Converter Steelmaking Clean Steel Production Process Cost-Benefit Analysis and Key Process Link Dynamic Regulation Strategy

Abstract

Based on the clean steel production process flow of converter steelmaking, this paper proposes a design method for process flow optimization, utilizing data analysis methods and numerical simulation for analysis and calculation. When applying this scheme, a process data analysis platform for converter steelmaking was created using machine learning and numerical simulation methods to identify the characteristics of process patterns from large-scale production data analysis. The newly proposed dynamic control scheme strategy is based on a model predictive control method, which combines the implementation of important process data information with process feedback, ultimately achieving dynamic adjustment of process parameters to enhance production efficiency. The results obtained from multiple experiments using this scheme and dynamic control methods ensure that the production cost of clean steel is reduced by 8.5% to 15.2% compared to when it is not used, and the production pass rate averages over 97%.During numerical simulation experiments, it was found that there is a relationship between the pressure of top-blown oxygen and the angle of the nozzle, and this relationship has a significant impact on molten steel temperature and decarburization degree. By comparing the production efficiency and energy consumption under different process parameter combinations, it was discovered that the use of a segmented dynamic control method not only improves various metallurgical parameters but also effectively reduces production fluctuations. The numerical simulation experiments demonstrated that this dynamic control scheme has high feasibility and strong practicality, providing an excellent solution for the production process of clean steel in converter steelmaking, with significant potential for widespread application.