基于KPCA和SVM的铝电解槽漏槽事故预警方法  

Early warning method of aluminum reduction cell leakage accident based on KPCA and SVM

作　　者：尹刚[1] 李伊惠 何飞 曹文琦 王民 颜非亚[5] 向禹 卢剑[5] 罗斌 卢润廷 YIN Gang;LI Yihui;HE Fei;CAO Wenqi;WANG Min;YAN Feiya;XIANG Yu;LU Jian;LUO Bin;LU Runting(State Key Laboratory of Coal Mine Disaster Dynamics and Control,College of Resource and Safety Engineering,Chongqing University,Chongqing 400044,China;Aluminium Corporation of China Limited,Guizhou Branch,Guiyang 510405,Guizhou,China;Bomei Qimingxing Aluminium Co.,Ltd.,Meishan 620010,Sichuan,China;Chongqing Qineng Electric Aluminum Co.,Ltd.,Chongqing 401420,China;Guiyang Aluminium Magnesium Design&Research Institute Co.,Ltd.,Guiyang 550081,Guizhou,China;Communication NCO Academy,Army Engineering University of PLA,Chongqing 400035,China;Sichuan Siwei Environmental Protection Equipment Co.,Ltd.,Suining 629000,Sichuan,China)

机构地区：[1]重庆大学资源与安全学院,煤矿灾害动力学与控制国家重点实验室,重庆400044 [2]中国铝业股份有限公司贵州分公司,贵州贵阳510405 [3]眉山市博眉启明星铝业有限公司,四川眉山620010 [4]重庆旗能电铝有限公司,重庆401420 [5]贵阳铝镁设计研究院有限公司,贵州贵阳550081 [6]陆军工程大学通信士官学校,重庆400035 [7]四川省四维环保设备有限公司,四川遂宁629000

出　　处：《化工学报》2023年第8期3419-3428,F0003,共11页CIESC Journal

基　　金：国家自然科学基金面上项目(51374268);科技转化重大项目(H20201555);重庆英才创新创业示范团队项目(A31700517)。

摘　　要：针对铝电解槽运行数据复杂多维、结构非线性且故障样本量相对正常运行样本量少的问题,提出了一种基于数据驱动的漏槽事故预警模型。模型采用简单经典的分类算法支持向量机,避免小样本数据带来的过拟合问题。并用交叉验证训练测试方法和麻雀搜索算法对参数寻优,提高分类器的性能。同时为了更好地挖掘铝电解运行数据中的特征信息采用核主成分分析法将数据降到8维,模型运行速度提高了65.51 s。另外在设置分类标签时结合实际情况选取了三个变化显著的特征参数作为辅助分类条件从而扩充了故障样本。最后对该漏槽事故预警模型进行性能验证,实验表明模型F1分数达到了0.995,AUC值达到了0.998。The slot leakage accident will cause enormous economic losses and casualties during the production process of aluminum reduction cell.Therefore,the accurate early warning of aluminum reduction cell leakage accident is significant for enterprises.As for the issue that the operation data of aluminum reduction cell is multidimensional,nonlinear,and the sample size of failure is smaller than that of normal operation,a data-driven model for early warning model of slot leakage accident is proposed.The support vector machine(SVM)algorithm with good robustness and high efficiency was adopted to avoid over-fitting due to small fault sample size.Then,cross-validation and sparrow search optimization algorithms(SSA)were used to optimize the parameters of SVM.The F1 score and AUC value of the optimized model were increased by 0.166 and 0.163 respectively.To better mine the characteristic information in the running data of aluminum electrolysis,the kernel principal component analysis(KPCA)method was used to reduce the data dimension.Eight principal components were selected when the cumulative variance contribution was 80%.The running time of the model was thereby decreased by about one minute.The F1 score and AUC value of optimized model were also increased by 0.046 and 0.038,respectively.Thirteen characteristic parameters were selected from many production parameters of aluminum electrolysis as input of model.Then,the missing data in the collected data were interpolated by K nearest neighbor(KNN)algorithm.When setting the classification labels,three characteristic parameters of temperature,Fe content,and Si content of the electrolysis process with obvious changes were selected as auxiliary classification conditions and combined with the actual situation.Thus,the fault feature range and the size of fault samples were expanded,indirectly solving the problem of small fault sample size to a certain extent.The experimental results showed that the F1 score and AUC value of the early warning of aluminum reduction cell leakage accide

关 键 词：电解 泄漏事故 算法 神经网络 故障预测 

分 类 号：TF821[冶金工程—有色金属冶金]