Application of RBF neural network based on discrete particle swarm optimization in denitrification control

WANG Lin; GAO Lin; GUO Yiwen; AN Chaorong; JIN Guoqiang; WANG Chenyu; SU Haolong

doi:10.19666/j.rlfd.201907149

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Application of RBF neural network based on discrete particle swarm optimization in denitrification control

更新时间：2025-09-28

- Application of RBF neural network based on discrete particle swarm optimization in denitrification control
- Thermal Power Generation Vol. 48, Issue 10, Pages: 22-27(2019)
- 作者机构：
  
  1. 西安热工研究院有限公司
  2. 黔桂发电有限责任公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.201907149
  CLC： TP273
- Published Online：29 September 2019，
  
  Published：2019
- 稿件说明：
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WANG Lin, GAO Lin, GUO Yiwen, et al. Application of RBF neural network based on discrete particle swarm optimization in denitrification control[J]. Thermal power generation, 2019, 48(10): 22-27.
DOI：

WANG Lin, GAO Lin, GUO Yiwen, et al. Application of RBF neural network based on discrete particle swarm optimization in denitrification control[J]. Thermal power generation, 2019, 48(10): 22-27. DOI： 10.19666/j.rlfd.201907149.

摘要

提出了一种选择性催化还原(SCR)烟气脱硝预测控制方法

该方法采用粒子群寻优的径向基(RBF)神经网络预测SCR脱硝系统入口NOx质量浓度

将整体寻优空间离散化

通过采用节点寻优并结合最速梯度方法重构寻优模型

同时将该RBF神经网络预测结果引入脱硝控制策略

应用到火电机组的喷氨控制。在某350 MW机组实际应用结果表明

该神经网络模型不仅能实现喷氨自动控制

而且能提高喷氨系统控制精度。

Abstract

A predictive control method for selective catalytic reduction of(SCR) flue gas denitrification is proposed

which uses the radial basis(RBF) neural network of particle swarm optimization to predict the mass concentration of NOx at entrance of the SCR denitrification system. The overall optimization space is discretized. The optimization model is reconstructed by combining node optimization with the fastest gradient method. Meanwhile

the prediction results of the RBF neural network are introduced into the denitrification control strategy

and applied to ammonia injection control of thermal power units. The practical application of this new developed control strategy in a 350 MW unit show that

this method can not only realize the automatic control of ammonia injection valves

but also improve the control precision of the ammonia injection system.

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Related Institution

Key Laboratory of Energy Heat Conversion and Process Measurement and Control, Ministry of Education, Southeast University

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College of Energy, Power and Mechanical Engineering, North China Electric Power University

School of Mechanical and Electrical Engineering, Nanchang University

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