Dewen Jiang

The permeability index of a blast furnace is an important parameter to characterize the reasonable countercurrent movement between the charge and the gas flow. The prediction modeling and regulation of the permeability index are of great significance for energy saving and emission reduction in the ironmaking process. Herein, predictive modeling of the permeability index after one hour (PI‐1h) is carried out by selecting appropriate machine learning models for the different clusters separately based on six machine learning methods and fuzzy‐C‐means. In addition, The SHapley Additive exPlanations (SHAP) method is used to gain insight into the relevance of each parameter to the PI‐1h. The simulation results show that within the allowable error range, the prediction accuracy of the support vector regression and Gaussian process regression models reaches 95.2% and 99.5%, respectively. Based on the data used in this article and the parameter importance analysis of SHAP, permeability index, pressure difference, wind velocity, and hourly coal injection rate are the main parameters to regulate PI‐1h.

The sulfur content of hot metal in a blast furnace is an important index that reflects the production effects and quality of the hot metal. Establishing an accurate prediction model for hot metal sulfur content can guide the production process. In the present study, the blast furnace production data were collected and then preprocessed using box plotting. Cross-validation was used in the training process of the model to improve the generalization performance and robustness of the model. Two models for predicting the sulfur content in hot metal were established based on extreme gradient boosting (XGBoost) and multilayer perceptron (MLP) algorithms. The results show that coal consumption (CC), coal ratio (CLR), and sinter consumption (SC) are all positively correlated with hot metal sulfur content. The oxygen enrichment rate (OER) was negatively related to hot metal sulfur content. Both the extreme gradient boosting (XGBoost) and multilayer perceptron (MLP) models predicted hot metal sulfur content effectively; however, the extreme gradient boosting (XGBoost) model had a higher hit rate, accuracy, and stability, with the hit rate achieving 95.07%.

The gas utilization rate (GUR) is an important indicator parameter for reflecting the energy consumption and smooth operation of a blast furnace (BF). In this study, the original data of a BF are pre-processed by two methods, i.e., box plot and 3σ criterion, and two data sets are obtained. Then, support vector regression (SVR) is used to construct a prediction model based on the two data sets, respectively. The state parameters of a BF are selected as input parameters of the model. Gas utilization after one hour (GUR-1h), two hours (GUR-2h), and three hours (GUR-3h) are selected as output parameters, respectively. The simulation result demonstrates that using the 3σ criterion to pre-process the raw data leads to better prediction of the model compared to using the box plot. Moreover, the model has the best predictive effect when the output parameter is selected as GUR-1h.