Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

The composition control of molten steel is one of the main functions in the ladle furnace (LF) refining process. In this study, a feasible model was established to predict the alloying element yield using principal component analysis (PCA) and deep neural network (DNN). The PCA was used to eliminate collinearity and reduce the dimension of the input variables, and then the data processed by PCA were used to establish the DNN model. The prediction hit ratios for the Si element yield in the error ranges of ±1%, ±3%, and ±5% are 54.0%, 93.8%, and 98.8%, respectively, whereas those of the Mn element yield in the error ranges of ±1%, ±2%, and ±3% are 77.0%, 96.3%, and 99.5%, respectively, in the PCA–DNN model. The results demonstrate that the PCA–DNN model performs better than the known models, such as the reference heat method, multiple linear regression, modified backpropagation, and DNN model. Meanwhile, the accurate prediction of the alloying element yield can greatly contribute to realizing a “narrow window” control of composition in molten steel. The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.