Chemical Engineering Science 64 (2009) 2522 -- 2524

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Chemical Engineering Science

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Shorter Communication

Pitfalls in gas sampling from fluidized beds

John Gracea,∗, Hsiaotao Bia, Yongmin Zhangb

aDepartment of Chemical and Biological Engineering, University of British Columbia, 2360 East Mall, Vancouver, Canada V6T 1Z3bState Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing, 102249, China

A R T I C L E I N F O A B S T R A C T

Article history:Received 29 September 2008Received in revised form 29 January 2009Accepted 11 February 2009Available online 20 February 2009

Keywords:SamplingFluidizationDispersionChemical reactorsMultiphase flowMultiphase reactors

It is shown that gas sampling from fluidized beds can provide misleading information due to hydrody-namic factors, biased sampling from the dense phase and radial gradients. Caution is needed to avoidthese problems and in the interpretation of gas-sampling data.

© 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Gas-fluidized beds experience significant axial and lateral disper-sion, affecting the conversion and selectivity of fluidized bed chemi-cal reactors. Their hydrodynamics are subject to complex two-phasebehavior and several flow regimes. Reactor modeling requires recog-nition of the two-phase nature of fluidized beds. Models also requireexperimental confirmation, preferably by measuring concentrationswithin the individual phases (Chavarie and Grace, 1975; Atkinsonand Clark, 1988). It is less useful, but better than measuring con-centrations only at the exit, to perform in-bed gas sampling alongthe height of the column. Sampling is also required when measuringgas mixing in fluidized beds. Efforts have been made to sample gasin large-scale industrial systems (e.g. Cooper and Ljungstrom, 1988;Hansen et al., 1995; Hartge et al., 2005), e.g. to better understandgeneration of pollutants.x

The two-phase nature of fluidized beds results in significantpitfalls when interpreting gas-sampling data which can cause sig-nificant errors. These issues have gone largely unrecognized. Forsimplicity and because this is where the challenges are greatest, wefocus on the bubbling flow regime, although similar considerationsapply in varying degrees to the other flow regimes (especially slug-ging and fast fluidization).

∗ Corresponding author. Tel.: +16048223121.E-mail address: jgrace@chml.ubc.ca (J. Grace).

0009-2509/$ - see front matter © 2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.ces.2009.02.012

2. Pitfall #1: bubble-induced pressure fluctuations

Local pressures recorded in gas-fluidized beds show substantialfluctuations. Among the various causes (Bi, 2007) are bubbles: Asbubbles approach, the pressure rises, reaching peaks when theirnoses reach the measurement level, then falling to a minimum at theback before recovering in the wake (Davidson and Harrison, 1963).The overall amplitude of the bubble-induced pressure fluctuationsis of order �p(1 − �mf )U2

b . A gas-sampling port within a bubblingbed experiences similar pressure fluctuations as bubbles pass. If thepressure drop through the sampling line and analysis instrumen-tation is small relative to the amplitude of these fluctuations, thesampling flowrate will vary significantly with time, with more gassampled from near the front of the bubble and less from the back.Thus the sampling flux will be skewed disproportionately to the noseregion, causing the sampling to be biased. Overall, the time-meanconcentration acts as if the sampling was carried out from belowthe actual sampling level. The extent of the bias, in addition to de-pending on the pressure drop through the sampling tubing and in-strumentation, depends on the vertical length and velocity of thebubbles. To avoid sampling errors associated with in-bed pressure-fluctuations, the pressure drop through the sampling system shouldbe much larger than the amplitude of the pressure fluctuations.This is readily achieved in pressurized or deep beds, but shallowatmospheric-pressure beds will almost certainly require a vacuumpump at the exit of the sampling lines, requiring careful attention toavoid leakage which could dilute the sampled gas and cause furthererrors.

