Optimization of CO2 Capture Process from Simulated Flue Gas by Dry

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Optimization of CO2 Capture Process from Simulated Flue Gas by Dry Regenerable Alkali Metal Carbonate-Based Adsorbent Using Response Surface Methodology (RSM) Mohsen Amiri, Shahrokh Shahhosseini, and Ahad Ghaemi Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b03303 • Publication Date (Web): 07 Mar 2017 Downloaded from http://pubs.acs.org on March 7, 2017

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Optimization of CO2 Capture Process from Simulated

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Flue Gas by Dry Regenerable Alkali Metal Carbonate-

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Based Adsorbent Using Response Surface

4

Methodology (RSM)

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Mohsen Amiri1, Shahrokh Shahhosseini1

6

1

School of Chemical Engineering, Iran University of Science and Technology, P.O. Box 16765163,

7

Tehran, Iran.

8

Abstract

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The low cost of K2CO3/Al2O3 adsorbent is encouraging to use it for CO2 capture from the flue gas of

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fossil-fuel power plants. In this study, optimization of CO2 capture process using K-based adsorbent

11

in a fixed-bed reactor has been investigated. The sorbent was also characterized by different

12

techniques such as SEM, BET, and XRD analysis before and after the reactions. Response surface

13

methodology (RSM) combined with Box–Behnken design (BBD) was employed to evaluate the

14

effects of the process variables (temperature, mole ratio of H2O/CO2 and vapor pretreatment time)

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and their interaction on the responses (CO2 capture capacity and deactivation rate constant) to

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achieve the optimal conditions. In addition to the experiments, the deactivation model in the non-

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catalytic heterogeneous reaction system was employed to evaluate the kinetic parameters (sorption

18

rate and deactivation rate constants) using nonlinear least square technique. According to the analysis

19

of variance (ANOVA), the vapor pretreatment time and temperature were found to be the most

20

important process variable, which affect CO2 adsorption capacity. Moreover, two quadratic semi-

21

empirical correlations were established to calculate the optimum operating conditions of CO2 capture

22

process. The predicted values of the correlations showed very good agreement with the experimental

23

data. The optimum process variables obtained from the numerical optimization corresponded to 61.3

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°C, 1 and 9 min for adsorption temperature, mole ratio of H2O/CO2 and min vapor pretreatment time,

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respectively. Based on the optimal condition, the highest adsorption capacity of 87.71 (mg CO2/g

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sorbent) in 100% CO2 removal zone (corresponding to 97.82% of theoretical adsorption capacity in

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the total zone) and the lowest deactivation rate constant of 0.1872 (min-1) were obtained.

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Furthermore, additional experiments performed in the optimal conditions resulted in 86.97 (mg

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CO2/g sorbent) adsorption capacity and deactivation rate constant of 0.1874 (min-1). The results

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indicate that the presented models could adequately predict the responses and provide suitable

31

information for the process scale-up.


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Keywords: CO2 capture; Optimization; Response surface methodology (RSM); Adsorption; Alkali

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metal carbonate; Kinetic.

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1. Introduction

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Environmental concerns such as global warming and climate change have inspired researchers to

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develop more effective and improved processes for carbon dioxide (CO2) capture 1. The climate of

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the earth is changing continuously due to numerous factors, particularly growth in greenhouse gas

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(GHG) concentrations 2. Among the greenhouse gases, CO2 contributes more than 60% to the global

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warming

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atmospheric concentration of CO2 has increased from 280 ppmv to 404 ppmv and as a result, the

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average global temperature has increased between 0.6 °C to 1 °C 5. International Panel on Climate

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Change (IPCC) predicts that, by the year 2100, the atmosphere may contain up to 570 ppmv CO2,

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causing a rise of mean global temperature of around 1.9 °C and an increase in mean sea level of 38 m

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2, 4, 6, 7

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Extreme atmospheric greenhouse gases are responsible for several environmental problems such as

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melting of the snow cover and ice caps, rising the sea levels, increasing number of the ocean storms

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and floods 2, 9.

3, 4

. From the origin of the industrial revolution in about 1850 till now, the mean

. Man-made sources of CO2 include power plants, refineries, and cement industries

1, 8

.

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CO2 Capture and sequestration (CCS) from the huge CO2 sources is recognized as the main option

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to address the problem of global warming and climate change. CCS consist of four principal stages:

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CO2 capture, compression, transport, and storage 9. In CO2 capture, CO2 emissions from thermal


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power plant flue gas can be diminished by any of the following methods: Pre-combustion CO2

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capture, post-combustion CO2 capture and oxy-fuel combustion 2. In pre-combustion CO2 capture,

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the fossil fuel is gasiﬁed and reacted in a water gas shift reactor to generate CO2 and H2. The CO2 is

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captured, while the H2 is used for energy generation. In post-combustion technology, the fossil fuels

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are combusted as in regular energy generation, and then the CO2 is captured from the discharge gas 9.

