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Enhanced CO2 absorption and desorption by MEA based nanoparticle suspensions Tao Wang, Wei Yu, Fei Liu, Mengxiang Fang, Muhammad Farooq, and Zhongyang Luo Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b00358 • Publication Date (Web): 01 Jul 2016 Downloaded from http://pubs.acs.org on July 5, 2016

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Industrial & Engineering Chemistry Research

Enhanced CO2 absorption and desorption by MEA based nanoparticle suspensions Tao Wang a,*, Wei Yu a, Fei Liu a, Mengxiang Fang a,*, Muhammad Farooq b, Zhongyang Luo a a

State Key Laboratory of Clean Energy Utilization, Zhejiang University, Zheda Road

38#, Xihu District, Hangzhou, Zhejiang Province, China b

Insitute of Mechanical Process & Energy Engineering, Heriot-watt University,

Edinburgh EH14 4AS, United Kingdom *Corresponding author e-mail address: [email protected]; [email protected] Tao Wang: [email protected] Wei Yu: [email protected] Fei Liu: [email protected] Mengxiang Fang: [email protected] Muhammad Farooq: [email protected] Zhongyang Luo: [email protected]

1

ACS Paragon Plus Environment


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Abstract In this study, three different nanoparticles, SiO2, TiO2 and Al2O3, were employed to enhance the CO2 gas absorption by monoethanolamine (MEA) solvent. It is observed that with increased solid loading, the total mass transfer enhancement tends to be dominated by the bubble breaking effect. It is concluded that nanoparticles result in increased CO2 absorption rate of over 10%. On the other hand, nanoparticles exhibit much more attractive impact on CO2 desorption. Under the same desorption extent, solvent with 0.1wt% TiO2 nanoparticles saved 42% desorption time compared to that without nanoparticles. Under higher heat flux density, more input heat would be supplied to the heat of desorption rather than the heat of water evaporation, which is due to the enhancement of desorption rate by nanoparticles. The issue of particle aggregation was also investigated by analyzing the size distribution of nanoparticle clusters and Zeta potential of MEA solvents.

2


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1.

Introduction Since CO2 is mainly responsible for the greenhouse effect for its global warming

rate, it is of great urge to control CO2 emissions globally, especially from fossil-fuel fired power plant. Carbon capture, utilization and storage (CCUS) is an effective option to mitigate CO2 emissions 1. Chemical absorption by amine based solutions is a well-understood and commercially mature technology which can be employed in CCUS 2. Monoethanolamine (MEA) based solvents are often regarded as the first chemical solvent to be used in the large-scale applications of post-combustion CO2 capture in coal-fired power plants 3. For the MEA technology, one of the primary challenges is the high energy consumption, which brings about a decrease in power plant efficiency of 10 points4. On the other hand, intensive capital expenditure is overwhelming for power stations, especially of the columns and packings, which account for about 50-60% of the total capital expenditure5. Enhancement of absorption and desorption processes are capable of reducing the size of absorber and stripper, and hence reducing the total cost of CO2 removal. Traditionally, optimization of packing design, modification of column structure etc. are employed to strengthen the turbulence and improve gas, liquid distribution inside the column in industry

6-8

. In recent years, the addition of

nanoparticles into solvent has turned to be a popular alternation for its remarkable effects on reducing liquid-side mass transfer resistance 9. Krishnamurthy et al. first visualized dye diffusion in nanoparticle suspensions and observed that dye diffuses 10

faster compared to that in base fluid

. Nanoparticle has been used in the

enhancement of gas absorption, such as NH3 and CO2, in the last decade. Recent studies of the effects of nanoparticles on CO2 absorption enhancement are listed in Table 1, mainly focusing on the selection of nanoparticles, match with solvents, and optimization of operation conditions, etc. With the addition of nanoparticles, it is inspiring to see that the mass transfer rate in different systems can be increased by 10-20%. Table 1 Literature review 3



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Authors

Gas/solvent

Nanoparticle

Lee et al.

CO2/DI water

SiO2, Al2O3

Jiang et al.

CO2/MEA, MDEA

TiO2, MgO, Al2O3, SiO2

Kim et al.

CO2/methanol

Al2O3

Wang et al.

