Model for predicting SCR flue gas denitration efficiency based on particle swarm optimization and twin support vector machine

FANG Xian; TIE Zhixin; CUI Shiwen; DING Chengfu; XIE Lei; LIU Jingjing

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Model for predicting SCR flue gas denitration efficiency based on particle swarm optimization and twin support vector machine

更新时间：2025-09-28

- Model for predicting SCR flue gas denitration efficiency based on particle swarm optimization and twin support vector machine
- Thermal Power Generation Issue 12, (2017)
- 作者机构：
  
  1. 浙江理工大学信息学院,浙江,杭州,310018
  2. 聚光科技（杭州）股份有限公司,浙江,杭州,310052
- 作者简介：
- 基金信息：
- DOI：
  CLC： X773
- Published Online：04 January 2018，
  
  Published：2017
- 稿件说明：
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FANG Xian, TIE Zhixin, CUI Shiwen, et al. Model for predicting SCR flue gas denitration efficiency based on particle swarm optimization and twin support vector machine[J]. Thermal power generation, 2017, (12).
DOI：

FANG Xian, TIE Zhixin, CUI Shiwen, et al. Model for predicting SCR flue gas denitration efficiency based on particle swarm optimization and twin support vector machine[J]. Thermal power generation, 2017, (12). DOI：

摘要

为了更好地分析燃煤电厂脱硝效率与相关影响因素之间的非线性关系，引入双支持向量机（TWSVM）作为分类预测框架，利用粒子群算法（PSO）对TWSVM的惩罚因子和核参数进行寻优，进而构建了针对选择性催化还原（SCR）烟气脱硝效率预测的PSO-TWSVM模型。通过提取某电厂工况监控系统近期数据，结合三倍标准差检验法与归一化法对数据进行预处理，并选取训练集与测试集。仿真结果表明：SCR烟气脱硝效率预测PSO-TWSVM模型最大相对误差小于±0.6%，平均相对误差保持在±0.3%以下，证明该模型的准确性高；对比支持向量机（SVM）模型发现，PSO-TWSVM模型既提高了预测精度，也节省了计算时间。

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