振动流化床中流动结构的混沌分析

在内径90mm、静床高800mm的高床层流化床中,用动态压力传感器检测了不同气速条件下普通流化床和振动流化床中沿轴向的压力脉动信号,通过小波变换对信号除噪后,用混沌理论对信号进行了分析.通过关联维数和Kolmogorov熵定量表征振动流化床中的流动结构特征.结果表明:压力脉动信号的关联维数和Kolmogorov熵能够描述振动流化床中的流化状态;振动流化床中床层的流动结构存在两个区,在近分布板区域为射流区,床层主体部分为均匀流化区.     

声场高温流化床流化特性

在内径50 mm、高1000 mm的声场高温鼓泡流化床中,研究Geldart A, B两类颗粒的流化特性,考察了床层温度、声波频率及声压级对流化床最小流化速度的影响. 结果表明,引入声场后,颗粒的最小流化速度随温度升高而下降;固定温度及频率,最小流化速度随声压级增大而减小;固定声压级与温度,颗粒最小流化速度随声波频率增大先减小后增大,存在一个最佳频率范围. 对床内压力波动信号进行分析,得出声场影响高温流化床流化质量的判据：当声压大于110 dB、频率在100~200 Hz范围内时压力波动偏差与最小流化速度值最小.     

工业废盐的流态化行为

目前工业废盐的处理方式较为单一,效果不理想。为了解决这一问题,提出了一种可用于处理工业废盐的流化床装置,采用自制的流化床对工业废盐进行流化,并对气固流化床的压力脉动信号进行实时监测,研究了流化数和床层压力波动的关系。介绍了温度对流化床临界流化速度的影响,并提出了临界流化速度与温度的关联式。对流化过程中产生的气泡进行了研究,讨论了气泡运动过程中的形貌和大小。介绍了一种可以考察床层物料流化效果的方法,并运用Improfile函数将三维空间内物料颗粒的混合情况转化为函数,通过强度和时间关系说明了返混程度的强弱。描述了工业废盐的床层塌落过程,最后通过扫描电镜对工业废盐表面上的有机物进行比较得出流化床处理工业废盐的可能性。     

气、固二相流化床在线分析系统

流化床中的压力波动和气泡行为是综合反映流化床流化质量的主要信息。流化床现场监测系统是对上述二种信号进行在线统计研究而设计的。该系统有 A/D 变换器、APPLE 微型计算机和打印机组成。采用 BASIC 和汇编语言编制了信号的幅值处理和谱分析软件来完成在线的信号处理工作、可实时采集气泡的峰值分布规律和变异曲线、频率分布和平均频率等有用信息、为进一步实现流化床现场实时运行监视系统打下了基础。     

用空隙率波动信号确定磁流化床的临界转换点

磁流化床由聚式流化向散式流化转化的实验判据,可以从磁流化床局部空隙率波动信号的主频率F_d随磁场强度H变化的曲线得到。该曲线的极值点就对应了从聚式流化转变为散式流化的临界转换点。由此确定的稳定流化区域与Rosensweig用微扰准则划定的稳定流化区域基本一致。     

大颗粒鼓泡流化床压力脉动信号的小波分析

利用小波分析对试验所测得的大颗粒流化床的压力脉动信号进行小波包的完全分解,并对各个频段的信号的方差进行统计分析。结果表明,分解前后信号的能量是守恒的,脉动能随着流化数的增加逐渐集中在低频段;气泡经过测点的频率在0~12.5H z的范围内,且气泡的运动变化是影响测点压力数据波动的主要因素;通过分析流化床压力信号中的低频成分,可以判断床层物料的流化状态。     

过热蒸汽流化床流化特性的实验研究

在底部直径为120 mm的锥型流化床中,以玻璃珠为流化颗粒,过热蒸汽为流化介质,研究了固体颗粒在过热蒸汽流化床中的流化特性,考察了操作温度和压力对临界流化速度(umf)的影响.结果表明,过热蒸汽流化床的流化行为与热空气相似,临界流化速度(umf)随床层温度的升高而减小,随床内压力的增大而减小;在相同温度条件下,过热蒸汽流化床的临界流化速度比热空气大.     

