Prediction of limestone-gypsum wet flue gas desulfurization efficiency based on T-S fuzzy neural network

LI Bin; DENG Yu; BIAN Yuming; QI Nianzhe

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Prediction of limestone-gypsum wet flue gas desulfurization efficiency based on T-S fuzzy neural network

更新时间：2025-09-28

- Prediction of limestone-gypsum wet flue gas desulfurization efficiency based on T-S fuzzy neural network
- Thermal Power Generation Vol. 45, Issue 6, Pages: 116-119(2016)
- 作者机构：
  
  华北电力大学能源动力与机械工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： X773
- Published：2016
- 稿件说明：
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LI Bin, DENG Yu, BIAN Yuming, et al. Prediction of limestone-gypsum wet flue gas desulfurization efficiency based on T-S fuzzy neural network[J]. Thermal power generation, 2016, 45(6): 116-119.
DOI：

LI Bin, DENG Yu, BIAN Yuming, et al. Prediction of limestone-gypsum wet flue gas desulfurization efficiency based on T-S fuzzy neural network[J]. Thermal power generation, 2016, 45(6): 116-119. DOI：

摘要

由于影响脱硫效率的因素较多

且相互之间均具有关联性

造成脱硫效率难以实时监测

且测量结果不精准。本文运用T-S模糊神经网络建立了脱硫效率的预测模型

基于某电厂DCS采集的湿法脱硫系统原始数据

在MATLAB平台上进行训练与检验

得到较精准的脱硫效率预测模型。模型验证结果显示:采用T-S模糊神经网络模型预测脱硫效率

85%的样本点相对误差分布在-1.0%~0.5%之间

最大误差不超过1.5%

说明该模型的预测精度较高

能较好地满足工程实际的需求。

Abstract

There are many relevant factors affecting the desulfurization efficiency

leading to difficulties in measuring the desulfurization efficiency.To solve this problem

the T-S fuzzy neural network was applied to establish prediction model of the desulfurization efficiency

on the basis of the original data of desulfurization system collected by apower plant's DCS in Jiangxi province.The original collected data were tested and trained to be accurate on the platform of MATLAB.The results show that

when predicting the desulfurization efficiency by using the T-S fuzzy neural network prediction model

relative error of 85%samples is between-1.0% and 0.5%

and the maximum error is no higher than 1.5%.It illustrates that the proposed model has higher prediction precision

which can better meet the actual needs of engineering.

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