Experimental Research and Numerical Simulation on Fine Particulate

Article pubs.acs.org/EF

Experimental Research and Numerical Simulation on Fine Particulate Matter Removal by Foam Agglomeration Method Zewen An,† Mingxin Gong,† Longlong Zhang,† Qingjie Guo,‡ Yongzhuo Liu,§ Huawei Jiang,† Yanhui Li,† and Cuiping Wang*,† †

College of Mechanical & Electrical Engineering, Qingdao University, Qingdao, China, 266071 College of Chemical Engineering, Ningxia University, Yinchuan, China, 750021 § College of Chemistry and Chemical Engineering, Qingdao University of Science and Technology, Qingdao, China, 261500 ‡

ABSTRACT: In this study, experiments were performed to investigate the effective removal of fine particulate matter (FPM) by the promising foam agglomeration method. Further, numerical simulation and thorough analysis were carried out based on the experimental data, and the population balance module was used as the agglomeration physical model. The results indicated that the aggregation size of FPM increased with the foam−liquid ratio because more bubbles with larger contact surface area played a major role. The viscosity of the spraying solution also contributed to the FPM agglomeration; however, extremely large viscosity had a negative influence on agglomeration for the growing solution flow resistance. Mechanism studies revealed the formation of agglomerates of FPM in foams is that FPM gets continuously adsorbed on the surface of the bubbles or droplets, and then the bubbles are broken when the force balance is destroyed between the gravity of the FPM cluster and the surface tension of the bubble, which causes the agglomeration of FPM into large particle clusters. The numerical simulation is in good agreement with the experimental results.

1. INTRODUCTION Fine particulate matter (FPM) released from coal firing is the main pollutant in the environment. FPM (mainly PM2.5) suspension in the atmosphere always imposes serious hazards to human health and the environment; for its small size, it can easily enter deep inside the respiratory tract, causing respiratory diseases and even lung cancer. PM2.5 particles not only can lead to an increase in human morbidity and mortality, but also are related to global climate change, atmospheric visibility reduction, high occurrence of haze episodes, and ozone layer damage. Although the existing dust removal devices have removal efficiency as high as 99% or more, they still fall short of catching very fine particulate matter, and a lot of FPM is still observed in the atmosphere.1−4 The main source of PM2.5 is the emission from coal combustion, the desulfurization products and secondary particulate matter related with the NOx emission;5 therefore, the study of FPM removal technology is particularly important before the flue gas flows out of the FGD tower outlet to capture the ultrafine particles, including the desulfurization salt product. Conventional filter operation always required the pretreatment of flue gas to enlarge the average size of the particle to a specific value. Among other innovative approaches for smallparticle filtration techniques, the foam agglomeration method has emerged as an efficient method for controlling small particle emissions. The agglomeration method includes the physical and chemical ways to agglomerate FPM into large particles, and then the normal dust removal equipment, for example, shell and tube type dust collector and wet electrostatic precipitatorm can easily be used to remove the “big” particles from the flue gas. Chemical agglomeration has been regarded as an effective method for the treatment of ultrafine particles in coal-fired flue gas. It is safe and nontoxic and can be easily used to remove many types of pollutants.6 Chemical agglomeration is divided into the © XXXX American Chemical Society

following two types: agglomeration during combustion and agglomeration after combustion. Zhuang et al.7 studied the effect of addition of gaseous adsorbents on agglomeration of FPM produced by pulverized coal combustion. Ninomiya et al.8 studied the effect of addition of magnesium or calcium-based particles on the reduction of emissions of FPM during the pulverized coal combustion. Durham et al.9 introduced a polymer chain with polar group as the viscous agent to agglomerate FPM. Nicola et al.10 studied the influence of types and concentration of agglomeration agents on FPM removal efficiency, and explored the novel chemical agglomeration mechanism, while achieving synergistic removal of various pollutants. Foam agglomeration is a type of chemical agglomeration methods. Foam has the characteristics of low density and large surface area, which can increase the chance of contact and adhesion between FPM and foam agglomerates. The foam should be the mixture of bubbles with fine droplets and be different with the water droplets. In this study, the foam agglomeration experiment and numerical simulation based on the experiments were investigated in order to explore the optimizing conditions and mechanism for the comprehensive understanding of foam agglomeration at the top of desulfurization tower.

