- Email: [email protected]
CFD modelling of moisture evaporation in an industrial dispersed system
Advanced Powder Technology xxx (2016) xxx–xxx
Contents lists available at ScienceDirect
Advanced Powder Technology journal homepage: www.elsevier.com/locate/apt
Original Research Paper
CFD modelling of moisture evaporation in an industrial dispersed system Paweł Wawrzyniak, Maciej Jaskulski ⇑, Ireneusz Zbicin´ski, Marek Podyma Faculty of Process and Environmental Engineering, Lodz University of Technology, 213 Wolczanska Str, Lodz 90-924, Poland
a r t i c l e
i n f o
Article history: Received 27 June 2016 Received in revised form 12 September 2016 Accepted 23 September 2016 Available online xxxx Keywords: CFD modelling Spray drying Multiphase flow Discrete phase Agglomeration Dispersed system
a b s t r a c t A methodology is presented here for the mathematical modelling of moisture evaporation in a dispersed system in an industrial tower. An empirical model using characteristic drying curves was applied to calculate moisture evaporation from a droplet and particle. A simple method was developed to calculate the agglomeration process of the dispersed phase in the drying towers, using transient functions between the initial and final particle size distributions, at the nozzle and in the final product. The developed model and simulation results were validated on the basis of industrial spray tower experiments. High instability of the airflow due to the geometry of the dryer and the construction of the air inlets was observed. The general methodology applied within this CFD model is universal, and can be applied to the scaling-up of installations for dewatering in dispersed systems in order to determine configurations of feeding systems and control the product quality and safety of the process. Ó 2016 Published by Elsevier B.V. on behalf of The Society of Powder Technology Japan. All rights reserved.
1. Introduction Thermal drying is a basic unit operation in chemical engineering and has been used for centuries, for example in food preservation. However, due to high energy demand, this process has been constantly improved and optimized in terms of energy consumption and properties of the resulting product, and this has led to the development of a number of convective drying techniques which differ in terms of the contact between the dried material and air or the form in which the material is fed into the drying chamber [1]. One of the methods for producing dry loose material without prior grinding is spray drying. This method involves the spraying (atomization) of a solution, slurry or paste within a stream of hot drying medium, usually air. As a result of the rapid extension of the phase contact area, there is intense moisture evaporation in the atomization zone without a significant increase in the temperature of the dried material. For this reason, spray drying is widely used in many industries, from pharmaceutical to food and chemical applications, including the drying of heat-sensitive materials. On an industrial scale, the drying process can be carried out in three ways: co-current, counter-current and mixed. Most spray drying applications use a co-current flow of air and a discrete phase [2–4]; however, a counter-current spray drying process has found applications in the chemical, pharmaceutical and ⇑ Corresponding author.
ceramic industries due to the intensive heat, mass transfer and particle agglomeration resulting from turbulent airflow. Moreover, the input of drying energy required for evaporation is reduced in comparison with co-current spray drying. Due to the complex flow of air and dried particles, counter-current spray drying is one of the processes for which the knowledge of aspects such as the mechanism of heat, mass and momentum transport and parameters controlling the drying process and quality of interactions is still limited [3]. There are relatively few works in the literature which refer to modelling [5,6] and experimental analysis of spray dryers with a counter-current flow of the phases [e.g., 6–8]. During the last 30 years, an intensive growth has been observed in theoretical research into the mechanism of continuous and discrete phase flow in spray drying based on computational fluid dynamics (CFD) calculations [14,15]. Most of the CFD models found in the literature correctly predict the parameters of the continuous phase (e.g., distributions of the drying air temperature and humidity) but do not accurately predict the behaviour of the discrete phase [16]. The main reason for this failure of CFD models is that the real initial particle size distribution, mass flow rate of the disperse phase and real evaporation kinetics are not introduced into the calculations or are extensively simplified [3,9]. The process of industrial spray drying is dynamic [9]. The massive agglomeration which occurs in the nozzle area makes it difficult to predict the transition of the particle size distribution from the initial to the final state during the drying process. In counter-current spray drying, droplets and particles are subjected
E-mail address: [email protected] (M. Jaskulski). http://dx.doi.org/10.1016/j.apt.2016.09.029 0921-8831/Ó 2016 Published by Elsevier B.V. on behalf of The Society of Powder Technology Japan. All rights reserved.
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
2
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
Nomenclature A c CD Cp d DAB F g i kc m n p r T v w X Y
surface area, m2 heat capacity, kJ/(kg dry air, K) drag coefficient [–] heat capacity, kJ/(kg K) diameter, m diffusion coefficient of vapour in air, m2/s force, kg m/s2 gravitational acceleration, m/s2 enthalpy of wet air, kJ/kg dry air external mass-transfer coefficient, kg/(m2 s) mass, kg spread parameter [–] vapour pressure, pressure, Pa latent heat, kJ/kg temperature, K velocity, m/s drying rate, kg/s average moisture content, kg/kg air humidity, kg/kg dry air
to intensive mixing due to air recirculation, during which colliding particles form bigger agglomerates. The collision of two droplets may result in the creation of a new droplet of a spherical shape, in a process called coalescence. In spray drying, coalescence occurs inside the atomization zone. The collision of two droplets does not always result in coalescence, since after the collision the droplets may be fragmented into smaller parts. Alternatively, when the resultant impact velocity is not high enough to break surface tension forces, the droplets bounce against each other and coalescence does not occur. The second type of collision during spray drying takes place between dry particles and liquid droplets. This may occur when the fine fraction of the dry product is recycled into the atomization zone; the dry particle may be coated with a new layer of the solution and take a form similar to a spherical one. If two particles collide, they may merge into one agglomerate if there is enough moisture on the particle surface to form a ‘liquid bridge’. The parameter controlling the development of the liquid bridge is particle stickiness. The bridge solidifies due to further drying, and the new, larger particle formed in this way deviates significantly from a spherical shape. The stickiness of the particle surface depends on the particle surface temperature and moisture content, and the individual properties of the material being dried. The collision of particles in which the moisture content is too low to form a stable liquid bridge may lead to an agglomeration due to electrostatic or Van der Waals forces; however, such structures are not stable due to the weakness of binding. An uncontrolled change in particle size affects the kinetics of drying, the temperature distribution of the two phases, and the hydrodynamics of the flow in the drying tower. As a consequence, spray drying kinetics for this process cannot be reproduced at laboratory scale with acceptable accuracy [3]. A counter-current spray dryer offers high rates of mass transfer and reduction of energy input for evaporation, as a result of introducing hot air close to the product outlet. The aim of this work was to develop a reliable model of the spray drying process which would enable the prediction of hot air temperature and the optimal configuration of hot air inlets in order to control the product quality and safety of the process. An additional objective was to take into account the particle agglomeration process in the calculation of the couplings between phases using a simplified version which does not involve sophisticated calculations of particle collision
Pr Re Sc
Prandtl number Reynolds number Schmidt number
Greek letters a heat transfer coefficient W/(m2 K) / characteristic moisture content [–] d Rosin-Rammler mean diameter, m Subscripts a dry air cr critical eq equilibrium fin final g gas phase init initial p particle v vapour vs equilibrium vapour
probability and shape of the created agglomerates. The simulation was performed on the base of a real, full-scale industrial spray drying installation to validate the calculation results with measured data. 2. Discrete phase model description The heat and mass transfer between the discrete and continuous phases is calculated on the basis of transport equations. The basic equations of the model are set out below. 2.1. Drying kinetics The mass transfer during drying between the particle and the surrounding air is a complex process. The first step in developing the model of spray drying is the formulation of the mathematical description of the drying of a single particle. The spray drying model should take into account the moisture evaporation from a droplet and, following this, the process of particle drying with reference to internal diffusion resistance. In the literature, models of drying kinetics can be divided into three groups [16]: deterministic models, empirical models based on characteristic drying curves (CDC), and models using the reaction engineering approach (REA). Within the description of drying kinetics, deterministic models take into account the phenomena associated with changes in particle morphology, such as the formation and accumulation of dry surfaces. The determination of dry-layer thickness, which changes during drying, makes it possible to calculate the resistance within the moisture transport inside the particle. One of the deterministic models of drying kinetics and agglomeration was proposed by Mezhericher [16] who developed a spatially-resolved diffusion model of spray drying integrated with a CFD solver. Mezhericher also calculated the agglomeration process for various kinds of inter-particle collisions. This method provides precise information of the agglomeration mechanism; however, it requires moving boundary conditions, complex calculations of particle collision probability and detailed information about material properties, which are often trade secrets. Methods for two-stage modelling of drying, which are the most frequently used, are based on experimental determination of the drying kinetics of the material being tested. In the REA method, molar concentrations of a volatile component on the particle
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
3
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
surface are modified [19]. The molar concentration of a volatile component on the surface is a function of saturation concentration, and is expressed by an equation analogous to the description of chemical reaction kinetics. However, the parameters of the REA model (in most cases temperature-dependent) must be determined experimentally. The last group of droplet drying models is based on the concept of the characteristic drying curve [21]. In this work, the CDC concept was used to model mass transfer. It is assumed that for each average moisture content there exists a corresponding specific relative drying rate. The relative drying rate is defined as the ratio of the actual drying rate to the drying rate independent of the external drying conditions, during the first drying period [20].
