Springer Science and Business Media LLC

The recycling rate of rejected electrolytic manganese metal (EMM) scrap can be increased by inhibiting the manganese metal (MM) vaporization during the remelting process with electroslag. However, if the latter is achieved by reducing the remelting temperature, the desulfurization behavior will deteriorate. Therefore, Na2O-containing electroslag and metallic additive were used to increase the rejected EMM scrap recovery ratio. The respective high-temperature experiment was conducted in a MoSi2 electrical resistance furnace filled with fluid argon at 1673 K using five different types of electroslag with the Na2O content ranging from 5.81% to 15.71%. High-purity metallic magnesium and magnesium calcium alloy additives were used as deoxidizers. The addition of Na2O and metallic additives effectively promoted the desulfurization and deoxidization of MM. The removal of sulfur and oxygen by the interaction between Na2O-containing electroslag melt and molten MM with metallic additive was analyzed from the thermodynamic and kinetic standpoints. The effect of Na2O-containing electroslag volatilization on desulfurization and deoxidization was considered. With an increase in Na2O content in the slag, the mass loss rates of Na2O and electroslag rose, as well as the final sulfur partition ratio. If the Na2O content volatilized in the slag melt did not exceed 10.44%, the sulfur removal ratio was increased by high sulfide capacity and CaO activity in all slags due to the addition of Na2O. The rejected EMM scrap deoxidization ratio grew with the increased activity of CaO and reduced activity of Al2O3 in the molten slag, caused by the increased Na2O content in the molten slag. The addition of metallic Mg and Mg–Ca alloy indirectly promoted desulfurization and deoxidization by reducing the MnO content in the rejected EMM scrap and growing slag oxidability. The Mg–Ca alloy could also react with dissolved sulfur and oxygen, directly promoting desulfurization and deoxidization processes. The Na2O content in slag should not exceed 10.44% to ensure the high desulfurization and deoxidization abilities, fluidity and low volatilization of slag.

Elongated MnS inclusions in rolled non-quenched and tempered steel tend to cause the mechanical anisotropy of steel, deteriorate the mechanical properties and degrade the quality and service life of the steel products. To reveal the mechanisms of morphological transformation of strip-shaped MnS inclusions during isothermal heating, the effects of heat treatment time and temperature on the morphology, number density and size distribution of elongated MnS inclusions were systematically studied and discussed. A diffusion couple experiment was also conducted to clarify the diffusion mode of MnS inclusions. The experimental results showed that with the increase in isothermal heating time (from 0 to 10 h at 1473 K) and temperature (from 1173 to 1573 K for 3.0 h), the number density and average aspect ratio of MnS inclusions generally showed an increase and decrease trend, respectively, while the area fraction remained stable and only slightly fluctuated around 0.4%. In the diffusion couple, after the isothermal heating at 1473 K for 3.0 h, the elements Mn and S in the steel near the steel–MnS interface were very stable without any concentration gradient. The morphology change sequence of the elongated MnS inclusions in the rolled non-quenched and tempered steel during the isothermal heating was strip → cylinderization → spindle → spheroidization. Relationship between the diameter of MnS inclusion and the spacing between two MnS inclusions after splitting, and the fitting goodness of different n values under different experimental time and temperature confirmed that the driving force for the transformation of MnS inclusions during the isothermal heating was surface diffusion, instead of volume diffusion.

The basic high-temperature properties of iron ore play a crucial role in optimizing sintering and ore blending, but the testing process for these properties is complex and has significant lag time, which cannot meet the actual needs of ore blending. A prediction model for the basic high-temperature properties of iron ore fines was thus proposed based on a combination of machine learning algorithms and genetic algorithms. First, the prediction accuracy of different machine learning models for the basic high-temperature properties of iron ore fines was compared. Then, a random forest model optimized by genetic algorithms was built, further improving the prediction accuracy of the model. The test results show that the random forest model optimized by genetic algorithms has the highest prediction accuracy for the lowest assimilation temperature and liquid phase fluidity of iron ore, with a determination coefficient of 0.903 for the lowest assimilation temperature and 0.927 for the liquid phase fluidity after optimization. The trained model meets the fluctuation requirements of on-site testing and has been successfully applied to actual production on site.

To improve the selective catalytic reduction of NO with NH3 over active coke (AC), coal–biomass ACs were prepared from the mixture of poplar and 1/3 coking coal for increasing the active sites. The resultant ACs were characterized by N2 adsorption and X-ray photoelectron spectroscopy. Furthermore, the denitrification performance was tested at laboratory scale. In addition, density functional theory was used to analyze active sites on the surface of AC. The result revealed that, with an increase in poplar content, the decrease in micropores volume appeared in the reduction of denitrification space. However, C−O group including hydroxyl and ether increased with the increase in poplar content, which was found to be most likely responsible for the promoted catalytic activity of AC toward NO reduction mainly because of enhancing NH3 adsorption. The comprehensive effect of two factors made the denitrification ability of AC increased first and then decreased.

FeO-containing slag originated from the basic oxygen furnace to the ladle is a major reoxidation source during the following secondary refining. Ladle slag reduction treatment (slag treatment) is one of the common countermeasures adopted to eliminate the steel contamination by FeO reoxidation. The oxygen transfer phenomenon between molten steel and slag was studied during the industrial production of interstitial-free (IF) steel, the measured and calculated oxygen activities in steel were compared, and the Fe–O equilibrium at the slag–molten steel interface was investigated by thermodynamic analysis. With slag treatment, the oxygen potential is higher in the molten steel than in the pre-deoxidation slag; this causes oxygen transfer from the molten steel to the slag, decreasing the efficiency of slag treatment. Based on this, a two-step slag deoxidation process was optimized. The second step further reduced the FeO content. On the other hand, the CaO/Al2O3 (C/A) ratio in the refining slag must be controlled, because it affects the FeO activity and inclusion absorption capacity of the slag. The results suggest that the C/A ratio of 1.2–1.5 and the FeO content of < 6% are beneficial to refine IF steel.

Aiming at the problem of insufficient prediction accuracy of strip flatness at the outlet of cold tandem rolling, the prediction performance of strip flatness based on different ensemble methods was studied and a high-precision prediction ensemble model of strip flatness at the outlet was established. Firstly, based on linear regression (LR), K nearest neighbors (KNN), support vector regression, regression trees (RT), and backpropagation neural network (BPN), bagging, boosting, and stacking ensemble methods were used for ensemble experiments. Secondly, three existing ensemble models, i.e., random forest, extreme random tree (ET) and extreme gradient boosting, were used to conduct experiments and compare the results. The research shows that bagging, boosting, and stacking three ensemble methods have the most significant improvement in the prediction accuracy of the regression trees model, which is increased by 5.28%, 6.51%, and 5.32%, respectively. At the same time, the stacking ensemble method improves both the simple model and the complex model, and the improvement effect on the simple base model is the greatest, which is 4.69% higher than that of the base model KNN. Comparing all of the ensemble models, the stacking ensemble model of level-1 (ET, AdaBoost-RT, LR, BPN) paired with level-2 (LR) was discovered to be the best model (EALB-LR) and can be further studied for industrial applications.