J. Grace et al. / Chemical Engineering Science 64 (2009) 2522 -- 2524 2523

3. Pitfall #2: two-phase flow averaging

Consider a bubbling fluidized bed operating at statistically steadystate with reactant A concentrations at height z of cAb(z) and cAd(z) inthe bubble and dense phases, respectively. Constant flow samplingat that height will give a time-mean concentration of

c̄Asample(z) = �b(z) × cAb(z) + [1 − �b(z)] × cAd(z) (1)

where �b(z) is the time-mean fraction of bed volume occupied by thebubble phase. The time-mean concentration based on flow throughthe two phases at that level should be

c̄Aflow(z) = {Qb(z) × cAb(z) + Qd(z) × cAd(z)}/Q (2)

where Q is the total gas volumetric flow, whereas Qb(z) and Qd(z)represent the flows associated with the bubble and dense phases,respectively. Since �b(z) is likely to be ∼0.1–0.4, the average mea-sured concentration from Eq. (1) is weighted heavily to the densephase concentration. On the other hand, since the flow passes pre-dominantly through the bubble phase, Eq. (2) shows that the ac-tual (flow) mean concentration should be heavily weighted towardscAb(z). Hence there is significant error in sampling when the concen-trations in the two phases, cAb(z) and cAd(z), differ appreciably.

This difference between the two means can be large as illustratedby comparing the sampled time-mean and flow-average concentra-tions for an illustrative case. To make this comparison, we could uti-lize various fluidized bed reactor models. For simplicity, we consideran irreversible constant-volume first-order reaction and the bubblingbed model summarized by Grace (1986). All of the gas is assumed topass through the bubble phase, the dense phase acting as a stagnantzone, but nevertheless contributing greatly to reaction due to inter-phase mass transfer. Radial gradients and temporal fluctuations areignored. With these assumptions, steady state dimensionless molebalances on the two phases yield:

Bubble phase :dCAbdZ

+ X(CAb − CAd) + k∗1�bCAb = 0 (3)

Dense phase : X(CAd − CAb) + k∗1�bCAd = 0 (4)

where CAb = cAb/cAo; CAd = cAd/cAo; cAo is the inlet concentration ofreactant A, and Z the dimensionless vertical coordinate= z/H with Hthe expanded bed height. X and k∗

1 are a dimensionless interphasemass transfer and rate constant, respectively, defined by

X = kmab�bHU

, k∗1 = k1H

U(5)

�bis the fraction of bed volume occupied by bubble-phase solids≈ 0.001�b–0.01�b, �d the fraction of bed volume occupied by densephase solids ≈ (1 − �b)(1 − �mf ), km an interphase mass transfer co-efficient, ab the bubble surface area per unit volume, �b the fractionof bed volume occupied by bubbles, �mf the bed voidage at mini-mum fluidization, k1the first order reaction rate constant and U thesuperficial gas velocity.

With boundary condition CAb = 1 at Z = 0, Eqs. (3) and (4) give

CAb = exp{−k∗

1[X(�b + �d) + k∗1�b�d]Z

X + k∗1�d

}and

CAd = CAbXX + k∗

1�d(6)

For very slow reactions (k∗1 → 0) or very large interphase mass

transfer (X → ∞), the concentrations of the two phases are nearlyequal at the same level, nullifying the difference between the meanconcentrations from Eqs. (1) and (2). However, consider an illus-trative case with realistic values and rate-limiting interphase masstransfer. Let �mf =0.5; �b=0.2;�d=0.4;�b=0.001; X=1.0; and k∗

1=10.

At Z = 0.1, substitution of these values into Eq. (6) gives CAb = 0.922,whereas CAd =0.184. Steady sampling at this height would then give(from Eq. (1)) a time-mean concentration of 0.332cAo, whereas thetrue (flow-average) concentration, given the stagnant flow in thedense phase, would equal the bubble-phase concentration, 0.922cAo.This difference is substantial, with the sampled gas concentrationonly 36% of the flow-average value. Failure to recognize errors as-sociated with sampling will lead to erroneous conclusions regardingthe progress of reactions along the reactor and the merits of reac-tor models. This effect explains the finding (Askins et al., 1951) thatmeasured in-bed oxygen concentrations in a commercial FCC regen-erator were consistently much lower than in the freeboard. Interpre-tation of sampling data requires care and caution to avoid misinter-pretation. We could find only a few other instances in the literature(Gilliland and Mason, 1952; Fontaine and Harriott, 1972; Cooper andLjungstrom, 1988; van der Vaart, 1992) where this bias resultingfrom over-representation of the dense phase has been recognized.

