Historical Tank Content Estimate (HTCE) and sampling estimate comparisons
- and others
There has been a substantial effort over the years to characterize the waste content in Hanford`s waste tanks. This characterization is vital to future efforts to retrieve, pretreat, and dispose of the waste in the proper manner. The present study is being conducted to help escalate this effort. This study compares estimates from two independent tank characterization approaches. One approach is based on tank sampling while the other is based on historical records. In order to statistically compare the two independent approaches, quantified variabilities (or uncertainty estimates) around the estimates of the mean concentrations are required. For the sampling-based estimates, the uncertainty estimates are provided in the Tank Characterization Reports (TCR`s). However, the historically based estimates are determined from a model, and therefore possess no quantified variabilities. Steps must be taken to provide quantified variabilities for these estimates. These steps involve a parameter influence study (factorial experiment study) and an uncertainty analysis (Monte Carlo study) of the Historical Tank Content Estimate (HTCE). The purpose of the factorial experiment is to identify in the Hanford Defined Wastes (HDW) model which parameters, as they vary, have the largest effect on the HTCE. The results of this study provide the proper input parameters for the Monte Carlo study. The two estimates (HTCE and sampling-based) can then be compared. The purpose of the Monte Carlo study is to provide estimates of variability around the estimate derived the historical records.
- Research Organization:
- Pacific Northwest Lab., Richland, WA (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC06-76RL01830
- OSTI ID:
- 135038
- Report Number(s):
- PNL--10840; ON: DE96002679
- Country of Publication:
- United States
- Language:
- English
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Hanford Defined Wastes model
Characterization of Hanford waste and the role of historic modeling