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This work first investigated the detection of slags, slag pool liquid level, and slag accumulation height in laboratory scale based on acoustic emission (AE) detection, and further tried the feasibility of this method in an industrial scale coal gasifier. Results show that the energy and variance of acoustic signals can realize the accurate detection of large slag (criterion: E > 1.5E0, S > 1.2S0), and the average relative error is only 0.28%. The acoustic energy in the frequency range of 20–40 kHz is defined as the characteristic energy, which can realize the accurate detection of slag accumulation height and slag pool liquid level, and the average relative error is only 3.94%. Furthermore, AE detection also realize accurate detection of large slag in an industrial scale gasifier and the acoustic signals at slag screen can be used to realize the early warning of the slag collapse (5 h earlier).

In this study, a non-invasive and environment-friendly acoustic emission (AE) technique, coupled with multiple analysis methods, was proposed to monitor the critical variation of separation efficiency of a cyclone separator by means of the gas-solid hydrodynamics. Acoustic energy analysis was used to reflect the characteristics of particle motion and particle-wall interaction. Shannon entropy analysis was adopted to reflect chaos and complexity of gas-solid two-phase motion. Multi-scale analysis (Hurst analysis and wavelet analysis) was used to obtain multi-scale characteristics inside the cyclone separator. To be specific, micro-scale collision behaviors of particle-particle and particle-wall, meso-scale interaction behavior between particle clusters and gas phase, and macro-scale interaction behavior (overall flow behavior). Furthermore, by comparison with the results of separation efficiency and pressure drop, the above-mentioned three methods based on AE technique can effectively reflect the critical variation of separation efficiency for cyclone separator.

Solids circulation establishment in a cold model and industrial scale reactor has been investigated via visual observation, bed density, pressure drop, particle loss and acoustic emission (AE) techniques. The gas-solid flow pattern in the cold model and industrial unit share similar features based on the visual observation and bed density detection respectively. The circulating fluidized bed (CFB) with sweeping bend return initially exhibits stable fluidization in the riser and downer. As time elapses, the resistance fluctuation in the riser and downer is induced by the imbalance between the inlet and the outlet solids flow in the downer, leading to the unstable gas-solids flow pattern in riser and downer. The alternation between fluidized bed and moving bed is observed in the downer during the unstable stage, and further induces high-pressure drop fluctuations, heavy particle loss. Besides, the detection of pressure drop, particle loss rate and corresponding AE energy could be used to determine the time range of unstable stage.

Acoustic emission technique in conjunction with multiscale processing method has been utilized to investigate the flow behavior of the dense-phase pneumatic conveying system at high pressure. A clearly defined classification of microscale, mesoscale, and macroscale signals has been put forward with the aid of wavelet transform and V statistics analysis. The detailed signals d1–d4, d5–d7, d8–d10 were recomposed into the microscale, mesoscale, and macroscale signals, respectively, which represent microscale particle-wall interactions, mesoscale interaction between gas phase and solid phase (such as bubbles, plugs, dunes), and macroscale flow-induced pipe vibration. Further analysis shows that as the mass flow rate of pulverized coal increases, the energy fraction (energy of detailed signal divided by the energy of original signal) of microscale signals decreases while that of mesoscale signals increases, which indicates that particles are more likely to move as particle aggregates than individual particles when mass flow rate increases. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2635–2648, 2016

This study mainly investigated the effects of liquid evaporation and liquid bridge actions on hydrodynamics in liquid-containing gas–solid fluidized bed reactor (LGSFBR). A liquid flow-rate increasing process and a gas velocity decreasing process were studied by using temperature, acoustic emission (AE) and pressure fluctuation (PF) measurement techniques. When the liquid was added with lower flow-rates, particle circulation pattern was dominated by gas flow/liquid evaporation action, and thus particles display ascending at the center and descending near the wall (ACDW) with particle motion enhanced, meanwhile bubble sizes increased at the beginning and then decreased to steady values. With increasing liquid flow-rate or decreasing gas velocity, liquid bridge action relative to gas flow action intensity (LRGI) increased, and thus the particle circulation pattern gradually became dominated by the liquid bridge action. Specifically, the downward motion of dynamic particle agglomerations was enhanced while particle motion was weakened, and the gas bubble size was repressed gradually. Several unstable fluidization states would be caused by continuous increases in the liquid bridge action.

