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

doi:10.16182/j.issn1004731x.joss.201807034

Abstract

Abstract: 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 NO_X 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 NO_X 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.

Key words: SCR flue gas denitration, neural network, feedback linearization algorithm, single neuron adaptive algorithm, artificial fish swarm algorithm, optimization

CLC Number:

TP27

Niu Yuguang, Pan Yan, Huang Wenyuan. Artificial Fish Swarm and Feedback Linearization of Flue Gas Denitration Control Based on Neural Network[J]. Journal of System Simulation, 2018, 30(7): 2707-2714.

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