CO2/MEA, MDEA, PZ

Al2O3, SiO2

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Conclusion

Reference

Maximum CO2 absorption/ regeneration performance enhancements are 23.5% and 11.8%, respectively. Enhancement performance is: TiO2> MgO > Al2O3> SiO2. Mass transfer coefficient is enhanced up to 26% at 0.01 vol% compared with pure methanol. Enhancement performance is: PZ> MEA> MDEA

11

12

13

14

A series of mass transfer theories, such as bubble breaking effect 15, shuttle effect 16

and boundary mixing effect

17

, were developed to explore the mechanisms of

enhancement by nanoparticles. However, these hypothesis are not easy to be compared directly with experiments. Wang et al. established mass transfer models in nanofluids to estimate the increased CO2 diffusivity into MEA in wetted-wall column and found that the micro-convective motion contributed around 80% of the increased CO2 diffusivity in liquid bulk, which backs up the reliability of shuttle effect and boundary mixing effect

14

. Kim used high-speed camera images of CO2 bubbles in

methanol/Al2O3 mixture, but did not observe bubbles breaking in nanofluids

13

. For

better understanding of nanoparticles roles in mass transfer enhancement, it is necessary to establish the methodology which can quantify these mechanisms. On the other side, the performance of gas desorption by amine based solvents with nanoparticles is absent. The effect of mass transfer enhancement by nanoparticles under high temperature and strong evaporation process are unclear. The high temperature also tends to result in agglomeration of nanoparticles and unexpected chemical reaction between particle and solvent 18. These parameters further impact the performance of absorption when the solvent with nanoparticle is cycled into absorber. It was found that several parameters, such as temperature, ionic composition and PH. dominate the interactions of nanoparticles with solvent

19, 20

. The study on

morphology and interfacial property of nanoparticle inside absorber and stripper would be of great importance for evaluation of absorption and desorption 4


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performance. In the present research, 30 wt% MEA solution enhanced by nanoparticles was investigated in the bubble column. Absorption and desorption analysis of CO2 were observed and mass transfer models were built to characterize the absorption enhancement induced by nanoparticles. Model calculations and absorption experiments were conducted to determine the diffusion coefficient and effective interfacial area, as well as the enhancement by nanoparticles. In addition, mechanism of aggregation and sedimentation of nanoparticles was observed during absorption, aiming to gain a comprehensive understanding of mass transfer enhanced by nanoparticle suspensions.

2. Theoretical fundamentals According to Fick’s law, the flux of CO2 is proportional to the CO2 concentration gradient in the diffusion direction. The proportionality factor is the overall mass transfer coefficient of CO2 in the medium. Inspired by the two-film model, the overall resistance to mass transfer of gas absorption can be expressed as the sum of resistance from gas side and liquid side: 1 1 1 = + K G kg kL

(1)

where KG is the overall mass transfer coefficient (mol/(s·m2 ·Pa)); kg is the gas-side mass transfer coefficient (mol/(s·m2 ·Pa)); kL is the liquid-side mass transfer coefficient (mol/(s·m2 ·Pa)). 2.1 Model for CO2 diffusivity in the liquid phase Due to our previous work 14, the CO2 absorption into MEA satisfies the condition of pseudo-first-order reaction. So, kL is the function of the kinetic rate constant bulk (m3/mol·s) k2, the bulk concentration of solvent in the liquid phase (mol/m3) Csol , L the CO2 diffusion coefficient in the liquid phase (m2/s) DCO and Henry’s law 2

constant for CO2 in the liquid phase (Pa•m3/mol) H CO2 :

5



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bulk L k 2 Csol DCO 2

kL =

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(2)

H CO 2

Correlation is employed in the calculation of the Arrhenius expression of rate constant k2 as a function of the temperature, as shown by 21.

k2 = 9.77 × 107 exp( −

4955 ) T

(3)

An empirical correlation is used for the Henry’s law constant of CO2 in MEA solutions, by Dang and Rochelle 22.

−2625 + 12.2) T

(4)

5076 ) − 16.999 T

(5)

MEA H CO = ω ⋅ exp( 2

with

ω = χ MEA exp(

where χMEA is the mole fraction of MEA in the unloaded solution. During CO2 absorption into nanoparticle suspensions, CO2 diffusivity could be enhanced by the shuttle effect and boundary mixing effect, which respectively considers the Brownian movement of nanoparticles as additional gas carrier inside the liquid bulk, and as additional turbulence inside the boundary layer. Hence, by measuring the liquid-side mass transfer coefficient, the deviation of CO2 diffusivity into 30 wt% MEA with and without nanoparticles can be calculated from Eq. 2-5.

