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New Methodology for Bias Identification and Estimation – Application to Nuclear Fuel Recycling Process

Duterme, Amandine and Montuir, Marc and Dinh, Binh and Bisson, Julia and Vigier, Nicolas and Floquet, Pascal and Joulia, Xavier New Methodology for Bias Identification and Estimation – Application to Nuclear Fuel Recycling Process. (2019) Computer Aided Chemical Engineering, 46. 1363-1368. ISSN 1570-7946

(Document in English)

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Official URL: https://doi.org/10.1016/B978-0-12-818634-3.50228-9


This paper focuses on the data reconciliation technique (DR) in case of numerous biases. DR improves the degree of confidence in available information and generates consistent data. The inventory and analysis of the plant data (position and type of sensors …) enable an evaluation of the process redundancy. Classical Gross Error Detection and Identification (GEDI) techniques delete the biased variables, decreasing the redundancy. This leads to information loss and possibly an inability to apply DR. The methodology proposed here combines DR, based on a reduced model, and rigorous simulations to locate and estimate multiple biases and to make data consistent in case of inter-connected flows. This methodology is applied to the nuclear fuel recycling process within the scope of a state estimation tool built on a process simulation code.

Item Type:Article
HAL Id:hal-02283390
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Commissariat à l'Energie Atomique et aux énergies alternatives - CEA (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > ORANO (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Laboratory name:
Deposited On:10 Sep 2019 15:34

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