Simulation of dust explosions in spray dryers

Transcription

1 Simulation of dust explosions in spray dryers Dr.-Ing. K. van Wingerden, Dipl. Ing. T. Skjold, Dipl. Ing. O. R. Hansen, GexCon AS, Bergen (N) and Dipl. Ing. R. Siwek, FireEx Consultant GmbH, Giebenach (CH); Abstract Dust explosion experiments performed in a representation of a spray dryer have been simulated with DESC, a dedicated CFD-tool for dust explosions. The simulations involved closed vessel explosions, vented explosions and suppressed explosions with and without dust layers. The simulations demonstrate that a description of dust explosions using a dedicated CFD-based tool such as DESC is fully possible. The results, however, also highlight some fundamental limitations associated with the current models in DESC which need improvement. 1. Introduction Explosion protection of conventional spray dryers has always been a challenge due to the fact that only a fraction of the volume will contain explosive mixtures and thereby contribute to explosion pressure generation. In the majority of the volume the water content in the sprayed product is sufficiently to cause the product to be either inert or little reactive. Only the dust-air mixture in the lower part of the dryer would normally contribute, possibly supported by dust whirled up from the cone/bottom of the dryer during the explosion. The reaction rate is important for explosion protection by venting or suppression and this will also be influenced by flow conditions and turbulence prevailing in the dryer. Other factors of secondary importance are the shape of the dryer (e.g. conical bottom, flat bottom), the volume of the dryer, and factors related to the explosion protection such as the location of vent openings. Simple methods used to design explosion protection systems often tend to give quite conservative answers resulting in the introduction of relatively large vent openings or the mounting of many HRD-suppressors. CFD-simulation tools would allow for a more detailed description of both physical and chemical processes occurring both prior to (drying process, description of flow conditions in dryer) and during an explosion (combustion processes and resulting pressure and flow field).

2 A far more optimal design of protective systems is possible provided adequate models are included, and extensive validation of the tool is performed. This document presents the results of simulations performed with the CFD-tool DESC [1, 2]. The simulations concern experiments performed in a representation of a spray dryer [3]. DESC is the first step towards a CFD-tool that can describe both process conditions prevailing in process equipment and ensuing explosions initiated in this process equipment. 2. Spray dryer explosion experiments The explosion experiments reported in [3] were performed in a 43.3 m 3 representation of a spray dryer. The experimental facility was a cylindrical vessel with a m high conical bottom section. The vessel was provided with vent openings of variable sizes (DN200 DN600) located near the top and just above the conical part. Dust clouds were generated in the conical part of the drying chamber only (dust cloud volume approximately 6 m 3 ) according to the methodology described in [4, 5]. Ignition was effected by two 5kJ igniters. The position of the ignition source was varied. In some of the tests, dust layers were introduced in the cone to simulate realistic conditions, i.e. the possibility of dust being whirled up by the explosion and contributing to the explosion. The tests were performed with both maize starch (maximum explosion pressure P max = 9.0 bar, dust explosion constant K St = 161 bar.ms -1 ) and cellulose (maximum explosion pressure P max = 8.6 bar; dust explosion constant: K St = 168 bar.m.s -1 ). The experimental program consisted of three main parts: - Closed vessel experiments - Explosion venting experiments - Explosion suppression experiments Simulations have been carried out for all three categories of experiments. 3. Dust explosion simulation code (DESC) DESC is based on the dedicated explosion CFD-tool FLACS. The most paramount development leading to DESC has been the development of a suitable combustion model [1,

