Research on Intelligent Manufacturing Problem in Process Industry. Issue 3 (December 2018)

• Record Type: Journal Article
• Authors: Jiang, Jiachen; Hao, Ziqing
• Is Part Of: IOP conference series. Volume 452:Issue 3(2018)
• Publication Date: 2018-12
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/452/3/032098
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: 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.