The mineralogical structure of flux films is a critical factor in controlling heat transfer in the mold and avoiding the longitudinal cracking of slabs during the continuous casting of peritectic steel. In this study, the layered structure, crystallization ratio, mineralogical species, and morphology features of flux films were characterized by optical microscopy, X-ray diffraction, and electron-probe microanalysis. Microstructural observation revealed that the normal flux films for peritectic steels present a multilayered structure and high crystallization ratio (60~90 vol%), mainly composed of well-developed crystalline akermanite and cuspidine. In contrast, the films with outstanding flux characteristics with abundant longitudinal cracks on the slab surface have a low crystallization ratio (80 vol%). Furthermore, heat transfer analysis showed that the low crystallization ratio and the vast crystallite content of flux films worsen the heat transfer rate or uniformity in the mold, whereas the appropriate thickness and cuspidine content of flux films can improve the heat transfer performance. From the above results, it is concluded that using strong crystalline flux to obtain the ideal mineral phase structure of flux film is one of the important measures for reducing longitudinal cracks during continuous casting of peritectic steel slabs.

The degree of homogeneity of the sintered ore phase structure directly determines its quality index. A sinter ore quality evaluation method based on the quantification of the homogeneity of the mineral phase structure is proposed. First, the magnetite particle size characteristics in the ore phase structures with different degrees of homogeneity were summarized under a polarized light microscope, and a criterion for evaluating the uniformity of the sintered ore phase structure based on the magnetite content of different particle size grades was determined. Second, a multiple regression model was established for the raw material composition ratio of magnetite with varying particle size grades. Finally, the multiple regression model was optimized using the second-generation non-dominated sorting genetic algorithm (NSGA2). The results show that mineral phase structure analysis categorized the magnetite particle sizes into 60 μm. The adjusted R² of the multiple regression model of the chemical composition of raw materials and the proportion of magnetite of each particle size grade were all greater than 0.95, and the p values were all <0.05, indicating a high degree of model fitting. Using model analysis, the single factor and the interaction between the multiple factors that significantly influence the proportion of magnetite in the three particle size grades were determined. The multivariate regression model was optimized using the NSGA2 algorithm to determine the ratios of Al₂O₃ mass% = 1.82, MgO mass% = 1.50, and R(CaO mass%/SiO₂ mass%) = 1.84 for the highest degree of uniformity of the sintered ores. Under this sintering condition, the micro-mineral phase structure became more homogeneous, confirming the model’s reliability.

Ultra-wideband (UWB) technology is widely used for high-precision indoor positioning due to its adaptability to various environments. However, in long linear areas, such as tunnels or corridors, the near-linear deployment of base stations caused by structural constraints significantly degrades UWB localization accuracy, rendering conventional algorithms ineffective. To address this issue, this study proposes a high-precision UWB+TOA-R positioning algorithm that incorporates Ridge estimation as a constraint condition. The algorithm introduces equivalent weights to refine the iterative computation of Ridge estimation, establishing an iteratively computed TOA-RR solution model. Experiments were conducted in a long linear corridor to compare the performance of three UWB localization models: the TOA-Least Squares (TOA-LS) model, the TOA-Ridge estimation (TOA-R) model, and the proposed TOA-Ridge estimation iterative (TOA-RR) model. The results indicate that the TOA-LS model suffers from significant coordinate distortions due to abnormalities in the inverse matrix of the coefficient matrix, regardless of the initial tag coordinates. The TOA-R model demonstrates improved accuracy and stability, particularly in cases of significant initial deviations, but still exhibits residual errors. In contrast, the TOA-RR model achieves enhanced stability and accuracy, with a positioning error of approximately 0.5 m. This study resolves the challenge of inaccurate UWB localization in long linear areas, providing a robust solution for such environments.