基于神经网络的鱼群寻优和反馈线性化烟气脱硝控制

Artificial Fish Swarm and Feedback Linearization of Flue Gas Denitration Control Based on Neural Network

针对火力发电厂单元机组SCR烟气脱硝控制系统现状,提出了在SCR反应器入口加装调节阀与测点的优化方案,并相应提出了SCR反应器入口烟气预处理和总阀控制方案。在SCR反应器入口,采用单神经元自适应-人工鱼群滚动寻优控制算法(RSNAAFS)控制喷氨支管调节阀,对烟气中NOX进行预处理;在SCR反应器出口,采用神经网络反馈线性化算法(NNFL)控制喷氨总管调节阀,对烟气中NOX进行优化控制,使其达到排放标准。结果表明:新控制方案在控制品质上优于传统控制方案,系统有很强的实用性。

According to the present situation of SCR flue gas dentration control system in thermal power plant, an optimum proposal that control valve and concentration transmitter are added in the inlet of the SCR reactor is presented, and the corresponding control strategy is given. At the entrance of the SCR reactor, the receding horizon algorithm combined with the single neuron adaptive algorithm and the artificial fish swarm algorithm（RSNAAFS） is used to control branch valves to pretreat NOX in the exhaust flue gas. At the outlet of the SCR reactor, the neural network based on feedback linearization algorithm（NNFL） is used to control the general valve to limit the concentration of NOX under the obligatory standard. The simulation result indicates that the presented strategy has a better effect on control quality compared with traditional control strategy, and has important practical significance.