An energy efficient decision-making strategy of burden distribution for blast furnace. (September 2018)

• Record Type: Journal Article
• Authors: Wu, Min; Zhang, Kexin; An, Jianqi; She, Jinhua; Liu, Kang-Zhi
• Is Part Of: Control engineering practice. Volume 78(2018)
• Page Start: 186
• Page End: 195
• Publication Date: 2018-09
• Subjects: Burden distribution -- Blast furnace -- Decision-making strategy -- Support vector regression -- Case-matching model; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2018.06.019
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Burden distribution plays an important role in the optimization of energy-consuming index of a blast furnace (BF). However, due to the complex mechanism of the burden distribution process and the poor understanding of this process, it is difficult for operators to make a suitable decision for burden distribution parameters. In this paper, a decision-making strategy of burden distribution parameters is devised for improving energy-consuming index, where carbon-monoxide utilization rate (CMUR) is taken as the energy-consuming index. Firstly, the support vector regression is used to build a model between the burden distribution parameters and BF state variables that are closely related to CMUR. Then, a probability-based case-matching model is built by the historical data to predict the trend of CMUR's change. Finally, based on the trend of CMUR's change, a decision for burden distribution parameters is obtained. Simulation results based on industrial data show that the devised decision-making strategy provides a good guide on making a suitable decision for burden distribution parameters. Highlights: Carbon-monoxide utilization rate (CMUR) is taken as an energy-consuming index. Support vector regression is used to predict the state variables of blast furnace. Trend of CMUR's change is predicted by a probability-based case-matching model. Burden distribution can be effectively decided according to the predicted trend.