摘要
引言铝电解槽在正常生产过程中处于高温状态,且其内的高温熔体具有很强的腐蚀性,一般的材料在其中会很快被腐蚀。因此铝电解槽的工作状态很难直接探测并实现在线显示,
As the object of anodic current signal of 160 kA pre-baked anode cells,these signals of different conditions were analyzed by the method of ＂spectrum-wavelet packet-neural network＂.The results show that the anode current signals of the different status have different peak frequency value,so it is possible to extract energy characteristics vectors of anode current signals in different cell states using wavelet packets decomposition and wavelet packets reconstruction.According to the wavelet packet energy characteristics vectors extracted from anode current signals,diagnosis model based on BP neural network was established and verified.The simulation results show that the model of network identification is simple in construction,high accuracy in recognition,and convenient to realize on-line monitoring and real-time identification for the anode current signals of the different status.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2011年第6期1770-1777,共8页
CIESC Journal
基金
国家自然科学基金项目(51004115)
中央高校中南大学自由探索计划项目(20101220062)
能源高效清洁利用湖南省高校重点实验室基金项目(2010NGQ001)~~
关键词
铝电解槽
阳极电流
频谱
小波包
神经网络
aluminum reduction cell
anode current
power spectral estimation
wavelet packets
neural network
