Neural network predictive control of converter inlet temperature based on event-triggered mechanism in flue gas acid production

The process of smelting non-ferrous metals results in significant emissions of flue gas that contains sulfur dioxide (SO 2$$ {}_2 $$), which is very harmful to the environment. Through precise control of converter inlet temperature, it is feasible to enhance the conversion ratio of SO 2$$ {}_2 $$ and simultaneously mitigate environmental pollution by generating acid from flue gas. Because of the high degree of uncertainty in smelting process, converter inlet temperature is challenging to regulate and controller frequently needs updating. To improve control performance and decrease controller update times, an event-triggered neural network model predictive control (ETNMPC) strategy is proposed. First, long short-term memory (LSTM) prediction model and model predictive controller are developed. Second, it is decided whether to update the existing controller by designing an event-triggered mechanism. Finally, using real data from a copper facility in Jiangxi Province, the temperature control experiment of converter inlet is carried out. Simulation results demonstrate that the proposed ETNMPC outperforms conventional time-triggered method in terms of control performance, greatly lowers the times of controller updates, and significantly lowers computation costs and communication burden. Key findings: (1) LSTM neural network is used to establish the predictive model of converter inlet temperature and model predictive controller is designed. (2) Two different event-triggered mechanisms with fixed threshold are designed. (3) Event-triggered neural network predictive control can effectively reduce the number of controller triggers, save computing resources, and improve system performance. image