Anode effect prediction based on a singular value thresholding and extreme gradient boosting approach
Anode effect prediction based on a singular value thresholding and extreme gradient boosting approach
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DOI：
10.1088/1361-6501/aaee5e
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发表时间：
2018-12
影响因子：
2.4
通讯作者：
Kaibo Zhou;Zhixin Zhang;Jie Liu;Zhongxu Hu;Xiaohui Duan;Qi Xu
中科院分区：
文献类型：
--
作者：
Kaibo Zhou;Zhixin Zhang;Jie Liu;Zhongxu Hu;Xiaohui Duan;Qi Xu
铝电解过程中经常出现阳极效应，导致铝生产能耗大、效率低，因此如何提前识别阳极效应成为一个重要问题。然而，传统方法忽略了获取数据中普遍存在的不完整信息问题，仅考虑单一预测时间，导致阳极效应预测结果不可靠。本文提出了一种基于奇异值阈值和极限梯度提升（SVT-XGBoost）方法的混合预测方法来识别铝电解过程中的阳极效应。 SVT用于全特征变换的数据填充，XGBoost用于阳极效应的分类。通过比较，预测时间设置为10分钟。实验结果表明，与之前的方法相比，所提出的方法具有使用 SVT-XGBoost 进行阳极效应分类的有效能力。在这里，还研究了训练样本数量的影响。所提出的方法可以应用于未来的实时阳极效应预测。
An anode effect often occurs during the process of aluminum electrolysis that will cause large energy consumption and low efficiency in aluminum production, thus how to identify the anode effect in advance has become an important issue. However, traditional approaches ignore the common incomplete information problem existing in the acquired data, and only consider a single predicting time, resulting in an unreliable result in anode effect prediction. In this paper, a hybrid prediction approach based on a singular value thresholding and extreme gradient boosting (SVT-XGBoost) approach is proposed to identify the anode effect in the aluminum electrolysis process. The SVT is used for data filling by the whole-features transformation, and the XGBoost is utilized for classification of the anode effect. The predicting time is set to 10 min by the comparison. The experimental results show that the proposed approach has an effective ability for anode effect classification using the SVT-XGBoost compared to the previous approaches. Here, the effect of the training sample number is also investigated. The proposed approach could be applied in real-time anode effect prediction in the future.