用神经网络精准预测电炉炼钢终期磷含量，助力绿色钢铁生产。
Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks
- 基于两年生产数据构建四层神经网络模型
- 预测误差低于10ppm，准确率高达99.98%
- 适合钢铁厂优化工艺与提升环保效率
以废钢为原料的电弧炉炼钢因其低碳潜力有望占据未来钢铁市场重要份额，但磷等杂质控制仍是难题。本研究利用机器学习模型预测电炉终点钢水磷含量，数据来自某钢厂两年生产记录，涵盖废钢成分与重量、氧气注入量及冶炼时长。经预处理后比较多种模型，人工神经网络（ANN）表现最优。最佳模型含四层隐藏层，训练500轮，批量大小50。该模型均方误差（MSE）为0.000016，均方根误差（RMSE）为0.0049998，决定系数（R²）达99.96%，相关系数（r）为99.98%。尤其在±0.001 wt%（±10 ppm）范围内预测命中率达100%。结果表明，优化后的ANN模型可实现对钢水终期磷含量的高精度预测。
原文摘要 · Abstract (English)
The scrap-based electric arc furnace process is expected to capture a significant share of the steel market in the future due to its potential for reducing environmental impacts through steel recycling. However, managing impurities, particularly phosphorus, remains a challenge. This study aims to develop a machine learning model to estimate the steel phosphorus content at the end of the process based on input parameters. Data were collected over two years from a steel plant, focusing on the chemical composition and weight of the scrap, the volume of oxygen injected, and process duration. After preprocessing the data, several machine learning models were evaluated, with the artificial neural network (ANN) emerging as the most effective. The best ANN model included four hidden layers. The model was trained for 500 epochs with a batch size of 50. The best model achieves a mean square error (MSE) of 0.000016, a root-mean-square error (RMSE) of 0.0049998, a coefficient of determination (R2) of 99.96%, and a correlation coefficient (r) of 99.98%. Notably, the model achieved a 100% hit rate for predicting phosphorus content within +-0.001 wt% (+-10 ppm). These results demonstrate that the optimized ANN model offers accurate predictions for the steel final phosphorus content.
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