Flow pattern of cohesive particles in gas-solid fluidized bed is complex, and particles in different regions exhibit various flow patterns. Characterization methods in literatures are deficient in detecting local motions of particles. In this work, particle motions distribution along the axial direction was investigated by acoustic emission detection and pressure fluctuation. The axial distribution of particle motions proved the “stratified fluidization” of cohesive particles and further investigation showed that the stratified fluidization was affected by both the superficial gas velocity and the initial height-to-diameter ratio (H0/D). Increasing the operating gas velocity, the fluidization of the fluidized bed with a relatively low H0/D changed from fixed bed with stable channeling to the two-layer stratified fluidization consisted of the fixed bed layer in the lower part and the bubbling fluidized bed layer in the upper part. While for the fluidized bed with a large H0/D, the fluidization transmitted from fixed bed with unstable channeling to two-layer stratified fluidization, then to multi-layer stratified fluidization with an inflatable fluidized bed layer appeared above the bubbling bed layer. Moreover, a flow pattern map of the fluidization of cohesive particles was established which could show guidance for the selection of optimized operation conditions for fluidization of cohesive particles.

The plug flow was investigated in both the single plug and the continuous plug experiments. In the single plug flow, the pressure drop might rise for two times with a longer plug, and the second increase was proved to be caused by the collision between the falling particles and the plug by acoustic emission detection. In continuous plug flow, the interaction between the bulk of the plug and the pipe wall was dominated by the friction. And the sources of acoustic signals in continuous plug flow can be mainly divided into two parts: the collision between the falling particles and the plug and the friction between the bulk of plug and the wall. Based on this, the acoustic model was derived and the energy fraction can be predicted accurately. And the model further provides a possibility for the non-intrusive, real-time and online detection of Janssen coefficient.

A novel approach based on the passive acoustic emission (AE) monitoring technique has been established for analyzing particle–wall collision and friction separately in the present work. Using power spectrum density analysis, the main frequency of AE signal caused from particle–wall collision is found to be higher than that generated by particle–wall friction. Besides, a method for quantitatively extracting the information on particle–wall collision and friction has been set up by wavelet transform analysis. On the basis of these analyses, a theoretical approach has been established for relating the AE signals and solids loading ratio in a vertical pneumatic conveying pipe. The model predictions are verified using experimental data and are in good agreement. Particle mass flow rates obtained using this model give errors less than 6.62%. Conclusions can be drawn that the AE technique has great potential in the measurement of hydrodynamics in pneumatic conveying as well as similar particulate processes.

A passive acoustic emission (AE) technique has been applied to monitor and characterize particle movement in a cross-flow moving bed. Experimental results show that AE signals are very sensitive to particles’ movement. By using spectrum analysis, both the relationship between particle discharging rate and the average signal energy and the relationship between particle moving velocity and frequency shift of the characteristic peak of acoustic signals have been found to be linear. Therefore, two correlations have been proposed to characterize the particle discharging rate and the particle moving velocity. The correlations have been validated using experimental data. The AE signal analysis was further used to detect the two adverse particle movement status: cavity and pinning. Convenient and accurate detection of particle movement is of important significance in the improvement of heat and mass transfer efficiency of the bed, thereby providing guidelines in the design of the radial flow moving bed reactor.

