Steelmaking-Casting of Molten Steel by Decarburization Ladle Matching

Abstract

Steelmaking-continuous casting is a complex process. The method of selecting a ladle, which also functions as a storage device, follows a specific process of the production plan. In ladle matching, several ladle attributes are considered. However, matching objectives are difficult to achieve simultaneously. Different molten steel properties have contributed to the complexity of matching constraints, and, thus, matching optimization is regarded a multiconflict goal problem. In the process of optimization, the first-order rule learning method is first used to extract key ladle attributes (performance indicators), including highest temperature, usage frequency, lowest-level material, and outlet. On the basis of a number of indicators, such as ladle temperature, quantity, material, and usage frequency, as well as skateboard quantity, the ladle matching model is established. Second, the rule of ladle selection is determined by the method of least-generalization rule learning. Third, a simulation experiment is carried out according to various scheduling order strategies and matching priority combinations. Finally, the heuristic ladle matching method based on the rule priority (RP) is determined for possible industrial applications. Results show that the accuracy of ladle selection can be improved. In particular, the numbers of ladles and maintenance times are reduced. Consequently, furnace production efficiency is also enhanced.