用于预测热金属中硫含量的改进型反向传播神经网络方法

Song Zhang, Maoqiang Gu, Yanbing Zong, Zhenyang Wang, Jianliang Zhang, Dewen Jiang, Jing Pang, Shushi Zhang, Ruishuai Si
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引用次数: 0

摘要

高炉冶炼是一种传统的炼铁工艺。其产品热金属是生产钢铁的重要原材料。使用适当的铁水可以提高炼钢效率,稳定钢铁产品质量。硫含量是反映铁水质量的重要指标,因此有必要建立一个准确的预测模型来预测铁水的硫含量,以有效指导生产过程。高炉冶炼过程中影响脱硫效果的因素之间存在非线性关系,反向传播神经网络(BPNN)模型具有很强的解决非线性问题的能力。但 BPNN 存在收敛速度慢、易陷入局部极小值等缺点。为了提高预测精度,本文提出了一种结合 Kmeans 和 BPNN 的改进算法。研究表明,与 BPNN 模型和基于案例的推理(CBR)模型相比,Kmeans-BPNN 模型的 RMSE 值和 MAPE 值最低,表明拟合度高,离散度小。Kmeans-BPNN 模型的 HR 值最大,表明预测精度最高。所提出的 Kmeans-BPNN 预测模型的命中率达到 96%,比改进前提高了 4.5%。它能有效提高热金属硫含量的预测精度。
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Improved back propagation neural network method for predicting sulfur content in hot metal
Blast furnace smelting is a traditional iron-making process. Its product, hot metal, is an important raw material for the production of steel. Steelmaking efficiency can be improved and steel product quality can be stabilized by using proper hot metal. Sulfur is an important indicator reflecting the quality of hot metal, it is necessary to establish an accurate prediction model to predict the sulfur content of hot metal, to effectively guide the production process. There is a non-linear relationship among the factors influencing the desulfurization effect during the blast furnace smelting process, and the back propagation neural network (BPNN) model has a strong ability to solve nonlinear problems. However, BPNN has the disadvantages of slow convergence speed and easy to fall into local minima. To improve the prediction accuracy, an improved algorithm combining Kmeans and BPNN is proposed in this paper. The study showed that compared with the BPNN model and case-based reasoning (CBR) model, the Kmeans-BPNN model has the lowest RMSE and MAPE values, which indicates a high degree of fit and a low degree of dispersion. The Kmeans-BPNN model has the largest HR value, which indicates the highest prediction accuracy. The proposed Kmeans-BPNN prediction model achieves a hit rate of 96%, which is 4.5% higher than before the improvement. It can effectively improve the prediction accuracy of hot metal sulfur content.
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