48. The research on intelligent extraction of furnace mouth flame characteristics based on DNN

• Published in: Journal of Mathematical Models in Engineering (MME) Vol. 4; no. 1; p. 42
• Main Authors: Tian, Lijia, Xing, Jiaying, Zhao, Heyu, Chang, Jincai
• Format: Journal Article
• Language: English
• Published: JVE International Ltd 01.03.2018
• Subjects: Algorithms; Applied research; Artificial neural networks; Iron and steel making; Machine learning; Methods; Technology application; China
• ISSN: 2351-5279
• DOI: 10.21595/mme.2018.19765

Deep neural networks are a focus of artificial intelligence and big data analysis in recent years. The monitor of the converter mouth is essential to the quality of the steel material production while the requirement of the steel material production is increasingly higher in China. The end-point control of converter blowing is the ultimate regulation of the carbon content and temperature. The severity of carbon-oxygen reaction and the temperature of molten steel can be reflected by the converter mouth flame. Operators judge the end of the steel by watching the converter mouth flame, the converter mouth spark and the time of oxygen supply. So, it is very important to offer a quantitative analysis to converter mouth flame characteristics. We quote the deep neural network into the intelligent extraction of the flame characteristics of the furnace mouth and construct a flame color recognition algorithm based on the deepness letter neural network. This paper belongs to the data science problem in the intelligent research of steel production. By observing the converter flame during the steel flame changes, this paper records the data of light intensity and end-point carbon content of each steel making furnace. When this paper then uses the temperature of flame emission spectrum to deduce and the absorption of the molten steel to judge the contents of the carbon during the converter steel blew process, it is more feasible and accurate than watching by operators. At the same time, by using deep learning algorithm, this paper makes the control process get automatic learning ability and achieve intelligent production so that we can provide a basis for solving the problem of predicting the end-point carbon content in molten steel during the blowing process. Keywords: deep neural network, deep learning, carbon content, end point control, spectrum.