2. EXPERIMENTAL SECTION The foam agglomeration experimental device is shown in Figure 1. The system is mainly composed of double fluidized beds. Secondary bed 9 is the feeding device which receives the FPM feeding and distributes the Received: April 26, 2017 Revised: July 21, 2017 Published: July 31, 2017 A

DOI: 10.1021/acs.energyfuels.7b01182 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

that promotes the clumping of particles. High molecular weight polymer is the most widely used flocculant. In this study, nonionic polyacrylamide (PAM), sodium carboxymethyl cellulose (CMC), and xanthan gum (XTG) were selected based on their cost effectiveness and comprehensive efficiency. The role of the surfactant in solution is to reduce the surface tension of foam, and promote the formation of rich and dense bubbles and thus increase the contact area with the suspended ash particles, in order to make it easier to wet the particle surface and improve its viscosity, which is beneficial for the agglomeration. Sodium dodecyl benzenesulfonate (SDBS) was used as a surfactant. Coconut diethanolamide (6501) was selected as a foam stabilizer. The flocculants concentrations were set as follows: concentrations of PAM and CMC were 0.1%, 0.2%, and 0.4%, while those of XTG were 0.05%, 0.1%, and 0.2% with a greater viscosity at the same solubility.9 The volume fraction of each component is listed in Table 1.

Table 1. Volume Fraction of Each Component in Agglomeration Solution Figure 1. Foam agglomeration experimental device.

flocculants

ultrafine particles into the main bed 8 through the gap inlet 14. Spray nozzles are set in the main bed and the spraying direction is opposite to that of the flue gas with FPM. The spray nozzles was set in two layers here, for in this little experimental riser, it is easy to ensure that the liquid−gas ratio is similar to that of the actual run of multilayers in the wet gas desulfurization and dust removal tower. Liquid−gas ratio is the running index to satisfy. By controlling the flow rate of the fluidized gas in the two beds, the concentrations of particulates are varied to the set values. The agglomeration solution is sprayed into the gas−solid flow in the main bed through one or two nozzles by the gear solution pump drive. The top of the main bed is equipped with an orifice disc defogger. The exhaust flue gas flows into the absorption bottle, known as the gas washing cylinder, to collect the residual fine particles in the tail gas. The solution containing agglomerated particles is collected at the bottom of the main bed with suspended agglomerated particles. The solution is then induced to a vacuum filtration device to collect the aggregated particles. The fly ash used in the experiment was obtained from the bag filter collection in a local power plant. According to the particle size analysis, the particle size of 10). Based on the observation of flow and agglomeration in experimental riser, the variation of fine particles and bubble droplet diameter is in the range of 0.05−160 μm; thus the calculated particles can be regarded as in free molecular zone. In this case, the frequency of collision is size-dependent and usually the following kernel is implemented as eq 1.

Figure 2. Particle size distribution in secondary bed. required PM2.5 size in the main bed had to be realized by the fluidization gas flow, which directs the FPM into the main bed through the gap on the clapboard between the two beds, and the large particles remain in the feeding bed as the bed materials, as shown in Figure 2, the curve after fluidized feeding. The difference between the two curves presents the reduction of ultrafine particles fed into the main bed. The foam agglomeration solution used in the experiment consisted of flocculants, surfactants, and foam stabilizers. Flocculant is a substance

βb(Li , Lj) =

2 2kBT (Li + Lj) 3μ LiLj

(1) −23

−1

where kB is the Boltzmann constant, kB = 1.381 × 10 J·K , T is the absolute temperature, T = 423.15 K, and μ is the viscosity of the agglomeration solution. Li is the diameter of particle i and Lj is the diameter of particle j. B

DOI: 10.1021/acs.energyfuels.7b01182 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels During the pneumatic transmission processes, the turbulence within the fluid always generates eddies, which in turn dissipate the energy. Energy is transferred through the largest eddies to the smallest eddies, where it is dissipated by viscous interaction. The size of the smallest eddies is the Kolmogorov microscale, η, which is expressed as a function of the kinematic viscosity and the turbulent energy dissipation rate as follows: η=

ν3 ε

(2)

where v is kinematic viscostiy, and ε is turbulence dissipation rate. In the turbulent flow field, according to the size of the two particles, aggregation or core collision could occur, which is described in terms of the following three models: (a) When the diameter of the two particles i and j is Li < η, Lj < η, then based on the study by Saffman and Turner,11 the collision rate is expressed as follows: βt (Li , Lj) = ξT

Figure 3. Physical model for simulation.