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Current power plants use air for combustion and produce a flue gas at atmospheric pressure and

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typically have a CO2 concentration of less than 15% 2. Oxy-fuel combustion consumes pure or nearly

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pure O2 for combustion, such that mainly CO2 and H2O are generated 9. Among these technologies,

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the post-combustion process is more appropriate for CO2 capture of the conventional power plants 5.

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Multiple post-combustion technologies are available for CO2 capture from fossil fuel power plant.

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These include mainly physical and chemical absorption, adsorption, cryogenic and membrane

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processes 2. Among these processes, chemical or physical absorption processes using amine solutions

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,such as monoethanol amine (MEA), are regularly used in the petroleum, natural gas and power

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plants as well as chemical industries for separation of CO2

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conventional amines are simply degraded and tend to lose its capacity because of the presence of

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classic flue gas pollutants (e.g., O2, SO2, HCl, and particulates) and need high capital and operating

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costs associated with CO2 separation from large volumes of flue gas at low CO2 concentrations 6. In

1, 2, 4, 9


. However, in this process

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addition, the sorption of water into the gas stream involves an extra drying process, the evaporation

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of the water rises the cost of the process 5.

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Reported alternative process for liquid absorption is adsorption using solid adsorbents such as

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zeolites, activated carbon and MOFs are not appropriate for working under flue gas condition due to

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the high affinity to the moisture, low selectivity or low CO2 adsorption capacities at relatively low

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CO2 partial pressure

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sorbents to capture CO2 from flue gas 6, 11, 12. Alkali metal carbonate, such as K2CO3 and Na2CO3 can

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react with CO2 in the existence of H2O and transform to alkali metal hydrogen carbonates salt

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(KHCO3 or NaHCO3) at low temperatures. Water vapor is consistently essential as shown in the

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reaction (1) 5:

1, 10

. A particularly promising option includes the use of dry alkali metal-based

M2CO3+CO2+H2O⟺2MHCO3

(1)

∆H= -141 kJmol-1, M=K ∴ -135 kJmol-1, M=Na 79

In the carbonation step, CO2 could react with M2CO3 in the presence of water vapor at low

80

temperature range of (50-100 °C). Thermal regeneration basically happens at a temperature lower

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than 300 °C. After steam condensation, high-concentrated CO2 can be obtained and compressed,

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ready for transportation and storage

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capacities than Na2CO3 13, 14. It has been suggested that to use porous matrix as support to place solid

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chemisorbent on it in order to improve the carbonation rate of K2CO3. Therefore several studies have

11, 13

. K2CO3 has been discovered to give more CO2 capture


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been performed by researches on various supports, such as Al2O3, TiO2, Ac, SiO2, ZrO2, CaO and

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zeolites

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and high attrition resistance, well-developed microstructure due to the Al2O3 support, and appears to

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be a nearly ideal sorbent for CO2 capture 6, 14, 21.

15-20

. K2CO3/Al2O3 in particular, appeared to have a high porosity, high mechanical strength

89

The process of CO2 capture using dry alkali metal carbonate-based adsorbents has been

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extensively studied in recent years, and was believed to have potential to be practical in fossil-fuel

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power plants

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amount of CO2 capture capacity are temperature, H2O concentration, CO2 concentration and water

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pretreatment time 18, 22, 25. However, by the fact that these variables have interactions with each other,

94

up to the present time no optimal operational condition has been proposed by the researchers in order

95

to maximize the CO2 capture capacity. It is worth noting that the best condition is the one, which has

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the highest possible removal capacity and reaction rate together. No systematic statistical study has

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been done on this subject

11, 18, 22-27

. As, reported by the researchers, most important variables that affect the

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Response surface methodology (RSM), initially described by Box and Wilson (1951), is useful for

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design of the experiments, estimation of the effects of multiple process variables with their

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interactions on response variables and finding the optimum conditions 28, 29. Latterly, RSM has been

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applied to decrease the required experimental data in order to attain the best operating conditions for


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a desired response in numerous chemical processes 30-34. Therefore, in the present work, RSM based

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on Box-Behnken design (BBD) has been used for experimental design, proposing empirical models

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and determination of the optimum values of the variables (carbonation temperature, mole ratio of

105

H2O to CO2 and water pretreatment time) for the desirable response variables (CO2 capture capacity

106

and reaction rate). Besides, the deactivation model was applied to determine the kinetic rate and

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deactivation rate constants using the breakthrough curve data with non-linear least square technique.

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The main objectives were to optimize the CO2 capture process and investigate the influence of

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process variables on adsorption capacity and reaction rate. In addition, two semi-empirical

110

correlations were developed for each response based on experimental data.

111 112

2. Experimental Section

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2.1.