f ¼
wV ^V w
ð1Þ
^ V is the drying rate in the where wV is the drying rate in kg/s, and w first drying period in kg/s. Normalized moisture content is defined as follows:
/¼
X X eq X cr X eq
ð2Þ
Thus, at the critical point between constant and falling drying rate period, f = 1 and / = 1, while at the equilibrium point, f = 0 and / = 0. In a simulated case, a solution of detergent components in water was used as a feed and spread inside the drying chamber. After moisture evaporation, the solidified particles create washing powder as the product of the spray drying process. The value of critical moisture content for dried detergent Xcr = 1.2615 kg/kg was determined experimentally [17]. As a result, a simple lumped-parameter expression for the drying rate is defined as
^ V ¼ f kc A ðpv s pv Þ wV ¼ f w
ð3Þ
Following Woo et al. [13], relative drying rate is described as a function of the characteristic moisture content.
f ¼
1
if / P 1
/ if / < 1
ð4Þ
If actual moisture content (X) is bigger than critical moisture content (Xcr) then the relative drying rate is equal to 1; otherwise, the relative drying rate is equal to the characteristic moisture content. 2.2. Mass transfer The stream of the evaporated component is calculated on the basis of a standard mass transfer equation:
dmp ¼ f kc Ap ðpv s pv Þ dt
ð5Þ
The coefficient of mass transfer (kc) between the particle and air is described by the Ranz-Marshall equation:
kc ¼
DAB ð2 þ 0:6 Re0:5 þ Sc0:33 Þ d
ð6Þ
2.3. Heat transfer Particle temperature is updated according to a heat balance that relates the sensible heat change in the particle to the convective heat transfer between the droplet and the continuous phase and heat used for evaporation:
mp C p
dT dmp ¼ a ðT g T p Þ þ r dt dt
ð7Þ
The heat transfer coefficient a is calculated using the RanzMarshall correlation:
a¼
k 2 þ 0:6 Re0:5 þ Pr 0:33 d
ð8Þ
2.4. Growth in particle diameter Robust agglomeration is observed in industrial spray towers, especially in the atomization zone, and this results in a significant increase in particle diameter. In the analysed case, the diameters of fractions grew from an initial 300 lm to a final 770 lm in the product. Agglomeration models which have been developed so far are based on complex, time-consuming and not fully validated calculations of particle collision frequency [18]. Additionally, conditions for particle-particle bonding depend mostly on the individual material properties (glass transition or stickiness) which are in many cases not available, for example due to industrial secrets. Here we present a simple method for calculating the agglomeration process of the discrete phase in the drying tower which can determine changes in particle size distribution (PSD) in spray drying due to agglomeration. The initial PSD, density of the slurry and final density of the product must be known. In the approach proposed here, particles are considered as spheres. The particle diameter of each fraction was determined from Eq. (9), where loss of particle mass mp is calculated from the evaporation model (Eqs. (1)–(8)) and particle density from Eq. (10).
dp;i ¼
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3 6 mp;i ; p qp;i
i 2 h1; ji
ð9Þ
where j is the number of particle fractions.
qp;i ¼ qfin þ
qinit qfin
ð10Þ
1 þ ða tÞn
where a and n are characteristic constants for a given spray drying process, qinit is the initial density of the slurry and qfin is the tapped density of the powder. It is assumed that the transition function describing the changes in particle density from the initial density of the slurry to the density of the product is as the logistic function shown in Fig. 1 and Eq. (10). The shape of the logistic function is governed by the time for agglomeration, which is estimated from preliminary CFD calculations. The residence time of particles within the spray drying tower was determined to be within the range 20–60 s. On the basis of CFD calculations, an average agglomeration time was set to 20 s, during which the initial PSD was changed into the final PSD of the product for each fraction in the tower. The constant a = 0.2 and exponent n = 4 were determined from CFD calculations of the particle trajectories. For the analysed detergent product, the initial density was set to qinit = 1550 kg/m3, and the final density qfin = 550 kg/m3. The transition from initial to final PSD is shown in Fig. 2. Cumulative PSD was determined on the basis of the volumetric contributions of particular fractions. 2.5. Particle trajectory calculations Changes in the particles’ positions are calculated on the basis of the balance of forces:
mp
dv p ¼ F drag þ F pres þ F v ir dt
mass
þ F grav ity
ð11Þ
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
4
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
Fig. 1. Changes in particle density inside the drying chamber.
3. Measurements Experiments were carried out within an industrial drying tower, the details of which are described in previous work [9,10]. 3.1. Description of the drying tower
Fig. 2. Transition from initial to final PSD.
where Fdrag is the hydrostatic resistance force, calculated from the relationship:
F drag ¼
qg C D 3 mp kv v p k 4 qp dp g
ð12Þ
Fpres is the force resulting from the difference in pressure behind and in front of the particle the particle:
F pres ¼
mp
qp
rp
ð13Þ
Fvir_mass is the so-called virtual mass force, needed to move the fluid in front of the particle:
F v ir
mass
¼
mp qg dðv g v p Þ dt 2 qp
ð14Þ
Fgravity is the force due to gravity:
F grav ity ¼ mp
qp qg ~ g qp
ð15Þ
The particle may also be affected by other forces, such as Saffman’s lift force, Brownian motion, the Magnus effect, or electromagnetic forces.
A schematic diagram of the drying tower is shown in Fig. 3. The total tower height is 37 m, and the inner diameter of the cylindrical part is 6 m. At the top of the tower there is a system of bag filters which purify the outlet air. This filter consists of 720 bags, 2.6 m long and 0.15 m in diameter. The height of the drying column is 33 m. The walls of the tower are insulated with a layer of mineral wool of thickness between 0.12 and 0.14 m. Slurry is sprayed by two systems of nozzles located on two levels. Twelve nozzles are located at a height of 18 m, and 10 nozzles at 10 m from the hot air inlets. During the process, 12 nozzles were used, that is, 6 at each level. The spray angle of the nozzles (SH118) was 65°. The nozzles were directed towards the axis of the dryer and inclined at an angle of 55°. At the higher level, the nozzles were mounted on shorter lances of length 650 mm, and at the lower level the lances were of length 1370 mm. Drying air is heated in a gas burner and supplied to the drying chamber through a distribution ring with rectangular cross section. Air is supplied to the ring through a tangential connection duct of circular cross section and diameter 1300 mm. Hot air from the distribution ring flows to the drying tower through 16 connection ducts perpendicular to the tower wall, and air flows to the drying chamber at an angle 45°. The inlets of the ducts are divided into three parts. In five connection ducts, the cross-sectional area was reduced locally to 2/3. Tangential supply of air and differences in cross-sectional areas of the connection ducts cause an uneven distribution in air supply to the drying tower. 3.2. Particle-air separation One of the most difficult parameters which must be measured during spray drying is the temperature of the drying air inside the spray chamber. Measurements of air temperature are hampered by particles hitting the probe, which makes correct determination of the air temperature difficult. A novel construction of shielded temperature sensors (K-type internal thermocouple) in order to carry out dynamic temperature measurements on an industrial scale is proposed in the frame of
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
5
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
Fig. 3. Schematic diagram of the drying tower and air distribution ring.
