Introduction

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Sampling Toolbox for Radiological Assessment To Enable Geostatistical and statistical Implementation with a Smart Tactic

The STRATEGIST web tool

  • Guides the expert in handling the problem definition and applying a strategy based on proper data analysis and sampling design.
  • Promotes the application of an integrated characterisation methodology and strategy during nuclear decommissioning and dismantling operations (D&D) of nuclear power plants, post-accidental land remediation or nuclear facilities under constrained environments.
  • Relies on state-of-the art statistical techniques for preliminary analysis and data processing.

The STRATEGIST web tool provides concise descriptions, lists relevant theoretical references, case studies and software implementations for helping the end user to get started, using the STRATEGIST diagrams:

Please do note the recommended browser for using this website is Google Chrome.

How should this strategy be applied?

  • For the application of this strategy, we recommend all involved parties to familiarize themselves with it, or at least the general workflow. This will ease the discussion on the different objectives and constraints, and create more realistic expectations on the work that has to be done.
  • People involved in the data analysis, and especially the selection of the appropriate methods, should at least have some general notions on all different types of methods discussed here, to enable proper judgement of the different options, and selection of the most appropriate one.
  • Please take into account the following important remarks:
    • This strategy is NOT intended to provide the nonspecialist with a comprehensive mode of operation for the complete process of initial nuclear state characterisation in view of decommissioning.
    • This is only a guideline, and should not be blindly followed. Special circumstances often ask for special solutions, which cannot all be covered by a generic strategy.
    • This strategy can be used to inform people with no or very little experience in statistics about the complexity of the issue, and provide them some relevant background, but it cannot justify not involving people experienced in the matter.

For the vocabulary used in the STRATEGIST tool we refer to the VIM (BIPM, IEC, IFCC, ILAC, ISO, IUPAC , IUPAP et OIML, International vocabulary of metrology – Basic and general concepts and associated terms (VIM), 3rd edition éd., Joint Committee for Guides in Metrology, JCGM 200:2012, 2012).

In the use cases section you can find details on the implementation of the strategy in four different use cases as well as a summary of the lessons learnt.

Overall strategy

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Data analysis & sampling design

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List of methods for data analysis

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List of approaches for sampling design

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Use cases introduction

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Intro

The INSIDER project aimed at developing and validating an improved integrated methodology of characterization based on different new statistical processing and modelling, coupled with present (and adapted) analytical and measurement methods, with respect to sustainability and economic objectives. For the validation of the STRATEGIST web tool, we used the below facilities and situations from ongoing D&D projects managed by consortium partners and further defined as use cases (UC). Further details are provided in four separate documents, as well as a summary of the lessons learnt.

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Use case 1 summary

UC1, concerns two tanks (VA001 and VA002), each about 50 m3 in volume, containing low level liquid waste (LLLW) and located in the liquid waste storage facility at the Joint Research Centre site of Ispra, Italy. The specific activities of the radionuclides present are between a few tenths to just over 100 Bq/g (2012-2013) for relevant nuclides, which include gamma emitters Co-60, Cs-137 and Am-241 as well as various alpha-and beta emitters.The licensee had not specified any waste acceptance criteria for this waste. Moreover, no information was available on what the conditioning process prior to waste acceptance should be. Within the INSIDER project, we therefore defined the following artificial objective: characterize the radionuclide content of the tanks, in view of deciding if the waste acceptance criteria for the selected waste category could be met. As a basis, we used the waste acceptance criteria for the Konrad repository (Germany).

More details.

Use case 2 summary

UC2 involves the biological shield of the Belgian Reactor 3 (BR3), a pilot pressurized water reactor of the SCK CEN (Belgian Nuclear Research Centre). The reinforced concrete shielding represents a volume of about 622 m³ (> 2000 tons). The concrete close to the reactor pressure vessel is activated. The main goal was to develop a radiological characterization program aiming at economically optimizing the biological shield dismantling strategy using a waste-led approach.

More details.

Use case 3a summary

UC3a relates to a nuclear facility that was devoted to radiochemistry on trans-uranium elements. It was under operation until 1992 on a CEA site in France. Due to different incidents decades ago, the soil beneath the tank room is contaminated with various alpha and beta emitters up to several TBq. For the preparation and management of a soil remediation project, some global quantities such as the average activity concentrations and total activity for the whole area (as well as its related uncertainty and confidence level) need to be estimated in a sound way.

More details.

Use case 3b summary

UC3b covers the graphite moderator and reflector (about 1300 tons) of the G2 UNGG reactor localized at the CEA site of Marcoule. The main objective is to provide a radiological inventory for the graphite volume. Some specific nuclides will be of key interest from a waste disposal perspective. A secondary objective is waste oriented with the classification of volumes according to different thresholds.

More details.

