Zhenzhong Shen

Hot metal temperature is a key factor affecting the quality and energy consumption of iron and steel smelting. Accurate prediction of the temperature drop in a hot metal ladle is very important for optimizing transport, improving efficiency, and reducing energy consumption. Most of the existing studies focus on the prediction of molten iron temperature in torpedo tanks, but there is a significant research gap in the prediction of molten iron ladle temperature drop, especially as the ladle is increasingly used to replace the torpedo tank in the transportation process, this research gap has not been fully addressed in the existing literature. This paper proposes an interpretable hybrid deep learning model combining Bi-LSTM and Transformer to solve the complexity of temperature drop prediction. By leveraging Catboost-RFECV, the most influential variables are selected, and the model captures both local features with Bi-LSTM and global dependencies with Transformer. Hyperparameters are optimized automatically using Optuna, enhancing model performance. Furthermore, SHAP analysis provides valuable insights into the key factors influencing temperature drops, enabling more accurate prediction of molten iron temperature. The experimental results demonstrate that the proposed model outperforms each individual model in the ensemble in terms of R², RMSE, MAE, and other evaluation metrics. Additionally, SHAP analysis identifies the key factors contributing to the temperature drop.

Utilizing panel data spanning from 2010 to 2021 across 30 Chinese provinces, this study examines the spatiotemporal dynamics of green finance and its correlation with high-quality economic development. Initially, the indicators for green finance and high-quality economic development were quantified by employing the entropy weight method. Secondly, we conducted a preliminary analysis of the spatiotemporal evolution patterns of green finance using the Mann-Kendall coefficient method and kernel density estimation, revealing an increasing trend in China’s green finance development level with regional disparities. Next, utilizing the Mann-Kendall coefficient method and spatial dynamic Markov model, we analyzed the spatiotemporal trends of green finance development and its coupling coordination with high-quality economic development across the 30 provinces in China. The research findings indicate a general upward trend in the degree of coordinated development between green finance and high-quality economic development from 2010 to 2021. Significant spatial differences in coupling coordination levels exist among different regions, with the highest level in the western regions, followed by the eastern regions, and the weakest in the central regions. This suggests an overall trend toward coordinated development between green finance and high-quality economic development in China, with green finance playing a significant role in promoting high-quality economic development. However, the growth rate of coupling coordination is relatively slow and exhibits regional heterogeneity. Lastly, drawing from these research findings, we put forward policy recommendations aimed at effectively advancing the development of green finance and high-quality economic growth in China.