDR) algorithms for the optimal estimation of variables in dynamic processes. More importantly, the DDR algorithms were implemented within the structures of feedback control loops, and the performance of the DDR algorithms as well as the controllers was quantitatively assessed via a series of process simulations. The DDR algorithms, acting as digital filters, were compared to commonly used filters, such as the exponentially weighted moving average (EWMA), moving average (MA), Kalman and extended Kalman filters. Methodologies to use the DDR algorithms to deal with autocorrelated noise were also investigated. The DDR algorithms integrate information from both measurements and process dynamic models such that, at each sampling time, the estimates obtained by the DDR algorithms provide more precise representations of the current state of the process. Three DDR algorithms were developed, namely, nonlinear programming (NLP) based DDR, predictor-corrector based DDR, and autoassociative neural network (AANN) based DDR. Evaluations of these DDR algorithms were conducted via simulations of three chemical processes, namely a cylindrical storage tank, a spherical storage tank and a binary distillation column. Results demonstrated that the DDR algorithms are efficient and effective tools for the estimation of dynamic processes. They perform significantly better than the EWMA and MA filters. Furthermore, compared to the Kalman filter, the DDR algorithm is easier to understand and to implement. Studies also showed that the structure of process models has considerable impact on the performance of the DDR. The use of the DDR algorithms embedded in feedback control loops significantly enhanced the controller performance. For example, the cost function of the control system in the distillation column was reduced by 28∼39% when linear, adaptive linear and nonlinear DDR algorithms were used. The cost function of the controller in the cylindrical storage tank was reduced by 46% using DDR, while it was reduced by 33% when using a EWMA filter