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UC1 - Liquid waste storage tanks

UC2 - Biological shield of the BR3 reactor

UC3a - Contaminated soil

UC3b - Activated graphite

Lessons learnt

Bibliography

Bibliography

  • Campolongo, F., Saltelli, A., & Cariboni, J. (2011). From screening to quantitative sensitivity analysis. A unified approach. Computer Physics Communications, 182(4), pp. 978-988.
  • Cukier, R., Fortuin, C., Shuler, K., Petschek, A., & Schaibly, J. (1973). Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory. The Journal of chemical physics, 59(8), pp. 3873-3878.
  • Helsel, D., & Cohn, T. (1988). Estimation of Descriptive Statistics for Multiple Censored Water Quality data. Water Resources Research Vol. 24, 1997-2004.
  • Helton, J. (1993). Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal. Reliability Engineering & System Safety, 42(2-3), pp. 327-367.
  • Iman, R., & Hora, S. (1990). A robust measure of uncertainty importance for use in fault tree system analysis. Risk analysis, 10(3), pp. 401-406.
  • Journel, A., & Huijbrechts, C. (1978). Mining Geostatistics. Academic Press, London, 600 p.
  • Kendall, M. (1938). A New Measure of Rank Correlation. Biometrika. 30 (1–2): 81–89. doi:10.1093/biomet/30.1-2.81.
  • Kim, J. H., Hornibrook, C., & Yim, M.-S. (2020). The impact of below detection limit samples in residual risk assessments for decommissioning nuclear power plant sites. Journal of Environmental Radioactivity, 222(ISSN 0265-931X, https://doi.org/10.1016/j.jenvrad.2020.106340), 106340.
  • Morris, M. (1991). Factorial sampling plans for preliminary computational experiments. Technometrics, 33, 161-174.
  • Park, C., & Ahn, K.-I. (1994). A new approach for measuring uncertainty importance and distributional sensitivity in probabilistic safety assessment. Reliability Engineering & System Safety, 46(3), pp. 253-261.
  • Pérot, N. (2020). France Patent No. 20 10176.
  • Pérot, N., Desnoyers, Y., Augé, G., Aspe, F., Boden, S., Rogiers, B., . . . de Groot, J. (2019). WP3 –Sampling strategy: state-of-the-art report. EC H2020 INSIDER project, http://insider-h2020.eu/, Deliverable D3.1.
  • Plischke, E., Borgonovo, E., & Smith, C. (2013). Global sensitivity measures from given data. European Journal of Operational Research, 226(3), pp.536-550.
  • Sacks, J., Schiller, S., & Welch, W. (1989). Designs for computer experiments. Technometrics, 1(1), pp. 41-47.
  • Saltelli, A., & Marivoet, J. (1990). Non-parametric statistics in sensitivity analysis for model output: a comparison of selected techniques. Reliability Engineering & System Safe, 28(2), pp. 229-253.
  • Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., . . . Tarantola, S. (2008). Global sensitivity analysis: the primer. robability and Statistics. John Wiley & Sons, Chichester NY.
  • Saltelli, A., Tarantola, S., & Chan, K. (1999). A quantitative model-independent method for global sensitivity analysis of model output. Technometrics, 41(1), pp. 39-56.
  • Shao, Q., Younes, A., Fahs, M., & Mara, T. (2017). Bayesian sparse polynomial chaos expansion for global sensitivity analysis. omputer Methods in Applied Mechanics and Engineering, 318, pp. 474-496.
  • Sobol’, I. (1993). Sensitivity analysis for non-linear mathematical models. Mathematical Modelling and Computational Experiment, 1, pp. 407-414.
  • Sudret, B. (2008). Global sensitivity analysis using polynomial chaos expansions. Reliability engineering & system safety, 93(7), pp. 964-979.

About

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About

The STRATEGIST web tool has been developed within work package 3 of the INSIDER EU Horizon 2020 project and received funding from the Euratom Research and Training Programme 2014-2018 under grant agreement No 755554.

The development consisted of:

  1. Providing an overview of the available sampling design methods and state-of-the-art techniques.
  2. Development of a strategy for data analysis and sampling design, referring to state-of-the-art techniques, and provide guidance to the end user through an application in which the strategy contents can be explored in a user-friendly way.
  3. Implementation and validation of the strategy in the following four use cases:
    1. Use case 1 concerns two tanks (VA001 and VA002), each about 50 m3 in volume, containing low level liquid waste (LLLW) and located in the liquid waste storage facility at the Joint Research Centre site of Ispra, Italy.
    2. Use case 2 involves the biological shield of the Belgian Reactor 3 (BR3), a pilot pressurized water reactor of the SCK CEN (Belgian Nuclear Research Centre).
    3. Use case 3a relates to a nuclear facility that was devoted to radiochemistry on trans-uranium elements. It was under operation until 1992 on a CEA site in France.
    4. Use case 3b covers the graphite moderator and reflector (about 1300 tons) of the G2 UNGG reac-tor localized at the CEA site of Marcoule.
  4. Summarizing the lessons learnt from the different use cases.
  5. Designing the STRATEGIST web tool.

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Cite

To cite the STRATEGIST web tool, use the following:

Rogiers B., Desnoyers Y., Pérot N., von Oertzen G., Sevbo O., Demeyer S., Boden S. 2022. STRATEGIST - Sampling Toolbox for Radiological Assessment To Enable Geostatistical and statistical Implementation with a Smart Tactic. https://strategist.sckcen.be, visited on