【作者】 张倩；

【导师】 阳永荣； 王靖岱； 黄正梁；

【作者基本信息】 浙江大学 ， 化学工程与技术， 2016， 硕士

【摘要】 气固流化床反应器广泛应用于石油、化工、生化、环保、制药等领域。结片/结块是气固流化床反应器中最常见的问题之一。结块过大时会堵塞分布板或出料系统,导致爆聚或紧急停车,严重影响反应器的安全稳定运行。气固流化床是一个流体力学行为高度复杂的系统,其工艺参数具有非线性和多尺度等特征,难以进行定性、定量分析,增大了故障预警难度。传统检测手段(如温度检测、压力检测等)不能满足现代工业生产的需求。因此,探究气固流化床内结块形成机制,建立可靠、有效的结块故障预警技术不仅具有重要的理论意义,而且具有极大的工业应用价值。本文针对气固流化床中的结块问题,以静电检测、温度和压力脉动等为在线测量手段,综合应用现代故障检测与诊断技术,对结块故障预警问题进行了深入分析；进一步,通过建立高温结块实验装置,从工业和冷模实验两方面探究了结块的演化规律以及结块与静电的相互作用关系,提出了强静电场下的结块模式；最后,开发了基于静电信号的结块早期检测技术,研究结果对于结块早期预警和聚乙烯装置的稳定运行具有重要意义。论文主要包含以下内容：1.通过考察结块过程中大量工艺参数的变化,分析了各工艺参数信号与结块的关联度并挖掘出与结块最相关的工艺参数,揭示了结块时信号波动的时间序列。研究发现,壁温信号与结块的关联度最高,相关系数达到了0.85,其次为静电信号,相关系数为0.71；结块过程中信号波动的时间序列为：静电波动→温度波动→铯源结块探测器信号波动,结块产生过程中静电最先发生波动,随后结片导致壁温发生波动,最后产生大结块并导致铯源信号波动。2.采用现代故障检测与诊断技术,从理论上分析了壁温的非线性波动,提取了其非线性特征,建立了结块检测模型(E检测法)。研究发现,壁温信号经经验模态分解得到的本征模函数的高频分量在有结片产生时会显著增大,其能量的滑动平均值Em能有效识别结片的产生。将Em作为特征参数输入PCA,计算结片检测统计量严并划定结片控制上限UCL,从而实现结片检测：当T2<UCL时,壁面无结片产生；当T2≥UCL时,壁面有结片产生。应用表明,当有结片产生时,E检测法的检测值T2显著增大,且与传统的壁温波动判别法和铯源结块探测法相比,E检测法不仅能提前约两小时预警结片的产生,而且能大幅降低误报率和漏报率。3.通过在冷模实验装置中采用热空气加热聚乙烯颗粒模拟结块过程,分析了结块过程中温度、静电、压力等信号的变化规律,揭示了静电等关键工艺参数与结块的相互作用机制,获得了流化气速和颗粒粒径对颗粒结块过程的影响规律。研究发现,结块过程中初始颗粒质量分数逐渐减少,颗粒聚团质量分数先增大后减小,颗粒结块质量分数逐渐增大；与此同时,床层料位逐渐降低,整床压降先减小后增大并再次减小。温度升高对静电水平的影响很小,可忽略不计;加热结块前,颗粒荷质比和静电压均为负值,且随着流化时间的延长先增大后减小并趋于平稳；加热结块后,颗粒荷质比再次增大,静电压先增大后减小,呈“V”形波动。随着流化气速的增大,结块过程中静电流的最大值、平均值、标准差值以及静电压均呈增大趋势,结块时间缩短；随着颗粒平均粒径的增大,结块过程中静电流的最大值、平均值、标准差值呈先减小后稍有增大的变化趋势,平均粒径为517 μm的聚乙烯颗粒的静电压最大且最先产生结块。4.通过分析工业装置结块过程中静电信号的变化规律,提出了强静电场下的结块模式,建立了基于静电信号IMF瞬时幅值的结块故障检测方法。研究发现,在工业装置结块过程中,壁面结片使床内静电水平减小,静电压在结块过程中呈“V”形波动；基于静电流信号IMF瞬时幅值的结块预警方法无法实现提前预警,基于静电压信号IMF瞬时幅值的结块预警方法效果较好,在冷模实验装置中与压降法相比能提前10～15 mmin预警结块,在工业装置中与E检测法相比能提前1.2～3.5 h预警结块。研究结果表明,静电压信号IMF瞬时幅值能提前预警结块,具有较好的普适性。更多还原

