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期刊信息
  • 主管单位:
  • 上海市教育委员会
  • 主办单位:
  • 上海理工大学
  • 主  编:
  • 庄松林
  • 地  址:
  • 上海市军工路516号
  • 邮政编码:
  • 200093
  • 联系电话:
  • 021-55277251
  • 电子邮件:
  • xbzrb@usst.edu.cn
  • 国际标准刊号:
  • 1007-6735
  • 国内统一刊号:
  • 31-1739/T
  • 邮发代号:
  • 4-401
  • 单  价:
  • 15.00
  • 定  价:
  • 90.00
耿铭垚,胡锐,李凌.火电机组锅炉烟气含氧量预测[J].上海理工大学学报,2021,43(4):319-324.
火电机组锅炉烟气含氧量预测
Prediction of oxygen content in boiler flue gas of thermal power unit
投稿时间:2020-10-30  
DOI:10.13255/j.cnki.jusst.20201030002
中文关键词:  烟气含氧量  BP神经网络  遗传算法  五点三次平滑滤波  偏最小二乘
英文关键词:oxygen content in flue gas  BP neural network  genetic algorithm  five-point three-time smooth filter  partial least squares
基金项目:国家自然科学基金资助项目(51476102)
作者单位E-mail
耿铭垚 上海理工大学 能源与动力工程学院上海 200093  
胡锐 杭州华源前线能源设备有限公司杭州 311106  
李凌 上海理工大学 能源与动力工程学院上海 200093 liling@usst.edu.cn 
摘要点击次数: 60
全文下载次数: 85
中文摘要:
      火电厂测量烟气含氧量主要是用氧化锆传感器和磁式氧气传感器,由于测量环境灰尘大,具有腐蚀性介质如硫化物等,容易发生测量环室堵塞和热敏元件腐蚀,所以其稳定性差,测量误差大,容易发生故障。针对这一情况提出了一种基于遗传算法和神经网络的测量模型。根据电厂已有的测点和机理分析初步选取模型辅助变量,在建模前对数据进行预处理,分别采用拉依达法则去除粗大误差、五点三次平滑滤波去除噪音。采用偏最小二乘进行主元分析,最后运用遗传算法对神经网络的权值阈值进行寻优,构建了基于遗传算法对初始权值和阈值优化的反馈神经网络模型。研究结果表明,基于遗传算法优化权值和阈值的神经网络预测烟气含氧量精度较高,且收敛速度快。
英文摘要:
      Thermal power plants mainly use zirconia sensors and magnetic oxygen sensors to measure the oxygen content in the flue gas. Due to the heavy dust in the measurement environment, and corrosive media such as sulfide, etc. the measurement ring chamber is prone to blockage and the thermal element corrosion. Therefore it has poor stability, large measurement errors, and is prone to failure. Aiming at this situation, a measurement model based on genetic algorithm and neural network was proposed. According to the existing measurement points and mechanism analysis of the power plant, the auxiliary variables of the model were preliminarily selected, and the data was preprocessed before modeling. The Laida rule was used to remove the gross errors and the five-point three-time smoothing filter was used to remove the noise. Partial least squares was used for principal component analysis. The genetic algorithm was finally used to optimize the weight threshold of the neural network. A feedback neural network model based on the genetic algorithm to optimize the initial weight and threshold was constructed. The results show that the neural network based on genetic algorithm optimization has high accuracy in predicting the oxygen content of flue gas, and the convergence speed is fast.
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