Data Fusion of Acoustic and Optical Emission from Laser-Induced Plasma for In Situ Measurement of Rare Earth Elements in Molten LiCl-KCl.

Molten salts have a significant potential for use as next-generation nuclear reactor coolants and in pyroprocessing for the recycling of used nuclear fuel. However, the molten salt composition needs to be known at all times, and high temperatures and intense ionizing radiation pose challenges for the monitoring instrumentation. Although the technique of laser-induced breakdown spectroscopy (LIBS) has been studied for in situ measurements of molten salts, trials to improve its monitoring accuracy using chemometrics are lacking. In this study, a data fusion technique using the LIBS optical and laser-induced acoustic (LIA) signals was investigated to enhance the measurement accuracy for molten salt monitoring. Prediction models were constructed using the partial least-squares method, and the variable importance in projection scores was analyzed to evaluate the effect of incorporating the LIA signal into the analysis. This study investigates rare earth elements Eu, Er, and Pr found not only in nuclear but also in other settings such as laser and magnetic materials. The analysis of LIBS data without data fusion resulted in a root-mean-square error of prediction (RMSEP) of 0.0774-0.0913 wt %, whereas the prediction model using data fusion led to approximately 18-40% enhanced RMSEP (0.0461-0.0679 wt %). The results suggest that fusing the LIBS data with the simultaneously recorded LIA data can improve the monitoring accuracy of rare earth element composition in molten salts.

Antimony(III/V) removal from industrial wastewaters: treatment of spent catalysts formally used in the SOHIO acrylonitrile process.

A treatment and volume reduction process for a spent uranium-antimony catalyst has been developed. Targeted removal, immobilization and disposal of the uranium component has been confirmed, thus eliminating the radiological hazard. However, significant concentrations of antimony ([Sb] ≥ 25-50 mg L-1) remain in effluent from the process, which require removal in compliance with Korean wastewater regulations. Antimony(III/V) removal via co-precipitation with iron has been considered with optimal pH, dose and kinetics being determined. The effect of selected anions - Cl-, SO4 2- and PO4 3- - have also been considered, the latter present due to a prior uranium removal step. Removal of Sb(III) from both Cl- and SO4 2- media and Sb(V) removal from Cl- media to below release limits were found to be effective within 5 minutes at an iron dose of 8 mM (molar ratio, [FeIII]/[Sb] = 20) and a target pH of 5.0. However, Sb(V) removal from SO4 2- was significantly hampered requiring significantly higher iron dosages for the same removal performance. Phosphate poses significant challenges for the removal of Sb(V) due to competition between PO4 3- and Sb(OH)6 - species for surface binding sites, attributed to similarities in chemistries and a shared preference for an inner vs outer binding mechanism.

Machine learning-assisted laser-induced breakdown spectroscopy for monitoring molten salt compositions of small modular reactor fuel under varying laser focus positions.

Next-generation advanced nuclear reactors based on molten salts are interested to apply machine learning (ML) technology to minimize human error and realize effective autonomous operation. Owing to harsh environments with limited access to molten salts, laser-induced breakdown spectroscopy (LIBS) has been investigated as a possible option for remote online monitoring. However, the height of molten salts is easily fluctuated by vibration. In addition, the level of molten salts could change during normal operation through the insertion of a controlling structure. While these uncertainties should be considered, their effects have not been studied yet. In this study, LIBS has been actively coupled with ML to automate the online monitoring of difficult-to-access molten salt systems. To practically apply a prediction model with ML, we intentionally defocus the measurement by manipulating the sample position. This study investigates the focusing and defocusing spectra of Sr and Mo as fission products for constructing the two prediction models using partial least squares and artificial neural network methods. For each method, the prediction models trained with focusing spectra only or focusing and defocusing spectra simultaneously are constructed and compared to each other. While the prediction model using only focusing spectra resulted in a root mean square error of prediction (RMSEP) of 0.1943-0.2175 wt%, a prediction model using both spectra led to approximately 10 times enhanced RMSEP (0.0210-0.0316 wt%). This study implies that not only focusing data but also defocusing data are needed to construct the prediction model while considering its practical usage in a real system, especially in the complex processes of the nuclear industry.

Pilot-Scale Treatment of a Spent Uranium Catalyst Formally Used in the SOHIO Process: Pilot Plant Verification of the SENSEI Process.

Approximately 7000 drums of waste uranium catalyst are currently present in Korea and require an appropriate treatment and waste management strategy. Recently, one such process has been developed and has proven successful at both laboratory and bench scales. The success of the process has culminated in its verification at a pilot plant scale. The purpose of this paper is to describe the catalyst treatment process and present results obtained from the pilot plant study that may be applicable to other such wastes. The individual unit technologies have been tested and verified, enabling process scale-up to be successfully proven. The final volume reduction of up to 80% has been confirmed with the successful separation, encapsulation, and immobilization of residue wastes, representing a potential cost saving of US$70 million compared to the direct disposal. The inactive silica component of the waste catalyst was purified and confirmed to be free of uranium. All effluents generated during the process were treated and satisfy the appropriate Korean release criteria. The process employs the concept of Selective Extraction of Nonradioactive Species, Encapsulation, and Immobilization, and is therefore introduced as the SENSEI process.

Effective removal of uranium via phosphate addition for the treatment of uranium laden process effluents.

We have investigated the suitability of phosphate addition, in the form MH2PO4 (M = Na+, K+ or NH4+), for the selective removal of uranium from a complex waste effluent. The effluent in question is generated as part of a treatment strategy for a spent uranium catalyst, used in the production of acrylonitrile (Sohio process), which has been in temporary storage in Korea since 2004. Both pH (3.0-11.0) and phosphate dosages (0.25-10 mM) have been screened to identify the optimal conditions of 6.25 and 1 mM, respectively, for an initial uranium concentration of 0.16 mM. Precipitation kinetics have been investigated revealing the rapid removal of uranium from solution, with 30 min found to be optimal. The effluent was effectively decontaminated via Meta-ankoleite (K(UO2)(PO4)·3H2O) formation to uranium levels below the Korean release limit of 1 ppm for uranium-bearing liquid wastes, with KH2PO4 addition being chosen for the real process. Final decontamination factors of the order of ≥ 8000 were readily achieved. Aluminium, calcium and iron containing coagulants were screened for the clean-up of the remaining supernatant, post-uranium removal, ensuring the final effluent meets the relevant release criteria (pH, total suspended solids, total phosphate and turbidity) for general, non-radioactive, effluents. A process scheme is presented and discussed for adaptation to similar uranium containing effluents.