Ind. Eng. Chem. Res. 2005, 44, 1585-1591
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GENERAL RESEARCH Oil-Polluted Sands in a Fluidized Bed Babu J. Alappat,† Sebastien Deon,‡ Pascaline Pre,‡ Arnaud Delebarre,*,‡ and Stephane Viazzo‡ Department of Civil Engineering, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi, India 110 016, and De´ partement Syste` mes Energe´ tiques et Environnement, Ecole des Mines de Nantes, 4 rue Alfred Kastler, BP 20722, F-44307 Nantes Cedex 3, France
This paper investigates the hydrodynamic behavior of oil-polluted sands in a cold bubbling bed. The minimum fluidization velocity of sand increased with the oil content of the bed. The quality of fluidization was assessed by measured and calculated coefficients and also by the power spectral density analysis of the pressure fluctuations. The increase of the dominant frequency at high contents of pollution indicated the occurrence of cohesive forces, leading to the deterioration of the quality of fluidization. Results of this study are useful for the design of industrial-size fluidized-bed remediation plants for oil-contaminated sands and soils. Among the different types of marine pollution, perhaps oil slicks stand first because of their frequency of occurrence, their magnitude, and the extent of damage they can inflict. Oil slicks usually happen as a result of the cleaning out of oil tankers and also shipwrecks or accidents. One such accident was Erika’s accident in the Atlantic Ocean near the French coast.1 The ship Erika, carrying 31 000 tons of heavy oil (no. 6 fuel oil), split into two on Dec 12, 1999, spilling about 15 000 tons of the fuel oil into the Atlantic Ocean. This oil slick polluted about 450 km of the Brittany coast (France). The oil-polluted sand was scrapped off the seashore and has been stored for decontamination. About 270 000 tons is being treated by physical and chemical separation by Bre´zillon (http://www.brezillon.fr). However, a research network named RITMER (http://www.ritmer.org) was funded by the French authorities to investigate the other decontamination routes. The Thermer project, selected by RITMER and funded by the French Ministry of Research, is aimed at assessing the feasibility of using a fluidized-bed furnace for the depollution of the oilcontaminated sand by combustion and/or co-combustion with municipal solid wastes. The feasibility of the decontamination of the oilpolluted sand in a fluidized-bed combustor depends on (i) the fluidization behavior of the oil-polluted sand, (ii) the extent of depollution obtained in the furnace, (iii) the quality of emissions through the flue gas, and (iv) its cost compared with conventional systems such as chemical washing and bioremediation. While bioremediation is slow, chemical washing generates other kinds of wastes that need be taken care of. The fluidization behavior of oil-contaminated sand plays a crucial role because it decides the efficiency of the system. Common * To whom correspondence should be addressed. Tel.: +33(0)251 858 253. Fax: +33(0)251 858 299. E-mail:
[email protected]. † Indian Institute of Technology. ‡ Ecole des Mines de Nantes.
sand falls in the Geldart B classification,2 and its fluidization behavior is well understood. However, the sand contaminated with oil may behave differently because of the cohesion between the particles.3,4 Defluidization, if it takes place, can even lead to the total shutdown of the plant. This paper investigates the hydrodynamic behavior of two sands for varying degrees of light and heavy oil contamination in a cold fluidized bed. Variation of the minimum fluidization velocity (Umf) and the minimum fluidization voidage (mf) was studied for various test conditions, in addition to the previous results and interpretation presented elsewhere.5 The influence of the pollutant concentration was then evaluated by calculating the ratio of the pressure drop as given by Ergun’s equation to the apparent specific weight of solids. The values of this ratio were then compared to the fluidization quality indices calculated after the bed pressure drop at the minimum fluidization divided by the bed weight per unit area of bed cross section. This comparison allowed one to determine whether the dominating mechanisms were channelling, agglomeration, or cohesion. The pressure fluctuation data were also used to assess the quality of fluidization. The variation of the dominant frequency of the pressure fluctuation power spectral density function was shown to be indicative of the fluidization quality and bed oil content. Materials and Methods Materials for the experimental studies were chosen based on the characteristics of the real wastes collected from the oil-polluted beaches considering the varying nature of oil that can be involved in maritime pollution, the variation in the water content, and the particle size distribution of sand. In fact, the wastes generated in an oil spill are highly heterogeneous in nature. However, the wastes of different nature and degree of pollution are usually collected separately so that they can be
10.1021/ie040189d CCC: $30.25 © 2005 American Chemical Society Published on Web 02/02/2005
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Table 1. Characteristics of Heavy Oil and Gas Oil heavy oil density at 15°C, kg/m3 kinematic viscosity, mm2/s chemical composition
gas oil
806-922 634 at 50 °C
845 52 at 50 °C
saturated hydrocarbons, 22-30% aromatic hydrocarbons, 42-50% resins and asphalt, 21-36% ash, 2.3%
CnH1.6n
Table 2. Polluted Sand Models Chosen series no.
