Intelligent Combustion Control of the Hot Blast Stove: A Reinforcement Learning Approach

Abstract

Combustion optimization of hot blast stoves is a promising approach for cost savings and energy conservation of ironmaking. Existing artificial intelligence methods for this optimization rely on air and gas flow meters, which can malfunction under harsh working conditions. To meet this challenge, we propose an intelligent combustion control system based on reinforcement learning (RL). Considering the difficulty of learning state feature representation, five RL models using different deep embedding networks were implemented and evaluated. The Attention-MLP-based RL model is distinguished through experimental testing, achieving an accuracy of 85.91% and an average inference time of 4.85 ms. Finally, the intelligent combustion control system with the Attention-MLP-based RL model runs in the hot blast stove of the blast furnace (1750 m3 in volume) at Tranvic Steel Co., Ltd. in China (Chengdu, China). The results show that our system can achieve good control performance by autonomously learning the implicit relationship between the state of the hot blast stove and the valve control action in the absence of flow meters.