Data/mechanism hybrid-driven modeling of blast furnace smelting system and global sequential optimization

Within the crucial domain of blast furnace ironmaking and sintering, the quality of sinter ore and molten iron holds supreme importance, with direct implications for downstream processes. However, the complexities of utilizing operational experience, understanding mechanisms, leveraging extensive data for precise modeling, and optimizing multiple objectives have persistently posed challenges for engineers. In this research, we propose an novel data/mechanism hybrid-driven modeling and global sequential optimization framework, with three core contributions: (1) Synthesizing field operation insights and mechanistic principles to construct models for molten iron production and energy consumption in ironmaking. (2) Crafting the broad learning approximate-aided subspace identification method (BLASIM), encapsulating the system’s dynamic and nonlinear characteristics. This method pioneers a parametric modeling strategy predicated on correlation error for dynamic nonlinear system identification, with its feasibility robustly underpinned by theoretical verification. (3) Streamlining the optimization process by applying expert knowledge to deconstruct a complex multi-objective optimization problem into manageable single-objective tasks. These tasks are addressed sequentially, reflecting operational chronology, and are adeptly resolved using gray wolf optimization algorithm with a sequence relaxant factor. To conclude, the proposed methods are thoroughly validated using real-world blast furnace smelting data, affirming the feasibility and efficiency of modeling accuracy and optimization performance.