Research on Intelligent Manufacturing Problem in Process Industry

2018 3rd International Conference on Insulating Materials, Material Application and Electrical Engineering

材料科学;无线电电子学;电工学

Jiang, Jiachen^1 ; Hao, Ziqing^1

Statistic Institute, Shanxi University of Finance and Economics, wucheng road, Taiyuan, China^1

关键词: Blast furnace iron-making;    BP neural network model;    Extracting parameter;    Furnace temperatures;    Intelligent Manufacturing;    Non-linear relationships;    Practical problems;    Production increase;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/452/3/032098/pdf
DOI  :  10.1088/1757-899X/452/3/032098

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

In this paper, the practical problems of blast furnace iron making are considered, taking into account the influence of sulfur content [S], coal injection volume PML, blast volume FL and molten iron silicon content [Si], and extracting parameters according to data and data analysis. Because the parameters of this problem are more parameters and the nonlinear relationship is stronger, the control of parameters in the BP neural network model has become the focus of this paper. This paper finds that the BP neural network prediction model has a relatively high success rate for both numerical and furnace temperature rise and fall directions, and has wide applicability. At the same time, in the context of intelligent manufacturing in the process industry, the blast furnace iron making process can be greatly optimized, which is reflected in the reduction of raw material usage, production increase, and emission reduction.