Lead Investigator: Martha Grover (Georgia Institute of Technology)
Additional Investigators: Ronald Rousseau (Georgia Institute of Technology)

Project Objectives:

• Combine existing knowledge of on-line monitoring with proposed mass-balance modeling and offline sampling to produce fault detection methodologies that include uncertainty quantification (Crouse, et al. 2024).
• Investigate the solution chemistry of the slurries inside the melter feed preparation vessel (MFPV).
• Understand the partitioning of sodium in solid and aqueous phases when waste is mixed with the glass forming chemicals (GFCs).
• Elucidate the nucleation and growth kinetics of sodium phosphate hydrate crystallization and related minerals at highly basic conditions, which represent a potential process fault at the Hanford site.
• Incorporate spectral data in kinetic modeling of Savannah River Site processes in collaboration with Dan Lambert from SRNL (Woodham et al. 2021).

Significance/Impact:
It is important to assess the feasibility of using in situ ATR-FTIR and/or Raman spectroscopy for monitoring the composition of nuclear waste during processing. The methodology has the potential to reduce the number of samples that must be drawn for off-line analysis during waste processing, thereby reducing the need for time-consuming and dangerous sample analysis. The turn-around time for analysis is suitable for the Hanford Real Time In-Line Monitoring (RTIM) initiative. The integration of the use of spectral data with kinetic models will improve the understanding of redox reactions at SRS and minimize offline data collection.

The planned remediation of the waste at Hanford includes mixing the high-level waste (HLW) with glass-forming chemicals (GFCs), followed by vitrification to immobilize the waste as borosilicate glass. The GFC addition is optimized according to the conditions of the incoming waste stream so that chemical and other constraints within the melter are satisfied. Therefore, it is crucial to understand the solution chemistry of the slurries inside the melter feed preparation vessel (MFPV), where the waste is mixed with several solid GFC mixtures.

Waste remediation at the Hanford Site will follow a batch processing structure, whereby each batch of waste is handled sequentially. In each batch, a variety of unanticipated process disturbances may occur, including mixing failure, species crystallization (i.e., phosphate or gibbsite), particle settling, and waste variation, to name some possibilities. Accurate and timely detection of process faults is important so that issues may be corrected before impacting the melter and potentially causing a process delay or even process (melter) damage. Therefore, it is important to have knowledge of conditions that may cause process faults and to detect when faults occur in a timely manner.

At the Hanford Site, a Monte-Carlo approach is planned to ensure waste reaching the melter satisfies glass-forming constraints while also maximizing waste loading under uncertainty. Therefore, any real-time measurement technologies would have to integrate seamlessly into this planned control system by also estimating and reporting measurement uncertainty. For this work to be useful at the Hanford or Savannah River Sites, uncertainty estimates are vital so that risk can be understood and managed.

Public Benefits:
This project will support the development of process monitoring technology applicable to many industries (e.g., pharmaceutical and mineral processing).  Graduate students, postdocs, undergraduate students, and high school teachers will be trained to support this research; these trained persons will be able to support not only DOE-EM activities but also those scientific and engineering endeavors outside of DOE.

References (* indicates CRESP publication)

*R. Prasad, S. H. Crouse, R. W. Rousseau, Martha A. Grover, “Quantifying Dense Multicomponent Slurries with In-line ATR-FTIR and Raman Spectroscopy: A Hanford case study,” Industrial & Engineering Chemistry Research, 62, 39, 15962–15973 (2023), https://doi.org/10.1021/acs.iecr.3c01249.

*S. Kocevska, G. M. Maggioni, R.W. Rousseau, and M. A. Grover, “Spectroscopic Quantification of Target Species in a Complex Mixture Using Blind Source Separation and Partial Least-Squares Regression: A Case Study on Hanford Waste,” Industrial & Engineering Chemistry Research, 60, 27, 9885–9896 (2021), https://doi.org/10.1016/j.cherd.2022.03.002.

*D. J. Griffin, Y. Kawajiri, M. A. Grover, R. W. Rousseau, “Feedback Control of Multicomponent Salt Crystallization,” Crystal Growth & Design, 15 (1), 305-317 (2015), https://doi.org/10.1021/cg501368y.

P. Tse, J. Shafer, S. A. Bryan, A. M. Lines, “Quantification of Raman-Interfering Polyoxoanions for Process Analysis: Comparison of Different Chemometric Models and a Demonstration on Real Hanford Waste,” Environmental Science & Technology, 55, 12943–12950 (2021), https://doi.org/10.1021/acs.est.1c02512.

A. M. Lines, J. M. Bello, C. Gasbarro, S. A. Bryan, “Combined Raman and Turbidity Probe for Real-Time Analysis of Variable Turbidity Streams,” Analytical Chemistry, 94, 3652–3660 (2022), https://doi.org/10.1021/acs.analchem.1c05228

*D. Griffin, M. A. Grover; Y. Kawajiri; R. W. Rousseau, “Robust multicomponent IR-to-concentration model regression,” Chemical Engineering Science, 116 (6), 77-90 (2014), https://doi.org/10.1016/j.ces.2014.04.013.

*G. M. Maggioni, S. Kocevska, M. A. Grover, R. W. Rousseau, “Analysis of multicomponent ionic mixtures using blind source separation: A processing case study”, Industrial & Engineering Chemistry Research, 58 (50), 22640–22651 (2019), https://doi.org/10.1021/acs.iecr.9b03214.

W. H. Woodham, A. M. Howe, M. J. Siegfreid, “Sludge Batch 10 Flowsheet Testing with Non-radioactive Simulants,” SRNL Report SRNL-STI-2021-00349, Revision 0 (2021)

R. J. Lascola, M. E. Stone, “Real-Time, In-Line Monitoring for High Level Waste Applications,” SRNL Report SRNL-RP-2023-01064, Revision 0 (2023).

*S. H. Crouse, S. Kocevska, S. Noble, R. Prasad, A. M. Howe, D. P. Lambert, R. W. Rousseau, M. A. Grover, “Real-time infrared spectroscopy coupled with blind source separation for nuclear waste process monitoring,” Frontiers in Nuclear Engineering, 2 (2023), https://doi.org/10.3389/fnuen.2023.1295995.

*S. Kocevska, G. M. Maggioni, S. H. Crouse, R. Prasad, R. W. Rousseau, M. A. Grover, “Effect of ion interactions on the Raman spectrum of NO3-: Toward monitoring of low-activity nuclear waste at Hanford, Chemical Engineering Research and Design, 181, 173–194 (2022),  https://doi.org/10.1016/j.cherd.2022.03.002.

*S. H. Crouse, R. Prasad, M. A. Grover, R. W. Rousseau, “Detecting Faults in Nuclear Waste Slurry Processing with In-Line Probes: A Computational Study,” Proceedings of the Waste Management Symposium (2024), Paper 24059.

R. A. Peterson, E. C. Buck, J. Chun, R. C. Daniel, D. L. Herting, E. S. Ilton, G. J. Lumetta, S. B. Clark, “Review of the Scientific Understanding of Radioactive Waste at the U.S. DOE Hanford Site,” Environmental Science and Technology, 52, 381–396 (2018), https://doi.org/10.1021/acs.est.7b04077.

Computer codes: https://github.com/magrover?tab=repositories