raw sand (95%)
oil pollutants (5 wt %)
1 2 3 4 5 6 7 8
coarse coarse coarse coarse fine fine fine fine
heavy oil alone (100%) a mixture of heavy oil (70%) and water (30%) light oil alone (100%) a mixture of light oil (70%) and water (30%) heavy oil alone (100%) a mixture of heavy oil (70%) and water (30%) light oil alone (100%) a mixture of light oil (70%) and water (30%)
subjected to suitable treatment processes based on their characteristics. A few polluted sand models were defined for this study based on the characteristics of the oilpolluted sand collected from the beaches after the Erika accident for decontamination. The main composition parameters were varied in this study from a low level to a high level. The materials and methods of this study are described below. Raw Sands. Two kinds of raw sand were used for the tests: coarse sand of an average harmonic diameter of 0.43 mm (particle size distribution from 0.1 to 4.0 mm) and fine sand of an average harmonic diameter of 0.10 mm (particle size distribution from 0.04 to 0.315 mm). Both sands had a density of 2650 kg/m3. The dry raw sands were mixed with different oil products (5 wt %) to obtain the polluted sands that were used in this study. Pollutants. Four kinds of pollution were simulated with heavy oil, gas oil, and water by mixing them in
Figure 1. Experimental facility.
different proportions. Light oil was obtained by mixing heavy oil and gas oil at a ratio of 25:75. The characteristics of the heavy oil and gas oil are given in Table 1. The water content for the experiments was fixed at 30 wt % because it was the usual moisture content of the sand collected from the seashore for depollution. Polluted Sands. Table 2 presents the polluted sand models chosen for this study. Emulsions (mixtures of oil and water) were prepared using a mechanical mixer, and then they were mixed with the appropriate quantity of dry raw sand manually (i.e., 95% raw dry sand + 5% pollutant). Mixing was continued to obtain uniform polluted sand without agglomeration. Experimental Methods. Experiments were carried out in a cold fluidized bed (Figure 1) of cross-sectional area 0.2 m × 0.2 m with three walls in high-density polyethylene plastic (two side walls and the wall at the back) and with a transparent front window. A blower supplied the fluidization air, and the flow was measured by a mass flowmeter (of capacity 0-100 Nm3/h) and a velocity meter. The fluidization air distributor was a perforated plate with 2-mm-diameter holes spaced at 10 mm center to center for the coarse sand and 1-mmdiameter holes spaced at 12 mm center to center for the fine sand. Pressure fluctuations were measured using a pressure transducer and data acquisition system (using PDI, Test Point software), as shown in Figure 1. Pressure drops at several points on the bed wall were measured with respect to a reference point, R, on the freeboard. The pressure fluctuation signals were recorded at 50 Hz for 2048 samples. These signals were treated by fast Fourier transformation (FFT) to obtain the power spectral density function. A bed inventory of 6 kg was used to start the experiments. Polluted sand with 5% of pollutants was first tested in the bed for its fluidization behavior. If it was not fluidizable, then 6 kg of raw unpolluted dry sand was used to initiate the experiment. To this, the previously prepared polluted sand was added progressively to vary the percentage of oil pollution in the bed, and the resulting mixture was mixed thoroughly. For every combination of experimental parameters, the
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Because the nature of the power spectrum is affected by many factors, a nondimensional representation is a must for comparing fluidized-bed data. Reference 13 recommended normalization by dividing the spectral density ordinate with the variance (which is equal to the sum of the power in the spectrum) and the frequency abscissa by the dominant frequency. Pressure drop fluctuation signals were used in the present study to assess the quality of fluidization of the bed as a complementary tool to visual observations. Analytical Method
Figure 2. Determination of Umf and Kmes.
