Fluidization of Nanoparticle Agglomerates at Elevated Temperatures

Subscriber access provided by Universitaetsbibliothek | Johann Christian Senckenberg

Article

Fluidization of Nanoparticle Agglomerates at Elevated Temperatures Ali Asghar Esmailpour, Navid Mostoufi, and Reza Zarghami Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b02921 • Publication Date (Web): 31 Oct 2017 Downloaded from http://pubs.acs.org on November 2, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Industrial & Engineering Chemistry Research

Fluidization of Nanoparticle Agglomerates at Elevated Temperatures Ali Asghar Esmailpour, Navid Mostoufi* and Reza Zarghami Multiphase Systems Research Lab., School of Chemical Engineering, College of Engineering, University of Tehran, P.O. Box 11155/4563, Tehran, Iran ABSTRACT The behavior of hydrophobic and hydrophilic nanoparticles was investigated. In a novel approach, the interparticle force (IPF) was altered by changing the temperature of the gas-solid fluidized bed in the range of 25 °C to 110 °C. The hydrodynamic state of the bed was monitored by examining pressure fluctuations using coherent and incoherent analysis. Formation, coalescence, eruption and mobility of bubbles in the fluidized bed were determined in addition to investigating the behavior of agglomerates. Diameter of agglomerate was calculated from the incoherent output of the power spectral density of pressure fluctuations in the agglomerate particulate fluidization (APF) regime at various temperatures. No bubbles were detected in the bed of silica nanopowder at low temperatures while small bubbles form at higher temperature. In contrast, hydrophilic titania nanopowder becomes fluidized in the ABF regime at lower temperature and tend to fluidize at the APF regime with increasing the bed temperature.

Keywords: Nanoagglomerate; Agglomerate particulate fluidization; Pressure fluctuations; Coherence; Temperature

1. INTRODUCTION *

Corresponding author, Tel.: (+98-21)6696-7797, Fax: (+98-21)6696-7781, E mail: [email protected]

1

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Fluidized beds have been widely applied in various chemical and physical processes due to their good mass and heat transfer rates. Therefore, considerable attention has been paid to understand the hydrodynamics of these beds.1-7 Nanoparticles are significantly used in fluidized beds in the recent years due to their high specific area and reactivity.8,9 Advanced Materials can be produced through fluidizing of nanoparticle agglomerates, especially in coating processes such as atomic layer deposition (ALD),10, 11 chemical vapor deposition (CVD)12 and molecular layer deposition (MLD).13

Among different measuring methods, pressure fluctuations measurement is a promising and reliable technique which has been successfully utilized to characterize the hydrodynamics and quality of fluidization.14-16 This technique is non-intrusive and inexpensive and can be employed in a wide range of operating conditions.17,

18

Pressure fluctuations include fast travelling

(dynamic) and slow travelling (kinematic) waves.17,

19

Fast travelling waves originate from

formation, coalescence, eruption and abruption of bubbles and slow travelling waves correspond to local fluctuations caused by rising of bubbles or motion of particles.

The quality of fluidization strongly depends on physical characteristics of the powder and its surface properties, such as size, density and hydrophobicity. In addition, strong interparticle cohesive forces among nanoparticles (including van der Waals, capillary and electrostatic) result in formation of nanoparticle agglomerates. Hence, interparticle forces (IPFs) play an important role in the hydrodynamics of nanoparticle fluidization. Fluidization of nanoparticles are classified into two regimes, namely, agglomerate particulate fluidization (APF) and agglomerate bubbling fluidization (ABF).6, 20, 21 The APF regime shows a bubble-less and smooth fluidization

2


Page 2 of 49

Page 3 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


with high bed expansion while the ABF regime is characterized by existence of large bubbles, low bed expansion by increasing the gas velocity and non-uniform distribution of agglomerates throughout the bed.20 Physical and surface properties of nanoparticles determine whether the agglomerates are fluidized as ABF or APF. Tahmasebpoor et al.22, 23 indicated that formation of hydrogen bonds between hydroxyl groups of nanoparticles strongly increase cohesive forces which leads to ABF fluidization and formation of larger agglomerates. Although the hydrogen bond is not a strong chemical bond, it is much more powerful than the van der Waals force.24 Nanoparticle agglomerates have porous and fragile structure and it is difficult to determine their size, especially in the APF regime. Esmailpour et al.7 demonstrated that APF-type behavior occurs with smaller and more delicate agglomerates than ABF-type. Various models have been developed for estimating the size of nanoagglomerates based on either force balance or energy balance.2, 3

Despite a number of studies conducted to indicate the effect of temperature on fluidization of micron particles,25-28 the influence of temperature on the fluidization of nanoparticles is still unclear. As mentioned earlier, strong IPFs among nanoparticles results in different behavior compared to micron-sized particles. van der Waals force, as the main IPF between hydrophobic nanoparticles, is directly related to temperature. Moreover, it has been found that the concentration of OH groups on the surface of hydrophilic titania decreases with increasing the bed temperature which also makes the van der Waals force to become dominant in such a condition.29 Therefore, the van der Waals force can prevail over other IPFs at high temperatures for both hydrophilic and hydrophobic nanopowders.

3



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

As mentioned before, measurement and analysis of pressure fluctuations can provide crucial and reliable information on fluidization of nanoparticles. Various techniques have been utilized to investigate the time-series of pressure fluctuations, including statistical, power spectral, wavelet, fractal and chaos methods. van der Schaaf et al.17 suggested a method to estimate the bubble size based on the coherence analysis of pressure fluctuations detected simultaneously in the bed and in the windbox.

The objective of this study was to investigate the behavior of hydrophobic and hydrophilic nanoparticles at temperatures above the room temperature on the basis of analyzing the bed pressure fluctuations. Particularly, the effect of change of interparticle forces on the behavior of bubbles and agglomerates in the bed in the temperature range of 25 °C to 110 °C was explored for the first time. Moreover, a model was developed based on coherent and incoherent analysis to calculate the nanoparticle agglomerate size formed during APF fluidization of nanoparticles.

2. EXPERIMENTS Schematic of the experimental setup used in this work is shown in Figure 1. The fluidized bed was a vertical cylindrical column made of glass with an inner diameter of 26 mm and 800 mm height. A series of ports was foreseen on this column for solids sampling and pressure measurments. To minimize the effect of moisture on the experimental results, pure and dry nitrogen (99.98 %) from a compressed nitrogen tank (supplied by Tehran Gas Co.) was used as the fluidizing gas. The gas was injected into the bed through a sintered galss distributor, 2 mm thick, with 20 µm nominal pore size. The gas flow rate was controlled through a mass flow controller (ALICAT model MC-5 SLPM). Starting from 100 mm/s, the superficial gas velocity

4


Page 4 of 49

Page 5 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


was decreased in small steps of 0.5 mm/s. The exhaust gas was cleaned by a two-stage water bubbler and a high-efficiency particulate arresting (HEPA) filter to trap any elutriated particle.

