A semi-supervised linear-nonlinear prediction system for tumbler strength of iron ore sintering process with imbalanced data in multiple working modes. (May 2021)

• Record Type: Journal Article
• Authors: Chen, Xiaoxia; Shi, Xuhua; Lan, Ting
• Is Part Of: Control engineering practice. Volume 110(2021)
• Publication Date: 2021-05
• Subjects: Iron ore sintering -- Semi-supervised learning -- Imbalanced data -- Tumbler strength -- Industrial-process modeling; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.104766
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: An iron ore sintering is a process that provides qualified sinter for the blast-furnace iron-making process. The tumbler strength is an important physical quality index of the sinter. Precise prediction of the tumbler strength is essential for solving the problem of how to improve it. In this study, a semi-supervised prediction system was devised for the tumbler strength of an iron ore sintering process. First, the process was briefly described with an analysis of the process characteristics. The characteristics of the existence of imbalanced data in multiple working modes, the lack of labeled samples, and the coexistence of linear–nonlinear components were taken into consideration for building the system. Then, the configuration of the prediction system was devised based on the characteristics. The system consists of three parts: working-modes decomposition based on a Gaussian mixture model (GMM) considering the existence multiple working modes, a GMM based just-in-time learning for nearest-samples selection in the relevant working modes considering the existence of imbalanced data, and the development of a semi-supervised linear–nonlinear least-square learning network considering the existence of the linear–nonlinear component and lack of labeled samples. Finally, comparisons of simulation results using actual run process data with scarce and abundant labeled samples verified the effectiveness of the proposed system. In addition, results of industrial application also verified the effectiveness of the prediction system.