Research progress of converter splash mechanism and prediction model technology

As a high-temperature, high-pressure, multi-phase reaction vessel, the converter is vulnerable to splashing or slag overflow. Good molten pool surge can expand the slag–gold reaction area and enhance steelmaking efficiency. Abnormal molten pool surge can cause metal loss, damage the furnace body and its auxiliary equipment, and even threaten the personal safety of workers working in front of the furnace. This paper summarizes the previous research findings on splashing mechanisms and influencing factors. According to the occurrence principle, converter splashes can be classified into explosive splashes, foam splashes, metallic splashes, and other splashes, among which explosive splashes are the most dangerous and foam splashes occur most frequently. The occurrence of splashing accidents can be generally attributed to the high-temperature melt splashing caused by bubbles produced during the vigorous chemical reaction in the furnace and the splashing produced by the flow energy generated during the top–bottom combined blowing of the molten pool. The influencing factors of splashing are discussed based on six aspects: loading system, slag making system, oxygen supply system, bottom blowing system, temperature system, and safety system, and the foam of slag, oxygen lance blowing parameters, and bottom blowing parameters are thoroughly examined. It is observed that the occurrence of a splashing accident is frequently caused by the coupling of multiple factors. It is mainly one-sided, and hence the cause of the splashing accident cannot be unilaterally analyzed. Currently, no methods are present that can effectively quantify the effect of each factor on the splashing. Thus, developing a set of safety evaluation models suitable for converter splashing is imperative. Furthermore, the author summarizes the existing splash prediction models, examines the benefits and drawbacks of some splash prediction models, and summarizes the prediction principles and some application outcomes of the furnace gas analysis, audio analysis, and image analysis methods. Although preliminary progress has been made in the study of prediction models, there are still challenges that need to be overcome. It is pointed out that the reason why the existing prediction models have not been widely used is due to the low prediction accuracy, short prediction time, high cost, and low practicability. Several researchers have used a combination of several models to predict splash in converter. The findings reveal that various models can learn from each other, and the prediction accuracy of the comprehensive model is higher than that of the single model. Furthermore, the splash prediction model will become more intelligent and refined in the future.