Bed pressure drop was measured with a digital manometer (Dwyer Model 477-000-FM) between two points, one at the top of the column and another at 3.5 cm above the distributor. In order to investigate the influence of temperature on fluidization of nanoparticles, the experiments were carried out at a temperature range of ambient up to 110 °C. For changing the bed temperature, the column was placed between two semi cylindrical ceramic heaters, each having 1200 W electric power. The temperature of fluidized bed was measured using a thin type-K thermocouple with the accuracy of ± 0.4 % of reading. The thermocouple was located inside the bed near the surface of the powders (see Figure 1). It was connected to a temperature controller in order to set the temperature at the desired value in each experiment. It is worth mentioning that the diameter of the thermocouple was approximately 3 mm and was positioned vertically such that having a negligible disruption to the hydrodynamics.

A Kistler dynamic pressure sensor type 7261 was utilized for measuring pressure fluctuations (absolute pressure) through a probe of 30 mm length and 4 mm diameter which was positioned 5 cm above the distributor. Tip of the probe was covered with a fine mesh to prevent the powder from entering. In each experiment, pressure fluctuations were recorded simultaneously in the column as well as in the windbox. It is important to note that the probe of the windbox was similar to that of the bed. Pressure signals were logged into a computer at 5 minute intervals, after stabilization of fluidization at each superficial gas velocity. The sampling frequency was 400 Hz.

5



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

The powders used in the experiments were hydrophobic (apolar) silica R972 and hydrophilic (polar) titania P25 nanoparticles (supplied by Evonik Industries). Properties of these nanoparticles are summerized in Table 1. Also, the distribution of intrinsic size of hyrophobic silica and hydrophilic titania are representated in Figures S1(a) and (b), respectively (see Supplementary Information). Prior to each experiment, the powder was passed through a 350 µm sieve on a shaker in order to remove large agglomerates that may have been formed during packing, storage and transportation. The experiments were performed using approximately 2.5 g hydophobic silica nanoparticles and 3.5 g hydophobic titania. Initial bed height was about 5 cm for both nanoparticles. Size and shape of nanoparticle agglomerates were determined by an optical microscope. In order to prevent deformation and breakage of agglomerates, a prudential method was utilized for sampling. This method involves delicately dipping a rod with a doublesided carbon tape into the column and sampling from the surface of the bed. The sampling was carried out with caution to avoid breaking the agglomerates. A typical image and histogram of the agglomerates of the hydrophobic silica and hydrophilic titania nanopowders at different temperatures are shown in Figures 2 and 3, respectively.

3. METHODS OF DATA ANSLYSIS Pressure fluctuations can provide significant information about various in-bed phenomena such as bubble formation, bubble coalescence, bubble eruption and bubble or particle movement. Several analysis methods in time domain, frequency domain, time-frequency domain and nonlinear state space can be utilized to analyze pressure signals. In this study, analysis of these signals was accomplished in coherent and incoherent detecting the dynamic changes in the

6


Page 6 of 49

Page 7 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


hydrodynamics and estimating the size of nanoparticle agglomerates and bubbles in the fluidized beds. These methods are described below.

3.1. Discrete Fourier Transform. In order to assess the pressure signals in the frequency domain, discrete Fourier transform (DFT) and its power spectral density function (PSDF) were utilized. The DFT evaluates the frequency content of a discrete time series and its PSDF determines the magnitude of square of Fourier transform of the raw signal. For a sample time series x(n) which includes N points, the DFT is given by30:

= π

(1)

In fact, the contribution of every frequency in the spectrum to the power of the overall signal can be described as the PSDF of the signal. Each phenomenon in the bed can be detected by its specific frequency which can be defined by the Fourier transform and the related PSDF.

The Welch’s method is one of the most applicable methods for estimating the PSDF of a signal 31

. It reduces the number of computations and core storage and can be used readily in

nonstationary tests. This method divides the original signal into sub-spectra and averages the magnitude of the square of discrete Fourier transform of these sub-spectra signals. The PSDF of each sub-spectra is defined by31: =

1

∑

π

7


(2)


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 49

where xi(n), w(n), j, Ns, and f denote the sampled pressure time series, the window function, the complex number, the length of each segment of time series and the frequency, respectively. The average power spectrum is then expressed as31:

1 =

(3)

here, L is the number of divisions of the time series.

Similarly, for two separate time series x and y, the cross power spectral density can be calculated from: =

1 〈 ∗ 〉

(4)

where Fx and Fy* denote the Fourier transform of signal x(t) and complex conjugate of Fourier transform of signal y(t), respectively, and 〈.〉 is the ensemble averaging. The exact value of cross power spectral density depends on the power represented in the PSDFs of both time series. In order to omit this dependency, the absolute value of cross power spectral density is normalized with the square root of PSDFs of both time series.17 The square of this value (γ2xy) is known as the coherence function which is given by:

γ =

∗

(5)

The coherence value ranges from 0 to 1. It is equal to unity when PSDFs of the time series are fully correlated. A coherence of zero demonstrates that these time series are not coupled.

3.2. Coherent and Incoherent Analysis. A gas-solid fluidized bed includes two types of pressure waves: dynamic waves which travel fast and kinematic waves which travel slow.17, 19

8


Page 9 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


The dynamic pressure wave corresponds to upward and downward fast waves. The upward fast waves arise from formation, coalescence and abruption of bubbles and the downward fast waves are related to bubble eruption. The local phenomena, such as rising of gas bubble and motion of agglomerates and clusters, attribute to the kinematic waves.

It has been found that the amplitude of upward pressure waves decreases linearly with distance from the point of origin. In contrast, the amplitude of downward pressure waves remains almost constant throughout the bed.17 Therefore, for distinguishing and extracting properties of these waves, pressure signals should be recorded simultaneously in the windbox and the bed. The power of pressure time series measured in the dense bed at position y is coherent and incoherent with the pressure time series measurement position x in the windbox. The coherent output PSDF (COPxy) is related to the dynamic pressure wave which demonstrates the probability of formation, coalescence, abruption and eruption of bubbles and is defines as17: #$ = γ

(6)

The standard deviation of COPxy corresponds to the intensity of fast pressure waves which indicates the intensity of formation, coalescence and eruption of bubbles:

σ,% = &' #$ ( ∞

(7)

)

The incoherent output PSDF (IOPxy) of pressure fluctuations is attributed to the kinematic pressure waves and reflects the local phenomena such as rising of bubble or motion of agglomerates. These waves originate from local fluctuations and do not generate pressure travelling wave throughout the fluidized bed. Thus, these local phenomena only measured in the dense bed and the IOPxy is defines as17:

9



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 49

*$ = 1 − γ

(8)

The standard deviation of IOPxy represents the intensity of movement of bubbles and agglomerates in the bed:

σ, = &' *$ ( ∞

(9)

)

Since the incoherent component is related to the local fluctuations induced by passage of bubbles, its standard deviation is related to the bubble size as follows17: ,- ∼

σ, ρ/ 011 − ε2 3

(10)

where ρp is the density of nanoparticles and εmf is the bed voidage at minimum fluidization.

