Multi-step prediction of blast furnace permeability index based on multi-time-scale analysis

The permeability index (PI) of a blast furnace (BF) is a key indicator of furnace performance, as it reflects the extent of indirect reduction, energy consumption, molten iron quality, and overall production efficiency. Accurate prediction of the PI is essential for ensuring stable and efficient BF performance. Due to the complex multi-time-scale characteristics of different operational parameters, this paper presents a multi-step prediction model based on multi-time-scale analysis to capture their long-term evolution trends. First, the multi-time-scale characteristics of BF operation are analyzed from both the smelting mechanism and data-driven perspectives. According to the characteristics, this paper constructs a single-step prediction model of PI on the long-time-scale, medium-time-scale, and short-time-scale, respectively, and introduces an iterative compensation strategy to extend each single-step model into a multi-step prediction framework, and then fuses the prediction results under the multi-time-scale to obtain the results of the future PI. Finally, the performance evaluation is shown based on actual industrial data, which verifies the significant advantages of the proposed multi-step prediction method based on iterative compensation in terms of accuracy and stability.