Monitoring Noble Gases (Xe and Kr) and Aerosols (Cs and Rb) in a Molten Salt Reactor Surrogate Off-Gas Stream Using Laser-Induced Breakdown Spectroscopy (LIBS). Issue 8 (August 2022)

• Record Type: Journal Article
• Authors: Andrews, Hunter B.; McFarlane, Joanna; Myhre, Kristian G.
• Is Part Of: Applied spectroscopy. Volume 76:Issue 8(2022)
• Page Start: 988
• Page End: 997
• Publication Date: 2022-08
• Subjects: Laser-induced breakdown spectroscopy -- LIBS -- molten salt aerosol -- xenon -- krypton -- off-gas -- online monitoring -- cesium -- rubidium -- molten salt reactor, MSR; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1177/00037028221088625
• Languages: English
• ISSNs: 0003-7028

Abstract

This study with surrogate materials shows that laser-induced breakdown spectroscopy (LIBS) is a robust tool with promising capability toward monitoring gaseous (Xe and Kr) and aerosol (Cs and Rb) species in an off-gas stream from a molten salt reactor (MSR). MSRs will continually evolve fission products into the cover gas flowing across the reactor headspace. The cover gas entrains Xe and Kr gases, along with aerosol particles, before passing into an off-gas treatment system. Univariate models of Xe and Kr peaks showed a strong correlation to concentration indicated by their coefficients of determination of 0.983 and 0.997, respectively. Multivariate models were built for all four analytes using partial least squares regression coupled with preprocessing steps including normalization, trimming, and/or genetic algorithm derived filters. The models were evaluated by predicting the concentrations of the analytes in four validation samples, in which all calibration models were successfully validated at a confidence interval of 99.9%. Lastly, pressure controllers were used to regulate the mass flow rate of Kr flowing into the measurement cell in sinusoidal and stepwise waveforms to test the real-time monitoring capabilities of the regression models. Both univariate and partial least squares Kr models were able to successfully quantify the gas concentration in the real-time evaluation. The root mean squared error of prediction (RMSEP) values for these real-time tests were calculated to be 0.051, 0.060, and 0.121 mol% demonstrating the measurement systems' capability to perform online monitoring with acceptable accuracy. Graphical Abstract