2.2 Approach for defining gas-liquid effective interfacial area in bubble column In the bubble columns, because of the finite volume inside the bubbles, gas diffusing from the gas bulk to the gas-liquid interface is considered much faster than the diffusion of gas from interface to the liquid bulk. The gas-side mass transfer coefficient can be neglected as suggested in the literature 23. Thus, overall mass transfer coefficient approximates to liquid side mass transfer coefficient: (6)

K G = kL

The bubble breaking effect would enhance the mass transfer rate by increasing 6


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the effective interfacial area 16. The effective interfacial surface area in the bubble column is not a constant value, altering with the rising and growing of bubbles. It can be calculated from CO2 absorption rate N (mol/s), the logarithm average partial pressure of CO2 in the flue gas (Pa) PCO2 , the CO2 equilibrium partial pressure above ∗ and the liquid-side mass transfer coefficient (mol/(s·m2 ·Pa)) the solution (Pa) PCO 2

kL. A=

Flux N = * * ) PCO 2 − PCO 2 k L ( PCO 2 − PCO 2

(7)

The logarithm average partial pressure of CO2 in the flue gas (Pa) PCO2 can be calculated by:

PCO 2 =

PCO 2 ,in − PCO 2 ,out

(8)

ln( PCO 2 ,in / PCO 2 ,out )

Where PCO2 ,in is CO2 partial pressure at the inlet of the column chamber and

PCO2 ,out is CO2 partial pressure at the outlet of the column chamber. When CO2 loading in solvent is low enough, the CO2 equilibrium partial pressure above the ∗ solution (Pa) PCO can be neglected compared to PCO2 . Based on Eq. 6-8, the 2

effective interfacial surface area between CO2 and MEA in the bubble column can be calculated as follows: A=

N k L PCO2

(9)

Hence, by measuring the inlet and outlet CO2 partial pressure, it is possible to calculate the effective interfacial surface area under different conditions.

3. Experimental 3.1 Sample preparation Analytic pure aqueous MEA (99 wt%, Aladdin company) was used without further purification. SiO2 (15 nm), Al2O3 (15 nm) and TiO2 (15 nm) nanoparticles (Hangzhou Wanjing New Material Company) were employed to enhance the CO2 7



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absorption. Deionized water is used to prepare the solutions. High purity CO2 and N2 gases were used. No surfactants were used in the preparation of nanoparticle suspensions in order to avoid their impact on CO2 absorption. To evaluate absorption performance, 30 wt% MEA solutions were prepared 24 with mixing of nanoparticles by mechanical stirrer for 30min, and then dispersed separately with Microfluidizer high shear fluid processor (Microfluidics M-110S). Appearance of suspension and aggregation is observed and characterized (Malvern Zetasizer NanoS90) to measure the sizes of nanoparticle clusters and Zeta potential of nanofluids. Solvent viscosity might change with addition of the nanoparticles in the system, which could impact the gas-liquid mass transfer process. As reported by Murshed et al. 25, the viscosity of nanoﬂuid is substantially higher than the value of the base fluid and increases with the nanoparticle volume fraction. The viscosities of nanoparticle suspensions are measured in a viscometer (Brookfield viscometer DV-II+Pro). 3.2 Experimental setup 3.2.1 Bubbling absorption experiment The schematic flow sheet of CO2 absorption experimental apparatus is presented in Fig.1 (a). Uniformly mixed gas flows into the bubble absorber through a tube with a bubble-shaped end where bubbles are formed and growing to rise up freely in the solvent. After absorption, the mixed gas was analyzed by infrared gas analyzer (GXH-3010E1, Beijing Huayun Company) and recorded by computer. Solution of 250 ml 30 wt% MEA nanoparticle suspensions are filled into the reactors with nanoparticle solid loading of 0-0.14 wt% with 400ml volume of bubble absorber. The absorption temperature was kept at 40oC for data reference and comparison with literatures. The total mixed gas flow of mixed gas ranges from 1 to 4 L/min and the CO2 concentration of the simulated gas was fixed at 12% by volume. The bubbling operation continues uniformly during experiment until the outlet CO2 concentration of the simulated gas keeps as steady as 12% for 2 minutes. 3.2.2 Desorption experiment Fig.1 (b) shows the experimental setup of CO2 desorption where CO2 rich solvents 8