Agglomeration occurring in horizontal stirred bed reactors (HSBR) for polyolefin production has negative impacts on the efficiency of the reactor operation and may sometimes lead to unscheduled shutdown of the plant. In this paper, an autoregression (AR) model based on acoustic emission (AE) technique has been proposed to establish the qualitative relationship between AE signals and agglomeration in the HSBR. In this method, the frequency of AE signal varies with particles of different sizes striking the reactor walls. From the cold model experiments, it was found that AR power spectrum became fluctuant after the addition of agglomerations into laboratorial scale HSBR, and meanwhile the low frequency band energy ratio and the variance of AE signals kept rising. Furthermore, this AE-based AR model was also successfully applied to detect the agglomeration in an industrial HSBR unit, showing that the method could monitor agglomerations in an environmentally friendly manner and with fairly good accuracy.

Particle and bubble motion plays an important role in determining the hydrodynamic characteristics of a fluidized system. The dynamic parameters of a fluidized bed are reflection of the complex correlation between particle–particle and particle–bubble in a system. A two-dimensional Eulerian–Eulerian model integrating the kinetic theory of granular flow is used to simulate the bubble and linear low density polyethylene (LLDPE) particle dynamic behavior in a gas–solid fluidized bed. The simulated method is validated by pressure fluctuation experiment. The computed vertical turbulent energy spectrum of particles is applied to identify the particle motion intensity and the inhomogeneity of turbulent energy dissipation. The energy spectrum captures the Levy–Kolmogorov law in inertial range at high frequency. Furthermore, the flatness factors of wavelet decomposition coefficients of particle fluctuation velocity are for the first time introduced to analyze the intermittence caused by coherent structures in the flow field. The results show that the intermittence in dissipation range is much stronger than that in energy-containing and inertial range, and reinforces rapidly as the radial distance and the bed height increase. Moreover, the acoustic emission (AE) energy is found to be able to indicate the flow regimes. By combing granular temperature and AE energy, the relationship between the spatial distribution of granular temperature and the flow regimes is established. To get more detail of bubble motion behavior, the power spectrum of voidage fluctuation is analyzed. This work provides valuable insights into the dynamic characteristics and the flow field information of a gas–solid fluidized bed by CFD simulation.

Agglomeration is one of the most challenging problems due to overheating of the particles in fluidized bed reactors (FBRs). Therefore, it is an urgent task to develop a reliable and sensitive metho...

Particle size distribution (PSD) is an important parameter in the process of fluidization, and it always plays a crucial role in a gas-solid fluidized system. A PSD model for on-line PSD determination based on acoustic emission (AE) measurement was developed according to the mechanism of particle collision with the inner wall of the cylinder and multi-scale wavelet decomposition analysis. This PSD model illuminates the quantitative relationship between the energy percentage of AE signals for different scales and the PSD, which indicates the feasibility of the application of the PSD model. Experiments were undertaken both in lab and plant gas-solid fluidized setup with polyethylene particles, and the parameters of the PSD model were calibrated and revised. The experimental conditions and results proved that the PSD model was suitable for on-line measurement and was sufficiently sensible and accurate. Concerning agglomeration, the PSD model also showed exact serviceability on detecting the onset of agglomeration by abnormal PSD, and the result agreed with that from the radiation method. Ultimately, AE measurement was found to be a reliable and credible means for understanding the PSD information that affects the behavior of a system, which can provide valuable guidance for practical applications.

In industrial propylene polymerization technology of Innovene and Chisso, agglomeration of polymer particles in horizontal stirred bed reactors (HSBR) has significant negative impacts on the reactor efficiency. If the hydrodynamic change could be detected early enough, then large chunks may not be formed nor cause an unscheduled shutdown of the plant. A novel non-invasive acoustic emission (AE) technique combined with an attractor comparison method is presented. It is based on the comparison of the time series of AE signals acquired from a normal state with the time series measured during operation of the bed. The nature of the method is to determine whether or not two time series are generated by the same mechanism. A statistical characteristic S is used to test the null hypothesis that the two multidimensional probability distributions are identical. Experiments were carried out at room temperature in a 475 mm i.d. laboratory HSBR. The results demonstrated that the method is sensitive to small changes in the particle size distribution. It is, therefore, indicated that the proposed method not only can offer “early and accurate warning,” but also has the potential to locate the agglomeration in HSBR with multiple AE sensors. © 2009 American Institute of Chemical Engineers AIChE J, 2009