Fig. 4. Schematic diagram of microseparator used in measurements within industrial spray drying installation.
this work, as a development of an idea proposed by Kievet and Kerkhof [11,12]. The microseparator device is presented in Fig. 4, and consisted of two 4 m long tubes; that is, an inner tube 12 mm in diameter, and an outer tube 40 mm in diameter and of the same
length. A further temperature sensor (external) was added, in order to monitor the temperature of the air entering the outer tube. The airflow in both tubes was controlled by two needle valves and rotameters. During these measurements, the air in the tubes was maintained at a similar velocity to that in the air stream, in order to avoid blocking of the tubes by powder particles. To reduce the heat capacity of the device, and to shorten the temperature readout the temperature readout, the element separating the particles from the air and mounted on the microseparator tip was made from Teflon. 3.3. Velocity measurements Measurements of air velocity were performed in selected areas of the tower to determine the flow pattern within the dryer and to
Table 1 Comparison of velocity measurements inside connection ducts and CFD simulation results. No. of air duct
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Air velocities at 35 °C m/s
Air velocities at 50 °C m/s
Mass flow at 259 °C kg/s
Measurements
CFD
Measurements
CFD
– 5.8 – – 4.9 – – – 9.3 – – 9.5 – – 13.5 –
4.91 5.88 5.72 6.86 5.11 6.30 6.86 7.22 9.75 9.79 11.75 13.70 13.73 10.70 13.76 14.68
– 7.0 – – 7.0 – – – 9.5 – – 12 – – 18.5 –
8.82 7.58 7.32 6.34 7.50 8.82 8.15 7.26 8.82 8.25 11.65 12.54 13.78 16.26 18.20 12.54
0.9525 0.7649 0.7939 0.8178 1.2981 1.3772 1.4253 1.4550 1.5261 1.5829 1.6340 1.6840 1.7417 1.7851 1.8200 1.2439 Total mass flow 21.9 kg/s
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
6
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
Fig. 5. Measured and calculated time-averaged radial air temperature profiles at various tower levels (see Fig. 3).
collect the data necessary to validate the results of the 3D CFD modelling. Local air velocity profiles along the radius inside the tower, together with approximate direction of the flow, were determined at two levels. Air velocity was measured 1.4 m below and 7.0 m above the hot air inlet (levels 1 and 3 respectively). At each level, velocity profiles along the radius were determined twice, to check the correctness of the measurements. Air velocity was measured with a thermoanemometer type AP471, manufactured by DeltaOhm. In order to estimate the distribution of the air flow rate at each inlet, air velocity was measured with a thermoanemometer placed in the centre of the connection duct. Due to the low thermal resistance of the thermoanemometer, the measurement could be taken at a temperature below 100 °C, i.e., much lower than during the drying tower operation. Measurements were taken for two
temperatures: 50 °C and 35 °C. Results were then rescaled, using similarity conditions, to air flow at 250 °C. 4. CFD simulations of hot air flow in the distribution ring Next, CFD simulations were carried out of isothermal airflow through the distribution ring for the tested temperatures. The model developed here allowed the velocity distribution of air supplied to the tower during the drying process to be determined. The velocities obtained inside the connection ducts for air temperatures 50 °C and 35 °C showed good agreement with the experimentally measured values (Table 1), confirming the correctness of the model. The mass flow rates inside the connection ducts obtained in the simulation for an air temperature of 259 °C were used as boundary
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
7
Fig. 6. Presentation of air temperature distribution for selected time steps obtained from CFD simulation.
Fig. 7. Measured and CFD-calculated air temperature and humidity profiles in the drying tower.
conditions at the inlet to the drying tower in subsequent simulations of the drying process. 5. CFD calculation of the spray tower In the first step of development of the CFD model, mesh densities were tested to obtain an accurate and grid-independent solution. On the basis of values taken from technical drawings, a 3D model of the drying tower was built. In accordance with this technical information, the cross sections of 5 of the 16 hot air inlets were reduced to 2/3. A series of numerical meshes were tested which differed in type and number of elements. The first meshes tested were orthogonal, and were composed of 350 k and 686 k elements. However, these meshes did not allow a convergent result for the calculations to be obtained. The next mesh tested was a structural mesh composed of 528 k elements. Due to flow instability within the column and large recirculation of airflow, the required accuracy for the calculations also could not be obtained.
It was therefore decided to use a non-structural mesh, to ensure better results in the case of such indefinite airflow. Finally, a mesh consisting of 745 k elements was used, concentrated in the feed zone of the dryer. Initial and boundary conditions for the CFD calculations were determined during inventory analysis of the industrial spray drying tower, and were presented in earlier work [9,10]. Initial mass flow rates for the drying air within each hot air inlet were taken from CFD simulations of airflow inside the distribution ring at a temperature of 259 °C (Table 1). The upper air outlet was defined as a free outflow (the pressure outlet condition). The lower outlet of particles was defined as a wall constituting a barrier for the air but permeable to DPM (Discrete Phase Modelling) particles. The mass flow rate of slurry was 3.238 kg/s and initial moisture content was equal to 44.46 wt%. The initial droplet distribution ranged from 20 lm to 600 lm. To determine initial atomization conditions, the RosinRammler distribution was used, and a mean diameter d = 285 lm and spread parameter n = 3.783 were calculated. The initial velocity of the particles was 50 m/s. Heat losses to the environment were calculated using the free convection model. The coefficients of heat transfer from the walls to surrounding air were determined on the basis of the heat balance of the tower. For insulated walls, these were 3.5 W/m2 K; for the cone chute, 4.75 W/m2 K. It was assumed that the wall in direct contact with the distribution ring was adiabatic. It was found that due to the construction of air inlets and despite constant boundary conditions, flow instability was so high that the only way to obtain an accurate solution was to run the calculations in unsteady state conditions with a time step of 0.1 s. Tests of the turbulence models showed that in such a dynamic flow, better solutions were obtained using the Reynolds stress model (RSM). All results presented here were obtained using the Fluent 13 software package from ANSYS. The single-phase Navier-Stokes equations with the RSM turbulence model were solved to determine the flow field, and the Euler-Lagrange approach was used to model continuous and discreet phase interaction.
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
8
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
Fig. 8. Measured (above) and calculated (below) airflow patterns in the spray drying tower (m/s).