4. RESULTS AND DISCUSSION Results of this work are presented on the basis of surface properties of nanoparticles in two parts. The first part deals with the hydrodynamics of fluidization of hydrophobic silica nanopowder and its agglomerate size. Fluidization behavior of hydrophilic titania nanopowder are presented in the second part. Hydrodynamics of these two powders are compared at the end.

4.1. Hydrophobic Nanoparticles 4.1.1. Coherent and Incoherent Analyses In order to investigate the behavior of hydrophobic nanoparticle agglomerates during fluidization, coherent and incoherent analyses were implemented in this study. An example of raw pressure signals for silica nanoparticle agglomerates at 0.08 m/s gas velocity and at 25 °C

10


Page 11 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


and 110 °C are shown in Figures S2(a) and (b), respectively (see Supplementary Information). Figure 4 illustrates the standard deviation of coherent pressure fluctuations and bed expansion of a fluidized bed of hydrophobic silica nanoparticles as a function of superficial gas velocity at various temperatures. It is clear that at lower temperatures, the coherent standard deviation is very small and independent of the gas velocity. This indicates that the intensity of bubble formation, bubble eruption and bubble coalescence is negligible which shows that fluidization of hydrophobic silica nanopowder at lower temperatures and gas velocity is bubbleless. Tahmasebpoor et al.15 and Zhu et al.6 also reported that hydrophobic silica becomes fluidized in the APF regime (bubbleless). Since there are no bubbles in the APF fluidization, the coherent standard deviation of the bed of silica nanopowder is insignificant at low temperatures. In addition, it can be seen from Figure 4 that the bed expansion is high at lower temperatures as expected in APF regime.6,

15

Therefore, the bed expansion confirms the results obtained from

coherent standard deviation that the APF regime occurs at low temperatures in this case. Figure S3(a) and (b) illustrate the bed expansion of the hydrophobic silica at room temperature before fluidization and at 0.032 m/s, respectively (see Supplementary Information). It can be found that that there are no bubbles at lower temperatures and a smooth and homogenous fluidization are occurred in the bed. By increasing the bed temperature, the coherent standard deviation increases and exhibits a small dependency on the gas velocity which indicates the tendency of the bed toward bubble formation. In other words, silica nanoparticle agglomerates tend to become fluidized in the ABF regime at higher temperatures. It can also be concluded from Figure 4 that bed expansion decreases with increasing the bed temperature. It has been shown that the bed expansion decreases when the fluidization moves toward the ABF regime.6 In other words, the APF regime exhibits a higher bed expansion and the ABF regime shows a lower bed expansion.

11



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 49

Powerful IPFs among nanoparticles lead to a different behavior compared to other particles. Hence, the simultaneous effect of temperature on properties of both gas and solids must be

considered. Shabanian and Chaouki25 found that the influence of IPFs on fluidization of Geldart C can be much greater than that of the change in gas properties. Increasing the gas viscosity with increasing the bed temperatures leads to reduction of minimum fluidization velocity, thus, higher bed expansion and better fluidization quality. Nevertheless, in this study, as well as in Shabanian and Chaouki,25 bed expansion and fluidization quality decrease with increasing the bed temperature. In fact, a better fluidization quality should have been encountered in this case if the gas properties would have been more effective. This trend suggests greater effect of IPFs on fluidization than gas properties which will be discussed in the followings.

As mentioned before, the van der Waals force is the main interparticle force in hydrophobic nanoparticles. van der Waals force can be evaluated as follows32, 33: 456 =

78 ,9 24<

(11)

here, AH is the Hamaker constant, Da is agglomerate size and Z is the minimum interparticle distance ∼ 0.4 nm.34. The Hamaker constant can be calculated from35: 3 ε − ε 3ℎνE − 78 = >? @ A B + H 4 ε − ε 16√2 + I

(12)

where T denotes the absolute temperature, kB is the Boltzman’s constant, h is the Planck’s constant, ve is the UV adsorptive frequency, n1 is the refractive index of agglomerates, ε1 is the dielectric constant of agglomerates, n2 and ε2 are the index of refraction and dielectric constant of fluid, respectively. It can be seen that the van der Waals force is directly proportional to temperature. Therefore, increasing the temperature leads to increase in the van der Waals force

12


Page 13 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


between particles. This increase in the IPFs causes formation of larger agglomerates, thus, formation of bubbles in the bed. It is worth to mention that larger agglomerates result in formation of greater voids in the bed which leads to formation bubbles. On the basis of these observations, it can be concluded that the increase of IPFs in the bed of hydrophobic silica eventually results in transformation of APF to ABF regime and bubble formation tendency at higher temperature is a sign of this fact. Moreover, it can be seen from this figure that the lowest bed expansion occurs at higher temperature which confirms the tendency of fluidization regime to change from APF to ABF.

The influence of gas velocity on the standard deviation of incoherent pressure fluctuations and of a fluidized bed of silica nanoparticles at various temperatures is plotted in Figure 5. According to this figure, the incoherent standard deviation is very small and independent of the gas velocity at low temperatures, but increases slightly with increasing the bed temperature. The incoherent standard deviation represents the intensity of the movement of bubbles and agglomerates.17, 19 At low temperatures, hydrophobic silica nanopowders become fluidized in the APF regime. i.e., bubbles do not exist and the agglomerates are small. Therefore, the incoherent standard deviation is very small at low temperatures and remains approximately constant versus the superficial gas velocity. In contrast, by increasing the bed temperature, the incoherent standard deviation enhances remarkably. There are two possible explanations for these trends. First, dependency of the incoherent standard deviation on the gas velocity indicates that small bubbles are formed in the bed. Second, the size of agglomerates increases with increasing the bed temperature due to increase in IPFs. Consequently, the movement of these larger agglomerates and motion of small bubbles lead to increase in the standard deviation.

13



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 49

By comparing Figures 4 and 5, it can be concluded that the standard deviation of coherent pressure fluctuations of the fluidized bed of silica nanopowder, which corresponds to formation, eruption or coalescence of bubbles, is much less than the standard deviation of incoherent pressure fluctuations, which is related to motion of bubbles and agglomerates. For instance, the incoherent standard deviation at 70 °C is almost seven times greater than the coherent standard deviation. It means that formation, coalescence and eruption of bubbles are negligible compared to other phenomena in the bed. Therefore, IOPxy is only related to movement of agglomerates since formation of bubbles can be ignored at low temperatures. Even at high temperatures, the intensity of COPxy is ∼2.5 times less than the intensity of IOPxy and this conclusion is still valid. Decrease in the ratio of the incoherent standard deviation to coherent standard deviation indicates that bubbles tend to start forming in the bed and the fluidization changes from APF to ABF regime and it may be happened at higher temperature.