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were first prepared by venting pure CO2 gas into the fresh solution to bring the CO2 loading up to 0.5mol CO2/mol MEA. During experiments, the heat for regeneration is supplied and controlled by oil bath. Two different heat flux densities (Q1 and Q2) were obtained at oil temperature of 150oC and 130oC respectively. Since the temperature of solvent keeps constant at 103oC during regeneration, a relationship of Q2=0.6Q1 could be estimated according to the definition of heat flux density. For sample analysis, small dose solution of about 1 ml was extracted from the reactor which contains 500 ml solvent every 2.5 minutes for first 30 minutes, every 5 minutes for the remaining 45minutes. CO2 loading in the sample solution is analyzed by CO2 titration method 26, which is conducted inside a water-based eudiometer which was balanced first with one end exposed to air. The other end is filled with extra amount of H2SO4 acid solution and 500 µL sample of CO2 loaded solution. After mixing of acid and sample, the released amount of CO2 was determined by the reading difference of the eudiometer. Meanwhile, the solvent before regeneration which has the same volume as the sample has been made up to the system. For each sample, CO2 loading is analyzed 3 times. The average value was used to avoid the analytical error. The gas volume was recorded every 30 seconds.

(a) Bubbling reactor for CO2 absorption (b) Thermal regeneration apparatus for CO2 desorption Figure 1 The schematic flow sheet of experiment setup

3.3 Parameters involved The absorption rate N (mol/m3·s) refers to the amount of CO2 absorbed into the chemical agents during the unit time. Considering that the simulated gas is measured under ambient pressure and temperature, it can be treated as ideal gas. The absorption rate of absorbents is defined in Eq. 10 below: 9



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N=

P (Vin - Vout )

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(10)

RT

where P is the operation pressure (ambient), Vin and Vout are the inlet simulated gas flow rate and outlet gas flow rate, respectively (m3/s), R is the ideal gas constant (kJ/mol·K), and T is the operation temperature (K). In order to compare the effect of CO2 absorption enhancement by nanoparticles, enhancement factor of absorption Eab is introduced. Eab is defined as the ratio of average absorption rate N ' (mol/s) with addition of nanoparticles to the average absorption rate N (mol/s) without nanoparticles. Eab =

(11)

N, N

Average absorption rate N (mol/s) is expressed as Eq. 12. It is the ratio of the integration of absorption rate with time t (s) to t, where t is the saturation time.

∫ N=

t

0

N

t

=

∫

t

0

p (Vin − Vout ) RT t

(12)

In the experiments, the solvent CO2 loading ߙ (mol CO2/mol MEA) is defined as follows: α=

f (V2 − V1 )

(13)

22.4V0 msolute

where V0 is the volume of tested sample (m3), V1 and V2 are the volume of the gauge tube before and after titration (L), msolute is the mole concentration of MEA (mol/m3), and f is the gas volume correction factor when the experimental operating conditions are converted into standard operating conditions. f is a dimensionless coefficient and can be obtained as: f =

273 P0 T P

(14)

where, P0 is the standard pressure, P is operation pressure. Desorption rate of rich solutions is recorded by mass flow controller in real time (mL/min). 3.4 Error analysis 10


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The uncertainty of data derives from inherent error of instrument and random error caused by measurement. As listed in Table 2, the type I variables can be measured directly in the experiments, which includes CO2 concentration CCO2 (%), reaction temperature T (K), saturation time t (s), mixed gas volume V (L/min) and CO2 volume VCO2 (ml) during titration. The measuring accuracy of CCO2 is ±0.01% of the reading, which brings about inherent error of 0.08%. The perturbation of the CO2 concentration during the experiment introduces a random error of 0.25% for CO2 measurement. The k-type thermocouple has ±0.5K inherent uncertainty at 273-473K, which introduces a relative error of 0.11% and 0.18% into T under desorption temperature (Tdes, 423 K) and absorption temperature (Tabs, 313 K), respectively. The details of error analysis results are shown in Table 2, in which the random error is the standard deviation of three repeated measurements. The original data and their standard deviation can be found in Table S1 in the supporting information. Table 2 Results of error analysis Variable

