During electrorefining, fission products, such as Sr and Cs, accumulate in a eutectic LiCl-KCl molten salt and degrade the efficiency of the separation process by generating high heat and decreasing uranium capture. Thus, the removal of the fission products from the molten salt bath is essential for reusing the bath, thereby reducing the additional nuclear waste. While many studies focus on techniques for selective separation of fission products, there are few studies on processing monitoring of those techniques. In-situ monitoring can be used to evaluate separation techniques and determine the integrity of the bath. In this study, laser-induced breakdown spectroscopy (LIBS) was selected as the monitoring technique to measure concentrations of Sr and Cs in 550°C LiCl-KCl molten salt. A laser spectroscopic setup for analyzing high-temperature molten salts in an inert atmosphere was established by coupling an optical path with a glove box. An air blower was installed between the sample and lenses to avoid liquid splashes on surrounding optical products caused by laser-liquid interaction. Before LIBS measurements, experimental parameters such as laser pulse energy, delay time, and gate width were optimized for each element to get the highest signal-to-noise ratio of characteristic elemental peaks. LIBS spectra were recorded with the optimized conditions from LiCl-KCl samples, including individual elements in a wide concentration range. Then, the limit of detections (LODs) for Sr and Cs were calculated using calibration curves, which have high linearity with low errors. In addition to the univariate analysis, partial least-squares regression (PLSR) was employed on the data plots to obtain calibration models for better quantitative analysis. The developed models show high performances with the regression coefficient R2 close to one and root-mean-square error close to zero. After the individual element analysis, the same process was performed on samples where Sr and Cs were dissolved in molten salt simultaneously. The results also show low-ppm LODs and an excellent fitted regression model. This study illustrates the feasibility of applying LIBS to process monitoring in pyroprocessing to minimize nuclear waste. Furthermore, this high-sensitive spectroscopic system is expected to be used for coolant monitoring in advanced reactors such as molten salt reactors.

Molten salt reactors and pyroprocessing are widely considered for various nuclear applications. The main challenges for monitoring these systems are high temperature and strong radiation. Two harsh environments make the monitoring system needs to measure nuclides at a long distance with sufficient resolution for discriminating many different elements simultaneously. Among available methodologies, laser-induced breakdown spectroscopy (LIBS) has been the most studied. The LIBS method can provide the required stand-off and desired multi-elemental measurable ability. However, the change of the level for molten salts induces uncertainty in measuring the concentration of the nuclides for LIBS analysis. The spectra could change by focusing points due to the different laser fluence and plasma shape. In this study, to prepare for such uncertainties, we evaluated a LIBS monitoring system with machine learning technology. While the machine learning technology cannot use academic knowledge of the atomic spectrum, this technique finds the new variable as a vector from any data including the noise, target spectrum, standard deviation, etc. Herein, the partial least squares (PLS) and artificial neural network (ANN) were studied because these methods represent linear and nonlinear machine learning methods respectively. The Sr (580–7200 ppm) and Mo (480–4700 ppm) as fission products were investigated for constructing the prediction model. For acquiring the data, the experiments were conducted at 550°C in LiCl-KCl using a glassy carbon crucible. The LIBS technique was used for accumulating spectra data. In these works, we successfully obtained a reasonable prediction model and compared each other. The high linearities of the prediction model were recorded. The R2 values are over 0.98. In addition, the root means square of the calibration and cross-validation were used for evaluating the prediction model quantitatively.

매년 많은 양의 플라스틱 폐기물이 발생되면서 폐플라스틱을 순환 자원화하기 위해 여러 공공기관, 연구소에서는 폐플라스틱 자동선별 시스템을 구축하기 위한 노력을 하고 있다. 이미 국내 지자체 재활용 선별장 등에서는 근적외선 분광법(NIR)을 활용한 자동선별 시스템을 구축 및 활용하고 있지만 검정색 플라스틱 제품군의 물리적 성상인 근적외선 파장의 과도한 흡수로 인한 스펙트럼 분석이 어려워 자동분류가 힘든 실정이다. 이러한 문제들을 해결하기 위해 NIR 분광장비가 아닌 LIBS 분광기를 사용하여 데이터를 구축 및 분석하고 지능형 알고리즘을 이용하여 자동 선별이 가능한 흑색 플라스틱의 재질별 선별 분류기 구축하고자 한다. LIBS분광장비는 시료가 기체, 액체 및 고체 상태와 관계없이 주기율표 상의 거의 모든 원소에 대하여 정성 정량 분석이 가능한 장비로 시료의 전처리 과정이 필요 없으며, 분석 시간이 매우 짧기 때문에 실시간 분석이 가능하다는 장점을 가진다. 이러한 LIBS 분광장비를 이용하여 데이터를 추출하고 이를 분석하여 인공지능 알고리즘을 이용한 분류기를 설계하고자 한다. 검은색 플라스틱을 인공지능 알고리즘을 통하여 재질별 자동 선별하도록 설계하여 산업적･경제적인 효율의 향상을 기대할 수 있다.