Figure 6 exhibits the COPxy of the fluidized bed of silica nanoparticles at various bed temperatures and constant gas velocity of 0.08 m/s. This figure illustrates that the amplitude of COPxy is negligible at low temperature. This indicates that no bubble is formed in the bed at lower temperatures (i.e., APF fluidization). This is also the result obtained from the coherent standard deviation of silica nanoparticles (Figure 4) and it was visually observed that there are no bubbles in the hydrophobic silica beds at such temperatures. At the highest temperature (110 °C), the COPxy demonstrates a dominant peak at ~42 Hz and a smaller peak at ~85 Hz. According to Tamadondar et al.,15 the first peak at 110 °C originates from the formation, eruption and coalescence of small bubbles in the bed and the second peak corresponds to interactions among

14


Page 15 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


nanoagglomerates and their fluctuations. In other words, by increasing the bed temperature, small bubbles (42 Hz) are formed in the bed and agglomerates-agglomerates, nanoparticlenanoparticle and nanoparticle-fluid interactions (85 Hz) become significant. It is worth noting that no peak exists at lower frequencies in the COPxy, even at the highest temperature. This demonstrates that large bubbles cannot be formed in the fluidization of hydrophobic silica at these operating conditions. As mentioned earlier, IPFs increase with increasing the bed temperature which leads to formation of larger agglomerates and a small peak at 85 Hz due to agglomerate-agglomerate interaction. When silica agglomerates become larger, they create more voids. This forms a peak at 42 Hz which represents formation, coalescence and abruption small bubbles.

The COPxy of the fluidized bed of silica nanoparticles at various superficial gas velocities and constant bed temperature of 110 °C is shown in Figure 7. It can be seen in this figure that there are two peaks at ∼42 Hz and ∼85 Hz in the COPxy at all gas velocities. The dominant frequency becomes wider by increasing the gas velocity due to the fact that the size of small bubbles becomes more distributed in these conditions. However, the trend of the COPxy at high frequencies is similar in all velocities which suggests that the velocity has a negligible effect on agglomerate-agglomerate interactions.

The IOPxy of hydrophobic silica at various temperatures and gas velocity of 0.08 m/s is shown in Figure 8. This figure demonstrates one peak at ∼15 Hz in the IOPxy curve at room temperature. By increasing the bed temperature to 110 °C, three peaks appear at approximately 15 Hz, 42 Hz and 85 Hz. It can also be seen in Figure 8 that the intensity of the first and the third peaks are

15



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 49

very low and that of the second one is significant. Therefore, it can be concluded that the phenomena related to this middle frequency (∼42 Hz) play an important role in the hydrodynamics of fluidized bed of nanopowders at high temperatures. It should be noted that the first peak at ∼15 Hz corresponds to the behavior of large scale species and the second peak at ∼42 Hz originates from to behavior of small bubbles and agglomerates.15 The third peak at ∼85 Hz shows the nanoagglomerate-nanoagglomerate and fluid-nanoagglomerate interaction.15 On the other hand, the IOPxy originates from movement of agglomerates and bubbles. Accordingly, the first peak at low frequency (∼15 Hz) that can be seen in all temperatures can be attributed to the mobility of large scale species, such as larger bubbles or agglomerates. As mentioned before, there are no large bubbles in the bed, even at high temperatures (see Figure 7). Thus, the peak at low frequency can be attributed only to the motion of larger agglomerates in the hydrophobic silica fluidization. Also, this peak is wide and its amplitude is very low. This denotes that size of agglomerates has a wide distribution. The low amplitude of this peak indicates that the number of these large agglomerates is small. At higher temperatures, two other peaks can be observed in the IOPxy. The middle frequency peak (at ∼42 Hz) significantly increases by increasing the bed temperature, especially at 110 °C. It was found from the COPxy (see Figure 7) that small bubbles begin to form at higher temperatures, causing the transformation of the APF regime into the ABF with formation of larger agglomerates. In other words, the fluidization regime does not change from APF to the ABF fluidization regime, but the bed inclines to become fluidized in the ABF regime by appearing of very small bubbles in the bed. In fact, by increasing the IPFs, more nanoparticles stick to an agglomerate and it becomes denser and larger. Consequently, the tendency of agglomerates to hold gas in between their particles decreases and more voids are formed in the bed. This leads to the formation of small bubbles in the bed and their number

16


Page 17 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


increases when increasing the IPFs. The third peak at approximately 85 Hz only was observed at the highest temperature, because the agglomerates are large enough at this temperature and their interactions can be sensed by the pressure sensor. It is important to mention that Tamadondar et al.15 also found the same three peaks in the PSDF of pressure fluctuations of hydrophilic silica nanoparticle. This suggests that the hydrophobic silica nanoparticles tend to become fluidized at high temperatures in a way similar to the hydrophilic ones due to increase in the IPFs. This is in agreement with the results obtained from the coherent standard deviation of hydrophobic silica nanoparticle bed of silica (Figure 4). This can be explained by the fact that increasing the bed temperature results in increase in the IPF between particles. As discussed earlier, the increase in the IPF leads to formation of larger agglomerates, thus, formation of bubbles in the bed. Therefore, the hydrophobic silica behaves similar to hydrophilic silica at higher temperatures as a result of bubble formation.

Figure 9 represents the IOPxy of silica nanoparticle agglomerates at various superficial gas velocities and constant bed temperature of 110 °C. As shown in this figure, there are three peaks at approximately 15 Hz, 42 Hz and 85 Hz at each velocity. The main difference between these curves can be observed in the second peak at ∼42 Hz. This peak shows the mobility of bubbles and agglomerates in the fluidized bed. Accordingly, it suggests that the movement of small bubbles and agglomerates increase with increasing the gas velocity. It is worth considering that although larger agglomerates move slower, but small bubbles, which are formed as a result of increase in the IPFs, can move easily in the bed and this increases the amplitude of the second peak at higher temperatures. It should be noted that although very small bubbles are formed, the bed is still in the APF regime. In fact, the bed tends to become fluidized in the ABF regime at

17



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 49

higher temperatures for hydrophobic silica nanoparticles. Furthermore, by increasing the gas velocity, the second peak becomes slightly broader which means that the size distribution of agglomerates and small bubbles have become wider due to increase in collision of nanoparticles with increasing the gas velocity.

4.1.2. Estimating Agglomerate Size In general, the incoherent of pressure fluctuations corresponds to the mobility of bubbles and agglomerates (solid clusters).17 In conventional beds, in the absence of clusters, the incoherent of pressure fluctuations originates from rise of gas bubbles. Based on this assumption, it has been shown that the diameter of bubbles is proportional to the incoherent standard deviation in a freely bubbling fluidized bed.17 This approach can also be used in fluidization of non-sticky particles, like Geldart group B which includes only bubbles but not agglomerates. Thus, Eq. (10) can be employed to estimate the size of bubbles for these beds. van der Schaaf et al.17 also found that in a gas-solid fluidized bed in the presence of clusters, the standard deviation of IOPxy can be utilized to either determine the size of particle clusters or the bubbles diameter.