The simulated flue gas at inlet is set as a two-phase flow with particle phase and gas phase. The agglomeration liquid from the nozzle is also set as a two-phase flow of droplet phase and bubble phase. The PBM model is introduced in Fluent software. 3.3. Boundary Conditions. The flue gas inlet was a velocity inlet of 0.2 m s−1 under the experimental conditions. FPM phase was CaCO3 particles, and the gas phase was air simulating the flue gas. The particle volume fraction was 0.1%. The volume ratio of the agglomerated liquid bubble to the droplet was 1:2, the agglomerate flow rate was 70 mL min−1, and the spray velocity was Vt = 1 m s−1. Near-Wall treatment included standard wall functions. The inhomogeneous discrete method was used in the PBM. According to the experimental research of jet spraying, including the particle size analysis, the particle size distribution of each phase is listed in Table 2. The physical properties of each

3

ε (Li + Lj) ν 8

8π 15

(3)

(b) When the diameter of the two particles i and j is Li > η, Lj > η, then the aggregation rate is expressed by using the Abrahamson’s model:12 3

βt (Li , Lj) = ξT 2

π

(Li + Lj)3 4

ui̅ 2 + uj̅ 2

(4)

(c) When the diameter of the two particles i and j is Li ≥ η, Lj < η or Li < η, Lj ≥ η, then for the aggregation rate, Zheng’s model13 is used βt (Li , Lj) =

(Li + Lj)3 8

8π 15

ε ν

⎛ 2η ⎞0.08 + 0.897St ⎜⎜ ⎟⎟ ⎝ Li + Lj ⎠

Table 2. Size Distribution of Each Phase FPM phase

(5)

where ξT is a prefactor that takes into account the capture efficiency coefficient of turbulent collision, and St is particle relaxation time scale and fluid characteristic time scale ratio, and ui̅ and uj̅ is the average velocity of particles i and j, respectively. The Brownian agglomeration and turbulence agglomeration have different effects on the bubble-ash particle collision and agglomeration; therefore, it can be assumed that the aggregation kernel functions are independent of each other13 and are defined as follows:13 β=

βb 2 + βt 2

particle diameter (μm)

volume fraction

0.5 1 2 4 8 16 32

0.1 0.15 0.2 0.25 0.2 0.1 0

bubble phase

droplet phase

particle diameter (μm)

volume fraction

60 84 120

0.2 0.3 0.5

particle diameter (μm)

volume fraction

35 55

0.5 0.5

(6)

phase are listed in Table 3. The aggregation kernel function was loaded by user-defined functions (UDFs), and the Luo model was used as the broken kernel.

The broken kernel function is based on the Luo model in the population balance module (PBM) model.14 3.2. Physical Model. According to the experimental process, the flow in riser can be divided into the following two sections: one is the spaying region where the collision is between the adverse current, and the other is the downstream region. The spraying region was first selected as the simulation section, i.e., the height of 800 mm up and down the nozzle. The physical model is shown in Figure 3. The 1/3 spherical face is used as a simulated nozzle outlet. The simulated flow riser, named the spraying section, is 800 mm high, 40 mm long, and 40 mm wide. The nozzle outlet lies in the center of gravity of the spraying section. The simulated flue gas is injected from the bottom inlet. The top is the pressure outlet of flue gas to the downstream region. The agglomeration solution is ejected from the nozzle atomizer.

