Abstract Liquidus temperature for primary crystallization is an important physical and chemical property for electrolyte system. It plays a crucial role on the stability of the electric cell in electrolysis production process. So how to accurately predict the liquidus temperature for primary crystallization of electrolyte based on the composition of electrolyte is a meaningful research subject. In this work, data mining assisted prediction of liquidus temperature for primary crystallization of electrolyte systems was proposed. The essential differences between the complex industrial electrolyte system and electrolyte system prepared in laboratory were revealed by means of comparing the micro-morphology, phase composition and thermal analysis. To some extent, it was verified that the empirical formula has no versatility in the two different electrolyte systems. The prediction model of liquidus temperature for primary crystallization of different electrolyte systems was constructed by using SVM(support vector machine), BPANN(back-propagation artifical neural networks), RFR(random forest regression) and GBR(gradient boosting regression) algorithm, respectively. The electroyte system inculdes Na3AlF6(CR)-Al2O3–AlF3–CaF2, Na3AlF6(CR)-Al2O3–MgF2–CaF2–LiF, Na3AlF6(CR)-Al2O3-MgF2-CaF2-KF-LiF, and Na3AlF6(CR)-Al2O3-AlF3-CaF2-MgF2-LiF-KF-NaF. For different electrolyte systems, ANN, SVM, RFR and other models all have good performances, they can effectively predict the liquidus temperature for primary crystallization of each electrolyte systems. For some electrolyte systems, ANN, SVM, RFR models are obviously superior to the prediction level of empirical formula described in the literature. It can be seen that data mining has a good application prospect in the prediction of the liquidus temperature for primary crystallization of electrolyte systems. We provide a new method for predicting the liquidus temperature for primary crystallization of different electrolyte systems based on the electrolyte composition dataset in this work.

中文翻译：

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不同电解质体系初结晶液相线温度的数据挖掘辅助预测

摘要 初晶液相线温度是电解质体系的重要物理化学性质。它在电解生产过程中对电池的稳定性起着至关重要的作用。因此，如何根据电解液的成分准确预测电解液一次结晶的液相线温度是一个有意义的研究课题。在这项工作中，提出了数据挖掘辅助预测电解质系统初级结晶的液相线温度。通过微观形貌、物相组成和热分析的比较，揭示了复杂工业电解质体系与实验室制备的电解质体系之间的本质区别。在某种程度上，经验证，经验公式在两种不同的电解质体系中没有通用性。分别采用SVM（支持向量机）、BPANN（反向传播人工神经网络）、RFR（随机森林回归）和GBR（梯度提升回归）算法构建了不同电解质体系初结晶液相线温度的预测模型. 电解质系统包括 Na3AlF6(CR)-Al2O3-AlF3-CaF2、Na3AlF6(CR)-Al2O3-MgF2-CaF2-LiF、Na3AlF6(CR)-Al2O3-MgF2-CaF2-KF-LiF 和 Na3AlF6(CR)-Al2O3 -AlF3-CaF2-MgF2-LiF-KF-NaF。对于不同的电解质体系，ANN、SVM、RFR等模型都具有良好的性能，可以有效预测各电解质体系初结晶的液相线温度。对于某些电解质系统，ANN、SVM、RFR 模型明显优于文献中描述的经验公式的预测水平。可见，数据挖掘在预测电解质体系初结晶液相线温度方面具有很好的应用前景。在这项工作中，我们提供了一种基于电解质成分数据集预测不同电解质体系初级结晶液相线温度的新方法。