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

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

Decarbonization options of the iron and steelmaking industry based on a three-dimensional analysis

Decarbonization is a critical issue for peaking CO_(2) emissions of energy-intensive industries,such as the iron and steel industry.The decarbonization options of China’s ironmaking and steelmaking sector were discussed based on a systematic three-dimensional low-carbon analysis from the aspects of resource utilization(Y),energy utilization(Q),and energy cleanliness which is evaluated by a process general emission factor(PGEF)on all the related processes,including the current blast furnace(BF)-basic oxygen furnace(BOF)integrated process and the specific sub-processes,as well as the electric arc furnace(EAF)process,typical direct reduction(DR)process,and smelting reduction(SR)process.The study indicates that the three-dimensional aspects,particularly the energy structure,should be comprehensively considered to quantitatively evaluate the decarbonization road map based on novel technologies or processes.Promoting scrap utilization(improvement of Y)and the substitution of carbon-based energy(improvement of PGEF)in particular is critical.In terms of process scale,promoting the development of the scrap-based EAF or DR-EAF process is highly encouraged because of their lower PGEF.The three-dimensional method is expected to extend to other processes or industries,such as the cement production and thermal electricity generation industries.