Intelligent control model of steelmaking using ferroalloy reduction and its application

The steel industry is a major energy consumer in China. As an effective measure for energy saving, cost and emission reduction, and higher efficiency among enterprises, ferroalloy reduction has attracted increased attention in our work to reduce carbon dioxide emissions and realize carbon neutrality. In the steelmaking process, the chemical composition of molten steel is required to meet the target ratio to maintain certain metallurgical and mechanical properties. The chemical composition of molten steel is mainly adjusted using ferroalloys. With the development of ferroalloy smelting technology, ferroalloys of various types are developed. These ferroalloys show major gaps in cost performance and composition. Before ferroalloy addition, it is essential to determine an appropriate and cost-effective type and its amount for cost-saving purposes. However, the traditional method of offering a manually determined amount cannot meet the above requirement. Therefore, it is necessary to explore an intelligent ferroalloy addition method without human intervention. Based on the K-means clustering algorithm, this paper studied ferroalloy loss in the basic oxygen furnace (BOF) steelmaking process. The key factors affecting the alloy loss were analyzed and divided into three clusters to obtain a process model of the lowest loss amount in the BOF steelmaking process. Using this model, an intelligent control system for alloy reduction was developed. The system is based on the principal component analysis and backpropagation neural network and mixed-integer linear programming. This system was implemented in a steelmaking plant, in which the accuracy and practicability of this model were verified by running it online. This model helped improve the accuracy of alloyed steel composition and reduce the unnecessary cost and extra composition, which are frequently seen in traditional calculations with a manual experience. The ferroalloy dosing scheme is also optimized, and the alloying cost of steelmaking is reduced. The total cost of adding ferroalloys of various types is reduced by 5.95% to 14.74%, with an average reduction of 11.72%.