The present study proves that the incoherent of pressure fluctuations of hydrophobic silica nanopowders is only related to mobility of agglomerates (unless at higher temperatures). Since there are no large bubbles, even at higher temperatures, the incoherent of pressure fluctuation of hydrophobic silica at lower frequency only attributes to the large agglomerates at all conditions. Therefore, the incoherent standard deviation of hydrophobic silica nanopowders is only proportional to the agglomerate diameter. In order to use Eq. (10) for calculating the size of

18


Page 19 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


agglomerate, the size of bubble (Db) and particle density (ρp) should be replaced by the size of agglomerate (Da) and agglomerate density (ρa), respectively: ,9 ∼

σ,

ρ9 011 − ε2 3

(13)

This needs a hypothesis for agglomerate density, such as Wang et al.'s21 assumption of ρa = 1.15ρb, where ρb is bed density. In addition, the bed voidage at minimum fluidization is calculated from:

ε2 = 1 −

J) 1 − ε) J2

(14)

where, Hmf, H0 and ε0 denote the bed height at minimum fluidization, the initial bed height and the initial bed voidage, respectively. The initial bed voidage was assumed to be 0.35 for hydrophobic silica in this work.7 Hence, the size of agglomerates can be obtained from Eq. (13) in hydrophobic nanoparticles fluidized beds.

To fulfill this aim, incoherent standard deviation of hydrophobic silica at lower frequency was used to determine the size of agglomerates, because this region of frequency corresponds to the movement of agglomerates only, even at higher temperatures. It is worth mentioning that larger agglomerates are more exposed to breakage during sampling and it is important to provide a model to estimate their sizes. The average size of agglomerates of hydrophobic silica in the APF regime at various temperatures is shown in Figure 10. It can be observed in this figure that the mean diameter of agglomerate increases with increasing the bed temperature. It is noticeable that an increase in the temperature causes an increase in the IPF among hydrophobic silica nanoparticles, which makes it possible for nanoparticles to form larger agglomerates.

19



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 49

In order to verify the model developed in this work, calculated sizes of agglomerates were compared with the experimental sizes obtained by the image analysis (see Figure 2). Figure 10 also demonstrates a comparison between experimental agglomerate sizes and the sizes calculated by the present model in the temperature range of ambient to 110 °C. It can be seen in this figure that the model follows the same trend as the experimental values with changing the bed temperature. However, the calculation over-predicts the experimental values by an average relative error of 12%. Of course, this error seems reasonable due to breakage of larger agglomerates during sampling. As shown in Figure 10, the relative error decreases with increasing the bed temperature. This can be attributed to increase in the IPF among hydrophobic silica nanoparticles by increasing the bed temperature. In fact, nanoparticles adhere together more firmly and form larger and more durable agglomerate when IPF is greater. This leads to reduction of the probability of agglomerate breakage in the course of taking the sample from the bed and its preparation for taking picture under the microscope.

4.2. Hydrophilic Nanoparticles 4.2.1. Coherent and Incoherent Analyses Raw pressure signals of the fluidized bed of hydrophilic titania nanoparticle agglomerates at 0.085 m/s gas velocity and at 25 °C and 110 °C can be seen in Figures S4(a) and (b), respectively (see Supplementary Information). The coherent standard deviation of pressure fluctuations and the bed expansion of the fluidized bed of titania nanoparticles at various temperatures is illustrated in Figure 11. This figure demonstrates that the standard deviation decreases significantly with increasing the bed temperature. This implies that the intensity of formation, eruption and coalescence of bubbles decrease with increasing the bed temperature. Besides, as

20


Page 21 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


shown in Figure 11, the bed expansion is low at lower temperature and it slightly increases with increasing the bed temperature. Low bed expansion an important characteristics of the ABF regime. Due to high standard deviation and low bed expansion, it can be concluded that hydrophilic nanoparticles become fluidized in the ABF regime at lower temperatures. Figure S5 (a) and (b) represent the bed expansion of the hydrophilic titania at room temperature before fluidization and at 0.075 m/s, respectively (see Supplementary Information). It can be seen that the bed expansion is low and there are bubbles at lower temperatures which confirms the results of standard deviation. Increase in the bed expansion indicates a better quality of fluidization. Therefore, increase in bed temperature leads to a better fluidization quality in a bed of hydrophilic titania nanopowders. The IPFs between hydrophilic nanoparticles, like titania, include hydrogen bond force and van der Waals force. The temperature rise results in increasing the van der Waals force while it decreases the hydrogen bond force through dehydroxylation of hydrophilic nanoparticles.36-40 Since the hydrogen bond force is more powerful than the van der Waals force,24 fluidization of hydrophilic titania nanopowder is more affected by changes in the hydrogen bonds. As matter of fact, the hydrogen bond force decreases (and even may vanish) by increasing the temperature of a bed of hydrophilic titania and,41 consequently, the IPFs decrease in the bed. In this situation, the agglomerates become smaller which in turn produce smaller bubbles in the bed and the coherent standard deviation decreases in the fluidized bed of hydrophilic titania nanoparticles with an increase in the bed temperature. As a result, titania nanoparticles tend to reach the APF behavior at higher temperature and bubbles become smaller since IPFs decrease in the bed. It is worth mentioning that higher quality of fluidization (higher bed expansion) and smaller bubbles in the bed were visually observed in the experiments. These results are in close agreement with those reported by Shabanian and Chaouki,25 who studied the

21



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 49

fluidization quality in the presence of IPFs. Figure 11 also demonstrates that the coherent standard deviation increases with increasing the gas velocity regardless of the temperature. It can be concluded that formation, eruption and coalescence of bubbles enhances with increasing the gas velocity due to increase in coalescence of smaller bubbles and formation of larger bubbles. In addition, it can be concluded that the highest bed expansion occurs at high temperature which confirms that the fluidization regime tends to change from ABF to APF.

Figure 12 illustrates the incoherent standard deviation and of the fluidized bed of hydrophilic titania nanoparticles as a function of gas velocity at various temperatures. As discussed above, the incoherent standard deviation of reflects the motion of bubbles and agglomerates. It can be concluded from Figure 12 that the intensity of the movement of bubbles and agglomerates decreases with increasing the bed temperature. By increasing the temperature, dihydroxylation of hydrophilic titania nanopowder occurs in the bed which leads to reduction of agglomerate size and bubble size. Hence, the effect of larger agglomerates and larger bubbles on the hydrodynamics of the bed decrease with increasing the temperature. Furthermore, Figure 12 shows that the intensity of motion of bubbles and agglomerates increase with increasing the gas velocity due to the fact that bubbles and agglomerates move faster at higher gas velocities.

The COPxy of the fluidized bed of titania nanoparticles at various temperatures and gas velocity of 0.085 m/s is demonstrated in Figure 13. It can be seen in Figure 13 that a peak in the range of 9-20 Hz exists in the PSDF in all conditions. Besides, the amplitude of the peak at the dominant frequency of the COPxy decreases with increasing the bed temperature. This indicates that formation, coalescence, abruption and eruption of bubbles significantly decrease with an increase

22


Page 23 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


in the bed temperature. As discussed above, the IPFs between titania nanoparticles decreases with increasing the bed temperature which leads to formation of smaller agglomerates and decrease in the number of large bubbles in the bed. This can be explained by the fact that by decreasing the IPFs, nanoagglomerates become looser and prone to maintain more gas between the particles. Therefore, bubbles become smaller when the bed temperature is increased.

