Huang Ao, Huo Yanzhu, Yang Juan, Li Guangqiang
The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, China.
National Engineering Research Center for E-learning, Central China Normal University, Wuhan 430079, China.
Materials (Basel). 2019 Mar 31;12(7):1059. doi: 10.3390/ma12071059.
Electrical conductivity is one of the most basic physical⁻chemical properties of oxide-based melts and plays an important role in the materials and metallurgical industries. Especially with the metallurgical melt, molten slag, existing research studies related to slag conductivity mainly used traditional experimental measurement approaches. Meanwhile, the idea of data-driven decision making has been widely used in many fields instead of expert experience. Therefore, this study proposed an innovative approach based on big data mining methods to investigate the computational simulation and prediction of electrical conductivity. Specific mechanisms are discussed to explain the findings of our proposed approach. Experimental results show slag conductivity can be predicted through constructing predictive models, and the Gradient Boosting Decision Tree (GBDT) model is the best prediction model with 90% accuracy and more than 88% sensitivity. The robustness result of the GBDT model demonstrates the reliability of prediction outcomes. It is concluded that the conductivity of slag systems is mainly affected by TiO₂, FeO, SiO₂, and CaO. TiO₂ and FeO are positively correlated with conductivity, while SiO₂ and CaO have negative correlations with conductivity.
电导率是氧化物基熔体最基本的物理化学性质之一，在材料和冶金工业中起着重要作用。特别是对于冶金熔体、熔渣，现有的与炉渣电导率相关的研究主要采用传统的实验测量方法。同时，数据驱动决策的理念已在许多领域广泛应用，取代了专家经验。因此，本研究提出了一种基于大数据挖掘方法的创新方法，用于研究电导率的计算模拟和预测。讨论了具体机制以解释我们提出方法的结果。实验结果表明，通过构建预测模型可以预测炉渣电导率，梯度提升决策树（GBDT）模型是最佳预测模型，准确率达90%，灵敏度超过88%。GBDT模型的稳健性结果证明了预测结果的可靠性。研究得出结论，炉渣体系的电导率主要受TiO₂、FeO、SiO₂和CaO的影响。TiO₂和FeO与电导率呈正相关，而SiO₂和CaO与电导率呈负相关。