4. RESULTS AND DISCUSSION 4.1. Particle Agglomeration Experiment and Simulation Comparison. The solution used for the agglomeration of FPM by foam method consisted of XTG 0.2%, SDBS 0.1%, and coconut diethanolamide 0.1%. Physical properties of the phases Table 3. Physical Properties of Each Phase phase FPM phase bubble phase droplet phase C

Viscosity kg m−1 s−1 −5

2.4 × 10 6.9 × 10−3 6.9 × 10−3

density kg m−3 2270 10 1000

DOI: 10.1021/acs.energyfuels.7b01182 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels are listed in Table 3. Figure 4 exhibits the comparison of the experimental and numerical simulation results. Clearly, the

Figure 6. Particle size distribution of different foam−liquid ratio.

Therefore, it is beneficial for the fine particles to contact with agglomerating foam bubbles and droplets, thus leading to agglomeration. 4.3. Influence of Agglomerating Solution Viscosity on Fine Particle Agglomeration. In this experimental study, the influence of dynamic viscosity on agglomeration was investigated by changing the content of flocculants in the agglomerating solution. The solution viscosity measured using a dynamic viscosity tester is listed in Table 4. PAM0.2%, CMC0.2%, or

Figure 4. Comparison of experimental and simulated results.

particle size in the experiment is unimodal, and mainly distributed at 4 μm. After the agglomeration, the particle size is mainly distributed at around 16 μm, in which the volume ratio of particles of 10 and 16 μm is significantly increased, and the volume ratio of particles with size below 4 μm is decreased evidently, in particular, for particles with size about 2 μm. For the numerical simulation, the particle size is also set as a single peak distribution, similar to the experimental input value. At the flue gas outlet of the spraying section, the simulated particle size distribution is basically consistent with the experimental result and mainly larger than16 μm. This indicates that the addition of foam significantly works on the agglomerations of FPM, and the models in the numerical simulations are accurate and adaptive. 4.2. Influence of Foam-Liquid Ratio on Fine Particle Agglomeration. The influence of foam bubbles and droplets on the FPM agglomeration is different. Foam−liquid ratio is used to define the volume ratio of bubbles and droplets. The evolution of FPM agglomeration with different foam−liquid ratios is shown in Figures 5 and 6. With the increasing foam−liquid ratio,

Table 4. Agglomerating Solution Viscosity (in mPa·S) XTG0.2%

XTG0.1%

PAM0.2%

CMC0.2%

XTG0.05%

8.5

6.9

6.4

6.7

4.1

XTG0.1% as the flocculant causes different viscosity of the agglomerating liquid. The viscosity values obtained by using different concentrations of flocculants such as XTG, PAM, and CMC in agglomerating liquid were selected in the numerical modeling to analyze the influence of solution viscosity on the FPM agglomeration. With the increase in the viscosity of spraying bubbles and droplets, the particle size after agglomeration first increased and then decreased. The volume fraction of particulate matter of 16 μm size is the maximum when the viscosity is 6.9 mPa·s, as shown in Figure 7. When the viscosity of agglomerating liquid increases

Figure 5. Effect of foam−liquid ratio on the number of particles. Figure 7. Viscosity influence on particle size distribution.

the size of the outlet particles is markedly increased to the size of more than 10 μm, and the volume fraction is increased significantly. The particle size corresponding to maximum volume fraction is enlarged from 8 to 16 μm. The agglomeration of FPM strengthened with the increasing foam−liquid ratio. The possible mechanism could be as follows: the density of bubbles is small and the specific surface area is large. The greater the volume fraction of bubbles is, the larger the area to increase the collision probability of FPM with bubbles.

to a larger value, the atomizing droplets are more and bubbles less, and the total quantity of droplets and bubbles decreases, thus leading to the reduction in the collision probability of particles and agglomerating liquid. Thus, the influence of spraying solution viscosity on the FPM agglomeration could not be ignored. D

DOI: 10.1021/acs.energyfuels.7b01182 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels 4.4. Influence of Broken Bubbles on Particle Agglomeration. The density of bubbles from agglomeration solution was small in the mixture, and thus it exhibited very different velocity from that of the ash and droplets. Furthermore, these bubbles underwent strong collision and adherence with particles during the aggregation or broken with other bubbles. Thus, it is extremely essential to evaluate the influence of fracture on particle agglomeration. The broken kernel function model additive used in the simulation brings forward the crushing effect as shown in Figure 8. Compared to the flow of only aggregation kernel model, the

