A Bayesian framework to update scaling factors for radioactive waste characterization
Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zaffora, Biagio [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Time-dependent shape factors for fractured reservoir simulation: Effect of stress sensitivity in matrix system - Wang, Lu ELSEVIER, 2018, a journal of nuclear and radiation techniques and their applications in the physical, chemical, biological, medical, earth, planetary, environmental and engineering science, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:159 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.apradiso.2020.109092 |
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ELV049877038 |
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| 520 | |a Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. | ||
| 520 | |a Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. | ||
| 650 | 7 | |a Characterization |2 Elsevier | |
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| 650 | 7 | |a Scaling factor |2 Elsevier | |
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| 700 | 1 | |a Ronchetti, Elvezio |4 oth | |
| 700 | 1 | |a Saporta, Gilbert |4 oth | |
| 700 | 1 | |a Theis, Chris |4 oth | |
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10.1016/j.apradiso.2020.109092 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001045.pica (DE-627)ELV049877038 (ELSEVIER)S0969-8043(20)30017-8 DE-627 ger DE-627 rakwb eng 660 VZ 38.51 bkl 57.36 bkl Zaffora, Biagio verfasserin aut A Bayesian framework to update scaling factors for radioactive waste characterization 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. Characterization Elsevier Radioactive waste Elsevier Scaling factor Elsevier Bayes Elsevier Demeyer, Severine oth Magistris, Matteo oth Ronchetti, Elvezio oth Saporta, Gilbert oth Theis, Chris oth Enthalten in Elsevier Science Wang, Lu ELSEVIER Time-dependent shape factors for fractured reservoir simulation: Effect of stress sensitivity in matrix system 2018 a journal of nuclear and radiation techniques and their applications in the physical, chemical, biological, medical, earth, planetary, environmental and engineering science Amsterdam [u.a.] (DE-627)ELV001919369 volume:159 year:2020 pages:0 https://doi.org/10.1016/j.apradiso.2020.109092 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 38.51 Geologie fossiler Brennstoffe VZ 57.36 Erdölgewinnung Erdgasgewinnung VZ AR 159 2020 0 |
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10.1016/j.apradiso.2020.109092 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001045.pica (DE-627)ELV049877038 (ELSEVIER)S0969-8043(20)30017-8 DE-627 ger DE-627 rakwb eng 660 VZ 38.51 bkl 57.36 bkl Zaffora, Biagio verfasserin aut A Bayesian framework to update scaling factors for radioactive waste characterization 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. Characterization Elsevier Radioactive waste Elsevier Scaling factor Elsevier Bayes Elsevier Demeyer, Severine oth Magistris, Matteo oth Ronchetti, Elvezio oth Saporta, Gilbert oth Theis, Chris oth Enthalten in Elsevier Science Wang, Lu ELSEVIER Time-dependent shape factors for fractured reservoir simulation: Effect of stress sensitivity in matrix system 2018 a journal of nuclear and radiation techniques and their applications in the physical, chemical, biological, medical, earth, planetary, environmental and engineering science Amsterdam [u.a.] (DE-627)ELV001919369 volume:159 year:2020 pages:0 https://doi.org/10.1016/j.apradiso.2020.109092 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 38.51 Geologie fossiler Brennstoffe VZ 57.36 Erdölgewinnung Erdgasgewinnung VZ AR 159 2020 0 |
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10.1016/j.apradiso.2020.109092 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001045.pica (DE-627)ELV049877038 (ELSEVIER)S0969-8043(20)30017-8 DE-627 ger DE-627 rakwb eng 660 VZ 38.51 bkl 57.36 bkl Zaffora, Biagio verfasserin aut A Bayesian framework to update scaling factors for radioactive waste characterization 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. Characterization Elsevier Radioactive waste Elsevier Scaling factor Elsevier Bayes Elsevier Demeyer, Severine oth Magistris, Matteo oth Ronchetti, Elvezio oth Saporta, Gilbert oth Theis, Chris oth Enthalten in Elsevier Science Wang, Lu ELSEVIER Time-dependent shape factors for fractured reservoir simulation: Effect of stress sensitivity in matrix system 2018 a journal of nuclear and radiation techniques and their applications in the physical, chemical, biological, medical, earth, planetary, environmental and engineering science Amsterdam [u.a.] (DE-627)ELV001919369 volume:159 year:2020 pages:0 https://doi.org/10.1016/j.apradiso.2020.109092 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 38.51 Geologie fossiler Brennstoffe VZ 57.36 Erdölgewinnung Erdgasgewinnung VZ AR 159 2020 0 |
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10.1016/j.apradiso.2020.109092 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001045.pica (DE-627)ELV049877038 (ELSEVIER)S0969-8043(20)30017-8 DE-627 ger DE-627 rakwb eng 660 VZ 38.51 bkl 57.36 bkl Zaffora, Biagio verfasserin aut A Bayesian framework to update scaling factors for radioactive waste characterization 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. Characterization Elsevier Radioactive waste Elsevier Scaling factor Elsevier Bayes Elsevier Demeyer, Severine oth Magistris, Matteo oth Ronchetti, Elvezio oth Saporta, Gilbert oth Theis, Chris oth Enthalten in Elsevier Science Wang, Lu ELSEVIER Time-dependent shape factors for fractured reservoir simulation: Effect of stress sensitivity in matrix system 2018 a journal of nuclear and radiation techniques and their applications in the physical, chemical, biological, medical, earth, planetary, environmental and engineering science Amsterdam [u.a.] (DE-627)ELV001919369 volume:159 year:2020 pages:0 https://doi.org/10.1016/j.apradiso.2020.109092 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 38.51 Geologie fossiler Brennstoffe VZ 57.36 Erdölgewinnung Erdgasgewinnung VZ AR 159 2020 0 |
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10.1016/j.apradiso.2020.109092 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001045.pica (DE-627)ELV049877038 (ELSEVIER)S0969-8043(20)30017-8 DE-627 ger DE-627 rakwb eng 660 VZ 38.51 bkl 57.36 bkl Zaffora, Biagio verfasserin aut A Bayesian framework to update scaling factors for radioactive waste characterization 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. Characterization Elsevier Radioactive waste Elsevier Scaling factor Elsevier Bayes Elsevier Demeyer, Severine oth Magistris, Matteo oth Ronchetti, Elvezio oth Saporta, Gilbert oth Theis, Chris oth Enthalten in Elsevier Science Wang, Lu ELSEVIER Time-dependent shape factors for fractured reservoir simulation: Effect of stress sensitivity in matrix system 2018 a journal of nuclear and radiation techniques and their applications in the physical, chemical, biological, medical, earth, planetary, environmental and engineering science Amsterdam [u.a.] (DE-627)ELV001919369 volume:159 year:2020 pages:0 https://doi.org/10.1016/j.apradiso.2020.109092 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 38.51 Geologie fossiler Brennstoffe VZ 57.36 Erdölgewinnung Erdgasgewinnung VZ AR 159 2020 0 |
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Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. |
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Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. |
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Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin. |
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