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Tapping weight is an important parameter in converter blowing process, wherein precisely predicting the quantity of steel required in an alloy baking converter can effectively guide the requirement of alloy ingredients. In practical production, the main approach is empirical estimation, despite its low accuracy. Employing a general neural network model for prediction requires to gather the converter blowing parameters and endpoint temperature measurement sampling parameters as the model inputs. However, this data cannot be obtained until the blowing process reaches its endpoint, rendering it impractical for alloy batching that requires advance preparation for baking. In this study, a principal component analysis–whale optimization algorithm–backpropagation algorithm (PCA–WOA–BP) neural network tapping prediction model is developed using raw material parameters available before alloy baking as input. This model is integrated into the intelligent alloy reduction model of a factory. The model achieves a regression coefficient R2 of 0.823, with 98.53% of furnaces having a prediction error of less than 2 t. The tapping weight of 20 consecutive heats in actual production is predicted; the error range is less than 80% within 1 t, and the converter tapping weight is predicted accurately. [ABSTRACT FROM AUTHOR]

When the molten steel contains high carbon and alloy elements, the continuous casting bloom is prone to internal defects. Soft reduction is an effectively means of improvement and is widely utilized in the continuous casting process. Herein, the generation mechanism of the central cracks in alloy steel ZKG223 is analyzed in detail; that is, the tensile stress is generated by the accelerated solidification and shrinkage in the core of the bloom at the end of solidification, which acts on the dendrites and causes central cracks. In order to reduce the shrinkage stress during the solidification of the bloom, the reasonable reduction parameters of the bloom are determined by establishing a soft reduction model, and the industrial test is carried out. Through the implementation of well‐designed soft reduction, the central cracks in the bloom are significantly bridged, and the secondary dendrite spacing in the center of the bloom is shortened from 315 to 230 μm. The maximum positive and negative carbon segregation indices in the cross section of the bloom are changed from 1.039 and 0.957 to 1.020 and 0.977, respectively, which greatly improve the uniformity of element distribution and the internal quality of the bloom. [ABSTRACT FROM AUTHOR]

The surface cracking problem in medium carbon sulfur‐containing steel is gradually emerging in continuous casting production. Therefore, this article investigates the effect of sulfur content on the surface cracks of medium carbon steel. In addition to macroscopic cracks, network cracks are visible on the surface of medium carbon sulfur‐containing bloom. 3D confocal laser scanning microscopy and industrial computed tomography are used to characterize the 3D realistic morphology of the network and macroscopic cracks. Thermodynamic calculations and hot ductility tests' results exhibit the higher precipitation of MnS in the sulfur‐containing steel, resulting in the second brittle zone of steel. The microstructure near the cracks revealed a high correlation between cracks and a high proportion of proeutectoid ferrite near the surface of the medium carbon sulfur‐containing bloom. In addition, the sulfur‐containing steel has 0.05% more linear shrinkage than the low‐sulfur steel during the transformation from austenite to ferrite, which increases its crack sensitivity. As a result, this article clarifies the effect of sulfur on the surface crack of medium carbon bloom. [ABSTRACT FROM AUTHOR]