During the smelting process of the AOD furnace, the unbalanced reaction of material will lead to the occurrence of splashing. It will not only damage the smelting equipment but also seriously injure the personnel. In this study, first, the information of liquid level, audio information and vibration information are detected by multiple sensors respectively. Then the fused information is used to forecast the splashing. Finally, the multitasking fuzzy controller is used to suppress splashing. The results show that the method of forecasting and suppressing splashing can accurately forecast and achieve rapid suppression. Thus the efficiency of smelting can be improved.

This paper presents an improved internal model control system to raise the efficiency of refining low-carbon ferrochrome. This control system comprises of a piecewise linearized transfer function and an improved internal model controller based on optimized time constant of the filter. The control system is mainly used to control the oxygen supply rate during the argon-oxygen refining for controlling the smelting temperature. The regulatory performance and servo of two closed-loop control schemes are compared between the improved internal model controller based on the optimized filter time 0000-0002-7606-6546and the internal model controller based on the fixed filter time constant. The simulation analysis shows that the piecewise linearized model and the optimization of the time constant of the filter improves the response time, stability, and anti-interference ability of the controller. Then, the proposed improved internal model controller is used to adjust the gas supply flow in 5 ton AOD furnace to control the smelting temperature. Ten production tests performed the effectiveness of the controlling refining optimal system. The analysis of the experimental data shows that the improved internal model control system can shorten the melting time and improve the melting efficiency. Thus, the application of the improved internal model control system in low-carbon ferrochrome refining is an interesting potential direction for future research.

In order to control the end-point carbon content in the process of argon–oxygen refining ferrochromium alloys, according to the mechanism model of the oxygen supply rate and carbon content variation, the mechanism model of the argon supply rate and carbon content variation, and the mechanism model of the supply rate of the two gases and molten iron temperature variation, this paper proposes an improved multivariable inference control system and uses expert control to realize the automatic adjustment of the filter time constant in the multivariable reasoning controller. The simulation results show that this method can ensure the stability of the control system without errors and maintain the furnace temperature below the maximum withstanding temperature, further improving the dynamic response of the system and effectively shortening the smelting time of the entire argon–oxygen refining ferrochromium alloy. It is beneficial for improving smelting efficiency and prolonging furnace service.