炼钢过程中的脱氧合金化是钢铁冶炼中的重要工艺环节。本文研究建立了关于碳锰元素收得率预测模型，仿真优化检验，定义出控制空间定理来评判模型预测准确率。其次，使用BP神经网络和拟合两种方法，分别建立出脱氧合金化过程的合金元素收得率预测模型，缩短学习训练时间，提高模型预测精度，得出BP神经网络预测值均在85%以上，拟合预测值均在82%以上，结果表明：随机选取100炉次的生产数据进行仿真优化，根据控制区间定理知，拟合的预测准确率达到84%以上，BP神经网络预测准确率均在89%以上，BP神经网络预测模型更符合生产要求。
Deoxidation alloying in the steelmaking process is an important process link in steel smelting. In this paper, a prediction model for the carbon and manganese yield is studied and established, and the simulation optimization test is defined. The control space theorem is defined to judge the model prediction accuracy. Secondly, using BP neural network and fitting methods, the prediction models of alloy element yield in the process of deoxidizing alloying are established respectively, which shortens the learning and training time and improves the prediction accuracy of the model. The predicted values of BP neural network are both 85%. Above, the fitting prediction values are all above 82%. The results show that: 100 production times are randomly selected for simulation optimization. According to the control interval theorem, the fitting prediction accuracy rate is above 84%, and the BP neural network prediction accuracy rate is above 89%, and the BP neural network prediction model is more in line with production requirements.
Biao, T., et al. (2017) The Research Process on Converter Steelmaking Process by Using Limestone. IOP Conference Series: Earth and Environmental Science, 81, Article ID: 012175.