3 2]. The purpose of a combustion model for premixed combustion is twofold: to define the reaction zone (i.e. the position of the flame), and to specify the rate of conversion from reactants to products (i.e. the rate of energy release). The flame model adopted both in DESC and FLACS is the β-model [6] where the flame thickness is constant, typically three grid cells, and the turbulent burning velocity S T is specified by an empirical burning velocity model: T = 15.1 L rms I S S u Where S L = laminar burning velocity u rms = turbulence intensity l I = turbulence length scale and is a reformulation of an empirical relationship suggested in [7], which is a correlation between ST S L, u rms SL, and the Karlovitz stretch factor K. In [8] it was suggested that a similar correlation would be valid for maize starch/air mixtures. Assuming this to be valid for any dust/air mixture it could be possible to simulate dust explosions on a similar basis as gas explosions use. For DESC lacking combustion parameters are derived from pressure-time histories measured in constant volume explosion vessels. The largest available database for such data is pressure-time histories measured in the now standardised 20-litre vessel [4, 5]. To describe lifting of accumulated dust layers into suspension, an empirical correlation obtained through experiments on dust lifting by turbulent flow or shock waves was implemented [9]. This correlation was used to describe the lifting of the dust layers introduced in the cone of the experimental spray dryer facility. The correlation gives the mass flux of dust as an injection velocity v z in m/s, assuming a dust concentration c d equal to 1 kg m -3 : v = h u d ρ A z l p p p where h l is layer thickness in millimetres, u is flow velocity above the layer in m s -1, d p is characteristic particle size in µm, ρ p is particle density in kg m -3, and A p is a dimensionless empirical constant.

4 Radiative heat loss from the hot combustion products may influence the simulation results considerably, especially in closed vessels or ducts. To some extent, such effects may be taken into account in the first version of DESC by invoking a radiation model [10] that follows the approach outlined by Hottel & Egbert [11]. An empirical model was implemented to describe explosion suppression. The model has been described in detail in [12]. Data derived from experiments performed in a 20 l sphere showed a linear decrease of the laminar burning velocity of dust-air mixtures as the concentration of suppressant (e.g. sodium bicarbonate) increases, up to the point of the minimum inerting concentration for the particular dust-air mixture. In the model, the results of [13] from a 1 m 3 -vessel were used for the minimum inerting concentration to exclude any effect caused by the small volume of the 20-l vessel [14]. 4. Simulation of closed vessel explosions As reference for the scenarios with explosion venting and explosion suppression, a first series of simulations considered only closed vessel experiments, with and without a dust layer in the cone. In the simulations, the process of dust cloud generation was represented by injecting dust from pressurised containers. The simulations were performed for maize starch. Two sets of experimental data for maize starch obtained from 20 l vessel data were used to extract the combustion parameters for the model in DESC: sample A : K St = 160 bar m s -1 ; P max = 8.65 bar, and sample B : K St = 114 bar m s -1 ; P max = 7.9 bar. Figure 1 shows a comparison of the maximum overpressures obtained for the two fuel models and experimental data, as function of average dust concentration in the cone of the vessel. Neither simulations nor experiments included dust layers. The simulated maximum pressures from the simulations are higher than the experimental values. This is not surprising, since several of the mechanisms that will reduce the actual pressure are not modelled in DESC (e.g. incomplete dust dispersion, condensation of vapour, etc.). The simulations that use the fuel model for the least reactive dust (sample B), and include the effect of radiative heat loss, show the best agreement with the experiment results. The results highlight the importance of radiation on thermodynamics determining the maximum explosion overpressure of dust explosions. Further, the simulations demonstrate the influence of the fuel data which is considerable affected by factors such as moisture content and particle size distribution.

5 1.5 Overpressure (bar) DESC: Fuel model A, no layer DESC: Fuel model A + radiation, no layer DESC: Fuel model B, no layer DESC: Fuel model B + radiation, no layer Experiment: No layer Average injected dust concentration in cone (g m -3 ) Figure 1: Maximum overpressure in dryer for varying amounts of injected dust, simulated with empirical models for both fuels (A and B) with and without radiation; the figure also contains some experimental data. Figure 2 shows simulation results for closed vessel explosions where 1.5 kg of dust was injected into the conical part of the dryer, i.e. a nominal dust concentration of 250 g m -3 in the 6 m 3 cone. In addition, various amounts of dust were present as a layer (0.4 kg to 1.6 kg), or more precisely as dense dust clouds in the bottom of the cone. The regular model for dust lifting in DESC proved unsatisfactory for this particular case (having the dust onto the walls of the cone of the vessel), presumably since the rate of lifting is determined by empirical correlations obtained in experiments where flow or shock waves pass over a dust layer on a horizontal surface [1, 9]. The simulations were performed for fuel model B, with the radiation model activated. Figure 3 shows results from experiments performed with maize starch dispersed into the cone of the vessel, and cellulose (1.65 kg) present as a dust layer [3]. Due to the unpredictable behaviour of the dust lifting process (bursts), different amounts of cellulose takes part in the explosion in the various experiments. The similarity of the experimental results with those of the simulations is however evident from comparing Figures 2 and 3. Note that the explosion indices (P max and K St ) of the injected maize starch were comparable

