Hot metal quality monitoring system based on big data and machine learning

The system of hot metal quality monitoring was established based on big data and machine learning using the real-time production data of a steel enterprise in China. A working method that combines big data technology with process theory was proposed for the characteristics of blast furnace production data. After the data have been comprehensively processed, the independent variables that affect the target parameters are selected by using the method of multivariate feature selection. The use of this method not only ensures the interpretability of the input variables, but also improves the accuracy of the machine learning process and is more easily accepted by enterprises. For timely guidance on production, specific evaluation rules are established for the key quality that affects the quality of hot metal on the basis of completed predictions work and uses computer technology to build a quality monitoring system for hot metal. The online results show that the hot metal quality monitoring system established by relying on big data and machine learning operates stably on site, and has good guiding significance for production.