pressure drop across the bed was determined and plotted against the velocity for both increasing and decreasing air flow rates. Umf was estimated from the decreasing air velocity part of the plot,2 as shown in Figure 2. Umf was taken as the bed air velocity at which the measured pressure drop divided by the bed weight per unit area became 1 or would have become 1 by extrapolation. A coefficient Kmes, deduced from the defluidization curve, is also shown in Figure 2. The bed height Hmf was measured at this minimum fluidization condition, except when the bed surface was fluctuating too much. The bed voidage at a minimum fluidization velocity, mf, was then calculated from the bed height. Analysis of Pressure Fluctuations Pressure fluctuations of the fluidized beds are related to the fluidization behavior of the bed, and it can provide an effective way for the online diagnosis of its behavior.6-10 As the quality of fluidization varies from the distributor plate upward, an overall measurement from a single point does involve some uncertainty. However, many prefer this method because it is easy to perform and is cost-effective. Some researchers prefer to study the pressure fluctuations of the windbox rather than those of the bed.11,12 In the bubbling-bed regime, it is common to treat the pressure fluctuation signals to assess the periodic components. Probability density, autocorrelation, and power spectral density functions are useful in identifying the periodic components in the pressure fluctuations.11 Power spectral density functions obtained by FFT analysis of the pressure fluctuation data can be used effectively for studying the fluidization behavior. The concentration of the power of the signal into a very narrow frequency band, with a width of less than 1 Hz, confirms the presence of a periodic component in the pressure signals. Here, the presence of a sharp peak is the most important feature. The location, size, and width of this peak depend on the bed parameters. The dominant frequency of the power spectrum is a strong function of the bed height. It decreases with an increase in the bed height because of bubble coalescence. It also decreases with an increase in the particle size at least in shallow beds.11 The amplitude of the dominant frequency is related to the size of the bubbles escaping from the bed. The amplitude increases with the bed height, particle size, particle density, and gas velocity.
According to the definition of fluidization, at the onset of fluidization, the bed weight (W) per unit area of bed cross section (A) is equal to the gas pressure drop ∆P across the bed. However, even for raw unpolluted sand, this equality does not always exist precisely. A coefficient Kcalc was then defined as the ratio of the bed pressure drop as evaluated by the Ergun equation14 at the minimum fluidization (with the measured velocity Umf, bed voidage mf, and solid characteristics dp, air viscosity µ, and density F) to the apparent specific bed weight, with g being acceleration and Fp the solid density (eq 1).
[
]/
1.75(1 - mf) 150(1 - mf)2 µUmf + FUmf2 3 2 3 mf dp mf dp g(Fp - F)(1 - mf) ) Kcalc (1)
The coefficient Kcalc gives an indication about the channelling, dead zones, and consolidation in the bed,15 as well as the friction of the fluidized bed along the reactor walls. When the sands were polluted, the walls were retaining a layer of sand and oil and the friction occurred mainly between the fluidized bed of the polluted sand and the fixed layers of the same polluted sand. Fatah et al.16 used a sort of coefficient Kcalc to determine their so-called “dynamic diameter” of agglomerates when fluidizing Geldart C particles that agglomerate during fluidization. Paola and Riccardo17 also have reported about the back-calculation of the particle diameter to assess the effect of cohesion on minimum fluidization at high temperatures. As a matter of fact, it is possible to evaluate a particle or agglomerate diameter knowing their behavior at minimum fluidization and accounting for the fact that Kcalc is equal to 1 if the Ergun equation or an equivalent applies. In the present study, Kcalc was numerically adjusted to evaluate such an agglomerate diameter when pollution was present in the sand bed assuming that Kcalc has to take the same value as that when there was no pollution. In other words, the value of Kcalc for each sand without pollution was considered as a correction that remained valid when pollutants were present and were supposed to provoke an increase of the mean diameter of the particles. Parameters tending to decrease the coefficient Kcalc, such as channelling, and to increase it, such as consolidation or friction, have cumulative effects on the coefficient Kcalc value, and these contrary effects cannot be thus distinguished while occurring at the same time.15 However, if Ergun’s law applies, Kcalc is less than unity when channelling or dead zones are predominant, while Kcalc is greater than unity when cohesion or agglomeration dominates.