6 to those of the cellulose in the layers, allowing the comparison between cellulose dust (experiments) and maize starch (simulations). In the pressure-time histories from both simulation and experiment, without the presence of a dust layer, the initial rate of pressure rise decreases after the flame has reached the top of the cloud and runs into lower concentrations and finally the non-flammable parts of the cloud. For the explosions with dust layers, the initial pressure rise in the experiments persists longer than in the simulations, probably due to immediate whirling up of the dust from the cone walls in the experiments (upon the injection of the dust from the containers). In the simulations, the dust is whirled up from the bottom of the cone, and this process introduces a delay in the volumetric rate of energy release. The initial rate of pressure rise is also considerably higher in the experiments, most likely due to the higher dust concentration. 3 2 Overpressure (bar) kg kg 1.5 kg kg 1.5 kg kg 1.5 kg kg 1.5 kg 0 Injected + Layer Fuel model B Radiation Time relative to ignition (s) Figure 2: Simulates pressure developments in the dryer for 1.5 kg fuel injected, and various amounts of fuel in the cone, with the empirical combustion model for fuel B and with the radiation model active.

7 1,2 1,0 Overpressure (bar) 0,8 0,6 0,4 1.2 kg maize starch injected + layer: 1.6 kg cellulose 1.6 kg cellulose 1.6 kg cellulose 0,2 No layer 0, Time relative to ignition (s) Figure 3: Examples of experimental pressure-time histories from explosions with maize starch dust clouds in the spray dryer, with and without cellulose in the conical bottom [3]; nominal dust concentration in cone from injected dust is 200 g m -3 ; central ignition 0.5 m above the bottom. 5. Simulation of vented dust explosions The vented dust explosion experiments reported in [3] comprise of experiments with and without a dust layer in the cone of the vessel and with various venting conditions: vent position (near top and on side wall of the vessel), the opening pressure and the vent size. Several of these experiments were simulated with DESC. Figure 4 shows a comparison of the effect of vent area on the maximum explosion overpressure seen in experiments performed without a dust layer onto the cone of the vessel (vent near the top of the vessel) and those simulated using DESC (Fuel B, radiation model activated). The predicted overpressures appear to be in the lower range of those found experimentally. Comparisons of measured and simulated pressure-time histories are shown in Figures 5 and 6 (vent openings of 0.28 m 2 and m 2 respectively). Both figures show that the initial rate of pressure rise in the simulations is slightly lower than seen in the experiments. This explains the lower overpressures in the simulations. The reason for the lower overpressure in the simulations could be too low turbulence intensity in the simulations and the representation of the fuel.

8 Overpressure (bar) Experiments DESC, Fuel B, radiation Vent opening size (m 2 ) Figure 4: Maximum overpressure for vented explosions (200 g m -3 maize starch, vent near top of vessel, no dust layer onto cone). Comparison of experiments and the results of DESC simulations (Fuel B, radiation model activated). 0,2 0,15 Experiment 1 Experiment 2 DESC simulation Overpressure (bar) 0,1 0, ,2 0,4 0,6 0,8 1-0,05 Time (s) Figure 5: Comparison of two pressure-time histories measured for explosions of 200 g m -3 maize starch-air mixtures and a pressure-time history for a simulation using DESC

