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Dynamic Data Reconciliation (dynamic + data_reconciliation)

Distribution by Scientific Domains

Chemistry	80%

Selected Abstracts

Enhancing Controller Performance via Dynamic Data Reconciliation

THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 3 2005
Shuanghua Bai
Abstract Measured values of process variables are subject to measurement noise. The presence of measurement noise can result in detuned controllers in order to prevent excessive adjustments of manipulated variables. Digital filters, such as exponentially weighted moving average (EWMA) and moving average (MA) filters, are commonly used to attenuate measurement noise before controllers. In this article, we present another approach, a dynamic data reconciliation (DDR) filter. This filter employs discrete dynamic models that can be phenomenological or empirical, as constraints in reconciling noisy measurements. Simulation results for a storage tank and a distillation column under PI control demonstrate that the DDR filter can significantly reduce propagation of measurement noise inside control loops. It has better performance than the EWMA and MA filters, so that the overall performance of the control system is enhanced. Les valeurs mesurées des variables de procédé sont affectées par les bruits de mesure. La présence de bruit de mesure force de régler à la baisse les régulateurs afin de prévenir des mouvements excessifs des variables manipulées. Des filtres numériques, tels que les filtres à moyenne mobile pondérée exponentiellement (EWMA) et les filtres à moyenne mobile (MA), sont communément utilisés pour atténuer le bruit de mesure avant les régulateurs. On présente dans cet article une autre approche, soit un filtre dynamique de réconciliation de données (DDR). Ce filtre emploie des modèles dynamiques discrets qui peuvent être phénoménologiques ou empiriques comme contraintes pour réconcilier les mesures bruitées. Les résultats de simulation pour un réservoir de stockage et une colonne à distiller utilisant un régulateur PI montrent que le filtre DDR peut réduire de manière significative la propagation du bruit de mesure dans les boucles de régulation. Sa performance est meilleure que celles des filtres EWMA ou MA, et par conséquent la performance globale du système de commande s'en trouve accrue. [source]

A new framework for data reconciliation and measurement bias identification in generalized linear dynamic systems

AICHE JOURNAL, Issue 7 2010
Hua Xu
Abstract This article describes a new framework for data reconciliation in generalized linear dynamic systems, in which the well-known Kalman filter (KF) is inadequate for filtering. In contrast to the classical formulation, the proposed framework is in a more concise form but still remains the same filtering accuracy. This comes from the properties of linear dynamic systems and the features of the linear equality constrained least squares solution. Meanwhile, the statistical properties of the framework offer new potentials for dynamic measurement bias detection and identification techniques. On the basis of this new framework, a filtering formula is rederived directly and the generalized likelihood ratio method is modified for generalized linear dynamic systems. Simulation studies of a material network present the effects of both the techniques and emphatically demonstrate the characteristics of the identification approach. Moreover, the new framework provides some insights about the connections between linear dynamic data reconciliation, linear steady state data reconciliation, and KF. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source]

Recursive estimation in constrained nonlinear dynamical systems

AICHE JOURNAL, Issue 3 2005
Pramod Vachhani
In any modern chemical plant or refinery, process operation and the quality of product depend on the reliability of data used for process monitoring and control. The task of improving the quality of data to be consistent with material and energy balances is called reconciliation. Because chemical processes often operate dynamically in nonlinear regimes, techniques such as extended-Kalman filter (EKF) and nonlinear dynamic data reconciliation (NDDR) have been developed for reconciliation. There are various issues that arise with the use of either of these techniques. EKF cannot handle inequality or equality constraints, whereas the NDDR has high computational cost. Therefore, a more efficient and robust method is required for reconciling process measurements and estimating parameters involved in nonlinear dynamic processes. Two solution techniques are presented: recursive nonlinear dynamic data reconciliation (RNDDR) and a combined predictor,corrector optimization (CPCO) method for efficient state and parameter estimation in nonlinear systems. The proposed approaches combine the efficiency of EKF and the ability of NDDR to handle algebraic inequality and equality constraints. Moreover, the CPCO technique allows deterministic parameter variation, thus relaxing another restriction of EKF where the parameter changes are modeled through a discrete stochastic equation. The proposed techniques are compared against the EKF and the NDDR formulations through simulation studies on a continuous stirred tank reactor and a polymerization reactor. In general, the RNDDR performs as well as the two traditional approaches, whereas the CPCO formulation provides more accurate results than RNDDR at a marginal increase in computational cost. © 2005 American Institute of Chemical Engineers AIChE J, 51: 946,959, 2005 [source]

Simultaneous Data Reconciliation and Parameter Estimation in Bulk Polypropylene Polymerizations in Real Time

MACROMOLECULAR SYMPOSIA, Issue 1 2006
Diego Martinez Prata
Abstract This work presents the implementation of a methodology for dynamic data reconciliation and simultaneous estimation of quality and productivity parameters in real time, using data from an industrial bulk Ziegler-Natta propylene polymerization process. A phenomenological model of the real process, based on mass and energy balances, was developed and implemented for interpretation of actual plant data. The resulting nonlinear dynamic optimization problem was solved using a sequential approach on a time window specifically tuned for the studied process. Despite the essentially isothermal operation conditions, obtained results show that inclusion of energy balance constraints allows for increase of information redundancy and, as a consequence, for computation of better parameter estimates than the ones obtained when the energy balance constraints are not considered (Prata et al., 2005). Examples indicate that the proposed technique can be used very effectively for monitoring of polymer quality and identification of process malfunctions in real time even when laboratory analyses are scarce. [source]