Inherent

random

Total

error

error

error

CCO2

0.08%

0.25%

0.26%

Tdes

0.11%

0.71%

0.52%

Tabs

0.18%

0.64%

0.66%

t

0.01%

1.90%

1.9%

V

0.5%

1.5%

1.58%

VCO2

1.0%

3.33%

3.48%

N

-

-

1.73%

Eab

-

-

2.57%

A

-

-

1.75%

ߙ

-

-

3.48%

Type I

Type II

The type II variables include CO2 absorption rate N, enhancement factor Eab, 11



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effective inter facial area A and solvent CO2 loading ߙ. The error is propagated from type I variables through Eq. 9-11, 13, respectively. It could be calculated by Eq. 15 suggested by Holman 27, where Etotal is total error and ei is the error of variable i. It can be seen from Table 2 that the total error of all the variables are less than 4%. (15)

2 Etotal = ei2 + eii2 + eiii2 + eiiii

4 Results and discussion 4.1 Absorption performance 4.1.1 Effect of nanoparticles on absorption rate

Figure 2 Effect of nanoparticles on CO2 absorption rate.

Fig.2 presents the CO2 absorption rate with respect to solvent CO2 loading in pure MEA solution and nanoparticle suspensions of 0.02 wt% 15nm TiO2-MEA, 15nm SiO2-MEA and 15nm Al2O3-MEA. The whole absorption process has been divided into three stages. In stage 1, it can be clearly observed that CO2 absorption rates into MEA with addition of nanoparticles are larger than those of pure MEA solution, which means the addition of nanoparticle can accelerate the CO2 to be absorbed into MEA. During this period, the mass transfer between CO2 and MEA is dominated by CO2 diffusivity, which could be enhanced significantly by nanoparticles. This is because of the relatively high MEA concentrations. In stage 2, more CO2 being dissolved into MEA, the concentration of MEA in the liquid bulk decreases and diffusion controlled mass transfer gradually turns into kinetics controlled. Thus, the enhancement effect of nanoparticles becomes less obvious. In stage 3, the addition of nanoparticles hinders CO2 absorption process and it is due to the increased viscosity 12


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caused by agglomeration of nanoparticles in high solvent CO2 loading. In general, the enhancement effect of nanoparticles on CO2 absorption into MEA depends on material of nanoparticles, solid loading, solvent CO2 loading, etc. The impacts of different parameters have been discussed in the following sections. 4.1.2 Enhancement factors of absorption with nanoparticles The CO2 absorption enhancements of nanoparticle suspensions using 15 nm SiO2, 15 nm Al2O3 and 15 nm TiO2 nanoparticles at different solid loadings are compared in Fig. 3. It can be observed that the enhancement factor augments to a peak value at first and decrease when solid loading increases. As shown in Fig. 3, the similar curve patterns were also reported by Jiang et al. 12 in MEA/TiO2, MDEA/TiO2 systems carried out in bubble absorber. The optimum solid loading of MEA/TiO2 system in this work is about 0.06%, which is in good accordance with Jiang’s work. For different base fluid like MDEA, the optimum solid loading differs, but presents similar curve patterns. According to previous study on mass transfer enhanced by nanoparticles in wetted-wall column, Brownian motion of nanoparticles and micro-convective movement in liquid phase are most likely to be responsible for mass transfer enhancement between CO2 and aqueous amines induced by nanoparticles, of which nanoparticle-induced micro-convective movement in gas-liquid interface account for around 80% 13. Brownian motion of nanoparticles helps transport additional amount of gas across gas-liquid interface as well as accelerate CO2 to diffuse in liquid bulk, resulting in the increase of CO2 diffusion coefficient in Eq. 2. For MEA solvent with TiO2, the enhancement factor is slightly higher than the work of Jiang et al. This might be due to smaller initial particles size is better for mass diffusion enhancement 28

. TiO2 is reported to have more adsorption of CO2 than Al2O3 nanoparticles, which

may lead to larger enhancement factors compared to the SiO2 and Al2O3 nanoparticles 12

. Unlike wetted-wall column study, the mass transfer in the bubbling reactor

proceeds with the bubbles rising and growing, which undergoes a changing gas-liquid interface area. The bubble breaking effect of nanoparticles can result in the augment 13



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of gas-liquid mass transfer area. Thus, the optimum enhancement factor of absorption in the bubbling reactor is slightly larger than that obtained in wetted-wall column. With the increase in the particle solid loading, the viscosity of nanoparticle suspensions increases significantly, especially for Al2O3 and TiO2 nanoparticles. The increased viscosity reduced the diffusion coefficient and restrained the growing of bubbles, resulting in reduction of gas-liquid mass transfer rate.