6. Results and discussion 6.1. Air temperature Fig. 5 demonstrates a good match between the calculated and experimental temperature profiles at various tower levels. The experimental results shown in Fig. 5 were obtained along the column radius only, from wall to dryer axis, and therefore are presented as calculated time-averaged temperature profiles. Fig. 6 displays the calculated scheme of air temperature patterns in the dryer. Results are shown in cross sections of the spray tower at consecutive time steps, to illustrate the dynamic changes in air and heat flow. Analysis of the figure shows that there is no uniform air temperature distribution in any cross-sectional area of the dryer which is a result of highly unstable operation of the tower. Both hotter and cooler areas can be found above the hot air inlets, which is a result of air flow instability. The highest temperatures occur in the conical section of the dryer. Fig. 7 presents the experimental (markers) and calculated timeaveraged (line) air temperature profiles along the tower height at three radial locations: the axis, 1 m from the axis and 2.5 m from the axis. The temperature of the drying air between the first and second levels decreases from 200 °C to 120 °C. 6.2. Estimation of air humidity As heat losses in the drying tower are low, it was possible to calculate the theoretical humidity of the air at each level and provide information at most locations of the drying process. The enthalpy of humid air can be described as follows:
i ¼ ca T g þ Yðr þ cv T g Þ
ð16Þ
Assuming that total air enthalpy (i) is constant in the tower (low heat losses) and that the apparent heat of air is transferred to latent heat, it is possible to find the air humidity from Eq. (16). Assuming the initial air humidity to be 5 g/kg dry air, and assuming a constant heat capacity of air and vapour, the average gas humidity at the different levels can be calculated. This approach has been described in earlier work [10]. Experimentally-determined air humidity inside the tower plotted against tower height (Fig. 7) shows a perfect match with the air humidity calculated in the CFD simulations. Analysis of the figure shows also good agreement between the experimentally-determined temperature and the profile calculated using CFD. 6.3. CFD calculations of air velocity in the tower The 3D CFD calculations show high instability of airflow in the dryer (lack of axial symmetry and oscillations of the flow field) due to the specific construction of the air distribution ring. Nonuniform airflow rate at the inlet, and the specific construction of air inlets, are responsible for the high flow instability in the industrial spray dryer. Verification of the CFD calculations was based on air velocity measurements in the tower. We compared theoretical and experimental airflow velocity 1.4 m below the hot air inlet. Fig. 8 shows the experimentally-determined average velocity profiles, obtained over about 1 h of measurement, and the calculated instantaneous air velocity profiles at selected times within the process. A comparison of the results shows good approximation of the absolute values of air velocity as well as flow directions. Considering the high instability of airflow in the tower, agreement is excellent both
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
9
Fig. 9. Particle flow trajectories after 100 s, injected from upper nozzle level (left) and lower nozzle level (right), coloured by diameter (A) and velocity magnitude (B).
for velocity values and flow directions, confirming the quality of the developed CFD model. 6.4. Particle diameter Fig. 9A shows the calculated trajectories of selected particles injected at the upper and lower nozzle levels, and their diameter change. A strong segregation of particles can be observed in the cylindrical part of the tower within the injection area; the smallest fractions (20–200 lm) are immediately dragged up, while the biggest fractions fall down, driven by downward airflow near the dryer wall. In the conical part of the dryer (air inlet area) small (20–400 lm) and medium (400–700 lm) particles move upward, following the robust air axis flow. However, the inertia of the large particles allows them to resist the upward axis airflow and fall down onto the conical wall. Upward flow near the wall in the conical section shifts the particles up, and promotes the build-up of the wall deposit. Similar conclusions can be drawn from the analysis of particle velocities in the column (Fig. 9B). The smallest velocities of the particles are observed near the wall of the conical section of the dryer. The upward flow of drying air drags the particles up along the cone wall. The bouncing shape of the particle trajectories near the wall indicates increased wall deposition in this part of the dryer. 7. Conclusions A robust CFD model is developed of continuous and dispersed phase flow, taking into account the agglomeration process within industrial towers. The CFD calculations and experimental measurements confirm the high instability of the airflow (no axial symmetry, oscillations of the flow field) due to the geometry of the dryer and the construction of the air inlet to the tower. No uniform air temperature distribution was found within any cross-sectional area of the dryer; this is a result of the highly unstable operation of the drying tower.
A strong segregation of particles was observed in the atomization zone; small fractions are immediately dragged up, while the largest fractions fall down, driven by downward airflow near the wall. In the conical section of the tower, the inertia of large particles allows them to fall down onto the conical wall and leave the tower. A straightforward method for determining agglomeration in the spray drying tower is developed here, based on transition functions, which allows for the correct prediction of particle flow patterns and product PSD. References [1] I.C. Kemp, Drying in the context of the overall process, Drying Technol. 22 (1– 2) (2004) 377–394. [2] D.F. Fletcher, B. Guo, D.J.E. Harvie, T.A.G. Langrish, J.J. Nijdam, J. Williams, What is important in the simulation of spray dryer performance and how do current CFD models perform?, Appl Math. Model. 30 (11) (2006) 1281–1292. [3] L. Huang, K. Kumar, A.S. Mujumdar, A parametric study of the gas flow patterns and drying performance of co-current spray dryer: results of a computational fluid dynamics study, Drying Technol. 21 (6) (2003) 957–978. [4] D.E. Oakley, Spray dryer modeling in theory and practice, Drying Technol. 22 (6) (2004) 1371–1402. [5] D.J.E. Harvie, T.A.G. Langrish, D.F. Fletcher, Numerical simulations of gas flow patterns within a tall-form spray dryer, Transact. Inst. Chem. Eng. 79 (A) (2001) 235–248. [6] A.E. Bayly, P. Jukes, M. Groombridge, C. McNally, Airflow patterns in a countercurrent spray drying tower simulation and measurement, in: Proceedings of the 14th International Drying Symposium, Sao Paulo, Brazil, August 22–25, vol. B, 2004, pp. 775–778. [7] W. Rahse, O. Dicoi, Spray Drying in Detergent Industry, in: Proceedings of Spray Drying’01 Conference, Dortmund, Germany, October 8–10, 2001, pp. 83– 87. [8] M. Piatkowski, I. Zbicinski, Analysis of the mechanism of counter-current spray drying, Transp. Porous Media 66 (1–2) (2007) 89–101. [9] P. Wawrzyniak, M. Podyma, I. Zbicinski, Z. Bartczak, J. Rabaeva, Modeling of air flow in an industrial countercurrent spray-drying tower, Drying Technol. 30 (2) (2012) 217–224. [10] P. Wawrzyniak, M. Podyma, I. Zbicinski, Z. Bartczak, A. Polanczyk, Rabaeva, Model of heat and mass transfer in an industrial counter-current spray-drying tower, Drying Technol. 30 (11–12) (2012) 1274–1282. [11] P. Wawrzyniak, I. Zbicinski, Z. Bartczak, Rabaeva, Advanced experimental analysis of industrial counter current spray drying process, in: Proceedings of the 18th International Drying Symposium, Xiamen, China, vol. B, November 11–15, 2012, pp. 118–125. [12] F.G. Kievet, J.A.M. Kerkhof, Using Computational Fluid Dynamics to Model Product Quality in Spray Drying: Air Flow, Temperature and Humidity
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
10
[13]
[14]
[15]
[16]
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx Patterns, in: Proceedings of the 10th International Drying Symposium, Krakow, Poland, July 30-August 2, 1996, vol. A, pp. 259–266. M.W. Woo, W.R.W. Daud, A.S. Mujumdar, S.M. Tasirin, M.Z.M. Talib, Role of rheological characteristics in amorphous food particle-wall collisions in spray drying, Powder Technol. 198 (2010) 251–257. P. Wawrzyniak, M. Podyma, I. Zbicinski, Z. Bartczak, J. Rabaeva, Modeling of air flow in an industrial countercurrent spray-drying tower, J.: Drying Technol. 30 (2) (2012) 217–224. M. Mezhericher, A. Levy, I. Borde, Theoretical drying model of single droplets containing insoluble or dissolved solids, Drying Technol. 25 (2007) 1025– 1032. M. Mezhericher, Theoretical Modelling of Spray Drying Process Volume 1. Drying Kinetics, Two and Three Dimensional CFD Modelling, vol. Volume 1, LAP Lambert Academic Publishing GmbH & Co. KG, Saarbrucken, 2011.
[17] Y. Rabaeva, Kinetics of Foam-spray Drying Process PhD Thesis, University of Technology, Lodz, 2012. [18] M. Jaskulski, P. Wawrzyniak, I. Zbicin´ski, CFD model of particle agglomeration in spray drying, Drying Technol. 33 (15–16) (2015) 1971–1980. [19] X.D. Chen, W. Pirini, M. Ozilgen, The reaction engineering approach to modelling drying of thin layer of pulped Kiwifruit flesh under conditions of small Biot numbers, Chem. Eng. Process. 40 (2001) 311–320. [20] R.B. Keey, M. Suzuki, On the characteristic drying curve, Int. J. Heat Mass Transf. 17 (1974) 1455–1464. [21] D.A. Van Meel, Adiabatic convection batch drying with recirculation of air, Chem. Eng. Sci. 9 (1958) 36–44.
