Machine Learning Enabled Models to Predict Sulfur Solubility in Nuclear Waste Glasses
Machine Learning Enabled Models to Predict Sulfur Solubility in Nuclear Waste Glasses
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机器学习模型可预测核废料玻璃中的硫溶解度
DOI：
10.1021/acsami.1c10359
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发表时间：
2021
影响因子：
9.5
通讯作者：
Goel, Ashutosh
中科院分区：
文献类型：
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作者：
Xu, Xinyi;Han, Taihao;Huang, Jie;Kruger, Albert A.;Kumar, Aditya;Goel, Ashutosh
美国能源部正在考虑在华盛顿州的汉福德工厂实施低放射性废物（LAW）和高放射性废物（HLW）玻璃化的直接进料方法。如果实施，碱金属/碱土金属硫酸盐浓度较高的核废料（比先前提出的玻璃化方案预期的浓度高）将被送往玻璃化设施。现有的经验模型难以预测硫酸盐在这些玻璃中的溶解度或设计在这种情况下具有增强的硫酸盐负载的玻璃配方。此外，当应用于其组成福尔斯落在用于开发/校准模型的数据库所涵盖的范围之外的HLW玻璃时，现有模型不能产生可靠的预测。因此，本研究利用人工智能（机器学习，ML）的力量，旨在解决现有模型的局限性。为此，使用大型数据库训练了三个ML模型;包括>1000 LAW和HLW玻璃，并包含广泛的玻璃组成和加工变量。接下来，具有最佳预测性能的ML模型已被用于定量评估和排名影响（即，玻璃的组成/加工变量对玻璃中SO 3溶解度的重要性。最后，在对有影响和无关紧要的变量有这样的理解的前提下，开发了两个封闭形式的分析模型─ ─具有不同程度的复杂性（一个高度参数化，一个输入变量较少）。结果表明，这两种分析模型都能预测LAW和HLW玻璃中SO 3的溶解度，其准确度与ML模型相似，并且远高于代表当前最先进水平的分析模型。总体而言，这项研究的结果提供了一个路线图-由数据提供信息并由人工智能引导-未来可以利用它来设计具有前所未有的硫负载的核废料玻璃。
The U.S. Department of Energy is considering implementing the direct feed approach for the vitrification of low-activity waste (LAW) and high-level waste (HLW) at the Hanford site in Washington state. If implemented, the nuclear waste with a higher concentration of alkali/alkaline-earth sulfates (than expected under the previously proposed vitrification scheme) will be sent to the vitrification facility. It will be difficult for the existing empirical models to predict sulfate solubility in these glasses or design glass formulations with enhanced sulfate loadings in such a scenario. Further, the existing models are unable to produce reliable predictions when applied to HLW glasses whose composition falls outside of the range encompassed by the database used to develop/calibrate the models. Accordingly, this study harnesses the power of artificial intelligence (machine learning, ML) with a goal to address the limitations of the existing models. Toward this, three ML models have been trained using a large database; comprising >1000 LAW and HLW glasses and encompassing a wide range of glass compositions and processing variables. Next, the ML model with the best prediction performance has been used to quantitatively assess and rank the influence (i.e., importance) of glasses’ compositional/processing variables on the SO3solubility in the glasses. Finally, on the premise of such understanding of influential and inconsequential variables, two closed-form analytical models─with disparate degrees of complexity (one highly parametrized and one with fewer input variables)─have been developed. Results show that both analytical models produce predictions of SO3solubility in LAW and HLW glasses with an accuracy analogous to ML models and substantially higher than the analytical models that represent the current state-of-the-art. Overall, this study’s outcomes present a roadmap─informed by data and channeled by artificial intelligence─that can be leveraged in the future to design nuclear waste glasses with unprecedented sulfur loadings.