Figure 3 Effect of solid loading on CO2 absorption enhancement factor

4.1.3 Characterization of bubble breaking effect Based on the established models for present study, the effective interfacial area was calculated by Eq. 9 and calculations were based on the data from the first 10s of absorption experiment (solvent CO2 loading SiO2 > Al2O3. Although, nanoparticle could reduce the heat resistance by increasing the heat conductivity and heat convection 31, the impact is generally insignificant for solid loading less than 1wt% 32. The kinetics enhancement should be contributed primarily by mass transfer enhancement. Whereas, the heat flux density also has significant impact on desorption performance. By increasing from Q2 to Q1, the time of desorption (from 0.4 to 0.25 mol CO2/mol MEA) for MEA without nanoparticle decreases from 50 mins to 26.5 mins.

(a) heat flux density of Q1 (150oC)

(b) heat flux density of Q2 (130oC)

Figure 5 Desorption time of different MEA CO2 loading range

Unlike absorption process, desorption is more of a process coupling with mass transfer and heat transfer. By considering the resistances from heat transfer and mass transfer act alone and in series, desorption rate r (mol/ m2 s) can be expressed as follows: (15)

1 dH 1 = + r ψ × h × dT dPCO2 ,eq × kd

dH of Eq. 15 represents the resistance from heat transfer. h, dT and dH ψ × h × dT

are the heat transfer coefficient (W/m2 K), temperature difference (K) between oil 16


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bath and solvent, and heat of desorption respectively (J/mol). ψ is a distribution factor which measures how much percentage of total input heat is distributed to the CO2 desorption.

1 dPCO2 ,eq × kd

of Eq. 15 represents the resistance from mass transfer. kd

(mol/m2 s) is the total mass transfer coefficient. dPCO2 ,eq (Pa) is the effective partial pressure of CO2 inside solvent and is reciprocal to the resistance from heat transfer: dPCO2 ,eq =

(16)

ϕ (ψ × h × dT) dH

where φ is the parameter which converts the amount of desorbed CO2 to the effective partial pressure of CO2 inside solvent. During CO2 desorption, the temperatures of solvent are the same at different conditions of heat flux density, the parameters of h, dH, kd and φ remains unchanged for the same reactor and solvent. Therefore, the ratio of desorption rate at different heat flux density is deduced as: t1 r2 ψ 2 dT2 = = × t2 r1 ψ 1 dT1

(17)

The CO2 desorption time (from 0.4 to 0.25 mol CO2/mol MEA) at different heat flux density is listed in Table 3. An interesting finding is that the values of ψ1/ψ2 are larger than 1, which indicates that much more input heat is supplied for the heat of desorption at higher heat flux density. During desorption, the total input heat is primarily supplied to the heat of desorption and heat of evaporation. The heat of evaporation would increase insignificant as it approaches to the critical heat flux. This might result in above phenomenon. Furthermore, compared to pure MEA solution, the addition of nanoparticle enhances the kinetics more significantly at higher heat flux density. The nanoparticles can provide more nucleation sites and improve the kinetics of bubble formation during desorption 11, as well as the distribution of heat for desorption. For example, the ψ1/ψ2 increases to 1.49 by adding TiO2 nanoparticles into MEA. From above analysis, it can be concluded that for current test conditions, nanoparticles enhance the CO2 desorption kinetics primarily through the mechanism of mass transfer enhancement. 17



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Table 3 CO2 desorption time from 0.40 to 0.25 mol CO2/mol MEA Items

Pure MEA

0.1 wt% TiO2

0.1 wt% SiO2

0.1 wt% Al2O3

Q1 (dT=50 K)

26.5min

15.5min

19.5min

21min

Q2 (dT=30 K)

50min

38.5min

40.5min

41.5min

t1/t2

0.53

0.40

0.48

0.50

ψ1/ψ2

1.13

1.49

1.25

1.2

4.3 Stability of nanoparticle suspensions As observed in the absorption experiment, aggregation and sedimentation of nanoparticles get worse during CO2 absorption process. The TEM images of wet 15 nm SiO2 nanoparticle clusters in Fig. 6 further verifies the observation. After CO2 absorption, nanoparticle agglomeration gets serious. Particle boundary is fuzzy and nanoparticles stick together like molten state. The nanoparticle suspensions were re-dispersed by Microfluidizer after absorption and it is found that the agglomeration of nanoparticles is irreversible.