Figure 10. Bubble volume fraction in different sections of 200 mm (dotted line lies the nozzle position).

the volume fraction of 120 μm bubbles decreased evidently. It could be deduced that the turbulence caused by the flow around the nozzle increased the collision and agglomeration and conglutinated more FPM, until the larger bubbles were broken and reduced. The amount of particles of size less than 2.5 μm is reduced to minimal at pipe output and the amount of particle cluster size greater than 15 μm is maximized. Moreover, the cluster size variation in the flow is more significant when below the nozzle. The volume fractions of FPM with size less than 2.5 μm and greater than 15 μm undergo little change when flowing above the nozzle, as shown in Figure 11. Under the nozzle, the spraying

Figure 8. Effect of crushing on agglomeration of fine particles.

volume fraction of particle size less than 4 μm decreased; however, the volume fraction of particle size greater than 10 and 16 μm increased evidently. Therefore, the bubble fracture exhibited an advantage toward agglomerating fine particles. As shown in Figure 9, the volume fraction of the bubbles on different heights is analyzed. The nozzle lies at 400 mm height,

Figure 11. Particle aggregation distribution in different sections.

leads to significant turbulence and increases the collision and agglomeration. The particle aggregation size, agglomeration or breaking of bubbles, and droplet agglomeration are always varied. However, the mixed flowing tends to be stable afterwards over the nozzle. The simulation results show that the bubbles always collide and adhere to the FPM when droplets and bubbles are sprayed from the nozzle against the current, and continuous breaking of the bubbles plays a major role in the FPM agglomeration. The agglomeration mechanism could be attributed to the diameter of bubbles much greater than FPM, which is constantly immersed in the bubble surface. The bubbles get broken when the force balance gets destroyed, which prompts the adhered FPM to agglomerate into large-sized clusters.

Figure 9. Bubble volume fraction along the riser.

but the highest bubble fraction is in the cross section of 200 mm height and then decreases very quickly along the flow upward. The bubbles could be crashed violently with FPM in the bubble abundant region, so the domain of the highest and lowest 200 mm of the nozzle is chosen as the simulation region. The mean bubble volume fraction on the cross section at 320, 340, 360, 380, 420, 440, 460, and 480 mm heights was, respectively, induced to analyze by using the two models. With the increase in the height of the cross sections, the volume fraction of bubbles with size of 120 μm continuously decreased and the volume fraction of bubbles with particle size of 60 μm steadily increased, as shown in Figure 10 (the dotted line is the nozzle position). When the bubbles moved upward to the nozzle,

5. CONCLUSION In this study, experiments were performed to investigate the effective removal of FPM by the promising foam agglomeration E

DOI: 10.1021/acs.energyfuels.7b01182 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

effect of pulsed corona discharge and acoustic wave enhanced by spray droplets[J]. Powder Technol. 2017, 312, 21−28. (5) Zhang, Q.; Quan, J.; Tie, X.; Li, X.; Liu, Q.; Gao, Y.; Zhao, D. Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China. Sci. Total Environ. 2015, 502, 578−584. (6) Hu, B.; Liu, Y.; Yang, C.; Hou, D.; Yuan, Z.; Yang, L. Simultaneous control of PM2.5 and SO3 by chemical agglomeration collaborative electrostatic precipitation [J]. CIESC Journal 2016, 67 (9), 3902−3909. (7) Ye, Z.; Biswas, P. Submicrometer Particle Formation and Control in a Bench-Scale Pulverized Coal Combustor[J]. Energy Fuels 2001, 15 (3), 130. (8) Ninomiya, Y.; Wang, Q.; Xu, S.; Mizuno, K.; Awaya, I. Effect of Additives on the Reduction of PM2.5 Emissions during Pulverized Coal Combustion[J]. Energy Fuels 2009, 23, 3412. (9) Chen, H. C.; Wu, W.; Liang, C.; Wu, X. Removal of Fine Particulate Matter by Spraying Attapulgite Suspending Liquid[J]. Energy Fuels 2016, 30, 4150−4158. (10) Careddu, N.; Medda, P.; Sarritzu, C.; Grasso, F. Particulate matter in fly-ash landfills: an abatement technology using anionic flocculant[J]. J. Cleaner Prod. 2015, 102, 477−484. (11) Saffman, P. G.; Turner, J. S. On the collision of drops in turbulent clouds[J]. J. Fluid Mech. 1956, 1 (1), 16−30. (12) Abrahamson, J. Collision rates of small particles in a vigorously turbulent fluid[J]. Chem. Eng. Sci. 1975, 30 (11), 1371−1379. (13) Zheng, J.; Xu, S.; Wang, J. Simulation Study of Ultrafine Particle Aggregation Models and Agglomerator Coagulation [J]. Proc. Chin Soc. Electrical Eng. 2016, 36 (16), 4389−4395. (14) Luo, H.; Svendsen, H. F. Theoretical model for drop and bubble breakup in turbulent dispersions[J]. Chem. Eng. Sci. 1996, 66 (5), 766− 776.

