Pellet Roasting Management System Based on Deep Learning and Internet of Things - Weixing Liu; Liyan Zhang; Jiahao Wang; Yiming Yang; Jie Li; Zhijie Zhang (Complexity)
Pellet is widely used in blast furnace ironmaking. Pellet quality affects the effect of ironmaking, the existing control system of grating-rotary kiln mainly adopts manual control mode, and the quality of pellet production largely depends on the experience, fatigue, and sense of responsibility of the site operators. The use of the Internet of things (IoT) technology in the integration and improvement of enterprise information level, to achieve fine, intelligent production management, at the same time, is conducive to promoting steel enterprises to reduce costs and increase efficiency, energy conservation and emission reduction, transformation and upgrading, and taking a new road to industrialization. According to the working principle and technological characteristics of the grate-rotary kiln at all stages, this paper designs the management system of firing pellets based on convolutional neural network (CNN) and IoT technology, so as to realize automatic recognition of image data obtained by the perceptual layer and make an intelligent analysis of it. The system can classify the working conditions of the current equipment, so as to judge whether the production process parameters of the grate-rotary kiln are up to the standard, thus achieving the goal of controlling the quality of the finished pellet.