Based on the historical data of a commercial blast furnace (BF), the evaluation and prediction models for the BF comprehensive operating status were established by big data mining methods. Firstly, based on the data resources of the data warehouse of BF ironmaking, clean data were obtained by processing the original data with the problem of null values, outlier data, and blowing-down operations data. Then, the AHP_EWM_TPOSIS evaluation model was built with the combined weight of AHP and EWM and the improved TOPSIS algorithm. Finally, the model evaluation results were verified with the actual production situation, and the comprehensive matching rate reached 94.49%, indicating that the model can accurately judge the comprehensive operating status of BF. The evaluation result was the target parameter for building the BF comprehensive operating status prediction model. The results showed that the stacking model achieved better results than the base models in all indicators. The accuracy index of the deviation between the predicted value and the actual value within ±0.05 reached 94.50%, which meets the practical needs of BF production. The evaluation and prediction models provided timely and accurate furnace condition information to the operators in the BF smelting process, which promoted the long-term stable operation of the BF condition.

Sinter ore quality directly affects the stability of blast furnace production. In terms of both physical and chemical properties, the main indicators of sinter quality are the TFe content, alkalinity, and drum index. By analyzing the massive historical data on the sinter production of a steel company, this study proposes a sinter quality prediction system based on Granger causality analysis and a stacking integration algorithm. First, based on real historical data of sintering production in steel enterprises (including coal gas pressure, ignition temperature, combustion air pressure, etc.), data preprocessing of raw data was carried out using a combination of feature engineering and the sintering process. Second, Pearson correlation analysis, Spearman correlation analysis, and Granger causality analysis were used to screen out the characteristic parameters with a strong influence on the target variable of sinter quality (drum Index, TFe, alkalinity). Third, a prediction model for sinter quality parameters was developed using a stacking integration algorithm pair for training. Finally, a program development tool was used to realize the establishment and online operation of a sinter ore quality prediction system. The test results showed that the predicted goodness of fit of the model for the TFe content, alkalinity (R), and drum index were 0.942, 0.958, and 0.987, respectively, and the model calculation time met the actual production requirements. By establishing a suitable model and running the program online, the real-time prediction of the main indicators of sinter quality was realized to guide production promptly.

The vanadium content of molten iron is an important economic indicator for a vanadium–titanium magnetite smelting blast furnace, and it is of great importance in blast furnace production to be able to accurately predict it and optimize the operation of vanadium extraction. Based on the historical data of a commercial blast furnace, the clean data were obtained by processing the missing data and outlier data for data mining analysis and model development. A combined wavelet-TCN model was used to predict the vanadium content of molten iron. The average Hurst index after wavelet transform was calculated to reduce the complexity of the wavelet transform layer selection and the model computation time. The results show that compared to single models, such as LSTM, LSTM with attention, and TCN, the combined model based on wavelet-TCN (a = 5) had an improvement of about 11~17% in R², and the prediction accuracy was high and stable, which met the practical requirements of blast furnace production. The factors affecting the vanadium content of molten iron were analyzed, and the measures to increase the vanadium content were summarized. A blast furnace should avoid increasing the titanium dioxide load, increase the vanadium load appropriately, and keep the relevant operating parameters within the appropriate range in order to achieve the optimization of vanadium extraction from molten iron.

Fluctuations in the sintering process have a significant impact on the overall performance and outcomes of sintering production. It is crucial to gain a comprehensive understanding of the sintering operation in a timely manner in order to minimise process fluctuations and ensure the stability of sinter production. In this paper, a sintering process evaluation model is presented, which is based on the analytic hierarchy process–entropy weight method–technique for order preference by similarity to an ideal solution (AHP–EWM–TOPSIS) and is constructed using data from a steel plant sintering production process. Firstly, the evaluation indices input to the system are selected by combining the principle of the sintering process and actual production requirements. The subjective and objective weights of the indices are then calculated using the AHP–EWM method, and the weights are combined through the principle of minimum discriminatory information to construct the set of weighted indices. Subsequently, the sintering process was evaluated by the TOPSIS model, and the results of this evaluation were compared with the actual fluctuation situation at the sintering site. This was done by combining the model results with the differential level method. It was found that the model can accurately reflect the fluctuation situation that occurs in the sintering process. Furthermore, the comprehensive matching rate reached 95.42%, which demonstrates that the model is capable of accurately evaluating the operation of the sintering process. The statistical analysis of historical data provides a basis for the optimised operation of the sintering production process by summarising the optimal range of each parameter when the sintering operates efficiently. Ultimately, a sintering process evaluation system is constructed through the use of computer technology, thereby facilitating the transformation of sintering production into an intelligent system. The model is capable of rapidly ascertaining the actual status of the sintering process in real time. The established system is designed to facilitate the optimisation of sintering production operations and to promote long-term stability in sintering.

To address the challenges of strong temporal dependencies, multivariable coupling, and nonlinearity in blast furnace hot metal output prediction, this study proposes an interpretable prediction framework that integrates a feature selection module based on Light Gradient Boosting Machine and Recursive Feature Elimination with Cross-Validation (LightGBM_RFECV) with a hybrid LSTM-Transformer model. First, an iterative two-stage feature optimisation framework is adopted: feature importance is evaluated using LightGBM (based on information gain), and redundant features are eliminated through RFECV with recursive cross-validation. Then, a combined prediction model that integrates temporal modelling and attention mechanisms (LSTM-Transformer) is constructed, achieving high prediction accuracy (MAE = 5.47, R ² = 0.8747, and a ± 20-ton accuracy rate of 96.17%), significantly outperforming baseline models. Furthermore, the Captum toolkit is introduced to perform feature attribution, quantifying the influence mechanisms of process variables on output and forming an intelligent ‘prediction–attribution–regulation’ closed loop. Experiments based on real production data verify the effectiveness of the proposed method, providing data-driven decision support for energy conservation, emission reduction, and stable blast furnace operation, thereby contributing to the intelligent transformation of the steel industry.