9 (Fuel B, radiation model activated). Vent of 0.28 m 2 in top of vessel, opening pressure of vent P stat = 0.1 bar, no dust layer onto the cone of the vessel Overpressure (bar) DESC simulation Experiment 1 Experiment 2 Experiment Time (s) Figure 6: Comparison of three pressure-time histories measured for explosions of 200 g m -3 maize starch-air mixtures and a pressure-time history for a simulation using DESC (Fuel B, radiation model activated). Vent of 0,126 m 2 in top of vessel, opening pressure of vent P stat = 0.1 bar. Simulations performed for scenarios with a dust layer onto the cone of the vessel were performed as well. The experiments were performed with 1.65 kg cellulose as a dust layer in the cone. In the simulations, this was represented as maize starch (1.5 kg). In the simulations a dust cloud with an average dust concentration of 250 g m -3 was used, whereas in the experiments the dust cloud had an average dust concentration of 200 g m -3. The simulations were performed for both vent opening positions (side wall and near top). The results have been summarized in Figure 7. The Figure clearly shows that the model does not reproduces the experimental findings. The main reason is the whirling up of dust from the cone walls upon injection of the dust from the two 5 l containers during the experiments. This causes the average dust concentration to be considerably higher, resulting in a considerably higher rate of pressure rise and thereby high vented explosion pressures. In the simulations this initial effect of whirling up of dust is limited since the dust is whirled up from the bottom of

10 the cone (as mentioned above the regular model for dust lifting in DESC proved unsatisfactory for skew walls, presumably since the rate of lifting is determined by empirical correlations obtained in experiments where flow or shock waves pass over a dust layer on a horizontal surface [1, 9]. A comparison of simulated and measured pressure-time histories clearly demonstrates this difference. The effect of vent opening position as described by the model is as expected. The side wall vent is close to there where the explosion occurs allowing for venting of combustion products (which is more effective than venting of unburned mixture), whereas through the vent near the top only air of unburned mixture is vented resulting in higher pressures. This effect is not seen in the experiments probably due to the non-reproducible nature of the experiments. Overpressure (bar) 1,2 1,0 0,8 0,6 0,4 DESC simulation, venting on side DESC simulation, venting on top Experiments, venting on side Experiments, venting on top 0,2 0,0 0 0,05 0,1 0,15 0,2 0,25 0,3 Vent opening size (m2) Figure 7: Venting of dust explosions with dust layers onto the cone of the vessel (1.65 kg cellulose in experiments, 1.5 kg maize starch in simulation). Effect of vent opening size and vent location. Comparison of DESC simulations (Fuel B, radiation model activated) and experiments from [3]. 6. Simulation of suppressed dust explosions Figure 8 shows the results from a few simulations of suppressed explosions, with either two 5 litre, or two 20 litre suppressors, with radiative heat losses. For comparison, the same

11 figure also includes the corresponding pressure-time histories from unsuppressed explosions, with and without dust layers. The simulated scenarios include explosions with 1.5 kg fuel injected (i.e. 250 g m -3 average dust concentration in the cone), and 1.2 kg fuel as a layer. The corresponding experiments concern an injected amount of maize starch of 1.2 kg (i.e. 200 g m -3 ), and 1.65 kg cellulose in the dust layers. 3 Overpressure (bar) 2 1 Injected + Layer Fuel model B Radiation 1.5 kg kg 2 x 5 litre HRD 1.5 kg kg 1.5 kg (no layer) 0 2 x 20 litre HRD 1.5 kg kg Time relative to ignition (s) Figure 8: Simulated pressure development for suppressed explosions in the vessel, with 1.5 kg fuel injected and 1.2 kg of fuel in the cone (Fuel B, radiation model active). Some pressure curves for scenarios without suppression are included for reference. There are several limitations to the implemented models for the suppressant in the present version of DESC. Dust clouds are represented as dense gases, i.e. gases with high molecular weight, and for a typical suppression scenario, the expansion of the compressed gas, typically from 60 bars initially, combined with the high volume fraction of solid (incompressible) material, results in unrealistically low temperatures in the release. This effect is evident from the simulation results for the two 20-litre suppressors shown in Figure 8: the simulated suppression system is overly effective for the two 20 litre suppressors, each injecting 16 kg of sodium bicarbonate, resulting in maximum pressures below 0.1 bar, and

