Application of Neural Network Predictive Control in SCR Flue Gas Denitration System

被引：1

作者：

Meng F.-W. ^{[1
]}

Xu B. ^{[2
]}

Lyu X.-Y. ^{[1
]}

Liu Y.-Q. ^{[1
]}

机构：

[1] School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao

[2] Jilin Electric Power Research Institute Co., Ltd., Changchun

来源：

Dongbei Daxue Xuebao/Journal of Northeastern University | 2017年 / 38卷 / 06期

关键词：

Model identification; Neural networks; Nonlinear auto regressive algorithm; Predictive control; Selective catalytic reduction (SCR);

D O I：

10.3969/j.issn.1005-3026.2017.06.004

中图分类号：

学科分类号：

摘要：

Based on the data collected from selective catalytic reduction (SCR) reaction system of a 350 MW coal-fired unit in a thermal power plant, neural network predictive control method was used to study the prediction and control of nitrogen oxides emission in power plant tail gas. Firstly, the non-linear model of SCR denitrification system was modeled and nonlinear autoregressive model was used to estimate the model. Then, by using the predictive control method to control the ammonia injection, the tail gas could achieve the standard of discharge limitation, and the amount of ammonia and ammonia escape could also be reduced, resulting in the enhancement of economic efficiency. The controller was optimized by the steepest gradient method and the control variable was constrained by the performance function to achieve the expected output. Finally, compared with the measured date in the field, the simulation results show that the neural network predictive control scheme can predict the amount of ammonia sprayed in the future at a finite time. © 2017, Editorial Department of Journal of Northeastern University. All right reserved.

引用

页码：778 / 782

页数：4

共 13 条

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