Multichannel Diffusion Graph Convolutional Network for the Prediction of Endpoint Composition in the Converter Steelmaking Process

The converter steelmaking process smelts hot metal to liquid steel and occupies an important position in industry. The composition of liquid steel at the endpoint is an essential quality index, including the concentrations of multiple elements, such as carbon, silicon, and manganese. Accurately predicting endpoint composition is the basis of production optimization. Hence, a multichannel diffusion graph convolutional network (MCDGCN) is presented in this article. Unlike conventional models, the developed MCDGCN describes the converter steelmaking process as a graph to exploit the correlations among element concentrations for an accurate endpoint composition prediction. We also develop a unique
$K$
-hop diffusion method to extract the globally consistent information over the graph for predicting each element. The proposed method addresses the composition prediction task for a realistic converter steelmaking process. To the best of our knowledge, this is the first time that up to 15 elements of liquid steel are covered and predicted to present a comprehensive process model. Compared with six benchmark models, MCDGCN presents state-of-the-art results, i.e., an average
$R^{2}$
of 0.8475 and an average MAE of 0.0189, which shows that the correlation mining of graph deep learning can indeed improve the prediction performance for endpoint composition.