method. Further, numerical simulation and thorough analysis were carried out based on the experimental data. Addition of foam to turbulence flow is significant for the agglomerations. The numerical simulation results are in good agreement with the experimental results. In the agglomeration process, the size of FPM aggregates increased with the increase in the bubble−liquid ratios, and the maximum volume fraction of aggregated particles increased from 8 to 16 μm in the flow pipe. With the increase of the solution viscosity, the size of the FPM aggregates increased at first and then decreased. Extremely large viscosity led to the weakening of the agglomeration, reduction of the probability of the collision, and breaking of the bubbles, thus affecting the agglomeration of FPM. This study indicates that the broken bubble plays a key role in the agglomeration of FPM. The agglomeration mechanism reveals that the large surface area of the bubbles improves the probability of collision and conglomeration between bubbles and particles. The FPM is constantly immersed in bubble surface or in droplets. When the force balance between the adhered FPM gravity and the surface tension is destroyed, FPM aggregates into clusters. Finally the large-sized FPM clusters are formed. Comparison of the results of experiments and numerical simulation provided comprehensive understanding of the mechanism and effect of the foam agglomeration method. Thus, the foam agglomeration method can aggregate PM2.5 into large-sized particle clusters, which can improve the efficiency of removal of FPM. Undeniably, a lot more systematic explorations are demanded to strengthen the efficiency of reunion, and to make PM2.5 agglomerates into clusters of more than 25 μm so that the normal dust removal device can be more convenient, and this will be pursued in the future study.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Cuiping Wang: 0000-0002-4249-709X Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (51676102) and the Natural Science Foundation of Shandong Province (ZR2015EM004). The authors greatly acknowledge the support from the Foundation of State Key Laboratory of Coal Clean Utilization and Ecological Chemical Engineering (Grant No. 2016-07) and Taishan Scholar Program of Shandong Province (201511029).

■

REFERENCES

(1) Zhou, D.; Luo, Z. Y.; Fang, M. X.; Lu, M. S.; Jiang, J. P.; Chen, H.; He, M. C. Numerical calculation of particle movement in sound wave fields and experimental verification through high-speed photography [J]. Appl. Energy 2017, 185, 2245−2250. (2) Chen, D.; Wu, K.; Mi, J. Experimental investigation of aerodynamic agglomeration of fine ash particles from a 330 MW PC-fired boiler[J]. Fuel 2016, 165, 86−93. (3) Liu, Z.; Liu, H.; Feng, X.; Zhang, W.; Li, H.; Xing, Z. Simulation for the Flow Field of the Turbulence Coalescence Device and the Trajectory of Particles[J]. Proc. Chin. Soc. Electrical Eng. 2012, 32 (14), 71−75. (4) Luo, Z.; Chen, H.; Wang, T.; Zhou, D.; Lu, M.; He, M.; Fang, M.; Cen, K. Agglomeration and capture of fine particles in the coupling F

DOI: 10.1021/acs.energyfuels.7b01182 Energy Fuels XXXX, XXX, XXX−XXX