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Table 3. Empirical and Calculated Values for the Coarse Sand
pollutant none heavy oil heavy oil + water
light oil
light oil + water
weight fraction of pollutants (%)
bed weight per unit area of bed cross section (Pa)
0 0.39 0.71 1.0 0.27 0.50 0.70 0.88 0.39 1.0 1.47 1.67 2.0 2.14 0.50 0.88 1.17 1.40 1.59 1.75
1472 1594 1717 1839 1594 1717 1839 1962 1594 1839 2084 2209 2453 2573 1717 1962 2210 2453 2696 2943
Umf (m/s) calcd measd
Hmf (m)
mf
0.26 0.31 0.47 0.68 0.31 0.40 0.50 0.62 0.31 0.45 0.42 0.42
0.1 0.12 0.15 0.18 0.12 0.14 0.16 0.19 0.12 0.15 0.17 0.18
0.46 0.48 0.55 0.62 0.48 0.52 0.56 0.60 0.48 0.54 0.53 0.53
0.14 0.17 0.18 0.19 0.21
0.52 0.56 0.51 0.51 0.50
0.40 0.50 0.37 0.37 0.35
0.24 0.33 0.48 0.87 0.31 0.40 0.62 0.89 0.28 0.47 0.52 0.51 0.74 0.96 0.37 0.47 0.53 0.53 0.54 0.80
∆Pmf (Pa) 1221 1398 1475 1538 1349 1434 1538 1295 1521 1555 1575 1717 1531 1447 1642 1788 1942 2004 2048
Kcalc ) ∆PErgun gradient at Umf/ apparent specific weight of the bed
Kmes ) ∆Pmf/ bed weight per unit area of bed cross section
dynamic diameter calculated (mm)
0.91 1.07 1.03 1.43 1.00 1.03 1.29 1.66 0.86 1.08 1.26 1.25
0.83 0.88 0.86 0.84 0.85 0.84 0.84
0.4276 0.4681 0.4514 0.5593 0.4496 0.4493 0.5282 0.6216 0.4218 0.4633 0.5209 0.5131
0.89 0.94 1.51 1.58 1.69
0.81 0.83 0.75 0.71 0.70 0.60 0.84 0.84 0.81 0.79 0.74 0.70
0.4265 0.5755 0.5754 0.6100
Table 4. Empirical and Calculated Values for the Fine Sand
pollutant none heavy oil
heavy oil + water
light oil light oil + water
weight fraction of pollutants (%)
bed weight per unit area of bed cross section (Pa)
0 0.2 0.39 0.56 0.71 1.0 1.13 1.25 0.14 0.27 0.5 0.7 0.86 0.12 0.24 0.31 0.085 0.17
1472 1533 1594 1655 1717 1839 1901 1962 1533 1594 1717 1839 1947 1508 1545 1570 1508 1545
Umf (m/s) calcd measd
Hmf (m)
mf
0.032 0.035 0.044 0.051 0.059 0.068 0.063
0.12 0.13 0.14 0.16 0.17 0.19 0.2
0.53 0.54 0.57 0.59 0.61 0.63 0.62
0.13 0.14 0.16 0.18
0.54 0.57 0.57 0.61
0.14 0.14 0.15 0.13 0.14
0.57 0.59 0.59 0.56 0.58
0.035 0.044 0.044 0.059 0.044 0.051 0.051 0.040 0.047
0.017 0.021 0.039 0.058 0.076 0.089 0.110 0.107 0.021 0.025 0.038 0.075 0.131 0.027 0.047 0.064 0.025 0.032
Results and Discussion All of the polluted sands, prepared by mixing 5% pollutant (heavy oil or light oil, with or without water) with 95% raw dry sand (coarse or fine), failed to fluidize. The experiments were thus carried out by starting with a bed of raw dry sand (6 kg) and increasing the percentage of pollution in the bed by adding polluted sand containing 5% pollutants. At every increase of the percentage of pollution in the bed, Umf was evaluated. Tables 3 and 4 present the variation of Umf versus the pollution fraction in the bed respectively for the coarse and fine sand, for the four kinds of pollutants that were tested. They also give the corresponding bed height (Hmf) measured at Umf, the voidage deduced from that Hmf, and the values of Kcalc and Kmes. Velocity and Voidage at Minimum Fluidization. From Tables 3 and 4, it is clear that Umf increased with an increase of the level of pollution in the bed for all kinds of pollutants tested in this study. This was expected because the oil contamination increases the cohesiveness of the particles or their agglomeration. However, there was an upper limit for the pollution level beyond which the bed was not at all fluidizable. More-
∆Pmf (Pa) 1280 1186 1181 1132 1131 1208 1140 1260 1202 1173 1253 1321 915 1164 953 424 1211 1089
Kcalc ) ∆PErgun gradient at Umf/ apparent specific weight of the bed
Kmes ) ∆Pmf/ bed weight per unit area of bed cross section
dynamic diameter calculated (mm)
0.51 0.62 0.89 1.17 1.29 1.35 1.72
0.87 0.77 0.74 0.68 0.66 0.66 0.60 0.64 0.78 0.74 0.73 0.72 0.47 0.77 0.62 0.27 0.80 0.71
0.1043 0.1122 0.1359 0.1543 0.1645 0.1657 0.1922
0.61 0.56 0.86 1.26 0.62 0.96 1.29 0.64 0.7
0.1116 0.1086 0.1342 0.1633 0.1129 0.1386 0.1623 0.1128 0.1184
