The process of smelting non‐ferrous metals results in significant emissions of flue gas that contains sulfur dioxide (SO), which is very harmful to the environment. Through precise control of converter inlet temperature, it is feasible to enhance the conversion ratio of SO 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.

中文翻译：

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烟气制酸中基于事件触发机制的转炉入口温度神经网络预测控制

有色金属冶炼过程中会排放大量含有二氧化硫（SO）的烟气，对环境危害极大。通过精确控制转炉入口温度，可以提高SO的转化率，同时减少烟气产酸对环境的污染。由于冶炼过程的高度不确定性，转炉入口温度的调节具有挑战性，控制器需要经常更新。为了提高控制性能并减少控制器更新次数，提出了事件触发神经网络模型预测控制（ETNMPC）策略。首先，开发了长短期记忆（LSTM）预测模型和模型预测控制器。其次，通过设计事件触发机制来决定是否更新现有控制器。最后利用江西某铜厂的实际数据，进行了转炉入口温度控制实验。仿真结果表明，所提出的ETNMPC在控制性能方面优于传统的时间触发方法，大大减少了控制器更新次数，并显着降低了计算成本和通信负担。