Digital Twin Modeling of the Blast Furnace CO Composition Field

As the core unit in the ironmaking process and a major contributor to industrial carbon emissions, the blast furnace presents substantial challenges for monitoring due to the coupling of multiphysical fields and the complexity of chemical reactions. To address this issue, this study develops a digital twin framework that integrates real-time sensor data to characterize the spatiotemporal dynamics of gas-phase compositions, thereby supporting intelligent control and process optimization of the blast furnace. The proposed model incorporates the unreacted core shrinkage model and Navier–Stokes (NSs) equations, and employs a grid-based finite difference method to solve a 2-D fluid model of the furnace, yielding gas velocity, temperature, and solid-phase temperature fields. Simultaneously, key chemical reactions, including coke combustion, carbon dissolution, and iron oxide reduction, are incorporated. The furnace is spatially partitioned into multiple reaction zones to compute localized reaction rate fields. By performing soft sensing of the carbon monoxide (CO) composition field through the fusion of top-gas sensor data with multiphysical field modeling, this method provides real-time insights into the internal distribution of gas-phase species within the furnace. It not only advances the theoretical modeling of gas–solid interactions and significantly enhances model interpretability, but also serves as a practical tool for real-time monitoring in industrial environments, demonstrating strong potential for improving energy efficiency, reducing emissions, and ensuring operational safety.