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  • ISSN 1006-3080
  • CN 31-1691/TQ

基于数据驱动的气化炉出口温度在线测量

杜旭鹏 王钰琪 许建良 于广锁 刘海峰

杜旭鹏, 王钰琪, 许建良, 于广锁, 刘海峰. 基于数据驱动的气化炉出口温度在线测量[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20211116001
引用本文: 杜旭鹏, 王钰琪, 许建良, 于广锁, 刘海峰. 基于数据驱动的气化炉出口温度在线测量[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20211116001
DU Xupeng, WANG Yuqi, Xu Jianliang, Yu Guangsuo, Liu Haifeng. Online Measurement of Outlet Temperature of Gasifier Based on Data Driven[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20211116001
Citation: DU Xupeng, WANG Yuqi, Xu Jianliang, Yu Guangsuo, Liu Haifeng. Online Measurement of Outlet Temperature of Gasifier Based on Data Driven[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20211116001

基于数据驱动的气化炉出口温度在线测量

doi: 10.14135/j.cnki.1006-3080.20211116001
基金项目: 国家自然科学基金(21878082)
详细信息
    作者简介:

    杜旭鹏(1997—),男,河南许昌人,硕士生,研究方向为气流床气化。Email:y30190671@mail.ecust.edu.cn

    通讯作者:

    许建良,Email: xujl@ecust.edu.cn

  • 中图分类号: TQ545

Online Measurement of Outlet Temperature of Gasifier Based on Data Driven

  • 摘要: 气化温度是气流床气化炉最重要的操作参数,现有煤气化装置缺失长期可靠的炉温监测。为实时监控气流床气化炉运行状态,收集气化炉激冷系统和反应系统等可测量数据,采用理论计算模型和遗传算法改进的BP神经网络模型(GABP)对气化炉出口温度进行了预测,并与工业测量数据进行了对比分析。研究结果表明,激冷系统理论计算可以得到气化炉出口温度,但因测量参数敏感度低,导致预测结果的精度和稳定性较差;采用GABP神经网络模型可以大幅提高预测效果;采用反应系统参数预测,因煤量波动和缺乏煤质数据,导致预测误差较大;采用激冷系统参数输入可大幅提高预测精度,预测误差保持在15 K以下,可满足不同工况下的气化炉温度实时在线监测的需要。

     

  • 图  1  水煤浆气化系统

    Figure  1.  Coal water slurry gasification system

    图  2  气化炉流股参数

    Figure  2.  Flow stock and parameters of gasifier

    图  3  理论计算相关因素的敏感度系数

    Figure  3.  Sensitivity coefficient of relevant factors is calculated theoretically

    图  4  常见的BP神经网络模型结构

    Figure  4.  Common BP neural network model structure

    图  5  不同输入参数的GABP神经网络

    Figure  5.  Neural network with different input parameters

    图  6  理论计算的出口温度及误差分布

    Figure  6.  Outlet temperature and error distribution of theoretical calculation

    图  7  激冷系统参数对出口温度的预测及误差分布

    Figure  7.  Outlet temperature and error distribution of GABP with quench system

    图  8  反应系统参数对出口温度的预测及误差分布

    Figure  8.  Outlet temperature and error distribution of GABP with reaction system

    图  9  稳定负荷与变化负荷条件下温度预测情况对比

    Figure  9.  Comparison of temperature prediction under steady load and variable load

    图  10  不同预测方法的误差分布

    Figure  10.  Error distribution of different prediction methods

    表  1  气化炉激冷系统相关参数

    Table  1.   Relevant parameters of quench system of gasifier

    ParameterParameter name
    Q1Syngas temperature out of water scrubber
    Q2Syngas volume out of water scrubber
    Q3Syngas pressure out of water scrubber
    Q4Quenching water flow
    Q5Quenching water temperature
    Q6Black water temperature
    Q7Syngas temperature out of quench chamber
    Q8Syngas pressure out of quench chamber
    Q9Liquid level of quench chamber
    下载: 导出CSV

    表  2  气化炉反应系统相关参数

    Table  2.   Relevant parameters of reaction system of gasifier

    ParameterParameter name
    R1Differential pressure of slag tap
    R2Gasification pressure
    R3Coal slurry flow rate
    R4Oxygen flow rate
    下载: 导出CSV

    表  3  气化炉参数与出口温度变化关系

    Table  3.   Variation of gasifier parameters and outlet temperature

    ParameterT/K
    Oxygen flow rate142.4
    Syngas temperature out of quench chamber71.7
    Coal water slurry flow rate42.8
    Quenching water temperature19.1
    Black water temperature18.5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-16
  • 录用日期:  2022-01-04
  • 网络出版日期:  2022-04-12

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