Cource

Clearance during Decommissioning with Statistical Models

A course on clearance using statistical models when handling large quantities of material. We delve into the statistical clearance methods developed through the decommissioning projects of the Uranium milling and mining facillity Ranstad, m/s Sigyn, Old Hospital Karolinska, and NPP Ågesta. The course covers effective clearance of materials and building structures.

Cource Objectivs

The goal of the course is to provide a basic understanding of which statistical models are effective in which contexts and how they can be combined with regular clearance measurements. The course includes many examples and exercises to equip participants with the tools they need to perform clearance using statistics.

Target Groups

Individuals who have previously taken the MARSSIM course or worked with Bayesian statistics and who work within the Swedish nuclear market.

Prerequisites

• Previously completed course on clearance
• University education at master's level

Course instructor

Jonatan Jiselmark

Content

• SSMFS 2018:3
  - Quick refresher on how the clearance and exemption regulations are structured in Sweden
• Clearance from an operational and decommissioning perspectiveFriklassning i ett drifts- och i ett avvecklingsperspektiv
• Risks and responsibilities in advanced clearance
• Strengths and weaknesses of different statistical approaches
  - Bayesian
  - MARSSIM
  - MCSOF
• Economic differences between different statistical approaches
  - Bayesian
  - MARSSIM
  - MCSOF
• SMARTSIM a combination of Bayes, MARSSIM and non statistical methods
  - Choosing a statistical model
  - Following the Bayes model for large materials 
  - Following MARSSIM's statistical models for large materials
• Examples and exercises
  - Planning
  - Choice of detectors
  - Nuclide vectors 
  - Measurement time
  - Statistical evaluation

Course duration

4 days

Cost per participants

20 000 SEK (ex. VAT)

Course Documentation

Presented material on paper and in electronic form (pdf)