over, the defluidization curves used to determine Umf were changing as the pollution content of the sand was increasing. As a matter of fact, the defluidization curve of the bed pressure drop versus gas superficial velocity tended to look like the defluidization curve obtained with dispersed solid or binary mixtures when the pollution fraction was increasing. When a greater segregation arises during slow defluidization, the gap between the ideal defluidization curves of a uniform solid increases. Instead of having a sudden fall of the pressure drop, the obtained curves for high levels of pollution had an important and extended part during which the bed pressure drop regularly decreased with the decrease of the gas velocity, without being completely defluidized. This phenomenon complicates the Umf determination and explains the common problems faced and the methods used in dispersed-solid characterization and fluidization.18 However, in this study, the experimental method used to prepare the batches of polluted sand before their fluidization characterization did not seem to have produced any visible segregation in the bed. Figure 3 presents the normalized Umf, that is Umf of
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Figure 4. Variation of the dominant frequency and Kcalc values for coarse sand.
Figure 3. Umf (normalized) for fine and coarse sands.
both coarse and fine sands for varying pollution contents, divided by Umf of the corresponding raw unpolluted sand. As the pollution content increased in the bed, there was a change in the fluidization behavior of the polluted sand. At a low pollution percentage, the increase of Umf was slow; then beyond a critical level, Umf increased rapidly with the pollution content in the bed.5 For each type of pollutant, a critical pollutant content corresponding to the change of slope of the Umf curves was then determined by fitting two straight lines to a curve and getting the critical pollutant concentration from the intersection of those lines.5 Another fluidizability criterion may be drawn from Figure 3 that emphasizes that the heavy oil has similar effects on both coarse and fine sands while the light oil behaves in an entirely different manner with coarse and fine sands. For the coarse sand, the pollutant made of a mixture of heavy oil and water was the most unfavorable because, beyond 0.88%, the bed was no longer fluidizable. The next most unfavorable pollutant was the heavy oil, fluidizable only up to 1% pollutant concentration. When light oil or a mixture of light oil and water were used, the bed of coarse sand was fluidizable even at higher pollutant concentrations. On the other hand, for fine sand, the most unfavorable pollutant was the mixture of light oil and water. With this pollutant, the bed was not fluidizable at concentrations above 0.17%. The next critical pollutant was the light oil, with which the bed was not fluidizable above 0.3%. However, with heavy oil and a mixture of heavy oil and water, the fine sand bed was fluidizable up to 1.25% and 0.86% of pollution, respectively. It is worth noting that the presence of water, for both coarse and fine sands, with heavy oil or light oil, decreased the fluidizability of the bed. Tables 3 and 4 present the voidage (mf) calculated based on the measured bed height at minimum fluidization velocity for both coarse and fine sands, respectively. In general, mf increased with pollution in the case of both fine and coarse sands for all kinds of pollutants tested in this study. As the percentage of oil pollution increased in the bed, the cohesion between the particles also increased. An increase of cohesion between particles is known to yield a higher void fraction than would be the case in the absence of cohesion.19 In Tables 3 and 4, for the high levels of pollution tested, it may be noted that no values are given for mf. This was because of
the difficulty in accurately measuring the bed height Hmf at these high levels of pollution. The minimum fluidization velocities for both sands, for all of the pollutants, were calculated with eq 1 using the measured values of mf and Kcalc equal to unity. These calculated Umf values are presented in Tables 3 and 4. The calculated Umf gives an idea about the scenario if the pollution would not have been there, except the pollution influence on the bed voidage. One can also see that the prediction of Umf by eq 1, and hence by Ergun’s law, is much better in the case of coarse sand than for fine sand. The measured Umf in comparison with the calculated Umf shows that for lower levels of pollution in the bed, the measured Umf is generally lower than the calculated Umf. As pollution in the bed increases, mf increases and hence Umf because a higher flow rate is required to produce enough pressure difference to support the bed weight.19 At higher levels