(a) TEM image of SiO2 before absorption

(b) TEM image of SiO2 after absorption

Figure 6 TEM image of 15 nm SiO2 before and after absorption in 30 wt% MEA

The stability of nanoparticle clusters can be explained by the theory of electrical double layer 33. For stable and uniformaly suspended nanoparticle clusters, the repulsion force between particles should dominate the total interaction. The electrical repulsion force, or the degree of double layer charge is usually estimated by Zeta potential. The higher the absolute value of Zeta potential is, less probable the aggregation and sedimentation of nanoparticles will occur. As shown in Fig.7, the absolute values of Zeta potential above 0.3 mol CO2/mol MEA are much smaller than 18


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those below 0.3 mol CO2/mol MEA, which explains the reason why nanoparticle suspensions get more unstable at high solvent CO2 loading. It is known that ions, such as AmHCOO-, AmCOO-, CO32-, HCO3- 34, will be formed during the reaction between CO2 and MEA (Am represrnts amine). As CO2 loading increases, these ions might interact with the functional group, such as silanol, on the particle surface, resulting in the negative-positive reversal of Zeta potential. As the CO2 loading continues to increase, the increased ion concentration would compress the electric double layers (EDLs) of nanoparticles and reduce the stability of nanoparticle clusters. The increase of average size of SiO2 nanoparticle clusters under different CO2 loading also proves that high solvent CO2 loading in the nanoparticle suspensions can induce the aggregation of nanoparticles. Particle aggregation and sedmentation will be an important issue to tackle with if nano-technology is going to applied into large scale. There are several ways developed to improve the stability of nanofluids, such as using surfactants and surface modification techniques 18. Although adding surfactants in the two-phase system is an easy and economic method to enhance the stability of nanofluids, it might place some unknown effects on CO2 absorption and desorption 35, which needs further investigation.

Figure 7 Zeta potential of SiO2 nanoparticle suspensions at different solvent CO2 loadings

5 Conclusion Experimental studies were conducted to investigate the effects of nanoparticles on 19



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the absorption and desorption of CO2 into aqueous MEA solutions in the bubble column and thermal regeneration apparatus. In the diffusion controlled mass transfer stage, nanoparticle presents more obvious enhancement effect on mass transfer. It is found that SiO2, Al2O3 and TiO2 nanoparticles can enhance the CO2 absorption by over 10% and the descending order is TiO2 > SiO2 > Al2O3. Three proposed mechanisms for the enhancement effects of nanoparticles on mass transfer process were compared and the calculation results reveal that bubble breaking effect contribute most to the overall mass transfer enhancement, which brings about the augment of effective interfacial area between liquid and gas. CO2 desorption out of rich MEA solutions enhanced by nanoparticles was also studied in this work under different input heat flux. The performance of the three particles ranks as: TiO2 > SiO2 > Al2O3. By regenerating the rich solvent from 0.4 to 0.25 mol CO2/mol MEA, solution with TiO2 nanoparticles can save desorption time by about 42%. Under desorption conditions, higher heat flux would make more heat distributed to the CO2 desorption rather than water evaporation, which is enhanced by the addition of nanoparticles. Finally, nanoparticle aggregation and sedimentation during CO2 absorption were investigated through cluster size and Zeta potential analysis. It is shown that, with increased CO2 loading, the electric double layers of nanoparticles might be compressed by carbamate and carbonate ions, which would then result in agglomeration of nanoparticles. Another factor influencing the stability of mass transfer enhancement by nanoparticles is the particle dissolving issue in alkaline environment. It would be valuable work to investigate particle loss after multiple cycles of absorption and desorption, which will be carried out in our future research.

Acknowledgements This work is supported by the National Natural Science Foundation (No.51306161, 51276161), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20130101120143) and the fundamental research funds for the central universities. 20


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Contents of Supporting Information Figure S1: Reproducibility studies of experimental system Table S1: Standard deviations of measurement Table S2: Mass transfer parameters of CO2 absorption into 30 wt% MEA at 40oC, ambient pressure with addition of 15nm SiO2 nanoparticles Table S3: Average sizes and PDI results by Malvern Zetasizer test with dispersion of Microfluidizer high shear fluid processor Figure S2: Viscosities of amine nanoparticle suspensions (T=40oC shear rate = 158.4 s-1) Figure S3: Average size of SiO2 nanoparticle clusters at different solvent CO2 loadings

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Table of Contents (TOC) Graphic

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MEA - ACS Publications - American Chemical Society

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