12 final pressures below ambient. This is clearly an effect of the unphysical representation of the dust cloud as a dense gas, as described above. In addition to that the model describing the heat capacity of sodium bicarbonate does not take into account disintegration of sodium bicarbonate at higher temperatures. Figure 8 also shows that the suppression fails for the two 5 litre suppressors, each injecting 4 kg of suppressant. In fact, the simulated explosion pressure with suppression and dust layer exceeds the simulated pressure for scenarios without suppression and without the presence of a dust layer. Furthermore, the same trends apply to both types of fuel (A and B), with and without radiative heat loss from the combustion products. Although the average inert concentration in the cone, more than 1300 g m -3, would normally be more than sufficient to suppress the explosion, there are several reasons why this does not occur in the simulations: the relatively poor distribution of the suppressant and the somewhat delayed dispersion of the simulated dust layer (partly helped by the injection of suppressant). The experiments showed a variation in the reduced explosion overpressure between 0.18 and 0.41 bar, indicating that similar processes actually may have occurred. In the experiments without suppression, the explosion overpressures varied between 0.47 and 1.76 bar. 7. Conclusions The simulations demonstrate that a description of dust explosions (in closed vessels, vented vessels and vessel provided with explosion suppression) using a dedicated CFD-based tool such as DESC is fully possible. In the future, this type of modelling will allow engineers to explore the capabilities and limitations of explosion venting systems, explosion suppression systems and extinguishing barriers. CFD modelling can also help researchers to improve the general understanding of various mechanisms associates with the dust explosions phenomenon. In particular, the results presented here suggest that radiative heat losses may influence the overpressures in dust explosions considerably. The results also highlight some fundamental limitations associated with the current models in DESC. Some models should be reasonably straightforward to improve, such as a modification of the equation of state that takes into account the incompressible nature of the dust particles, or an improved expression for the specific heat of the suppressant (sodium bicarbonate) that accounts for thermal decomposition at elevated temperatures. However, other shortcomings may turn out to be more challenging to remedy, such as finding a model describing dust dispersion from accumulated dust layers from any surface.

13 8. References [1] Skjold, T., & Hansen, O. R.: The development of DESC - A dust explosion simulation code. International ESMG symposium, October 2005, Nuremberg, Germany [2] Skjold, T.: Review of the DESC project, Journal of Loss Prevention in the Process Industries, 20, , 2007 [3] Siwek, R., van Wingerden, K., Hansen, O.R., Sutter, G., Kubainsky, C., Schwartzbach, C., Giger, G., Meili, R.: Dust explosion venting and suppression of conventional spray dryers, Paper presented during the 11 th Int. Symp. On Loss Prevention and Safety Promotion in the Process Industries, Prague, 31 May- 3 June, 2004 [4] European Committee for Standardisation: Determination of explosion characteristics of dust clouds Part 1: Determination of the maximum explosion pressure pmax of dust clouds, Standard EN , 2004 [5] European Committee for Standardisation: Determination of explosion characteristics of dust clouds Part 2: Determination of the maximum rate of explosion pressure rise (dp/dt)max of dust clouds, Standard EN , 2006 [6] Arntzen, B.J.: Modelling of turbulence and combustion for simulation of gas explosions in complex geometries. Dr. Ing. Thesis, NTNU, Trondheim, Norway, 1998 [7] Bray, K.N.C.: Studies of the turbulent burning velocity. Proc. R. Soc. Lond. A, 431, , 1990 [8] Bradley, D., Chen, Z. & Swithenbank, J.R.: Burning rates in turbulent fine dust-air explosions. 22 nd Symp. (Int.) on Combustion, , 1988 [9] Zydak, P. & Klemens, R.: Modelling of dust lifting process behind propagating shock wave. Journal of Loss Prevention in the Process Industries, 20, , 2007 [10] Sand, I.Ø., & Storvik, I.E. (1998). Thermal radiation heat exchange between gas volume and black enclosure. Technical note, Christian Michelsen Research. [11] Hottel, H.C., & Egbert, R.B. (1942). Radiant heat transmission from water vapor, AIChE Transactions, 38: 531. [12] Van Wingerden, K. and Skjold, T. (2008). Simulation of explosion suppression systems and extinguishing barriers using the CFD code DESC. Paper accepted for presentation during the 42 nd Loss Prevention Symposium, April 6-10, New Orleans

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