In recent years, the University of Science and Technology Beijing has delved into the electric arc furnace (EAF) steelmaking process by applying the theory of spatiotemporal multi - scale structure. It has identified multiple spatiotemporal scales within the material transformation process, including micro - scale, meso - scale, unit operation scale, and station scale.
Building on a comprehensive review of existing process control models from both domestic and international steel enterprises, the university has integrated an EAF cost control model and an EAF steelmaking process expert guidance model. This integration has led to the construction of a multi - scale model for the EAF, refining, and continuous casting processes. This model serves a dual purpose: cost monitoring and providing online optimization guidance for the EAF steelmaking process.
The model has been successfully implemented in the EAF production processes of several enterprises, such as Xinyu Xinliang Special Steel, Hengyang Steel Pipe, Malaysia Anyu Steel, Taiwan Yisheng Steel, Xining Special Steel, and Tianjin Steel. Remarkable results have been achieved, with an average reduction of 2 Nm³ in oxygen consumption per ton of steel, a 2 kWh decrease in power consumption, a 10 kg reduction in metal material consumption, and a cost reduction of over 30 yuan per ton of steel. These achievements have brought about significant economic and social benefits.
In recent times, with the advancement of intelligent algorithms, researchers have introduced techniques like artificial neural networks, support vector machines, and genetic algorithms into the realm of EAF steelmaking. This has led to the development of a series of end - point prediction models, which have demonstrated favorable application results in practical scenarios.
However, the "black - box models" based on intelligent algorithms heavily rely on data and lack the guidance of the production process. To address this limitation, in recent years, a hybrid end - point prediction model that combines reaction mechanisms with intelligent algorithms has gradually emerged.
It is foreseeable that in the field of end - point control of EAF steelmaking, the development of more effective monitoring technologies and highly reliable intelligent models, along with their organic combination, will become a key research focus. This combination is expected to enhance the accuracy and reliability of end - point control, thereby improving the overall quality and efficiency of the steelmaking process.
With the progress of monitoring methods and computer technology, intelligent control of EAF steelmaking is no longer confined to the monitoring and control of individual links. Instead, it should take a holistic approach, starting from the entire process. By integrating the information collected during the smelting process with the fundamental mechanisms of the process, it can conduct analysis, make decisions, and implement control measures to pursue the overall optimization of the EAF steelmaking process.
Real - time overall control of the steelmaking process has significantly improved energy utilization, production efficiency, and the safety of the production process. The system employs the latest detection technology and a condition - monitoring control scheme to optimize the control of the EAF steelmaking process. This ensures maximum production efficiency, the best energy conversion rate, and the lowest production cost.
The overall intelligent control of the EAF steelmaking process depends on the level of intelligent control of each individual link. Currently, research in this area is still in its early stages. The continuous optimization of monitoring methods and control models in the smelting process will drive the further development of overall intelligent control in EAF steelmaking, paving the way for more efficient and sustainable steel production.
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