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    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), 2023, 49(2): 168-175. 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), 2023, 49(2): 168-175. 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, 2023, 49(2): 168-175. 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, 2023, 49(2): 168-175. 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

    Online Measurement of Outlet Temperature of Gasifier Based on Data Driven

    • 摘要: 为实时监控气流床气化炉的气化温度和运行状态,收集气化炉激冷系统和反应系统等系统的可测量数据,采用理论计算模型和遗传算法改进的BP(GABP)神经网络模型对气化炉出口温度进行预测,并与工业测量数据进行对比分析。结果表明,由激冷系统理论计算模型可以得到气化炉出口温度,但因测量参数敏感度低,导致温度预测精度和稳定性较差。采用GABP神经网络模型总体上可以提高预测温度的精度和稳定性,但反应系统中由于煤量波动和煤质数据缺乏等原因,导致部分区间预测误差较大;采用激冷系统参数可大幅提高绝大部分区间内温度的预测精度,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需要。

       

      Abstract: Gasification temperature is the most important operating parameter of entrained flow gasifier. However, reliable methods to long-term measure the gasification temperature of coal gasifier units remain elusive. In order to monitor the operation state of entrained flow gasifier in real time and ensure the safe and stable operation of gasification system, measurable data including gasifier cooling system and reaction system are collected. The outlet temperature of gasifier was predicted by a theoretical calculation model and BP neural based on the genetic algorithm model (GABP). The results were compared to those obtained from industrial measurement. The outlet temperature of gasifier can be obtained by the theoretical calculation of quench system, while the accuracy and stability of the prediction results are unsatisfactory due to the low sensitivity of measurement parameters. GABP neural network model greatly improves the prediction performances. Based on the gasification chamber parameters, the prediction error is large due to the fluctuation of coal water slurry flow rate and a lack of coal property data. Using quench system parameters as the input of GABP neural network greatly improves the prediction accuracy, and the absolute value of the prediction error is less than 15 K. Both the train set and verification set have excellent prediction results, and the average absolute error of GABP model with quench system parameters as input is about 5 K. The GABP model has good performances in the face of complex working conditions. When predictions are carried out under different conditions, the results under steady and variable coal loads are produced with good prediction precision and stability, which meet the requirements of online monitoring of gasifier temperature.

       

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