Figure 14 shows the COPxy of the fluidized bed of titania nanoparticles at various gas velocity and 110 °C bed temperature. As shown in this figure, amplitude of the dominant peak in the range of 9-20 Hz increases with increasing the gas velocity. This trend suggests that by increasing the gas velocity, more bubbles are formed in the bed due to coalescence of smaller bubbles.

Figure 15 presents the IOPxy of fluidized bed of titania nanoparticles at various temperatures and gas velocity of 0.085 m/s. similarities between this figure and Figure 13 can be easily recognized. The amplitude of the dominant peak of the IOPxy decreases with an increase in the bed temperature. This indicates that the size and the number of bubbles and agglomerates decreases with increasing the bed temperature due to decrease in IPFs and bubble size. Reducing IPFs leads to formation of looser and smaller nanoagglomerates which tend to keep more gas in between the particles. This results in formation of smaller bubbles in the bed. Moreover, the fluidity of smaller particles is higher than larger ones which leads to increase in the breakage of larger bubbles into smaller ones. Therefore, the amplitude of the IOPxy of pressure fluctuations of the fluidized bed of hydrophilic titania decreases with increasing the bed temperature because both agglomerates and bubbles become smaller in this situation.

23



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 49

Comparing Figures 15 and 13 reveals that the amplitude of the IOPxy of titania is much higher than the COPxy of titania. This peak in the IOPxy curve (Figure 13) is related to the mobility of bubbles and agglomerates and in the COPxy (Figure 15) corresponds to the formation, coalescence and eruption of bubbles. Therefore, this significant difference can be due to mobility of bubbles and agglomerates simultaneously. In fact, the frequency of mobility of bubbles is much more than that of formation, coalescence and eruption of bubbles. It also demonstrates that the movement of bubbles and agglomerates play a more important role in the hydrodynamic state of the bed than formation, coalescence and eruption of bubbles.

The IOPxy of the fluidized bed of titania nanoparticles at various gas velocities and bed temperature of 110 °C is plotted in Figure 16. As can be seen in this figure, there is a dominant frequency in the range of 9-20 Hz and the IOPxy of titania agglomerates increases with increasing the gas velocity. This reveals that the motion of bubbles and agglomerates enhances by increasing the gas velocity. In fact, the drag force on agglomerates and bubbles increases by increasing the gas velocity. Consequently, agglomerates and bubbles can move more easily in such condition.

4.3. Comparing Fluidization of Hydrophobic Silica and Hydrophilic Titania Comparing coherent and incoherent standard deviations of pressure fluctuations of fluidized beds of hydrophobic silica (Figures 4 and 5) and hydrophilic titania nanopowders (Figures 11 and 12) indicates that the standard deviation of the bed of titania is considerably greater than that of the bed of silica. This implies that larger bubbles exist in the fluidized bed of hydrophilic titania

24


Page 25 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


nanoparticles. By comparing the coherent standard deviation of these two powders (Figures 4 and 11) at lower bed temperature, it can be additionally found that there are no large bubbles in the fluidized bed of hydrophobic silica, unlike the hydrophilic titania. It is worth mentioning that it was also visually observed that bubbles exist in the bed of titania while no bubbles were observed in the bed of silica.

It can be seen from Figures 4 and 5 that the standard deviation of COPxy and IOPxy in the bed of hydrophobic silica increases with increasing the bed temperature, while a reverse trend occurs in the bed of hydrophilic titania in Figures 11 and 12. This can be explained by considering the change in IPFs of these nanoparticles with temperature. As mentioned earlier, the IPF between hydrophobic silica nanoparticles increases with increasing the bed temperature, while this trend is opposite for titania nanoparticles. Consequently, increase in the bed temperature leads to formation of larger agglomerates which results in appearing small bubbles in the bed. On the contrary, the hydrophilic titania agglomerates and bubbles become smaller with increasing the bed temperature. This causes reduction of coherent and incoherent standard deviation in the bed of hydrophilic titania while they increase in in the bed of hydrophobic silica with increasing the bed temperature.

By considering the COPxy of fluidized bed of hydrophobic silica nanopowder in Figure 6, it can be found that its value is very small at the lower temperature and two peaks appear in the range of middle and high frequencies at the higher temperature. This means that there are no large bubbles in the bed of hydrophobic silica, even at high temperatures, and only small bubbles can form at high temperature. On the other hand, looking at the COPxy of fluidized bed of

25



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 49

hydrophilic titania (Figure 13), it can be seen that there is a lower frequency peak in the PSDF which implies that large bubbles exist in the bed. Note that the IPFs between hydrophilic nanoparticles include hydrogen bond force as well as van der Waals force which leads to a greater IPF than among hydrophobic nanoparticles. Therefore, the size of agglomerates of hydrophilic titania is larger than that of hydrophobic silica at lower temperatures. Consequently, the larger agglomerates cause formation of larger bubbles in the bed. Moreover, although the size of titania agglomerates decrease with increasing the bed temperature, number of bubbles in the bed of hydrophilic nanopowders are much more than hydrophilic silica.

By comparing the IOPxy of the bed of hydrophobic silica (Figure 8) and hydrophilic titania (Figure 15) it can be seen that in the case of hydrophobic silica there are three peaks at the highest temperature with low amplitude while there is only one broad peak for hydrophilic titania. In fact, by increasing the bed temperature, the IPF among silica nanoparticles increases, thus, nanoagglomerates become larger and small bubbles are formed in the bed. However, the peak in the IOPxy of fluidized bed of hydrophilic titania nanopowder demonstrates that the mobility of bubbles and agglomerates decreases with increasing the bed temperature. This can be explained by the fact that the IPF among hydrophilic titania decreases with increasing the bed temperature which leads to formation of smaller agglomerates and smaller bubbles in the bed.

The amplitude of the COPxy and the IOPxy in the case of titania is greater than in silica. This can be attributed to presence of larger bubbles in the bed of hydrophilic titania than in hydrophobic silica.Number of larger agglomerates and larger bubbles in the bed of hydrophilic titania at lower temperatures are much more than hydrophilic silica due to existence of hydrogen bond force

26


Page 27 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


between hydrophilic nanoparticles. However, increasing the bed temperature causes decrease in the hydrogen bond force, thus, the size of agglomerates in the bed of hydrohplic titania decreases at higher temperatures. Nevertheless, the number of bubbles are still much more than in the bed of hydrophilic silica at various temperatures which results in a greater amplitude of COPxy and IOPxy.

5. CONCLUSIONS In this study, the influence of bed temperature and interparticle forces on the hydrodynamics of hydrophobic and hydrophilic nanoparticles fluidized bed was investigated. The analyses of coherent and incoherent of pressure fluctuations were used to determine the behavior of bubbles and agglomerates. It was found that there are no bubbles (agglomerate particulate fluidization regime) at lower temperatures in the bed of hydrophobic silica nanoparticles. By increasing the bed temperature, small bubbles start to form due to increase in IPFs and formation of larger agglomerates. It was concluded that the hydrophobic silica nanoparticles tend to become fluidized in the agglomerate bubbling fluidization regime at higher temperatures.