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

A novel method, based on acoustic emission (AE) techniques, for detecting agglomeration in fluidized beds is presented. Particle size characteristics are determined based on the principle that AE signals with different frequency band energies are emitted when particles of different sizes impact an internal wall. By applying chaotic analysis to the AE signals, the malfunction coefficients are well defined. Agglomeration in the fluidized bed can then be detected by the sudden variation of malfunction coefficients. AE signals were investigated in a laboratory scale heated fluidized bed and an industrial polyethylene fluidized bed. Experimental data showed that the malfunction coefficients increased with the growth of agglomeration. The results indicated that agglomeration in fluidized beds can be predicted and diagnosed effectively and precisely using AE techniques based on chaotic analysis.

Flow pattern of the fluidized bed is a key factor for heat transfer and the design of a new reactor. It is important to comprehend the fluidization conditions because the complex correlation between particle−particle and particle−environment in the reactor. In this study, we applied acoustic emission (AE) measurement in monitoring the particle fluidization pattern in a gas−solid bed fluidized with different sorts of particles classified by Geldart. With AE axial time average energy analysis, the flow structure of polyethylene particles was investigated both in the laboratory and plant apparatus. The results showed that the fluidization pattern in the bed is multicirculation, including the main-circulation zone, subcirculation zone, and the stagnant zone. Moreover, the influence of operating variables, such as particle size, superficial gas velocity, static bed height, and particle sorts had also been considered. It was found that the particle size, gas velocity, and particle sorts have significant impacts...

The occurrence of agglomeration in fluidized-bed production of polyolefin can have negative impacts on the efficiency of the reactor operation and may lead to defluidization and unscheduled shutdown of the plant. A novel method by using acoustic emission (AE) sensors was developed to detect different types of agglomeration in fluidized bed. Chaos analysis was discussed, and coefficients of malfunction were defined to recognize agglomeration. AE signals we divided into micro-, meso-, and macroscales by wavelet transform, and on the basis of the energy of mesoscale fraction analysis of AE signals, a prediction model was developed to predict the size of moving chunks in the bed. The application effect of AE method was illustrated with experiments carried out both in laboratory and the plant.

An energy distribution theory was presented based on regular evolvement of energy fraction of acoustic signals with fluidization velocity. Wavelet packet analysis was used in processing the acoustic signals originated from particle impact on the wall of a fluidized bed. A new criterion of judging incipient fluidization (U mf) velocity and minimum turbulent velocity (U mt) was proposed according to the energy distribution theory. Experiments were performed with five groups of high density polyethylene (PE) particles and one bimodal PE to acquire incipient fluidization velocity and minimum turbulent velocity by using the criterion. The feasibility of this method in obtaining characteristic fluidization parameters was further verified by comparing it to results from the pressure drop method and the empirical value from industry.

The underlying structure characteristics of acoustic emissions (AE) measured in a gas-solid fluidized bed was investigated detailedly by resorting to wavelet transform and rescaled range analysis. A general criterion was proposed to resolve AE signals into three characteristic scales, i.e. micro-, meso- and macro-scale, and a so-called structure diagram was introduced. Compared with the structure diagram of pressure signals, it was found that AE signals in micro-scale reflect mainly the particles motion while pressure signals in meso-scale reflect mainly the bubbles motion. Energy distribution analysis further revealed that the most energies in AE and pressure signals were distributed mainly by the micro-scale and meso-scale signals respectively. Moreover, the structure characteristics of AE signals collected from gas-solid fluidized bed and liquid-solid stirred tank were compared based on structure diagram and energy distribution analysis. The results indicated that although the same measurement technique was adopted, the structure characteristics of AE signals measured in gas-solid fluidized bed and liquid-solid stirred tank still exhibited larger difference. As an illustrative application of AE technique in process monitoring, a prediction model for particle size distribution was proposed and the satisfactory results were obtained both for laboratory scale and plant scale fluidized beds.