Similar errors occur when withdrawing gas samples in determin-ing axial gasmixing in fluidized beds. For example, gas samples takenupstream of a steady-state tracer injector, predominantly come fromthe dense phase, where the concentration is likely to be significantlyhigher than in the bubble phase, leading to overestimation of gasbackmixing. Sampling errors of this nature are likely one cause ofthe wide scatter of gas axial dispersion coefficients reported in theliterature (van Deemter, 1980). In order to compensate for this prob-lem, sampling data should be interpreted with the aid of a two-phasemodel appropriate to the flow regime which is present in the bed.

4. Pitfall #3: hydrodynamic interference

Gas sampling lines need a screen or filter at their entrances toprevent ingress of particles. Excessive withdrawal of gas through asampling port can cause local defluidization or build-up of stagnantsolids, with the resulting lump of solids impeding the movement ofparticles. To minimize flow disruption, the average velocity alongthe sampling line should be small, preferably ∼Umf/�mf. However, fortypical fluidized bed catalyst particles, the resulting velocities willthen be onlymillimetres/second, causing long delay times. Moreover,such velocities, coupled with small sampling-tube diameters, givesuch low Reynolds numbers that the sampling tube flow will belaminar, causing Taylor dispersion to distort transient signals.

If sampling tubes intrude into the bed, their presence can alsointerfere with the flow, e.g. causing bubble splitting. Non-intrusivemeasurement techniques such as NMR species mapping, can over-come this problem. Ports flush with the vessel wall also avoid theproblem. However, sampling from thewall region leads to challengesassociated with radial gradients, as outlined in the next section.

5. Pitfall #4: neglect of radial gradients

Most fluidized-bed reactor and dispersion models assumeone-dimensional flow, ignoring radial gradients of concentration,voidage and velocity, despite considerable evidence that, for ex-ample, voidages are considerably greater in the interior than nearthe outer walls. Since gas samples are usually withdrawn from thewalls (as suggested above), measured concentrations are atypicalof cross-sectional averages at the given level. For example, solidsdownward flow occurs predominantly along the walls, inducinggas backmixing in this region. Taylor dispersion associated withnon-uniform gas velocity profiles may also cause axial mixing. In-terpretation in terms of a one-dimensional axial model is likely toinadequately represent the actual mechanisms of mixing, whichare dominated by macroscopic phenomena such as gulf-streaming(Merry and Davidson, 1973). Given these circumstances, two- or

2524 J. Grace et al. / Chemical Engineering Science 64 (2009) 2522 -- 2524

three-dimensional dispersion mixing models are needed to providea proper interpretation of the mixing phenomena.

6. Pitfall #5: adsorption and trapping

Sampling errors can also occur if a gas species adsorbs on theparticles. The species in question then travels in a different man-ner from non-adsorbing species. The consequences have been rec-ognized (e.g. Nguyen and Potter, 1974; Bohle and van Swaaij, 1978;Krambeck et al., 1987) and are not discussed here. Note, however,that similar considerations apply to non-adsorbing species with non-porous particles. In such cases, resistance to internal diffusion willcause delays in gas species entering and leaving the particles. As aresult, measured gas mixing and local concentrations differ accord-ing to whether the particles are porous or non-porous, the extent ofthe difference depending on the internal porosity and the effectivediffusivity of the gas species of interest.