of pollution, the measured Umf is considerably larger than the calculated Umf. This indicates that, beyond certain levels, the pollution brings a complementary effect to the bed voidage increase that might be particle size enlargement by agglomeration. The sizes of these agglomerates were not measured because they were produced during fluidization and hence were too fragile to be withdrawn from the bed without any breakage. Agglomeration was neither evaluated nor measured during and after the experiments. Instead, the so-called dynamic diameter was calculated at each level of pollution by forcing Kcalc in eq 1 to take the value it had when there was no pollution at all in the bed. This diameter (Tables 3 and 4) increased with the pollution concentration in the sand, for both sands and for all pollutants. The pressure drop of the bed, as calculated by Ergun’s equation at minimum fluidization, divided by the apparent specific weight of the bed is thus the same as that if some agglomeration was occurring because of pollution. The higher the pollution, the higher the dynamic diameter is. The maximum dynamic diameter calculated with that method was nearly 50% greater than the diameter of the real raw sand in the case of the coarse sand (with the highest content of heavy oil or light oil with water) and 85% and 60% in the case of the fine sand (with respectively heavy oil and light oil without water). Quality of Fluidization. The quality of fluidization was assessed in different ways: using the coefficients Kmes and Kcalc and by the analysis of pressure fluctuations. As defined in Figure 2, Kmes gives an idea about the channelling and/or dead zones present in the bed,
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Figure 5. Variation of the dominant frequency and Kcalc values for fine sand.
although the effect of consolidation cannot be completely ruled out. An increase of Kmes indicates a decrease in channelling and vice versa. The raw coarse sand has a Kmes value of 0.83 (Table 3), indicating 17% channelling. Similarly, the raw fine sand has a value of 0.87 (Table 4), where the channelling is only 13%. The further decrease of Kmes from raw sand values (Tables 3 and 4) showed that, as the pollution increased, the ideality of the defluidization curve decreased. That is, as the pollution increased, channelling increased or the property distribution of solids became wider, providing a change from the fluidized state to the fixed state that was more and more progressive as the gas velocity was decreased. Moreover, when the pollution fractions were relatively high, it was often visually observed that fluidization was taking place predominantly in the center of the bed and was sometimes producing a central spout. Kcalc values presented in Tables 3 and 4 increased with the pollution content of the bed in all of the cases. This is mainly due to the increase in Umf with the pollution in the bed. Umf appears in Ergun’s equation and thus in Kcalc. In all of the cases, for no pollution or very low levels of pollution, Kcalc values were less than unity. However, the Kcalc value increased, soon exceeding one showing a complementary effect to channelling that might be agglomeration. With higher concentrations of pollutants in the bed, Kcalc values increased manyfold, for instance, 3 times for fine sands. As a result, the increase of the Kcalc values with respect to pollution in the sand bed evaluates the “efficiency” of the pollutants on the degradation of the fluidizability due to cohesion and agglomeration. In Tables 3 and 4, for the high levels of pollutions tested, it may be noted that no values are given for Kcalc. This was because it was difficult to measure the bed height Hmf accurately and, hence, no mf values were available. Analysis of Pressure Fluctuations. The analysis of the pressure fluctuation signals revealed that all of the experiments of the present study were performed in the bubbling-slugging regimes. This was clear from the periodicity observed with the pressure signals. The pressure signals were analyzed using the FFT for periodicity, and it was found that the peaking frequency is a strong function of the bed height. In the present study, the progressive addition of polluted sand to increase the percentage pollution in the bed increased the bed height also. The dominant frequency would have decreased every time with the addition of polluted sand. However, in the present study, this was true only for low levels of pollution. At higher levels of pollution, the
