Intelligent optimization method for dynamic scheduling of hot metal ladles of one-ladle technology on ironmaking and steelmaking interface in steel plants

The one-ladle technology requires higher ironmaking and steelmaking interface technology. The scheduling of the hot metal ladle in the steel plant determines the overall operation efficiency of the interface. Considering the strong uncertainties of the real-world production environments, the paper studies the dynamic scheduling problem of hot metal ladles and develops a data-driven based three-layer approach to cope with the problem. In the approach, a dynamic scheduling optimization model of the hot metal ladle operation with the minimum average turnover time as the optimization objective is constructed. Further, the intelligent perception of industrial scenes and autonomous identification of disturbances, the adaptive configuration of dynamic scheduling strategies, and real-time adjustment of schedules can be realized. To be specific, the upper layer generates a demand-oriented pre-scheduling scheme for hot metal ladles. The middle layer adaptively adjusts it to obtain an executable schedule according to the actual supply-demand relationship. In the lower layer, three types of dynamic scheduling strategies are designed from the characteristics of the dynamic disturbance in the model: real-time flexible fine-tuning, local machine adjustment and global rescheduling. The case test using 24-hour production data of a certain day during the system operation of a steel plant shows that the method and system can effectively reduce the hot metal ladle operation time and its fluctuation, and improve the stability of the ironmaking and steelmaking interface production rhythm. The data-driven dynamic scheduling strategy is feasible and effective, which shows that the method can obviously improve the operation efficiency of hot metal ladles.