Modeling of SCR denitrification system based on wavelet denoising and PCA-ANFIS

ZHANG Xiaowen; XIANG Wenguo; CHEN Shiyi; LIU Quanjun; XU Longfei

doi:10.19666/j.rlfd.202009235

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Modeling of SCR denitrification system based on wavelet denoising and PCA-ANFIS

更新时间：2025-09-28

- Modeling of SCR denitrification system based on wavelet denoising and PCA-ANFIS
- Thermal Power Generation Vol. 50, Issue 6, Pages: 114-120(2021)
- 作者机构：
  
  1. 东南大学能源热转换及过程测控教育部重点实验室
  2. 江苏新海发电有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202009235
  CLC： X701
- Published：2021
- 稿件说明：
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ZHANG Xiaowen, XIANG Wenguo, CHEN Shiyi, et al. Modeling of SCR denitrification system based on wavelet denoising and PCA-ANFIS[J]. Thermal power generation, 2021, 50(6): 114-120.
DOI：

ZHANG Xiaowen, XIANG Wenguo, CHEN Shiyi, et al. Modeling of SCR denitrification system based on wavelet denoising and PCA-ANFIS[J]. Thermal power generation, 2021, 50(6): 114-120. DOI： 10.19666/j.rlfd.202009235.

摘要

针对选择性催化还原(SCR)脱硝系统非线性、时变和大滞后的特点

本文提出基于小波去噪和主成分分析、自适应神经模糊推理系统(PCA-ANFIS)而建立的SCR脱硝系统预测模型。通过分析不同阈值选取原则及不同小波基和分解层数的去噪效果

选取最适合系统数据去噪的rigrsure原则、软阈值函数、Sym10小波3层分解方式

对数据进行去噪处理

并利用主成分分析法进行数据降维。然后基于减法聚类构建ANFIS模型的初始网络结构

采用混合算法优化模型参数。最后利用某燃煤机组实际运行数据对模型进行验证

并与BP神经网络模型预测结果进行对比。结果表明

基于小波去噪和PCA-ANFIS的SCR脱硝系统模型具有较好的拟合精度和泛化能力。

Abstract

Against the non-linear

time-varying and time-delayed characteristics of selective catalytic reduction(SCR) denitrification system

this paper established a predictive model of the SCR denitrification system based on wavelet denoising

principal component analysis and adaptive neuro-fuzzy inference system(ANFIS). By analyzing the results of different threshold selection principles

wavelet bases and decomposition levels

the most suitable denoising method for the system data signals was selected

and the principal component analysis method was adopted for data dimensionality reduction. Then

the initial network structure of the ANFIS model was constructed based on subtractive clustering

and the model parameters were optimized by a hybrid algorithm. Finally

the model was verified by the operational data of a coal-fired power plant

and the results were compared with the BP neural network model. The results showed that the ANFIS denitrification system model based on wavelet denoising and principal component analysis has higher fitting accuracy and better generalization ability.

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