High‐level waste (HLW) glasses with high alumina content are prone to nepheline crystallization during the slow canister cooling that is experienced during large‐scale production. Because of its detrimental effects on glass durability, nepheline (NaAlSiO₄) precipitation must be avoided; however, developing robust, predictive models for nepheline crystallization behavior in compositionally complex HLW glasses is difficult. Using overly conservative constraints to predict nepheline formation can limit the waste loading to lower than the achievable capacity. In this study, a robust data‐driven model using five compositional features has been developed to predict nepheline formation. A new descriptor is introduced called the “difference based on correlation”, which has higher accuracy compared to previous descriptors and also has more balanced false positive and false negative rates. The analysis of the model and the data show an overlap, instead of a distinct compositional boundary, between glasses that form and do not form nepheline. As a result, the model's predictive accuracy is not the same throughout the feature space and instead is dependent on the location of the glass composition in the dimensionally reduced feature space.

中文翻译：

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预测高级废玻璃中霞石结晶的数据驱动方法

氧化铝含量高的高级废料（HLW）玻璃在大规模生产过程中经历的缓慢罐冷却过程中容易发生霞石结晶。霞石（NaAlSiO ₄）必须避免沉淀；但是，很难为组成复杂的HLW玻璃中的肾上腺素结晶行为建立可靠的预测模型。使用过度保守的约束条件来预测霞石的形成会限制废物的装载量，使其低于可达到的容量。在这项研究中，已经开发了使用五种成分特征的可靠的数据驱动模型来预测霞石的形成。引入了一个新的描述符，称为“基于相关性的差异”，与以前的描述符相比，它具有更高的准确性，并且还具有更均衡的误报率和误报率。对模型和数据的分析表明，形成和不形成霞石的眼镜之间存在重叠，而不是明显的成分边界。结果，该模型