Efficient data reconciliation and estimation for dynamic processes using nonlinear programming techniques

Leibman, M. J.; Edgar, Thomas F.; Lasdon, Leon S.

doi:10.1016/0098-1354(92)80030-d

Cited by 229 publications

(192 citation statements)

References 21 publications

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“…(4), may impose process variable limits. For more details one can refer to the paper by Liebman et al (1992).…”

Section: The Original Nddr Problem Formulation and Solution Strategymentioning

confidence: 99%

“…Their approach was based on simultaneous optimization and solution techniques where efficient state estimation was performed. There was no GED and identification included in the NDDR approach of Liebman et al (1992), but, since then, to complete their work, there have been some new approaches which are capable of detection of gross errors, or, more specifically, identification of gross errors as well. For instance, Soderstrom et al (2001) proposed an approach to simultaneously tackle the problem of GED and identification together with DR. Abu-elzeet et al (2002) proposed a combined method of bias and outlier identification in dynamic DR where a history of all previous bias detection and identification methods is briefly presented.…”

Section: Data Reconciliaton Backgroundmentioning

confidence: 99%

“…In the next step Liebman et al (1992) developed their main NDDR algorithm. Their approach was based on simultaneous optimization and solution techniques where efficient state estimation was performed.…”

Section: Data Reconciliaton Backgroundmentioning

confidence: 99%

“…The solution adopted by Liebman et al (1992) is a moving horizon approach which enables the user to utilize all the information at hand (process measurements) from start-up to the current time. This moving horizon window (MHW) approach has the advantage of reduced optimization problem size while giving the user the advantage of having only one tuning parameter, the window horizon (H), compared to other nonlinear approaches such as the EKF where more tuning parameters need to be adjusted.…”

Section: The Original Nddr Problem Formulation and Solution Strategymentioning

confidence: 99%

“…The original NDDR formulation was developed in Liebman et al (1992), and it may be re-stated as follows: …”

Section: The Original Nddr Problem Formulation and Solution Strategymentioning

confidence: 99%

See 4 more Smart Citations

ANDDR with novel gross error detection and smart tracking system

Laylabadi¹,

Taylor²

2006

Information Control Problems in Manufacturing 2006

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Data reconciliation is a well-known method in on-line process control engineering aimed at estimating the true values of corrupted measurements under constraints. An adaptive nonlinear dynamic data reconciliation (ANDDR) method is proposed that includes the application to processes with an unknown statistical model. ANDDR enables gross error detection (GED) as well. Finally, a novel smart tracking system is combined to ameliorate the problem of delay seen in both the original and later NDDR methods. This package with its smart tracking features is suggested for use in distributed control systems (DCSs) for process control and manufacturing applications such as paper making.

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“…(4), may impose process variable limits. For more details one can refer to the paper by Liebman et al (1992).…”

Section: The Original Nddr Problem Formulation and Solution Strategymentioning

confidence: 99%

Section: Data Reconciliaton Backgroundmentioning

confidence: 99%

Section: Data Reconciliaton Backgroundmentioning

confidence: 99%

Section: The Original Nddr Problem Formulation and Solution Strategymentioning

confidence: 99%

“…The original NDDR formulation was developed in Liebman et al (1992), and it may be re-stated as follows: …”

Section: The Original Nddr Problem Formulation and Solution Strategymentioning

confidence: 99%

See 3 more Smart Citations

ANDDR with novel gross error detection and smart tracking system

Laylabadi¹,

Taylor²

2006

Information Control Problems in Manufacturing 2006

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Diagnostic and Control of Energy Systems in Power Generation Systems and Process Industries

Dahlquist

Avelin

2015

Handbook of Clean Energy Systems

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A lot of efforts are normally done to design plants in an efficient way. Still, most plants are having varying production qualities and quantities and both the diagnostics of sensors and process performance and advanced control for optimal production are essential. Useful methods and applications of these for both power plant and process industry applications are presented. The benefits of using, for example, simulators are described in connection with start‐up of new plants and the importance of diagnostics for maintenance of demand. Examples are shown where statistical methods are used to predict pulp paper quality properties and physical models to perform data reconciliation and decision support. The data reconciliation predicts true values for sensors that are drifting while decision support is giving advice when any kind of possible problem has been detected. In addition, process optimization and model‐based control are discussed.

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Process Monitoring and Data Reconciliation

Heyen¹,

Kalitventzeff²

2006

Computer Aided Process and Product Engineering

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Efficient data reconciliation and estimation for dynamic processes using nonlinear programming techniques

Cited by 229 publications

References 21 publications

ANDDR with novel gross error detection and smart tracking system

ANDDR with novel gross error detection and smart tracking system

Diagnostic and Control of Energy Systems in Power Generation Systems and Process Industries

Process Monitoring and Data Reconciliation

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