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
Contents lists available at ScienceDirect
Advanced Powder Technology journal homepage: www.elsevier.com/locate/apt
Original Research Paper
CFD modelling of moisture evaporation in an industrial dispersed system Paweł Wawrzyniak, Maciej Jaskulski ⇑, Ireneusz Zbicin´ski, Marek Podyma Faculty of Process and Environmental Engineering, Lodz University of Technology, 213 Wolczanska Str, Lodz 90-924, Poland
a r t i c l e
i n f o
Article history: Received 27 June 2016 Received in revised form 12 September 2016 Accepted 23 September 2016 Available online xxxx Keywords: CFD modelling Spray drying Multiphase flow Discrete phase Agglomeration Dispersed system
a b s t r a c t A methodology is presented here for the mathematical modelling of moisture evaporation in a dispersed system in an industrial tower. An empirical model using characteristic drying curves was applied to calculate moisture evaporation from a droplet and particle. A simple method was developed to calculate the agglomeration process of the dispersed phase in the drying towers, using transient functions between the initial and final particle size distributions, at the nozzle and in the final product. The developed model and simulation results were validated on the basis of industrial spray tower experiments. High instability of the airflow due to the geometry of the dryer and the construction of the air inlets was observed. The general methodology applied within this CFD model is universal, and can be applied to the scaling-up of installations for dewatering in dispersed systems in order to determine configurations of feeding systems and control the product quality and safety of the process. Ó 2016 Published by Elsevier B.V. on behalf of The Society of Powder Technology Japan. All rights reserved.
1. Introduction Thermal drying is a basic unit operation in chemical engineering and has been used for centuries, for example in food preservation. However, due to high energy demand, this process has been constantly improved and optimized in terms of energy consumption and properties of the resulting product, and this has led to the development of a number of convective drying techniques which differ in terms of the contact between the dried material and air or the form in which the material is fed into the drying chamber [1]. One of the methods for producing dry loose material without prior grinding is spray drying. This method involves the spraying (atomization) of a solution, slurry or paste within a stream of hot drying medium, usually air. As a result of the rapid extension of the phase contact area, there is intense moisture evaporation in the atomization zone without a significant increase in the temperature of the dried material. For this reason, spray drying is widely used in many industries, from pharmaceutical to food and chemical applications, including the drying of heat-sensitive materials. On an industrial scale, the drying process can be carried out in three ways: co-current, counter-current and mixed. Most spray drying applications use a co-current flow of air and a discrete phase [2–4]; however, a counter-current spray drying process has found applications in the chemical, pharmaceutical and ⇑ Corresponding author.
ceramic industries due to the intensive heat, mass transfer and particle agglomeration resulting from turbulent airflow. Moreover, the input of drying energy required for evaporation is reduced in comparison with co-current spray drying. Due to the complex flow of air and dried particles, counter-current spray drying is one of the processes for which the knowledge of aspects such as the mechanism of heat, mass and momentum transport and parameters controlling the drying process and quality of interactions is still limited [3]. There are relatively few works in the literature which refer to modelling [5,6] and experimental analysis of spray dryers with a counter-current flow of the phases [e.g., 6–8]. During the last 30 years, an intensive growth has been observed in theoretical research into the mechanism of continuous and discrete phase flow in spray drying based on computational fluid dynamics (CFD) calculations [14,15]. Most of the CFD models found in the literature correctly predict the parameters of the continuous phase (e.g., distributions of the drying air temperature and humidity) but do not accurately predict the behaviour of the discrete phase [16]. The main reason for this failure of CFD models is that the real initial particle size distribution, mass flow rate of the disperse phase and real evaporation kinetics are not introduced into the calculations or are extensively simplified [3,9]. The process of industrial spray drying is dynamic [9]. The massive agglomeration which occurs in the nozzle area makes it difficult to predict the transition of the particle size distribution from the initial to the final state during the drying process. In counter-current spray drying, droplets and particles are subjected
E-mail address: [email protected] (M. Jaskulski). http://dx.doi.org/10.1016/j.apt.2016.09.029 0921-8831/Ó 2016 Published by Elsevier B.V. on behalf of The Society of Powder Technology Japan. All rights reserved.
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
2
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
Nomenclature A c CD Cp d DAB F g i kc m n p r T v w X Y
surface area, m2 heat capacity, kJ/(kg dry air, K) drag coefficient [–] heat capacity, kJ/(kg K) diameter, m diffusion coefficient of vapour in air, m2/s force, kg m/s2 gravitational acceleration, m/s2 enthalpy of wet air, kJ/kg dry air external mass-transfer coefficient, kg/(m2 s) mass, kg spread parameter [–] vapour pressure, pressure, Pa latent heat, kJ/kg temperature, K velocity, m/s drying rate, kg/s average moisture content, kg/kg air humidity, kg/kg dry air
to intensive mixing due to air recirculation, during which colliding particles form bigger agglomerates. The collision of two droplets may result in the creation of a new droplet of a spherical shape, in a process called coalescence. In spray drying, coalescence occurs inside the atomization zone. The collision of two droplets does not always result in coalescence, since after the collision the droplets may be fragmented into smaller parts. Alternatively, when the resultant impact velocity is not high enough to break surface tension forces, the droplets bounce against each other and coalescence does not occur. The second type of collision during spray drying takes place between dry particles and liquid droplets. This may occur when the fine fraction of the dry product is recycled into the atomization zone; the dry particle may be coated with a new layer of the solution and take a form similar to a spherical one. If two particles collide, they may merge into one agglomerate if there is enough moisture on the particle surface to form a ‘liquid bridge’. The parameter controlling the development of the liquid bridge is particle stickiness. The bridge solidifies due to further drying, and the new, larger particle formed in this way deviates significantly from a spherical shape. The stickiness of the particle surface depends on the particle surface temperature and moisture content, and the individual properties of the material being dried. The collision of particles in which the moisture content is too low to form a stable liquid bridge may lead to an agglomeration due to electrostatic or Van der Waals forces; however, such structures are not stable due to the weakness of binding. An uncontrolled change in particle size affects the kinetics of drying, the temperature distribution of the two phases, and the hydrodynamics of the flow in the drying tower. As a consequence, spray drying kinetics for this process cannot be reproduced at laboratory scale with acceptable accuracy [3]. A counter-current spray dryer offers high rates of mass transfer and reduction of energy input for evaporation, as a result of introducing hot air close to the product outlet. The aim of this work was to develop a reliable model of the spray drying process which would enable the prediction of hot air temperature and the optimal configuration of hot air inlets in order to control the product quality and safety of the process. An additional objective was to take into account the particle agglomeration process in the calculation of the couplings between phases using a simplified version which does not involve sophisticated calculations of particle collision
Pr Re Sc
Prandtl number Reynolds number Schmidt number
Greek letters a heat transfer coefficient W/(m2 K) / characteristic moisture content [–] d Rosin-Rammler mean diameter, m Subscripts a dry air cr critical eq equilibrium fin final g gas phase init initial p particle v vapour vs equilibrium vapour
probability and shape of the created agglomerates. The simulation was performed on the base of a real, full-scale industrial spray drying installation to validate the calculation results with measured data. 2. Discrete phase model description The heat and mass transfer between the discrete and continuous phases is calculated on the basis of transport equations. The basic equations of the model are set out below. 2.1. Drying kinetics The mass transfer during drying between the particle and the surrounding air is a complex process. The first step in developing the model of spray drying is the formulation of the mathematical description of the drying of a single particle. The spray drying model should take into account the moisture evaporation from a droplet and, following this, the process of particle drying with reference to internal diffusion resistance. In the literature, models of drying kinetics can be divided into three groups [16]: deterministic models, empirical models based on characteristic drying curves (CDC), and models using the reaction engineering approach (REA). Within the description of drying kinetics, deterministic models take into account the phenomena associated with changes in particle morphology, such as the formation and accumulation of dry surfaces. The determination of dry-layer thickness, which changes during drying, makes it possible to calculate the resistance within the moisture transport inside the particle. One of the deterministic models of drying kinetics and agglomeration was proposed by Mezhericher [16] who developed a spatially-resolved diffusion model of spray drying integrated with a CFD solver. Mezhericher also calculated the agglomeration process for various kinds of inter-particle collisions. This method provides precise information of the agglomeration mechanism; however, it requires moving boundary conditions, complex calculations of particle collision probability and detailed information about material properties, which are often trade secrets. Methods for two-stage modelling of drying, which are the most frequently used, are based on experimental determination of the drying kinetics of the material being tested. In the REA method, molar concentrations of a volatile component on the particle
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
3
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
surface are modified [19]. The molar concentration of a volatile component on the surface is a function of saturation concentration, and is expressed by an equation analogous to the description of chemical reaction kinetics. However, the parameters of the REA model (in most cases temperature-dependent) must be determined experimentally. The last group of droplet drying models is based on the concept of the characteristic drying curve [21]. In this work, the CDC concept was used to model mass transfer. It is assumed that for each average moisture content there exists a corresponding specific relative drying rate. The relative drying rate is defined as the ratio of the actual drying rate to the drying rate independent of the external drying conditions, during the first drying period [20].
