A feedforward-feedback control method of blast furnace ironmaking for energy saving and carbon reduction

The blast furnace (BF) is a high energy consumption and high carbon emission equipment. Dynamic variations in production orders, raw material prices, and equipment conditions lead to fluctuations in energy consumption and carbon emissions during the ironmaking process, making it difficult to maintain long-term stable, low-energy and low-emission operation. To achieve energy saving, carbon reduction, and stable efficient operation of the BF, it is of great significance to develop feedforward optimization and feedback control strategies for energy consumption and carbon emissions. Therefore, this paper proposes a feedforward-feedback control method for BF ironmaking aimed at energy conservation and carbon reduction. A feedforward control model for BF energy consumption, CO2 emissions, and production costs is developed and solved using the non-dominant sorting beetle optimization algorithm (NSDBO). Meanwhile, a feedback control model is constructed based on model predictive control (MPC) integrated with a firefly algorithm-optimized long short-term memory network (FA-LSTM). Case study results demonstrate that the feedforward model reduced energy consumption, CO2 emissions, and production cost by 11.78%, 12.05%, and 8.13%, respectively. The feedback model enabled rapid and stable regulation of these three indicators. Furthermore, the combined feedforward-feedback control maintained them stably at lower levels, achieving reductions of 11.76%, 11.75%, and 8.12%, respectively, which confirms the practical validity of the proposed method.