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Data Reconciliation (data + reconciliation)

Distribution by Scientific Domains

Chemistry	58%
Life Sciences	25%

Kinds of Data Reconciliation

dynamic data reconciliation

Selected Abstracts

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]

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]

Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry

BIOTECHNOLOGY & BIOENGINEERING, Issue 2 2010
Hilal Taymaz-Nikerel
Abstract Metabolic network models describing growth of Escherichia coli on glucose, glycerol and acetate were derived from a genome scale model of E. coli. One of the uncertainties in the metabolic networks is the exact stoichiometry of energy generating and consuming processes. Accurate estimation of biomass and product yields requires correct information on the ATP stoichiometry. The unknown ATP stoichiometry parameters of the constructed E. coli network were estimated from experimental data of eight different aerobic chemostat experiments carried out with E. coli MG1655, grown at different dilution rates (0.025, 0.05, 0.1, and 0.3,h,1) and on different carbon substrates (glucose, glycerol, and acetate). Proper estimation of the ATP stoichiometry requires proper information on the biomass composition of the organism as well as accurate assessment of net conversion rates under well-defined conditions. For this purpose a growth rate dependent biomass composition was derived, based on measurements and literature data. After incorporation of the growth rate dependent biomass composition in a metabolic network model, an effective P/O ratio of 1.49,±,0.26,mol of ATP/mol of O, KX (growth dependent maintenance) of 0.46,±,0.27,mol of ATP/C-mol of biomass and mATP (growth independent maintenance) of 0.075,±,0.015,mol of ATP/C-mol of biomass/h were estimated using a newly developed Comprehensive Data Reconciliation (CDR) method, assuming that the three energetic parameters were independent of the growth rate and the used substrate. The resulting metabolic network model only requires the specific rate of growth, µ, as an input in order to accurately predict all other fluxes and yields. Biotechnol. Bioeng. 2010;107: 369,381. © 2010 Wiley Periodicals, Inc. [source]

Data reconciliation of concentration estimates from mid-infrared and dielectric spectral measurements for improved on-line monitoring of bioprocesses

BIOTECHNOLOGY PROGRESS, Issue 2 2009
Michal Dabros
Abstract Real-time data reconciliation of concentration estimates of process analytes and biomass in microbial fermentations is investigated. A Fourier-transform mid-infrared spectrometer predicting the concentrations of process metabolites is used in parallel with a dielectric spectrometer predicting the biomass concentration during a batch fermentation of the yeast Saccharomyces cerevisiae. Calibration models developed off-line for both spectrometers suffer from poor predictive capability due to instrumental and process drifts unseen during calibration. To address this problem, the predicted metabolite and biomass concentrations, along with off-gas analysis and base addition measurements, are reconciled in real-time based on the closure of mass and elemental balances. A statistical test is used to confirm the integrity of the balances, and a non-negativity constraint is used to guide the data reconciliation algorithm toward positive concentrations. It is verified experimentally that the proposed approach reduces the standard error of prediction without the need for additional off-line analysis. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009 [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]

Integrated estimation of measurement error with empirical process modeling,A hierarchical Bayes approach

AICHE JOURNAL, Issue 11 2009
Hongshu Chen
Abstract Advanced empirical process modeling methods such as those used for process monitoring and data reconciliation rely on information about the nature of noise in the measured variables. Because this likelihood information is often unavailable for many practical problems, approaches based on repeated measurements or process constraints have been developed for their estimation. Such approaches are limited by data availability and often lack theoretical rigor. In this article, a novel Bayesian approach is proposed to tackle this problem. Uncertainty about the error variances is incorporated in the Bayesian framework by setting noninformative priors for the noise variances. This general strategy is used to modify the Sampling-based Bayesian Latent Variable Regression (Chen et al., J Chemom., 2007) approach, to make it more robust to inaccurate information about the likelihood functions. Different noninformative priors for the noise variables are discussed and unified in this work. The benefits of this new approach are illustrated via several case studies. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [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]