f ¼
wV ^V w
ð1Þ
^ V is the drying rate in the where wV is the drying rate in kg/s, and w first drying period in kg/s. Normalized moisture content is defined as follows:
/¼
X X eq X cr X eq
ð2Þ
Thus, at the critical point between constant and falling drying rate period, f = 1 and / = 1, while at the equilibrium point, f = 0 and / = 0. In a simulated case, a solution of detergent components in water was used as a feed and spread inside the drying chamber. After moisture evaporation, the solidified particles create washing powder as the product of the spray drying process. The value of critical moisture content for dried detergent Xcr = 1.2615 kg/kg was determined experimentally [17]. As a result, a simple lumped-parameter expression for the drying rate is defined as
^ V ¼ f kc A ðpv s pv Þ wV ¼ f w
ð3Þ
Following Woo et al. [13], relative drying rate is described as a function of the characteristic moisture content.
f ¼
1
if / P 1
/ if / < 1
ð4Þ
If actual moisture content (X) is bigger than critical moisture content (Xcr) then the relative drying rate is equal to 1; otherwise, the relative drying rate is equal to the characteristic moisture content. 2.2. Mass transfer The stream of the evaporated component is calculated on the basis of a standard mass transfer equation:
dmp ¼ f kc Ap ðpv s pv Þ dt
ð5Þ
The coefficient of mass transfer (kc) between the particle and air is described by the Ranz-Marshall equation:
kc ¼
DAB ð2 þ 0:6 Re0:5 þ Sc0:33 Þ d
ð6Þ
2.3. Heat transfer Particle temperature is updated according to a heat balance that relates the sensible heat change in the particle to the convective heat transfer between the droplet and the continuous phase and heat used for evaporation:
mp C p
dT dmp ¼ a ðT g T p Þ þ r dt dt
ð7Þ
The heat transfer coefficient a is calculated using the RanzMarshall correlation:
a¼
k 2 þ 0:6 Re0:5 þ Pr 0:33 d
ð8Þ
2.4. Growth in particle diameter Robust agglomeration is observed in industrial spray towers, especially in the atomization zone, and this results in a significant increase in particle diameter. In the analysed case, the diameters of fractions grew from an initial 300 lm to a final 770 lm in the product. Agglomeration models which have been developed so far are based on complex, time-consuming and not fully validated calculations of particle collision frequency [18]. Additionally, conditions for particle-particle bonding depend mostly on the individual material properties (glass transition or stickiness) which are in many cases not available, for example due to industrial secrets. Here we present a simple method for calculating the agglomeration process of the discrete phase in the drying tower which can determine changes in particle size distribution (PSD) in spray drying due to agglomeration. The initial PSD, density of the slurry and final density of the product must be known. In the approach proposed here, particles are considered as spheres. The particle diameter of each fraction was determined from Eq. (9), where loss of particle mass mp is calculated from the evaporation model (Eqs. (1)–(8)) and particle density from Eq. (10).
dp;i ¼
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3 6 mp;i ; p qp;i
i 2 h1; ji
ð9Þ
where j is the number of particle fractions.
qp;i ¼ qfin þ
qinit qfin
ð10Þ
1 þ ða tÞn
where a and n are characteristic constants for a given spray drying process, qinit is the initial density of the slurry and qfin is the tapped density of the powder. It is assumed that the transition function describing the changes in particle density from the initial density of the slurry to the density of the product is as the logistic function shown in Fig. 1 and Eq. (10). The shape of the logistic function is governed by the time for agglomeration, which is estimated from preliminary CFD calculations. The residence time of particles within the spray drying tower was determined to be within the range 20–60 s. On the basis of CFD calculations, an average agglomeration time was set to 20 s, during which the initial PSD was changed into the final PSD of the product for each fraction in the tower. The constant a = 0.2 and exponent n = 4 were determined from CFD calculations of the particle trajectories. For the analysed detergent product, the initial density was set to qinit = 1550 kg/m3, and the final density qfin = 550 kg/m3. The transition from initial to final PSD is shown in Fig. 2. Cumulative PSD was determined on the basis of the volumetric contributions of particular fractions. 2.5. Particle trajectory calculations Changes in the particles’ positions are calculated on the basis of the balance of forces:
mp
dv p ¼ F drag þ F pres þ F v ir dt
mass
þ F grav ity
ð11Þ
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
4
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
Fig. 1. Changes in particle density inside the drying chamber.
3. Measurements Experiments were carried out within an industrial drying tower, the details of which are described in previous work [9,10]. 3.1. Description of the drying tower
Fig. 2. Transition from initial to final PSD.
where Fdrag is the hydrostatic resistance force, calculated from the relationship:
F drag ¼
qg C D 3 mp kv v p k 4 qp dp g
ð12Þ
Fpres is the force resulting from the difference in pressure behind and in front of the particle the particle:
F pres ¼
mp
qp
rp
ð13Þ
Fvir_mass is the so-called virtual mass force, needed to move the fluid in front of the particle:
F v ir
mass
¼
mp qg dðv g v p Þ dt 2 qp
ð14Þ
Fgravity is the force due to gravity:
F grav ity ¼ mp
qp qg ~ g qp
ð15Þ
The particle may also be affected by other forces, such as Saffman’s lift force, Brownian motion, the Magnus effect, or electromagnetic forces.
A schematic diagram of the drying tower is shown in Fig. 3. The total tower height is 37 m, and the inner diameter of the cylindrical part is 6 m. At the top of the tower there is a system of bag filters which purify the outlet air. This filter consists of 720 bags, 2.6 m long and 0.15 m in diameter. The height of the drying column is 33 m. The walls of the tower are insulated with a layer of mineral wool of thickness between 0.12 and 0.14 m. Slurry is sprayed by two systems of nozzles located on two levels. Twelve nozzles are located at a height of 18 m, and 10 nozzles at 10 m from the hot air inlets. During the process, 12 nozzles were used, that is, 6 at each level. The spray angle of the nozzles (SH118) was 65°. The nozzles were directed towards the axis of the dryer and inclined at an angle of 55°. At the higher level, the nozzles were mounted on shorter lances of length 650 mm, and at the lower level the lances were of length 1370 mm. Drying air is heated in a gas burner and supplied to the drying chamber through a distribution ring with rectangular cross section. Air is supplied to the ring through a tangential connection duct of circular cross section and diameter 1300 mm. Hot air from the distribution ring flows to the drying tower through 16 connection ducts perpendicular to the tower wall, and air flows to the drying chamber at an angle 45°. The inlets of the ducts are divided into three parts. In five connection ducts, the cross-sectional area was reduced locally to 2/3. Tangential supply of air and differences in cross-sectional areas of the connection ducts cause an uneven distribution in air supply to the drying tower. 3.2. Particle-air separation One of the most difficult parameters which must be measured during spray drying is the temperature of the drying air inside the spray chamber. Measurements of air temperature are hampered by particles hitting the probe, which makes correct determination of the air temperature difficult. A novel construction of shielded temperature sensors (K-type internal thermocouple) in order to carry out dynamic temperature measurements on an industrial scale is proposed in the frame of
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
5
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
Fig. 3. Schematic diagram of the drying tower and air distribution ring.
