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 Issue 5, (2016)
- 作者机构：
  
  华北电力大学能源动力与机械工程学院,河北,保定,071003
- 作者简介：
- 基金信息：
- DOI：
  CLC： X773
- Published Online：15 June 2016，
  
  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, (5).
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, (5). 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。

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