Operating mode recognition of iron ore sintering process based on the clustering of time series data. (March 2020)

• Record Type: Journal Article
• Authors: Du, Sheng; Wu, Min; Chen, Luefeng; Cao, Weihua; Pedrycz, Witold
• Is Part Of: Control engineering practice. Volume 96(2020)
• Publication Date: 2020-03
• Subjects: Dynamic time warping -- Fuzzy C-Means clustering -- Operating mode -- Sintering process -- Time series; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2020.104297
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: 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. Highlights: An operating mode recognition method for the sintering process is presented. The Fuzzy C-Means clustering based on the dynamic time warping distance is used. The naive Bayesian classification method is used to identify the operating mode.