Research on closed-loop optimisation control model based on blast furnace upper adjustment

The upper adjustment of the blast furnace, a key aspect of furnace management, plays a vital role in maintaining stable operation. Currently, it largely depends on manual experience, often leading to deviations in burden composition and distribution. To address this, this paper integrates burden structure and charging system design as a multi-objective optimization problem. A FastICA-GELM-WNN model is proposed to predict production indicators. Based on this, an initial parameter setting model using the Multi-objective Grey Wolf Optimizer is developed. A feedback compensation strategy based on an Improved Adaptive Genetic Algorithm is introduced to correct deviations between predicted and target values. Experimental results show that the proposed Model Predictive Control system achieves around 96% prediction accuracy and effectively adapts to production changes. This enables reductions in carbon emissions and energy consumption while ensuring stable iron output, supporting the broader optimization of blast furnace ironmaking.