## 6 Conclusions  
1. Based on the data from the actual hourly production process of a steel enterprise's #2 blast furnace in June and July, outliers were detected using the isolated forest algorithm with the raw data, and the detected outliers are analyzed, eliminated, and/or retained. The hourly level data-driven samples were obtained through data deviation normalization, which can accurately predict the blast furnace permeability index after an hour to ensure the stable, smooth running of the blast furnace. Through the analysis of the coupling mechanism of blast furnace permeability and gas flow, Spearman correlation analysis, and MIC analysis of the selection of 25 key parameters affecting the production of the blast furnace, 14 characteristic variables were ultimately selected. These were: the roof pressure, the volume fraction of gas CO, the amount of coal spraying, the pressure difference, the volume fraction of gas CO\({}_{2}\), the furnace belly gas index, the utilization of the gas, the hot air pressure, the temperature of the top of the furnace, the amount of oxygen enrichment, the oxygen enrichment rate, the kinetic energy of the blowing air, the air temperature, and the flow rate of the cold air. 2. The wavelet neural network (WNN) has a large advantage over a BP neural network (BP), a particle swarm-optimized BP neural network (PSO-BP), and Xgboost in the data-driven prediction of the blast furnace permeability index. The RMES (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error) with the model accuracy (error at \(\pm 0.1\)) indicators are better than the other three models, and the model prediction accuracy reached 95.71% at an error of \(\pm 0.1\), which offers a great impact on blast furnace production compliance. 3. Making full use of the data collected during the actual production of the blast furnace, we carried out the data-driven construction of a predictive model of the blast furnace permeability index and put the constructed model into the actual production of a No. 2 blast furnace of an iron and steel enterprise. The results showed that the predicted value of the blast furnace permeability index is very much in line with the actual value of real-time blast furnace production and maintains excellent characteristics. The aim of this study was to develop an efficient and reliable gas permeability index prediction model for blast furnaces to ensure the stable operation of the blast furnace smelting process. Therefore, it has considerable application prospects and a popularization value in actual blast furnace production. Figure 11: Comparison of predicted values and actual production values. **Author Contributions:** Conceptualization, K.T. and Z.L. ; methodology, K.T. ; software, K.T. and Z.L. ; validation, K.T., Z.L. and Y.H. ; formal analysis, K.T. ; investigation, K.T. ; resources, Y.H. ; data curation, K.T. ; writing--original draft preparation, K.T. ; writing--review and editing, K.T. ; visualization, K.T. ; supervision, Z.L., Y.H., W.W. and X.Q. ; project administration, K.T. ; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 52074126); Tangshan Science and Technology Program (No. 22130201G); Natural Science Research Program of Higher Education Institutions in Hebei Province (No. QN2023153) and the Natural Science Foundation of Hebei Province (E202209110). **Data Availability Statement:** Not applicable. **Acknowledgments:** Thanks are given to the Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes for their help with training. Special thanks to [PERSON] for his meticulous guidance. We would also like to extend our appreciation to our colleagues for their unwavering support and companionship. Additionally, we would like to thank [PERSON], [PERSON], and [PERSON] for their valuable contributions to the manuscript.