气固流化床流型特性及其识别的复杂性研究

运用复杂性C2、涨落复杂性Cf及Lempel-Ziv复杂性C(n)等复杂性参数对气固流化床压力脉动信号进行分析,研究它们随流化床操作气速增大历经不同流型的变化趋势并将结果作了比较,进一步探讨了流化床流型特性的内在规律性,研究结果表明,在起始流化致鼓泡态转变的过程中,气－固体系会进行一种所谓的“重构”现象,并证实了气泡的存在是影响压力脉动信号复杂性的重要因素,同时实验显示复杂性参数能明确地指示固定床,鼓泡流化及湍动流化等不同流型之间的转变过程,为流型识别提供了新思路。     

气固流化床多传感器信号时间序列分析

利用光纤、压力传感器同时获取气固流化床不同位置颗粒浓度、压差、压力信号时间序列：运用吸引子和涨落复杂性参数分析三种信号时间序列，研究细颗粒在低气速下的流化状况。实验结果证实了Takens的相空间嵌入原理，且在流化床由散式流化到聚式流化过渡的过程中，三种信号的复杂性分析都体现床内运动存在涨落过程，这与Wackerbauer研究的结论是吻合的。同时结果表明不同信号涨落复杂性参数随气速的变化趋势是一致的，有理由认为涨落复杂性参数对应的仅是系统本身的一定运动状况，且和轴向位置关系不大。     

基于小波变换的气固流化床压力波动信号的分析

将小波分析技术应用于气固流化床压力波动信号的分析中,在不同尺度上提取信号能量特征,由此来对压力波动信号中所含丰富信息进行分析,提出了应用小波分析技术来判别流化床从固定床向鼓泡床转变的新方法,并确定了起始流化速度的范围。初步研究表明,所提取的特征信息反应了气固流化床从固定床向鼓泡床转变的过程,通过此特征信息可以确定流化床的起始流化速度的范围。     

Characterization of flow regime transition and particle motion using acoustic emission measurement in a gas‐solid fluidized bed

Particle motion is a major determinant of the dynamical performance of a fluidized bed. It plays an important role in determining and optimizing the complex correlation of fluidization condition between particle‐particle and particle‐environment in a system. A passive acoustic emission (AE) technique is applied to monitor, characterize, and control the fluidization condition of polyethylene particles in a gas‐solid fluidized bed. Experimental results show that AE signals are very sensitive to the particle movements by analyzing energy distribution, which can help to understand the status of the system. The AE energy temporal analysis is further used to identify the transition of flow regimes. Moreover, the activity of particle motion can be quantitatively determined by using a combination of granular temperature and AE spatial energy analysis. This work provides valuable insights into the dynamic behavior of particles in a gas‐solid fluidized bed based on AE technique. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

烯烃聚合流化床反应器内动结块的声发射检测

烯烃聚合流化床反应器近分布板区域的动结块是分布板堵塞的诱因,严重影响流化床的正常操作,甚至引发事故。本文在冷模实验装置中,使用声发射技术对近分布板区域的动结块进行检测,分别对比了正常流化、加入丝状结块和加入块状结块时近分布板区域声信号能量和频谱的差异。实验发现,与正常流化时相比,加入结块使得声信号能量下降,且加入块状结块时声信号能量下降更为显著;加入结块后声信号频谱的低频部分（0~20 kHz）和高频部分（70~150 kHz）均出现不同程度的降低,且高频部分下降比低频部分更为显著,块状结块导致的下降比丝状结块更为显著。实验结果表明,声发射技术可以用于流化床反应器内近分布板区域动结块的检测。     

Resolution of structure characteristics of AE signals in multiphase flow system—From data to information