Fig. 4. Schematic diagram of microseparator used in measurements within industrial spray drying installation.
this work, as a development of an idea proposed by Kievet and Kerkhof [11,12]. The microseparator device is presented in Fig. 4, and consisted of two 4 m long tubes; that is, an inner tube 12 mm in diameter, and an outer tube 40 mm in diameter and of the same
length. A further temperature sensor (external) was added, in order to monitor the temperature of the air entering the outer tube. The airflow in both tubes was controlled by two needle valves and rotameters. During these measurements, the air in the tubes was maintained at a similar velocity to that in the air stream, in order to avoid blocking of the tubes by powder particles. To reduce the heat capacity of the device, and to shorten the temperature readout the temperature readout, the element separating the particles from the air and mounted on the microseparator tip was made from Teflon. 3.3. Velocity measurements Measurements of air velocity were performed in selected areas of the tower to determine the flow pattern within the dryer and to
Table 1 Comparison of velocity measurements inside connection ducts and CFD simulation results. No. of air duct
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Air velocities at 35 °C m/s
Air velocities at 50 °C m/s
Mass flow at 259 °C kg/s
Measurements
CFD
Measurements
CFD
– 5.8 – – 4.9 – – – 9.3 – – 9.5 – – 13.5 –
4.91 5.88 5.72 6.86 5.11 6.30 6.86 7.22 9.75 9.79 11.75 13.70 13.73 10.70 13.76 14.68
– 7.0 – – 7.0 – – – 9.5 – – 12 – – 18.5 –
8.82 7.58 7.32 6.34 7.50 8.82 8.15 7.26 8.82 8.25 11.65 12.54 13.78 16.26 18.20 12.54
0.9525 0.7649 0.7939 0.8178 1.2981 1.3772 1.4253 1.4550 1.5261 1.5829 1.6340 1.6840 1.7417 1.7851 1.8200 1.2439 Total mass flow 21.9 kg/s
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
6
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
Fig. 5. Measured and calculated time-averaged radial air temperature profiles at various tower levels (see Fig. 3).
collect the data necessary to validate the results of the 3D CFD modelling. Local air velocity profiles along the radius inside the tower, together with approximate direction of the flow, were determined at two levels. Air velocity was measured 1.4 m below and 7.0 m above the hot air inlet (levels 1 and 3 respectively). At each level, velocity profiles along the radius were determined twice, to check the correctness of the measurements. Air velocity was measured with a thermoanemometer type AP471, manufactured by DeltaOhm. In order to estimate the distribution of the air flow rate at each inlet, air velocity was measured with a thermoanemometer placed in the centre of the connection duct. Due to the low thermal resistance of the thermoanemometer, the measurement could be taken at a temperature below 100 °C, i.e., much lower than during the drying tower operation. Measurements were taken for two
temperatures: 50 °C and 35 °C. Results were then rescaled, using similarity conditions, to air flow at 250 °C. 4. CFD simulations of hot air flow in the distribution ring Next, CFD simulations were carried out of isothermal airflow through the distribution ring for the tested temperatures. The model developed here allowed the velocity distribution of air supplied to the tower during the drying process to be determined. The velocities obtained inside the connection ducts for air temperatures 50 °C and 35 °C showed good agreement with the experimentally measured values (Table 1), confirming the correctness of the model. The mass flow rates inside the connection ducts obtained in the simulation for an air temperature of 259 °C were used as boundary
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
7
Fig. 6. Presentation of air temperature distribution for selected time steps obtained from CFD simulation.
Fig. 7. Measured and CFD-calculated air temperature and humidity profiles in the drying tower.
conditions at the inlet to the drying tower in subsequent simulations of the drying process. 5. CFD calculation of the spray tower In the first step of development of the CFD model, mesh densities were tested to obtain an accurate and grid-independent solution. On the basis of values taken from technical drawings, a 3D model of the drying tower was built. In accordance with this technical information, the cross sections of 5 of the 16 hot air inlets were reduced to 2/3. A series of numerical meshes were tested which differed in type and number of elements. The first meshes tested were orthogonal, and were composed of 350 k and 686 k elements. However, these meshes did not allow a convergent result for the calculations to be obtained. The next mesh tested was a structural mesh composed of 528 k elements. Due to flow instability within the column and large recirculation of airflow, the required accuracy for the calculations also could not be obtained.
It was therefore decided to use a non-structural mesh, to ensure better results in the case of such indefinite airflow. Finally, a mesh consisting of 745 k elements was used, concentrated in the feed zone of the dryer. Initial and boundary conditions for the CFD calculations were determined during inventory analysis of the industrial spray drying tower, and were presented in earlier work [9,10]. Initial mass flow rates for the drying air within each hot air inlet were taken from CFD simulations of airflow inside the distribution ring at a temperature of 259 °C (Table 1). The upper air outlet was defined as a free outflow (the pressure outlet condition). The lower outlet of particles was defined as a wall constituting a barrier for the air but permeable to DPM (Discrete Phase Modelling) particles. The mass flow rate of slurry was 3.238 kg/s and initial moisture content was equal to 44.46 wt%. The initial droplet distribution ranged from 20 lm to 600 lm. To determine initial atomization conditions, the RosinRammler distribution was used, and a mean diameter d = 285 lm and spread parameter n = 3.783 were calculated. The initial velocity of the particles was 50 m/s. Heat losses to the environment were calculated using the free convection model. The coefficients of heat transfer from the walls to surrounding air were determined on the basis of the heat balance of the tower. For insulated walls, these were 3.5 W/m2 K; for the cone chute, 4.75 W/m2 K. It was assumed that the wall in direct contact with the distribution ring was adiabatic. It was found that due to the construction of air inlets and despite constant boundary conditions, flow instability was so high that the only way to obtain an accurate solution was to run the calculations in unsteady state conditions with a time step of 0.1 s. Tests of the turbulence models showed that in such a dynamic flow, better solutions were obtained using the Reynolds stress model (RSM). All results presented here were obtained using the Fluent 13 software package from ANSYS. The single-phase Navier-Stokes equations with the RSM turbulence model were solved to determine the flow field, and the Euler-Lagrange approach was used to model continuous and discreet phase interaction.
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
8
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
Fig. 8. Measured (above) and calculated (below) airflow patterns in the spray drying tower (m/s).
6. Results and discussion 6.1. Air temperature Fig. 5 demonstrates a good match between the calculated and experimental temperature profiles at various tower levels. The experimental results shown in Fig. 5 were obtained along the column radius only, from wall to dryer axis, and therefore are presented as calculated time-averaged temperature profiles. Fig. 6 displays the calculated scheme of air temperature patterns in the dryer. Results are shown in cross sections of the spray tower at consecutive time steps, to illustrate the dynamic changes in air and heat flow. Analysis of the figure shows that there is no uniform air temperature distribution in any cross-sectional area of the dryer which is a result of highly unstable operation of the tower. Both hotter and cooler areas can be found above the hot air inlets, which is a result of air flow instability. The highest temperatures occur in the conical section of the dryer. Fig. 7 presents the experimental (markers) and calculated timeaveraged (line) air temperature profiles along the tower height at three radial locations: the axis, 1 m from the axis and 2.5 m from the axis. The temperature of the drying air between the first and second levels decreases from 200 °C to 120 °C. 6.2. Estimation of air humidity As heat losses in the drying tower are low, it was possible to calculate the theoretical humidity of the air at each level and provide information at most locations of the drying process. The enthalpy of humid air can be described as follows:
i ¼ ca T g þ Yðr þ cv T g Þ
ð16Þ
Assuming that total air enthalpy (i) is constant in the tower (low heat losses) and that the apparent heat of air is transferred to latent heat, it is possible to find the air humidity from Eq. (16). Assuming the initial air humidity to be 5 g/kg dry air, and assuming a constant heat capacity of air and vapour, the average gas humidity at the different levels can be calculated. This approach has been described in earlier work [10]. Experimentally-determined air humidity inside the tower plotted against tower height (Fig. 7) shows a perfect match with the air humidity calculated in the CFD simulations. Analysis of the figure shows also good agreement between the experimentally-determined temperature and the profile calculated using CFD. 6.3. CFD calculations of air velocity in the tower The 3D CFD calculations show high instability of airflow in the dryer (lack of axial symmetry and oscillations of the flow field) due to the specific construction of the air distribution ring. Nonuniform airflow rate at the inlet, and the specific construction of air inlets, are responsible for the high flow instability in the industrial spray dryer. Verification of the CFD calculations was based on air velocity measurements in the tower. We compared theoretical and experimental airflow velocity 1.4 m below the hot air inlet. Fig. 8 shows the experimentally-determined average velocity profiles, obtained over about 1 h of measurement, and the calculated instantaneous air velocity profiles at selected times within the process. A comparison of the results shows good approximation of the absolute values of air velocity as well as flow directions. Considering the high instability of airflow in the tower, agreement is excellent both
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx
9
Fig. 9. Particle flow trajectories after 100 s, injected from upper nozzle level (left) and lower nozzle level (right), coloured by diameter (A) and velocity magnitude (B).
