On-line tracking simulator with a hybrid of physical and Just-In-Time models

Nakaya, Makoto; Li, Xinchun

doi:10.1016/j.jprocont.2012.06.007

Cited by 20 publications

(17 citation statements)

References 6 publications

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“…In contrast, existing SBDTs [4], [6], [27], [105] rely on online FPMs based on an up-to-date condition of the physical plant that can be built from plant design data and that can be applied to explore operating regions not captured by the process measurement systems. SBDTs are based on online model-based applications (OMBAs) [10], which consist of an online simulation configuration that runs a dynamic FPM in parallel with the modelled plant, while being controlled by the plant control application.…”

Section: Digital Twinsmentioning

confidence: 99%

“…Similar to OMBAs, tracking simulators consist of dynamic FPMs, which are continuously adjusted by dynamic estimation to synchronize the states of the model and the physical plant. These systems provide information that can be used for supporting operator training [113], process operation [35], process maintenance [105] and plant optimization [114]. Tracking simulators serve as the SBDTs for process plants, as they provide a holistic set of simulation-based applications for operation support.…”

Section: Tracking Simulation Systemsmentioning

confidence: 99%

“…However, the drawbacks of tracking simulators in terms of expensive and laborious development of FPMs, restrict broader adoption of these systems in industry [10]. Existing systems [4], [6], [27], [105] have mainly focused on proposing different methods for parameter estimation of FPMs. Moreover, no work has attempted to address issues related to time consuming model development or laborious implementation of tracking simulators.…”

Section: Tracking Simulation Systemsmentioning

confidence: 99%

“…To further improve the model results, a tracking simulation system is presented in [105] that applies offline dynamic data reconciliation [115] for adjusting multiple model parameters. This and other existing model optimization methods could be applied not only to aid online dynamic estimation but also to optimize existing outdated models or models generated automatically based on AMG methods.…”

Section: Tracking Simulation Systemsmentioning

confidence: 99%

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Automatic Generation of a Simulation-Based Digital Twin of an Industrial Process Plant

Martínez

Sierla

Karhela

et al. 2018

IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

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The author wrote the manuscript in collaboration with Mr. Miettinen, Mr. Aikala and Mr. Savolainen. The author implemented and tested the proposed method on the Aalto University laboratory process under the guidance of Dr. Karhela and Prof. Vyatkin. Publication IV: "Sliding Mode SISO Control of Model Parameters for Implicit Dynamic Feedback Estimation of Industrial Tracking Simulation Systems" The author wrote the manuscript in collaboration with Mr. Ruusu. Mr. Ruusu developed the conceptual designed of the proposed method and implemented the parameter controller. The author implemented and tested the proposed method on the Aalto University laboratory process under the guidance of Dr. Karhela and Prof. Vyatkin.

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Section: Digital Twinsmentioning

confidence: 99%

Section: Tracking Simulation Systemsmentioning

confidence: 99%

Section: Tracking Simulation Systemsmentioning

confidence: 99%

Section: Tracking Simulation Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Generation of a Simulation-Based Digital Twin of an Industrial Process Plant

Martínez

Sierla

Karhela

et al. 2018

IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

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“…Soft-sensor structures according to how property variables are estimated by the combination of white and black box models[13].…”

mentioning

confidence: 99%

Data-driven development and maintenance of soft-sensors

Abonyi

Farsang

Kulcsár

2014

2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

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Product quality related process variables have significant role in advanced process control (APC). Online analyzers and software sensors can provide accurate and timely information for APC systems. In this paper we give an overview of data based soft-sensor development. We show that soft-sensor models of APC require maintenance and demonstrate that statistical quality control (SQC) techniques can be effectively used to automatize the related fault detection tasks. I. INTRODUCTIONAdvanced process control systems should have the following functionalities to ensure and continuously improve product quality [1]:• prediction of product quality from operating conditions, • optimisation of operating conditions to improve product quality, and • detection of faults or malfunctions for preventing undesirable operation. These functionalities require timely and accurate information about process variables characterizing and influencing product quality. Offline laboratory tests mostly take more than two hours which time delay can cause control problems resulting economic loss. Online analysers have faster response time (1-4 minutes) and can eliminate the dependence on laboratory data by providing timely information for corrective action or real time control (see Fig. 1) [2]. However, due to the high instrumentation and maintenance costs and low reliability of online analyzers there is a need for an easily implementable, maintainable and robust alternative.

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