To address the challenges of low accuracy and poor robustness of traditional single prediction models for blast furnace molten iron temperature, a hybrid model that integrates the improved complete ensemble empirical mode decomposition with adaptive noise, kernel principal component analysis, support vector regression and radial basis functional neural network is proposed for precise and stable iron temperature prediction. First, the complete ensemble empirical mode decomposition is employed to decompose the time series of iron temperature, yielding several intrinsic mode functions. Second, kernel principal component analysis is used to reduce the dimensionality of the multi-dimensional key variables from the steel production process, extracting the major features of these variables. Then, in conjunction with the K-means algorithm, support vector regression is utilized to predict the first column of the decomposed sequence, which contains the most informative content, evaluated using the Pearson correlation coefficient method and permutation entropy calculation. Finally, radial basis function neural network is applied to predict the remaining time series of iron temperature, resulting in the cumulative prediction. Results demonstrate that compared to traditional single models, the mean absolute percentage error is reduced by 54.55%, and the root mean square error is improved by 49.40%. This novel model provides a better understanding of the dynamic temperature variations in iron, and achieves a hit rate of 94.12% within a range of ±5℃. Consequently, this work offers theoretical support for real-time control of blast furnace molten iron temperature and holds practical significance for ensuring the stability of blast furnace smelting and implementing intelligent metallurgical processes.

Temperature control in bath smelting processes is crucial for optimizing the efficiency and quality of metal extraction, especially for nickel and copper. Traditional prediction methods often fail to account for the nonlinear and complex nature of these processes. This work introduces a novel hybrid nonlinear analysis algorithm combining the random forest–least squares support vector machine (RF-LSSVM) and random forest–relevance vector machine (RF-RVM) models to enhance the accuracy of temperature prediction. Utilizing 868 datasets collected from an oxygen-enriched top-blown furnace, key parameters such as the feeding amount (X₁), oxygen pressure (X₂), oxygen flow (X₃), total air flow (X₇), and lance windpipe back pressure (X₅) were analyzed. The RF-LSSVM model achieved superior predictive performance, with a mean absolute error (MAE) of 7.58 and a root mean square error (RMSE) of 9.82 for matte temperature (Y₁), and an MAE of 10.47 and an RMSE of 13.31 for slag temperature (Y₂). Comparatively, traditional methods showed higher errors, with MAE values of up to 23.64 and RMSE values as high as 59.14 in some cases. Additionally, the RF-RVM model performed significantly better than conventional models, with MAE and RMSE improvements of approximately 10~20%. These results demonstrate that the hybrid models effectively capture the intricate dynamics of the smelting process, offering a robust and adaptive framework for real-time temperature prediction. The improved accuracy in temperature control leads to enhanced smelting efficiency, reduced energy consumption, and higher quality of the extracted metals, ultimately benefiting the metallurgical industry by enabling more precise and sustainable production processes.

Particle suspension and deposition dynamics are significant factors affecting the level of mixing quality in solid-liquid two-phase stirring processes. In general, the ability to increase the suspension rate and minimize deposition effects is instrumental in improving the uniformity of particle mixing, accelerating the reaction of involved solid-liquid two-phase, and improving the efficiency of production operations. In this work, suspension and deposition indicator based on the Betti number and a uniformity indicator are introduced and obtained by means of image analysis. The influence of the blade type, rotation speed, blade diameter and blade bottom height on the particle suspension/deposition characteristics and mixing uniformity are carefully investigated. The experimental results show that the two-phase motion region can be divided into three local regions, including a bottom motion along the wall, a low-degree suspension region under the blade and a high suspension region above the blade. The best degree of particle suspension is attained by the double-inclined blade paddle at a speed of 270 r/min, a paddle diameter ratio of 0.414, and a height-diameter ratio of 0.086. The double-inclined blade paddle has a better effect on promoting particle suspension and solid-liquid two-phase mixing uniformity.