for velocity values and flow directions, confirming the quality of the developed CFD model. 6.4. Particle diameter Fig. 9A shows the calculated trajectories of selected particles injected at the upper and lower nozzle levels, and their diameter change. A strong segregation of particles can be observed in the cylindrical part of the tower within the injection area; the smallest fractions (20–200 lm) are immediately dragged up, while the biggest fractions fall down, driven by downward airflow near the dryer wall. In the conical part of the dryer (air inlet area) small (20–400 lm) and medium (400–700 lm) particles move upward, following the robust air axis flow. However, the inertia of the large particles allows them to resist the upward axis airflow and fall down onto the conical wall. Upward flow near the wall in the conical section shifts the particles up, and promotes the build-up of the wall deposit. Similar conclusions can be drawn from the analysis of particle velocities in the column (Fig. 9B). The smallest velocities of the particles are observed near the wall of the conical section of the dryer. The upward flow of drying air drags the particles up along the cone wall. The bouncing shape of the particle trajectories near the wall indicates increased wall deposition in this part of the dryer. 7. Conclusions A robust CFD model is developed of continuous and dispersed phase flow, taking into account the agglomeration process within industrial towers. The CFD calculations and experimental measurements confirm the high instability of the airflow (no axial symmetry, oscillations of the flow field) due to the geometry of the dryer and the construction of the air inlet to the tower. No uniform air temperature distribution was found within any cross-sectional area of the dryer; this is a result of the highly unstable operation of the drying tower.
A strong segregation of particles was observed in the atomization zone; small fractions are immediately dragged up, while the largest fractions fall down, driven by downward airflow near the wall. In the conical section of the tower, the inertia of large particles allows them to fall down onto the conical wall and leave the tower. A straightforward method for determining agglomeration in the spray drying tower is developed here, based on transition functions, which allows for the correct prediction of particle flow patterns and product PSD. References [1] I.C. Kemp, Drying in the context of the overall process, Drying Technol. 22 (1– 2) (2004) 377–394. [2] D.F. Fletcher, B. Guo, D.J.E. Harvie, T.A.G. Langrish, J.J. Nijdam, J. Williams, What is important in the simulation of spray dryer performance and how do current CFD models perform?, Appl Math. Model. 30 (11) (2006) 1281–1292. [3] L. Huang, K. Kumar, A.S. Mujumdar, A parametric study of the gas flow patterns and drying performance of co-current spray dryer: results of a computational fluid dynamics study, Drying Technol. 21 (6) (2003) 957–978. [4] D.E. Oakley, Spray dryer modeling in theory and practice, Drying Technol. 22 (6) (2004) 1371–1402. [5] D.J.E. Harvie, T.A.G. Langrish, D.F. Fletcher, Numerical simulations of gas flow patterns within a tall-form spray dryer, Transact. Inst. Chem. Eng. 79 (A) (2001) 235–248. [6] A.E. Bayly, P. Jukes, M. Groombridge, C. McNally, Airflow patterns in a countercurrent spray drying tower simulation and measurement, in: Proceedings of the 14th International Drying Symposium, Sao Paulo, Brazil, August 22–25, vol. B, 2004, pp. 775–778. [7] W. Rahse, O. Dicoi, Spray Drying in Detergent Industry, in: Proceedings of Spray Drying’01 Conference, Dortmund, Germany, October 8–10, 2001, pp. 83– 87. [8] M. Piatkowski, I. Zbicinski, Analysis of the mechanism of counter-current spray drying, Transp. Porous Media 66 (1–2) (2007) 89–101. [9] P. Wawrzyniak, M. Podyma, I. Zbicinski, Z. Bartczak, J. Rabaeva, Modeling of air flow in an industrial countercurrent spray-drying tower, Drying Technol. 30 (2) (2012) 217–224. [10] P. Wawrzyniak, M. Podyma, I. Zbicinski, Z. Bartczak, A. Polanczyk, Rabaeva, Model of heat and mass transfer in an industrial counter-current spray-drying tower, Drying Technol. 30 (11–12) (2012) 1274–1282. [11] P. Wawrzyniak, I. Zbicinski, Z. Bartczak, Rabaeva, Advanced experimental analysis of industrial counter current spray drying process, in: Proceedings of the 18th International Drying Symposium, Xiamen, China, vol. B, November 11–15, 2012, pp. 118–125. [12] F.G. Kievet, J.A.M. Kerkhof, Using Computational Fluid Dynamics to Model Product Quality in Spray Drying: Air Flow, Temperature and Humidity
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029
10
[13]
[14]
[15]
[16]
P. Wawrzyniak et al. / Advanced Powder Technology xxx (2016) xxx–xxx Patterns, in: Proceedings of the 10th International Drying Symposium, Krakow, Poland, July 30-August 2, 1996, vol. A, pp. 259–266. M.W. Woo, W.R.W. Daud, A.S. Mujumdar, S.M. Tasirin, M.Z.M. Talib, Role of rheological characteristics in amorphous food particle-wall collisions in spray drying, Powder Technol. 198 (2010) 251–257. P. Wawrzyniak, M. Podyma, I. Zbicinski, Z. Bartczak, J. Rabaeva, Modeling of air flow in an industrial countercurrent spray-drying tower, J.: Drying Technol. 30 (2) (2012) 217–224. M. Mezhericher, A. Levy, I. Borde, Theoretical drying model of single droplets containing insoluble or dissolved solids, Drying Technol. 25 (2007) 1025– 1032. M. Mezhericher, Theoretical Modelling of Spray Drying Process Volume 1. Drying Kinetics, Two and Three Dimensional CFD Modelling, vol. Volume 1, LAP Lambert Academic Publishing GmbH & Co. KG, Saarbrucken, 2011.
[17] Y. Rabaeva, Kinetics of Foam-spray Drying Process PhD Thesis, University of Technology, Lodz, 2012. [18] M. Jaskulski, P. Wawrzyniak, I. Zbicin´ski, CFD model of particle agglomeration in spray drying, Drying Technol. 33 (15–16) (2015) 1971–1980. [19] X.D. Chen, W. Pirini, M. Ozilgen, The reaction engineering approach to modelling drying of thin layer of pulped Kiwifruit flesh under conditions of small Biot numbers, Chem. Eng. Process. 40 (2001) 311–320. [20] R.B. Keey, M. Suzuki, On the characteristic drying curve, Int. J. Heat Mass Transf. 17 (1974) 1455–1464. [21] D.A. Van Meel, Adiabatic convection batch drying with recirculation of air, Chem. Eng. Sci. 9 (1958) 36–44.
Please cite this article in press as: P. Wawrzyniak et al., CFD modelling of moisture evaporation in an industrial dispersed system, Advanced Powder Technology (2016), http://dx.doi.org/10.1016/j.apt.2016.09.029