This investigation was performed to study the underlying structure characteristics of acoustic emission (AE) signals, which could be helpful not only to understand a relatively complete picture of hydrodynamics in multiphase flow systems, but also to extract the most useful information from the original signals with respect to a particular measurement requirement. However, due to AE signals are made up of emission from many acoustic sources at different scales, the resolution of AE signals is often very complicated and appears to be relatively poorly researched. In this study, the structure characteristics of AE signals measured both in gas–solid fluidized bed and liquid–solid stirred tank were researched in detail by resorting to wavelet transform and rescaled range analysis. A general criterion was proposed to resolve AE signals into three physical‐related characteristic scales, i.e., microscale, mesoscale, and macroscale. Multiscale resolution of AE signals implied that AE signals in microscale represented totally the dynamics of solid phase and could be applied to measure particle‐related properties. Furthermore, based on the structure characteristics of AE signals, useful features related to particles motion were extracted to establish two new prediction models, one for on‐line measurements of particle size distribution (PSD) and average particle size in gas–solid fluidized bed and the other for on‐line measurement of the suspension height in liquid–solid stirred tank. The prediction results indicated that (1) measurements of PSD and average particle size using AE method showed a fairly good agreement with that using sieve method both for laboratory scale and plant scale fluidized beds, and (2) measurements of the suspension height using AE method showed a fairly good agreement with that using visual method. The results thus validated that the extracted features based on analyses of structure characteristics of AE signals were very useful for establishing effective on‐line measurement models with respect to some particular applications. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

气固流化床中声发生机理及在工业装置中的应用

利用声测量技术,结合频谱分析,建立了颗粒碰撞的声波频率模型,可定量描述声波主频随颗粒粒径、弹性模量和密度的变化规律.通过改变流化颗粒的粒径、弹性模量参数和密度,发现声波主频与频率模型计算值之间的最大偏差为8.3%,说明声波主频可以代表颗粒在壁面的碰撞频率.讨论了热态和冷态条件下声波主频之间变化规律,通过对弹性模量参数的校正,声测量技术可以用于预测工业装置中物料的平均粒径变化,并将该模型应用于线性低密度聚乙烯、高密度聚乙烯和双峰聚乙烯工业生产装置中的平均粒径测量,发现与传统的取样筛分方法所得测量结果十分接近.同时,发现当系统产生聚合物颗粒结块时,声波主频将急剧降低,声波频谱的能量分布将明显集中增大,这可作为判断流化床稳定运行的一个判据.     

基于声发射信号递归分析的气固流化床流型转变

利用声发射技术采集不同流化气速下流化床内颗粒与壁面碰撞的声信号,结合声能量及递归分析法研究不同流型下颗粒运动特征,得到鼓泡流态化到湍动流态化的临界转变速度及流型转变规律。特别是针对声能量分析无法准确区分不同床层高度处流型转变的不足,利用递归分析可有效预测系统周期性的特点,将声信号进行递归分析,研究了流化床不同位置的流型转变性质。结果表明,鼓泡流态化下颗粒运动的周期性较湍动流态化强,并能够清晰地检测到由鼓泡流态化向湍动流态化的流型转变速度,而且床层较低处的流型转变速度比床层较高处大。由此获得了一种便捷灵敏、安全环保的非侵入式流化床流型转变速度的测量技术,可用于对整个流化床内不同位置流型转变过程的实时在线监控。     

Non‐intrusive characterization of particle size changes in fluidized beds using recurrence plots

An on‐line method is developed for monitoring of mean particle size in fluidized beds using pressure fluctuations (PFs) and acoustic emissions (AE) signal by recurrence plot (RP) and recurrence quantification analysis (RQA). PFs and AE signals of a lab‐scale fluidized bed were measured simultaneously at various superficial gas velocities and mean particle sizes. Although the AE signals are often very complicated due to many different acoustic sources in the bed, applying RP analyses showed that small changes in mean particle size can be detected by visual comparison of AE‐RP structures, while this cannot be distinguished by graphical RP analysis of PFs. Moreover, the hydrodynamics of the bed was inspected through RQA analysis of both signals. For this purpose, recurrence rate, determinism, laminarity, average length of diagonal and vertical lines were extracted from RPs showing the effect of an increase in the mean particle size. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3547–3561, 2016     

