Operating mode recognition of iron ore sintering process based on the clustering of time series data

被引：22
作者：
Du, Sheng [1 ,2 ,3 ]
Wu, Min [1 ,2 ]
Chen, Luefeng [1 ,2 ]
Cao, Weihua [1 ,2 ]
Pedrycz, Witold [3 ,4 ,5 ]
机构：
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Peoples R China
[2] Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan 430074, Peoples R China
[3] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada
[4] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia
[5] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland
来源：
CONTROL ENGINEERING PRACTICE | 2020年 / 96卷
基金：
中国国家自然科学基金;
关键词：
Dynamic time warping; Fuzzy C-Means clustering; Operating mode; Sintering process; Time series; CARBON EFFICIENCY; PREDICTION MODEL; IDENTIFICATION; OPTIMIZATION; ALGORITHM;
D O I：
10.1016/j.conengprac.2020.104297
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Operators often make different control decisions for different operating modes to meet the production requirement of the iron ore sintering process. Recognizing the operating modes is important to improve the quality and quantity of the sinter ore. An operating mode recognition method based on the clustering of time series data for the iron ore sintering process is presented in this paper. First, the Spearman rank correlation analysis and the information entropy analysis are combined to select parameters. Next, the operating mode recognition submodel is built by the fuzzy C-Means clustering method based on dynamic time warping distance and the naive Bayesian classifier method. Then, the outputs of the submodels are fused to obtain the final recognized operating mode. Finally, the productivity and combustion efficiency are regarded as the classification criteria, and the raw data collected from an iron and steel plant are used for the experiment. The experimental results show that the proposed method can effectively recognize the operating mode of the sintering process.
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页数：11
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