A model was developed to estimate the size of agglomerates in the agglomerate particulate fluidization regime. The close agreement between experimental and calculated values of agglomerate size indicates that the model is close reality. It was further found that the intensity of formation, eruption and coalescence of bubbles decrease with increasing the bed temperature as well as the movement of bubbles. On the other hand, the interparticle forces among titania nanoparticles decreases with enhancing the bed temperature which leads to formation of smaller agglomerates and smaller bubbles. In fact, fluidization of hydrophilic titania nanopowder tends

27



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

from agglomerate bubbling fluidization to agglomerate particulate fluidization regime with increasing the bed temperature.

ACKNOWLEDGMENT Financial support provided by the Iran National Science Foundation, INSF, (Grant no. 93033268) is gratefully acknowledged.

LIST OF SYMBOLS Acronyms ABF APF COPxy DFT IOPxy IPF PSDF

Agglomerate Bubbling Fluidization Agglomerate Particulate Fluidization Coherent Output PSDF Discrete Fourier Transform Incoherent Output PSDF Interparticle Force Power Spectral Density Function

Symbols AH Db Da f F(f) h H0 Hmf kB L N Ns Pxx Pxy t T w(n) x Z

Hamaker constant (J) diameter of bubble (m) diameter of agglomerate (m) frequency (Hz) Fourier transform of signal (-) Plank’s constant (m2kg/s) initial bed height (m) bed height at minimum fluidization (m) Boltzman’s constant (J/K) number of division of time series (-) length of time series (-) length of each segment (-) PSDF of signal x(t) (Pa2/Hz) cross PSD of signals x and y (Pa2/Hz) time (s) temperature (K) window function (-) pressure time series (-) minimum interparticle distance (m)

Greek symbols

28


Page 28 of 49

Page 29 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


dielectric constant (-) initial bed voidage (-) bed voidage at minimum fluidization (-) density of agglomerate (kg/m3) density of fluidized particle (kg/m3) standard deviation (Pa) coherent standard deviation (Pa) incoherent standard deviation (Pa) mother wavelet (-) complex conjugate of the mother wavelet (-)

ε ε0 εmf ρp ρp σ σxy,c σxy,i ψ ψ*

SUPPLEMENTARY INFORMATION Size distribution of nanoparticles at room temperature (Figure S1) Raw signals of pressure fluctuations in the fluidized bed of hydrophobic silica (Figures S2) Bed expansion and fluidization of hydrophobic silica nanopowder (Figure S3) Raw signals of pressure fluctuations in the fluidized bed of hydrophilic titania (Figures S4) Bed expansion and fluidization of hydrophilic titania nanopowder (Figure S5)

REFERENCES (1) Quintanilla, M.; Valverde, J.; Espin, M.; Castellanos, A. Electrofluidization of silica nanoparticle agglomerates. Ind. Eng. Chem. Res. 2011, 51, 531. (2) Chaouki, J.; Chavarie, C.; Klvana, D.; Pajonk, G. Effect of interparticle forces on the hydrodynamic behaviour of fluidized aerogels. Powder Technol. 1985, 43, 117. (3) Morooka, S.; Kusakabe, K.; Kobata, A.; Kato, Y. Fluidization state of ultrafine powders. J. Chem. Eng. Jpn. 1988, 21, 41. (4) Liu, H.; Guo, Q.; Chen, S. Sound-assisted fluidization of SiO2 nanoparticles with different surface properties. Ind. Eng. Chem. Res. 2007, 46, 1345. (5) Matsuda, S.; Hatano, H.; Muramoto, T.; Tsutsumi, A., Modeling for size reduction of agglomerates in nanoparticle fluidization. AIChE J. 2004, 50, 2763. (6) Zhu, C.; Yu, Q.; Dave, R. N.; Pfeffer, R. Gas fluidization characteristics of nanoparticle agglomerates. AIChE J. 2005, 51, 426. (7) Esmailpour, A. A.; Zarghami, R.; Mostoufi, N. An improved model for determining fractal structure of nano‐agglomerates. Can. J. Chem. Eng. 2015, 93, 1753. (8) van Ommen, J. R.; Valverde, J. M.; Pfeffer, R. Fluidization of nanopowders: a review. J. Nanopart. Res. 2012, 14, 737. (9) Zhu, X.; Zhang, Q.; Wang, Y.; Wei, F. Review on the nanoparticle fluidization science and technology. Chinese J. Chem. Eng. 2016, 24, 9. 29



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 49

(10) King, D. M.; Spencer, J. A.; Liang, X.; Hakim, L. F.; Weimer, A. W. Atomic layer deposition on particles using a fluidized bed reactor with in situ mass spectrometry. Surf. Coatings Technol. 2007, 201, 9163. (11) Beetstra, R.; Lafont, U.; Nijenhuis, J.; Kelder, E. M.; van Ommen, J. R. Atmospheric pressure process for coating particles using atomic layer deposition. Chem. Vap. Dep. 2009, 15, 227. (12) Ying, W.; Chun, L.; Jing, Z. Plasma treated TiO2 nanoparticles for dispersion enhancement. Plasma Sci. Technol. 2009, 11, 78. (13) Liang, X.; King, D. M.; Li, P.; George, S. M.; Weimer, A. W. Nanocoating hybrid polymer films on large quantities of cohesive nanoparticles by molecular layer deposition. AIChE J. 2009, 55, 1030. (14) Aghabararnejad, M.; Mostoufi, N.; Sotudeh-Gharebagh, R.; Zarghami, R. Evaluating the probabilities of fluidization regimes. Ind. Eng. Chem. Res. 2011, 50, 4245. (15) Tamadondar, M. R.; Zarghami, R.; Tahmasebpoor, M.; Mostoufi, N. Characterization of the bubbling fluidization of nanoparticles. Particuology 2014, 16, 75. (16) Abbasi, M.; Mostoufi, N.; Sotudeh-Gharebagh, R.; Zarghami, R. A novel approach for simultaneous hydrodynamic characterization of gas–liquid and gas–solid systems. Chem. Eng. Sci. 2013, 100, 74. (17) Van der Schaaf, J.; Schouten, J.; Johnsson, F.; Van den Bleek, C. Non-intrusive determination of bubble and slug length scales in fluidized beds by decomposition of the power spectral density of pressure time series. Int. J. Multiphase Flow 2002, 28, 865. (18) Johnsson, F.; Zijerveld, R.; Schouten, J.; Van den Bleek, C.; Leckner, B. Characterization of fluidization regimes by time-series analysis of pressure fluctuations. Int. J. Multiphase Flow 2000, 26, 663. (19) Dong, L.; Zhao, Y.; Peng, L.; Zhao, J.; Luo, Z.; Liu, Q.; Duan, C. Characteristics of pressure fluctuations and fine coal preparation in gas-vibro fluidized bed. Particuology 2015, 21, 146. (20) Yao, W.; Guangsheng, G.; Fei, W.; Jun, W. Fluidization and agglomerate structure of SiO2 nanoparticles. Powder Technol. 2002, 124, 152. (21) Wang, H.; Zhou, T.; Yang, J. S.; Wang, J. J.; Kage, H.; Mawatari, Y. Model for Calculation of Agglomerate Sizes of Nanoparticles in a Vibro‐fluidized Bed. Chem. Eng. Technol. 2010, 33, 388. (22) Tahmasebpoor, M.; Ghasemi Seif Abadi, R.; Rahimvandi Noupoor, Y.; Badamchizadeh, P. Model Based on Electrostatic Repulsion and Hydrogen Bond Forces To Estimate the Size of Nanoparticle Agglomerates in Fluidization. Ind. Eng. Chem. Res. 2016, 55, 12939. (23) Tahmasebpoor, M.; de Martín, L.; Talebi, M.; Mostoufi, N.; van Ommen, J. R. The role of the hydrogen bond in dense nanoparticle–gas suspensions. Phys. Chem. Chem. Phys. 2013, 15, 5788. (24) Wu, X.; Sacher, E.; Meunier, M. The effects of hydrogen bonds on the adhesion of inorganic oxide particles on hydrophilic silicon surfaces. J. Appl. Phys. 1999, 86, 1744. (25) Shabanian, J.; Chaouki, J. Hydrodynamics of a gas–solid fluidized bed with thermally induced interparticle forces. Chem. Eng. J. 2015, 259, 135. (26) Guo, Q.; Zhang, J.; Hao, J. Flow characteristics in an acoustic bubbling fluidized bed at high temperature. Chem. Eng. Process: Process Intensif. 2011, 50, 331. (27) Formisani, B.; Girimonte, R.; Mancuso, L. Analysis of the fluidization process of particle beds at high temperature. Chem. Eng. Sci. 1998, 53, 951.

