Enhancing model predictive control using dynamic data reconciliation

Abu-el-Zeet, Z. H.; Roberts, P.D.; Becerra, Victor M.

doi:10.1002/aic.690480216

Cited by 23 publications

(15 citation statements)

References 31 publications

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“…Abu-el-zeet, et al, 2002 andZhu, et al, 2004) have discussed process control applications that contain dynamic data reconciliation, in particular advanced control strategies, such a model predictive control (MPC). In this paper one potential application of dynamic data reconciliation is the filtering of control signals, particularly the filtering of noise for the derivative action of controllers.…”

Section: Process Control Applicationmentioning

confidence: 97%

“…The dynamic data reconciliation problem will be solved utilizing the moving horizon estimator (MHE), proposed by Abu-el-zeet et al, (2002). This estimation problem is defined as a non-linear dynamic optimization problem with a discrete time performance index and a continuous time model with constraints.…”

Section: Dynamic Data Reconciliation and Moving Horizon Estimatormentioning

confidence: 99%

See 1 more Smart Citation

On-Line Dynamic Data Reconciliation Incorporating Dynamic Simulation

Tellez-Schmill¹,

Svrcek²,

Young³

2007

Chemical Product and Process Modeling

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The formulations for dynamic data reconciliation found in the literature assume that the error minimization problem is solved as a non-linear optimization problem constrained by the material and energy balances in the form of time-dependant non-linear differential equations. In this paper we introduce a new approach or an unconstrained formulation of this problem using a dynamic process simulator or model calculator and its potential application to chemical process control.Brought to you by | University of Connecticut Authenticated Download Date | 5/30/15 2:36 PM

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Section: Process Control Applicationmentioning

confidence: 97%

Section: Dynamic Data Reconciliation and Moving Horizon Estimatormentioning

confidence: 99%

On-Line Dynamic Data Reconciliation Incorporating Dynamic Simulation

Tellez-Schmill¹,

Svrcek²,

Young³

2007

Chemical Product and Process Modeling

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“…Indeed, data reconciliation methods have been employed in control systems either with the goal of minimizing the noise level in process measurements or with the target of detecting faults in combination with data-based fault detection methods. In particular, in [17,18], data reconciliation was exploited to reduce the noise level in measurements and improve the performance of the control strategy for a continuous stirred tank. In [19], data reconciliation was applied with the same purpose in a distributed control system for a distillation column.…”

Section: Introductionmentioning

confidence: 99%

Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler

et al. 2017

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This paper presents a novel, effective method to handle critical sensor faults affecting a control system devised to operate a biomass boiler. In particular, the proposed method consists of integrating a data reconciliation algorithm in a model predictive control loop, so as to annihilate the effects of faults occurring in the sensor of the flue gas oxygen concentration, by feeding the controller with the reconciled measurements. Indeed, the oxygen content in flue gas is a key variable in control of biomass boilers due its close connections with both combustion efficiency and polluting emissions. The main benefit of including the data reconciliation algorithm in the loop, as a fault tolerant component, with respect to applying standard fault tolerant methods, is that controller reconfiguration is not required anymore, since the original controller operates on the restored, reliable data. The integrated data reconciliation-model predictive control (MPC) strategy has been validated by running simulations on a specific type of biomass boiler-the KPA Unicon BioGrate boiler.

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“…Ramamurthi et al (1993) proposed a DDR algorithm that led to better closed-loop performance for a nonlinear predictive controller. Abu-el-zeet et al (2002) claimed that DDR, in conjunction with systematic bias detection, enhanced a model predictive control scheme. However, the degree of improvement for the controller performance was not specifi ed.…”

mentioning

confidence: 99%

Enhancing Controller Performance via Dynamic Data Reconciliation

2008

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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.

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Enhancing model predictive control using dynamic data reconciliation

Cited by 23 publications

References 31 publications

On-Line Dynamic Data Reconciliation Incorporating Dynamic Simulation

On-Line Dynamic Data Reconciliation Incorporating Dynamic Simulation

Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler

Enhancing Controller Performance via Dynamic Data Reconciliation

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