Dong Bowen, Lv Wu, Xie Zhi
Northeastern University, Shenyang, China.
Sci Rep. 2024 Nov 9;14(1):27428. doi: 10.1038/s41598-024-78611-z.
To achieve the desired superheat of molten steel during the continuous casting process, optimization of process parameters such as molten steel temperature in ladle furnace, casting speed, and baking temperature is necessary. Therefore, obtaining the superheat corresponding to these process parameters in advance is particularly important. To address this issue, a model for predicting the temperature of molten steel in the tundish during continuous casting is designed. The model adopts a combined modeling approach of mechanistic model and data model. To address the issue of the mechanism model's inability to capture the variation of the lining's thermal parameters, this article improves the traditional physics-informed neural network (PINN) algorithm. It combines the constraints from both the forward and inverse problems, allowing for obtaining solutions to the equations while capturing the variation of equation parameters. Actual data from multiple casting sequences at a steel plant are collected to validate the accuracy and interpretability of the model. The results show that the error of the model is about 2.1k which has better accuracy compared to pure mechanistic model and pure data model. Additionally, it can capture the variation patterns of tundish lining thermal parameters under different operating conditions. Therefore, the model designed in this article can provide both profound physical interpretation ability and more practical predictions of molten steel temperature.
为了在连铸过程中实现所需的钢水过热度，优化诸如钢包精炼炉中的钢水温度、铸造速度和烘烤温度等工艺参数是必要的。因此，提前获得与这些工艺参数对应的过热度尤为重要。为了解决这个问题，设计了一种用于预测连铸过程中间包内钢水温度的模型。该模型采用了机理模型和数据模型相结合的建模方法。为了解决机理模型无法捕捉内衬热参数变化的问题，本文改进了传统的物理信息神经网络（PINN）算法。它结合了正向问题和反向问题的约束，在捕捉方程参数变化的同时求解方程。收集了某钢铁厂多个浇铸序列的实际数据，以验证模型的准确性和可解释性。结果表明，该模型的误差约为2.1K，与纯机理模型和纯数据模型相比具有更好的准确性。此外，它可以捕捉不同操作条件下中间包内衬热参数的变化规律。因此，本文设计的模型既可以提供深刻的物理解释能力，又可以对钢水温度进行更实际的预测。