30


Page 31 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(28) Lei, C.; Zhu, Q.; Li, H. Experimental and theoretical study on the fluidization behaviors of iron powder at high temperature. Chem. Eng. Sci. 2014, 118, 50. (29) Finnie, K. S.; Cassidy, D. J.; Bartlett, J. R.; Woolfrey, J. L. IR spectroscopy of surface water and hydroxyl species on nanocrystalline TiO2 films. Langmuir 2001, 17, 816. (30) Rao, K. R.; Kim, D. N.; Hwang, J. J. Fast Fourier transform-algorithms and applications. Springer Science & Business Media: 2011. (31) Welch, P. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 1967, 15, 70. (32) Rabinovich, Y. I.; Adler, J. J.; Ata, A.; Singh, R. K.; Moudgil, B. M. Adhesion between nanoscale rough surfaces: I. Role of asperity geometry. J. Coll. Interface Sci. 2000, 232, 10. (33) de Martín, L.; van Ommen, J. R. A model to estimate the size of nanoparticle agglomerates in gas− solid fluidized beds. J. Nanopart. Res. 2013, 15, 2055. (34) Krupp, H. Particle adhesion, theory and experiment. Adv. Coll. Interface Sci. 1967, 1, 111. (35) Zhou, T.; Li, H. Estimation of agglomerate size for cohesive particles during fluidization. Powder Technol. 1999, 101, 57. (36) Cant, N. W.; Hall, W. K. Studies of the hydrogen held by solids. Part 13-Effect of temperature on hydroxyl band intensities of some solid oxides. Trans. Faraday Soc. 1968, 64, 1093. (37) Digne, M.; Sautet, P.; Raybaud, P.; Euzen, P.; Toulhoat, H. Hydroxyl groups on γ-alumina surfaces: A DFT study. J. Catal. 2002, 211, 1. (38) Hair, M. L. Hydroxyl groups on silica surface. J. Non-Cryst. Solids. 1975, 19, 299. (39) Zhuravlev, L. T. Concentration of hydroxyl groups on the surface of amorphous silicas. Langmuir 1987, 3, 316. (40) Zarubin, D. P. Modelling the OH stretch envelope shape and temperature effects in infrared spectra of hydrous silica and silicate glasses. Phys. Chem. Miner. 2000, 27, 590. (41) Tang, Z.; Zhang, J.; Cheng, Z.; Zhang, Z. Synthesis of nanosized rutile TiO2 powder at low temperature. Mater. Chem. Phys. 2003, 77, 314.

31



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 49

Table 1. Properties of silica and titania nanoparticles

Wettability

Chemical Formula

Particle Size (nm)

Particle Density (kg/m3)

Bulk Density (kg/m3)

Aerosil R972

Hydrophobic

SiO2

16

2200

85

Aeroxide P25

Hydrophilic

TiO2

21

4000

130

Commercial Name

32


Page 33 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1. The experimental setup (A) Mass flow controller; (B) Windbox; (C) Sintered glass distributor; (D) Ports for sampling and pressure measurement; (E) Two-stage water bubbler; (F) HEPA filter; (G) Heater; (H) Thermocouple

33



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(a)

(b)

(c)

(d)

Figure 2. Optical microscope image and histogram of agglomerates of hydrophobic silica. (a) 45 °C (b) 90 °C (c) 45 °C (d) 90 °C

34


Page 34 of 49

Page 35 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(a)

(b)

(c)

(d)

Figure 3. Optical microscope image and histogram of agglomerates of hydrophilic titania. (a) 25 °C (b) 110 °C (c) 25 °C (d) 110 °C

35



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 4. Coherent standard deviation and bed expansion of the fluidized bed of hydrophobic silica vs. gas velocity at various temperatures.

36


Page 36 of 49

Page 37 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 5. Incoherent standard deviation of the fluidized bed of hydrophobic silica vs. gas velocity at various temperatures.

37



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 6. The COPxy of the fluidized bed of hydrophobic silica at various temperatures at gas velocity of 0.08 m/s.

38


Page 38 of 49

Page 39 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 7. The COPxy of the fluidized bed of hydrophobic silica at various gas velocities at temperature of 110 °C.

39



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 40 of 49

Figure 8. The IOPxy of the fluidized bed of hydrophobic silica at various temperatures at gas velocity of 0.08 m/s.

40


Page 41 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 9. The IOPxy of fluidized bed of hydrophobic silica at various gas velocities at temperature of 110 °C.

41



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 10. Comparison of estimated and experimental mean agglomerate size as a function of bed temperature.

42


Page 42 of 49

Page 43 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 11. Coherent standard deviation and bed expansion of the fluidized bed of hydrophilic titania vs. gas velocity at various temperatures.

43



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 44 of 49

Figure 12. Incoherent standard deviation of the fluidized bed of hydrophilic titania vs. gas velocity at various temperatures.

44


Page 45 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 13. The COPxy of the fluidized bed of hydrophilic titania at various temperatures at gas velocity of 0.085 m/s.

45



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 46 of 49

Figure 14. The COPxy of the fluidized bed of hydrophilic titania at various gas velocities at temperature of 110 °C.

46


Page 47 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 15. The IOPxy of the fluidized bed of hydrophilic titania at various temperatures at gas velocity of 0.085 m/s.

47



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 48 of 49

Figure 16. The IOPxy of the fluidized bed of hydrophilic titania at various gas velocities at temperature

of 110 °C.

48


Page 49 of 49


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 ACS Paragon Plus Environment

Fluidization of Nanoparticle Agglomerates at Elevated Temperatures

Recommend Documents