Dynamic flame feature-driven prediction model for basic oxygen furnace steelmaking endpoint carbon content based on three-dimensional multi-layer complex networks

Accurate prediction of carbon content at the endpoint is crucial for the endpoint management of Basic Oxygen Furnace (BOF) steelmaking. The carbon content in the molten pool is closely related to the dynamic and static characteristics of the flame at the furnace’s mouth. However, the flame’s texture change exhibits multidirectional and multiscale properties, posing challenges for existing algorithms to effectively extract dynamic color texture features. To address this issue, this paper proposes a dynamic texture feature extraction model based on a three-dimensional multi-layer complex network (3D-MLCN). The model constructs an unbounded complex network for a single-frame flame picture by integrating spatiotemporal position information of the image region’s centroid with color information, thereby quantizing the single-frame image into a complex network with spatiotemporal properties. Subsequently, a multi-scale multi-direction weighted dynamic color texture complex network is built for the flame video at the furnace mouth, utilizing the temporal index of the video frames in combination with vertex color values to capture the time-varying features of the flame video. The proposed method quantifies network characteristics through vertex degree distribution features to obtain dynamic color texture feature descriptors. These descriptors are then combined with static color texture features and color features to construct dynamic and static feature descriptors for the flame video, enabling the prediction of the endpoint carbon content using a regression model. By analyzing the actual production data of BOF steelmaking, the prediction accuracy of carbon content within the error range of ±0.02% is 87.91%, the $R^2$ value is 0.8547, and the RMSE value is 2.0959, which verifies the effectiveness of the proposed method.