一种铝电解槽阴极压降聚类分析方法
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摘要
铝电解槽的阴极是电解槽的重要组成部分,目前大多从工艺角度对阴极进行研究,结合阴极数据特征的分析和挖掘尚不多见。现采集到包括阴极压降在内的一些数据,针对数据的变化特点等信息对其进行相关分析,并在此基础上对阴极压降进行聚类分析,提出一种阴极压降聚类分析方法。该方法能够有效地将阴极压降划分为若干类,并对阴极压降的正常和异常状态做出基本判断。
The cathode is an important part of the electrolytic cell. At present,most of the researches on cathode are from the perspective of technology,and the analysis and mining of the characteristics of the cathode data is still rare. Some data including cathode voltage drops were collected and correlative analysis was performed on the characteristics of data changes. Based on this,the cathode voltage drops were clustered and analyzed,and a cathode voltage drop clustering analysis method was proposed. This method can effectively divide the cathode voltage drops into several categories and make basic judgments on the normal and abnormal states of the cathode voltage drop.
The cathode is an important part of the electrolytic cell. At present,most of the researches on cathode are from the perspective of technology,and the analysis and mining of the characteristics of the cathode data is still rare. Some data including cathode voltage drops were collected and correlative analysis was performed on the characteristics of data changes. Based on this,the cathode voltage drops were clustered and analyzed,and a cathode voltage drop clustering analysis method was proposed. This method can effectively divide the cathode voltage drops into several categories and make basic judgments on the normal and abnormal states of the cathode voltage drop.
引文
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[2]王令丰.铝电解槽阴极炭块组装压降与炉底压降关系分析[J].轻金属,2013(2):42-44.
[3]曹丹阳,杨炳儒,李晋宏.多维分析技术在铝电解生产中的应用研究[J].冶金自动化,2010,34(1):16-21.
[4]ZENG S,YI W. Design and application of multidimensional decision system for aluminum electrolysis[C]//IEEE International Conference on Information and Automation,2015:1808-1811.
[5]赵晓军.时间序列的相关性及复杂性研究[D].北京:北京交通大学,2015.
[6]陈湘涛,李明亮,陈玉娟.基于时间序列相似性聚类的应用研究综述[J].计算机工程与设计,2010,31(3):577-581.
[7]贾澎涛,何华灿,刘丽.时间序列数据挖掘综述[J].计算机应用研究,2007,24(11):15-18.
[8]RODPONGPUN S,NIENNATTRAKUL V,RATANAMAHATANA CA. Selective Subsequence Time Series clustering[J]. Knowledge-Based Systems,2012,35(15):361-368.
[9]范雪莉,冯海泓,原猛.基于互信息的主成分分析特征选择算法[J].控制与决策,2013,28(6):915-919.
[10]王千,王成,冯振元. K-means聚类算法研究综述[J].电子设计工程,2012,20(7):21-24.