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Challenges include: The multiscale interactions among water, microbial respiration, and sediment under realistic riverbed conditions; The transferability of the scientific understanding of interactions at multiple sites; and the upscaling of these interactions from pore to reach scales.
This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cycle, biogeochemistry, drivers and responses in the Earth system, and data-model integration.
In preparation for implementing the NG acid flowsheet for the SRS LWS, analytical methods for determining glycolate at low concentration are desired to support system management and safety. Previous work documented that the IC method performed well for samples with low to moderate ionic strength. That work also included a scoping effort to determine if an alternate analytical strategy using H NMR would complement and extend the capabilities of the IC method. The H NMR scoping results indicated that the method had the potential to expand glycolate analysis in LWS samples to higher ionic strength tanks/solutions. The scope of this work is to develop innovative proton NMR techniques, including demonstrating ion exchange decontamination protocols needed for application of the technique to real waste samples. Part of radioactive waste processing at SRS uses formic acid to reduce oxidized (Hg 2+ ) to more volatile elemental Hg for steam striping, collecting, and disposal. Under acidic conditions found in the Chemical Processing Cell (CPC) at the Defense Waste Processing Facility (DWPF), formic acid has a much higher hydrogen generation rate than an alternative reductant, glycolic acid. Thus, an NG acid flowsheet has been developed utilizing glycolic acid with the benefit of easing the need for headspace monitoring requirements for hydrogen and ammonia at DWPF. Low concentrations of glycolate are conservatively assumed to be in the recycle stream, which will collect in the Recycle Collection Tank (RCT). The DWPF recycle stream collected in the RCT has a distinct pathway to the LWS waste tanks that feed the 2H and 3H Evaporator. This route involves transfer of the DWPF recycle to Tank 22 in the Concentration, Storage, and Transfer Facilities (CSTF) followed by transfer to the LWS tank farm/evaporators.
C/Q behaviors describe how concentrations of dissolved geochemical (or biogeochemical) species respond to changes in stream flow (i.e., discharge). C/Q relations can vary between different types of solutes (e.g., nutrients, contaminants, other biogeochemical species)
International Atomic Energy Agency Safeguards Analytical Services (IAEA-SGAS) oversees coordinating the second Controlled Potential Coulometry Quality Control Interlaboratory Comparison (CPC QC ILC) on behalf of the Nuclear Material Laboratory (NML). The purpose of the exercise is to: (1) verify the ability of participating laboratories to measure the Pu concentration in the sample solutions, and (2) estimate the method performance (repeatability and intermediate reproducibility). Sample shipping coordination was performed by IAEA-SGAS. The test sample is a plutonium nitrate solution (estimated molarity of 3-5 M), delivered in two PTFE screw-capped quartz ampoules. Each participating laboratory is to receive two ampoules. Each ampoule holds approximately 8 mL of solution containing approximately 10 mg Pu/g. The gross weights of the ampoules were provided. The test sample is assumed to have an iron content below the level of significance for the CPC. The isotopic composition of the plutonium test sample was provided; however, no analysis date was provided. In addition, IAEA-SGAS provided analytical instructions to participants. On September 20, 2022, SRNL received the samples sent by IAEA-SGAS.
his chapter presents the site and facility characteristics that potentially influence ELLWF performance, the development of the PA waste inventory, including uncertainty quantification, and the screening approaches used to reduce the number of radionuclides included in the PA model simulations.
This chapter summarizes safety functions and features, events, and processes; conceptual models which apply to all DUs; property data packages; and modeling tools developed and implemented in this PA to analyze ELLWF performance. Conceptual models of GW flow and transport in the VZ, which are specific to type of DU, are presented in Chapter 4.
This chapter describes the GW flow and transport conceptual models in the VZ for both generic and special waste forms in STs, ETs, LAWV, ILV, and NRCDAs. The development and implementation of the GoldSim® system model for trenches is also introduced. The Trench System Model is used for sensitivity analysis and uncertainty quantification as reported in Chapter 6.
This chapter presents selected GW flow and radionuclide contaminant transport results from PORFLOW for the VZ and aquifer zone for each type of DU (STs, ETs, LAWV, ILV, and NRCDAs). Results for the nominal PA compliance case and various sensitivity cases are given. Results for the VZ include water saturation spatial profiles and radionuclide flux-to-the-water- table time profiles. Aquifer zone results include maximum concentration spatial contours; radionuclide concentration time profiles at the 100-meter POA; and peak concentrations at the 100-meter POA and time of occurrence for each modeled radionuclide.
This chapter provides a description of the methods used for the sensitivity and uncertainty quantification analyses and identifies the parameters and assumptions found to be most important in the determination of compliance with PA performance objectives, development of WAC, establishment of individual DU inventory limits, and other regulatory decisions.
For the GW and air pathways described in Sections 3.5, 3.6, and 3.7, POs are evaluated for MOPs based on a 100-meter POA for all DUs. In this chapter, POs are assessed for the IHI who may physically disrupt the facility at some time after the end of IC and when the POA is at the DUs (Aleman et al., 2021).
This chapter, together with Appendix H, provides the necessary CWTS inventory limits and trigger values for every parent radionuclide not screened out in Sections 2.3.6, 2.3.7, and 2.3.8. Also provided are details associated with how generic and special waste forms are handled on a DU-specific basis, and a discussion of the conversion of preliminary inventory limits (via transport runs summarized in Chapter 5) into final inventory limits for use in the CWTS limits system. Using the final inventory limits, a projected 2065 CWTS inventory is generated for use in the PA closure analysis outlined in Chapter 9.
This chapter summarizes the results of PA compliance against all relevant PA POs and measures. The final inventory limits presented in Chapters 7 and 8, as well as the methodology employed, provide assurance that POs will be met throughout the compliance periods. Deterministic and stochastic closure analyses also demonstrate a minimal likelihood of exceeding POs. Potential peaks post compliance are also addressed where future work is proposed to improve the understanding in actual uncertainties.
This chapter summarizes activities performed to ensure the quality of the information throughout the PA.
The work documented within this PA is the result of years of multidisciplinary research and modeling activities accomplished through the efforts of the individuals named in this section. Individuals who directly helped prepare this report are listed in Section 11.1; those who significantly contributed to the work described herein are acknowledged in Section 11.2.
Chapter 12 includes references for the entire report.
Table A-1, Table A-2, and Table A-3, respectively, list SAs, UDQEs, and other analyses and assessments prepared in support of the ELLWF since the previous ELLWF PA was issued in early 2008.
The total relative uncertainty, U, reported for each isotope in each waste cut is given by (Eq. 2-2) in Section 2.3.5.3. Waste Cut 1 of Container SD00003950 has a total activity of 737.990 Ci distributed among the isotopes H-3 and Am-241. Table B-1 summarizes the calculation results for the best-effort analysis example presented in Section 2.3.5.9.