【Abstract】 Fluidized bed reactors are widely used in petroleum, chemical, biochemical, environmental, pharmaceutical and other fields. Agglomeration/sheeting is one of the most common problems in fluidized bed reactor. Large agglomeration would block distribution plate or discharge system, leading to implosion and emergency shutdown, which affects the safe and stable operation of the reactor seriously. Since the hydrodynamic behavior in industrial fluidized bed is highly complex, thus forming a complicated system with nonlinear and multi-scale characteristics, fault detection and diagnosis in multiphase flow reactors is rather difficult. Traditional detection methods (such as temperature detection, pressure detection, etc.) cannot meet the needs of modern industrial production. Therefore, researches regarding agglomeration issue in the fluidized bed and developing fast and accurate fault detection technology are not only challenging, but also of great theoretical importance and industrial application value.Based on the online measurement of electrostatic detection, temperature and pressure pulsation technique, combining modern fault detection and diagnosis technology, this thesis has an in-depth study of the early warning of agglomeration in fluidized bed. Moreover, by establishing a high-temperature agglomeration experimental device, the evolution of the agglomeration and the interaction between agglomeration and electrostatic potential are investigated in industrial as well as cold model experiments; the agglomeration model under high level electrostatic potential is proposed. Furthermore, early warning of agglomeration based on the electrostatic signal is realized. These research findings are of great importance for the fault detection of agglomeration and the stable production of polyethylene unit. The main contents in this paper are as follows.1. By examining the variance of a large number of process parameters during agglomeration process, the correlation degree between each process parameter and agglomeration is analyzed. By extracting the most relevant process parameter, the time sequence of signal fluctuations during agglomeration is revealed. The study finds that wall temperature shows the highest correlation coefficient with agglomeration (0.85), followed by electrostatic potential signal (0.71). The time sequence of signal fluctuations in agglomeration process is as follows:electrostatic potential fluctuation →temperature fluctuation→cesium source signal fluctuation. This sequence reveals that when agglomerations are formed, the electrostatic potential reacts first, followed by wall temperature due to wall sheeting, and lastly cesium source detector when the agglomerations are too large.2. Based on fault detection and diagnosis technology, nonlinear fluctuations of wall temperature are investigated theoretically and the nonlinear characteristics are extracted and the agglomeration detection method (E detection method) is established. This study finds that the high-frequency components of the intrinsic mode functions (IMF), extracted from empirical mode decomposition results of wall temperature signal, increase significantly when wall sheets are formed. The energy moving average value, Em, is found to be effective in indicating initial sheeting. By importing Em into principal component analysis, the statistical T2 and upper control limit (UCL) are achieved, and the wall sheeting detection method is proposed:when T2 is less than UCL, no sheets are formed; when T2 is greater than or equal to UCL, sheets are formed. The industrial application results show that, compared with traditional wall temperature fluctuation discriminant or cesium source detection method, E detection method can achieve early warning of sheeting of about two hours in advance, and can greatly reduce false positive rate and false negative rate.3. Through simulating agglomeration process by heating polyethylene particles using hot air in the cold model device, the variation rules of temperature, electrostatic potential and electrostatic current, pressure signals are analyzed. The interaction mechanism of key process parameters (electrostatic potential or other parameters) and agglomeration is revealed and the effect regularities of fluidized gas velocity and particle diameter on particle agglomeration are acquired. This study finds that the mass fraction of initial particles decreases, and the mass fraction of particle agglomeration first increases and then decreases. Meanwhile, the bed level declines gradually and the whole bed pressure drop decreases before increasing, and finally declines. The effect of temperature on electrostatic level is ignorable. The charge-to-mass ratio and electrostatic potential is negative before heating, and first increases before decreasing to steady value as the fluidization time lasts. After heating and agglomeration, charge-to-mass ratio increases again and electrostatic potential first increases and then decreases, which shows a "V" shape variance. As the gas velocity increases, all of the maximum value, mean value and standard deviation of electrostatic current and electrostatic potential show an upward tendency during agglomeration process, and the time from heating to agglomeration decreases. As the average particle diameter increases, the maximum value, mean value and standard deviation of electrostatic current first decreases and then increases slightly. The polyethylene particle with an average diameter of 517 μm shows the highest electrostatic potential and forms agglomeration firstly.4. Based on the analysis of variation regularity of electrostatic signal during industrial agglomeration process, the agglomeration model in strong electrostatic field is proposed, and the agglomeration detection method based on the IMF instantaneous amplitude of electrostatic signal is developed. This study finds that electrostatic level decreases because of wall sheeting during industrial agglomeration process, and electrostatic potential presents V shape variance during agglomeration process. The agglomeration detection method based on the IMF instantaneous amplitude of electrostatic current signal cannot achieve early warning of agglomeration, while that of electrostatic potential signal shows great results since it can achieve early warning of agglomeration of 10～15 min in advance compared with pressure drop method in cold model experiment, and 1.2～3.5 h in advance compared with E detection method in industrial device. The result reveals that IMF instantaneous amplitude of electrostatic potential signal can achieve early warning of agglomeration and shows great universality.更多还原