ABSTRACT

The temperature control of molten steel is essential to ensure operational stability in a steelmaking plant. The calculation of thermal losses in the steelmaking plant’s operations depends on highly dynamic variables, which motivates the construction of predictive models for the steel temperature. This paper proposed a hybrid ensemble method using multiple linear and random forest regression to predict the end molten steel temperature at the secondary refining required to achieve a target tundish temperature. Combining these two methods makes it possible to account for the linear and non-linear relationships in the data. The implemented models were trained on industrial data, and their performance was assessed using root mean squared error (RMSE) and a custom accuracy metric. The results showed that the proposed hybrid method achieves up to 5% better accuracy compared to linear regression or random forest regression methods alone, thus can enhance molten steel prediction in steelmaking plants.

摘要

钢水温度控制对于确保炼钢厂的运行稳定性至关重要。炼钢厂运行中热损失的计算取决于高度动态的变量，这促进了钢温度预测模型的构建。本文提出了一种混合集成方法，使用多重线性和随机森林回归来预测达到目标中间包温度所需的二次精炼的最终钢水温度。结合这两种方法可以解释数据中的线性和非线性关系。所实施的模型根据工业数据进行训练，并使用均方根误差 (RMSE) 和自定义准确度指标评估其性能。