dominant frequency increased in the present study, indicating the dominance of cohesion over the effect of the bed height. The increase in cohesiveness may decrease the size of the bubbles and/or limit its growth. Smaller bubbles bring forth high-frequency components in the signal, which is the case in the present study. The variation of the dominant frequency has been compared with the variation of Kcalc for both coarse and fine sands in Figures 4 and 5, respectively. Initially, the dominant frequency decreased with pollution because of an increase in the height of the bed. Then at higher pollution levels, it increased, showing the dominance of the effect of cohesive forces over the effect of the bed height. This increase in the dominant frequency was more or less at the same pollution percentage at which the slope of the Kcalc value changes drastically. The increase of the dominant frequency with pollution indicates the deterioration of the quality of fluidization. As long as the bed was fluidizable, there was a clear sharp peak in the power spectrum, showing the presence of bubbles. At higher velocities, the bubbling regime gives way to the slugging regime, which is indicated by an increase in the amplitude of the peaking frequency. Conclusions The presence of oil contaminants in sand changes its fluidization behavior. While the unpolluted sand belongs to the Geldart B category, oil pollution changes its behavior, considerably affecting its capacity to fluidize well in a cold model. There is a percentage of pollution in the bed above which the bed is not fluidizable. However, this percentage varies with the types of pollutants in the bed and the type of sand in use. The fluidization behavior of the coarse and fine sands also varied for the different pollutants tested in this study. While heavy oil pollution was the most critical for the coarse sand, light oil pollution was the most critical for the fine sand. The presence of water further worsened the scenario in both cases. The increase in the percentage of oil pollution in the bed increases Umf slowly initially and then dramatically at higher pollution levels. A coefficient has been defined to represent friction forces and agglomeration in the bed along with channelling. The changes of this coefficient versus percentage pollution indicate that cohesion and then agglomeration were dominating above a critical pollution level. Oil pollution in the bed increased the dominant frequency of the power spectral density function
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at higher percentages of pollution. This indicates the deterioration of the quality of fluidization with an increase in oil pollution in the bed. This may be used for the online monitoring of the quality of fluidization and for diagnosing the regime of fluidization. The results of the present study may be useful in the design of an industrial-scale fluidized-bed incineration plant for depolluting oil-contaminated sands and soils and deciding on some large-scale tests to be carried out on a incineration plant burning municipal wastes. Acknowledgment Gaz de France, la Socie´te´ Nationale d’Electricite´ et de Thermique, Techniques Modernes de Combustion (Group CNIM), and Bre´zillon are acknowledged for their participation in the Thermer project. RITMER and the Ministe`re de la Recherche of France are acknowledged for their financial support to the project. Nomenclature A ) area of the bed, m2 dp ) harmonic diameter of the sand particle, m fd ) dominant frequency, Hz Hmf ) height of the sand bed at minimum fluidization velocity, m Kmes ) coefficient measured on defluidization curves at Umf Kcalc ) coefficient calculated after eq 1 with measured values of Umf and mf Umf ) minimum fluidization velocity, m/s W ) weight of the bed, kg‚m/s2 Greek Letters ∆P ) pressure drop across the bed, Pa ∆Pmf ) pressure drop across the bed at minimum fluidization, Pa mf ) bed voidage at minimum fluidization velocity µ ) dynamic viscosity of air, kg/m‚s F ) density of air, kg/m3 Fp ) particle density, kg/m3
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Received for review June 24, 2004 Revised manuscript received November 29, 2004 Accepted November 30, 2004 IE040189D