Characterization of gas-solid fluidization: A comparative study of acoustic and pressure signals

The hydrodynamics of fluidized beds strongly influence their operation, but are complicated and chaotic. There are many measurement techniques, but none fully characterizes gas-solid fluidized beds. Acoustic signals from fluidized beds cover a wide frequency spectrum and can be correlated to bed characteristics. Experiments were conducted to study the acoustic signals from ultrasonic transducers mounted on the outer wall of a two-dimensional fluidization column. The acoustic signals were related to bubble behavior in 550 μm glass beads. Simultaneous acoustic and pressure measurements allowed direct comparison of these signals for single bubbles, pairs and chains of bubbles. The envelope of acoustic signals, generated by particle collisions and particle-wall impacts, provided information on the behavior of bubbles. Significant peaks appeared as the top portions of the bubble wakes approached the acoustic sensor. Pressure waves propagated considerably in the horizontal direction, whereas acoustic signals propagated little in the lateral direction, but transmitted forward in the wall in the direction of bubble motion, maintaining the wave profile invariant during transmission. The strong lateral localization of acoustic signals is promising for determining the lateral bubble position in the bed. Acoustic signals provide a potential means of determining such bubble properties as velocity, frequency and volume, with some advantages relative to pressure signals.     

Measuring Average Particle Size for Fluidized Bed Reactors by Employing Acoustic Emission Signals and Neural Networks

Average particle size is one of the key parameters of fluidized bed reactors. A novel method to measure the average particle size in fluidized bed reactors by acoustic emission (AE) signal is proposed. The measurement of the average particle size by AE is superior to other methods since it is inherently safe to use and it is also a non‐invasive method. AE signals originating from different particle sizes were 8‐level decomposed by a Daubechies wavelet of order 3 after being denoised with a sym8 wavelet filter combined with the rigrsure threshold method. Principal component analysis (PCA) was applied to overcome the complex collinearity and reduce the number of input variables of the neural network. A feed‐forward back propagation neural network with two hidden layers was used to predict the average particle size according to the principal components. The results show that this soft‐measuring model is suitable for measuring the average particle size in the fluidized bed reactors by online AE. It achieved high accuracy when applied to a model‐scale fluidized bed.     

声场流化床流动特性研究进展

声场流化床是将声场引入普通流化床,采用颗粒为床层介质的流固相处理系统。声波可以有效降低颗粒聚团尺寸,显著改善超细颗粒的流化质量。本文介绍了声场流化床的基本原理以及近年来在基础研究和应用方面取得的进展及成果,综述了声场流化床在流体力学特性、颗粒特性、声场参数、流态化模型、颗粒团聚以及流化质量机理等方面的研究,并对声场流化床目前存在的问题及发展趋势提出了一些建议。     

声波的多尺度分解与气固流化床故障检测

引言 在流化床乙烯气相聚合过程中,由于传热速率较低(与淤浆工艺相比),聚合物小颗粒容易聚集在一起形成结块,若不及时采取措施,将影响流化状态.而不良的流化状况经常导致装置不能满负荷、长周期地运转,严重时会"熔床",继而停产.为了保证流化床长期稳定的运行,聚合物颗粒结块的监测就变得十分重要.目前工业生产中主要根据流化松密度、反应温度、床高等参数的突变和γ射线法来检测结块.前者多依赖人为经验的判断,漏报、误报的情况时有发生.而γ射线法则对人体存在致命的伤害.因此,发展一种简单快捷、灵敏准确、安全环保的结块测量技术成为当务之急.