Predictive Control of Gas Recovery System Based on Neural Network
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摘要: 针对传统煤气回收系统存在回收效率低、烟气大等问题,文章提出了一种基于神经网络的煤气回收系统预测控制策略,运用神经网络自适应预测控制与模糊控制相结合的方法,对某钢厂转炉煤气回收系统进行了优化。仿真结果表明,神经网络自适应预测炉口煤气涌出量的误差为-5~5 L/h,预测效果较为理想。实际应用表明,采用神经网络自适应预测控制后,煤气平均回收率达到97.5 m3/t,达到了节能降耗、成本低、保护环境的目的。Abstract: In allusion to the problems of low gas recovery rate and big smoke of traditional gas recovery system,the paper put forward a predictive control strategy of gas recovery system based on neural network,which optimizes gas recovery system of a steel plant’s converter by applying neural network adaptive and predictive control and fuzzy control.The simulation results showed that the predictive error of furnace gas emissions was-5～5 L/h and the preditive effect was better.The practice application proved the average recovery of gas reached 97.5 m3/t after applying neural network adaptive and predictive control and it reached the purpose of energy saving,low cost,and protection of environment.
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Key words:
- steel plant /
- converter /
- gas recovery /
- neural network /
- fuzzy control /
- predictive control
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