Burden Control Strategy Based on Reinforcement Learning for Gas Utilization Rate in Blast Furnace⁎This work was supported in part by the National Natural Science Foundation of China under Grants 61973287 and 61333002, and the 111 project under Grant B17040. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Shen, Xiaoling; An, Jianqi; Wu, Min; She, Jinhua
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 11704
• Page End: 11709
• Publication Date: 2020
• Subjects: Blast furnace -- gas utilization rate -- burden control strategy -- reinforcement learning algorithm -- long-time scale; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.667
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Gas utilization rate (GUR) is an important state parameter to reflect the energy consumption, the quality and production of the pig iron, and the distribution of the gas flow in a blast furnace. The GUR is mainly adjusted by burden distribution and hot-blast supply. According to the analysis of mechanism and data, burden distribution and hot-blast supply affect the GUR on a long-time scale and short-time scale, respectively. However, few of the previous researches proposed the control method for the GUR and they did not consider multi-time-scale characteristics. Thus, it is necessary to design a control strategy or system for the GUR considering the multi-time-scale characteristics, which can make the GUR have a reasonable development trend. This paper presented a burden control strategy based on a reinforcement learning algorithm for the GUR. The method improved the development trend of the GUR on a long-time scale. The experimental results demonstrated that the sequence of the parameters of the burden distribution given by the presented method ensured a reasonable development trend of the GUR on a long-time scale.