7. Conclusions

Fluidized-bed reaction and mixing studies often require gas con-centration measurements to test models, understand behavior, andderive mixing coefficients. However, the two-phase nature of gas-fluidized beds and their radial gradients can cause substantial errorsin gas sampling. It is critical to minimize the pitfalls identified inthis paper and to interpret data derived from in-bed gas sampleswith great care. Failure to do so has doubtless contributed to widevariations in literature data and may cause misinterpretation of therelative merits of alternative reactor and dispersion models. Somesuggestions for overcoming the pitfalls are provided in this paper.

Acknowledgement

The authors are grateful to the Chinese University of Petroleumand the Natural Sciences and Engineering Research Council ofCananda for financial support

References

Askins, J.W., Hinds, G.P., Kunreuther, F., 1951. Fluid catalyst-gas mixing in commercialequipment. Chem. Eng. Prog. 47, 401–404.

Atkinson, C.M., Clark, N.N., 1988. Gas sampling form fluidized beds: a novel probesystem. Powder Technol. 54, 59–70.

Bi, H.T., 2007. A critical review of the complex pressure fluctuation phenomenon ingas–solids fluidized beds. Chem. Eng. Sci. 62, 3473–3493.

Bohle, W., van Swaaij, W.P.M., 1978. The influence of gas adsorption on masstransfer and gas mixing in a fluidized bed. In: Davidson, J.F., Keairns, D.L. (Eds.),Fluidization. Cambridge University Press, Cambridge, UK, pp. 167–172.

Chavarie, C., Grace, J.R., 1975. Performance analysis of a fluidized bed reactor, partI—visible flow behaviour. Ind. Eng. Chem. Fundam. 14, 75–91.

Cooper, D.A., Ljungstrom, E.B., 1988. In-bed gas measurements in a commercial 16MW fluidized bed combustor. Chem. Eng. J. Biochem. Eng. J. 39, 139–145.

Davidson, J.F., Harrison, D., 1963. Fluidised Particles. Cambridge University Press,Cambridge.

Gilliland, E.R., Mason, E.A., 1952. Gas mixing in beds of fluidized solids. Ind. Eng.Chem. 44, 219–224.

Fontaine, R.W., Harriott, P., 1972. Effect of molecular diffusion on mixing in fluidizedbeds. Chem. Eng. Sci. 27, 2189–2197.

Grace, J.R., 1986. Fluid beds as chemical reactors. In: Geldart, D. (Ed.), Gas FluidizationTechnology. Wiley, New York, pp. 287–341 (Chapter 11).

Hansen, P.F.B., Bank, L.H., Dam-Johansen, K., 1995. In-situ measurement in fluidizedbed combustors: design and evaluation of four sample probes. In: Proceedingsof 13th International Conference on Fluidized Bed Combustion, ASME, vol. 1,pp. 391–397.

Hartge, E.U., Fehr, M., Werther, J., Ochodek, T., Noskievic, P., Krzin, I., Kallner, P.,Gadowski, J., 2005. Gas concentration measurements in the combustion chamberof the 235MWe circulating fluidized bed boiler Turow no. 3. In: Proceedings of18th International Conference on Fluidized Bed Combustion, ASME, pp. 813–822.

Krambeck, F.J., Avidan, A.A., Lee, C.K., Lo, M.N., 1987. Predicting fluid-bed reactorefficiency using adsorbing gas tracers. A.I.Ch.E. J. 33, 1727–1734.

Merry, J.M.D., Davidson, J.F., 1973. Gulf-stream circulation in shallow fluidized beds.Trans. Inst. Chem. Eng. 51, 361–368.

Nguyen, H.V., Potter, O.E., 1974. Gas mixing in fluidized beds as a function of theadsorbency of the solids for the gas. Adv. Chem. Ser. 13, 290–300.

van Deemter, J.J., 1980. Mixing patterns in large-scale fluidized beds. In: Grace, J.R.,Matsen, J.M. (Eds.), Fluidization. Plenum, New York, pp. 69–89.

van der Vaart, D.R., 1992. Prediction of axial concentration profiles in catalyticfluidized reactors. A.I.Ch.E. J. 38, 1157–1160.