Solid Sorbent Preparation

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The potassium based sorbent used in this study was prepared by dry impregnation of K2CO3 (99%

115

purity from Merck) on porous γ-Al2O3 (from Merck) as a support. The preparation process consisted

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of three steps: (1) mixing and impregnation in deionized water, (2) drying at 100 °C for dehydration

117

and calcination at 300°C. Then, the sorbent was grained and sieved for collecting the solid sorbent


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particle size in the range of 150-300 µm. In addition, the designated loading of K2CO3 on γ-Al2O3

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was 35 %wt.

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2.2.

Adsorbent Characterization

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Phase composition of the sorbent before and after the carbonation reaction was examined with a

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PHILIPS PW 3830 X-ray diffraction (XRD) system. A PHS-1020 (PHSCHINA) system with N2

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adsorption-desorption was used to determine surface area and pore size distributions. The

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microscopic shapes of the sorbent was observed by a Philips XL30 scanning electron microscopy

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(SEM). The impregnated amount of the alkali metal was determined by a PHILIPS PW1480 X-ray

126

fluorescence (XRF).

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2.3.

Apparatus and Procedure

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The cyclic carbonation reactions were conducted in lab-scale fixed bed reactor (FBR) unit, as

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shown in Figure 1. The experimental apparatus mainly consisted of four sections: (1) simulated flue

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gas system and injection, (2) temperature controlled bath, (3) fixed-bed reactor (FBR), (4) CO2

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analysis in discharge stream. N2 and CO2 were supplied from high-purity cylinders and transported

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to the experimental apparatus with individual mass flow controllers. Besides, water was supplied by

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a high-precision liquid pump and then heated to guarantee complete vaporization of the water before

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mixing with other gases. Additionally, heat tracing was applied to keep the temperature of the 9 ACS Paragon Plus Environment

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pipeline high enough to avoid condensation of the steam. The temperature of the reactor was

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controlled with a circulating fluid through the FBR jacket. The reactor temperature was measured by

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the thermocouples at the inlet and outlet of the reactor. The diameter and height of the reaction zone

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were 12 mm and 35 mm, respectively. The mass of the loaded solid adsorbent was about 3.5 g for

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each experimental run. The CO2 concentration in the treated discharge stream was continuously

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measured by an online infrared (IR) analyzer (Vaisala, Finland, measurement limit 0-20 vol%). The

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total flow rate of the feed gas was 80 ml/min. In addition, temperature ranges for carbonation and

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decarbonation stages were 55-80 °C and 300 °C, respectively. The simulated flue gas consisted of

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different amount of CO2 and H2O with a balanced amount of N2. The adsorbent was pretreated with

144

the vapor for (3-9) min to achieve higher adsorbent capacity.


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Figure 1. Schematic diagram of the fixed-bed reactor apparatus

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The sorption operation is as follows. Firstly, N2 gas stream flows through the FBR in each

148

experimental run in order to achieve isothermal condition of the sorbet particles and also to prevent

149

water condensation at low temperature. Secondly, water vapor is added to the N2 stream for about 3-

150

9 min. This time is considered as the vapor pretreatment stage. Then, CO2 is added to the mixture

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stream of N2 and water vapor. After that, carbonation reaction starts through the bed and CO2 is

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adsorbed.

153 154

3. Theoretical Basis 11 ACS Paragon Plus Environment

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156 157

3.1.

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CO2 capture capacity calculation

CO2 capture capacity during carbonation reaction, Ac (mg CO2/g sorbent), was determined through Eq (1):

Ac =

1000C i w

t

∫ Q (1 −ψ (t) ) ρ .dt 0

 mg CO 2     g sorbent 

(1)

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Where w, Ci, Q, t,  and  are sorbent mass (g), inlet CO2 concentration (%vol), gas flow rate

159

(cm3/min), time (min), dimensionless outlet CO2 concentration (C/Ci) and CO2 density (g/cm3),

160

respectively. In addition, the fractional CO2 removal, Fr, was expressed as Eq (2):

Fr =

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CO2 capture capacity during carbonation reaction (Aci ) Theoretical CO2 capture capacity

3.2.

(2)

Mathematical Modelling

162

Along with the progress of CO2 reaction, a dense product layer is formed on the solid surface of

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the adsorbent particles. Moreover, the amounts of pore volume and surface area as well as the

164

activity of the adsorbent change over the reaction time. In the deactivation model (DM), all of these

165

effects on reduction of CO2 reaction rate are combined into a deactivation rate term 35.

166 167

The mass conservation equation of CO2 in the fixed bed with the assumption of the pseudo-steady state and isothermal condition is given as Eq (3):


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(1 − εb )τ K ψ a dψ =− εb dζ ψ=

C z L , ζ = ,τ = Ci L ug

(3)

B .C . ζ = 0 ⇒ ψ = 1

168

Where ψ, ζ, εb, τ, z, L, ug, Co, C and K are dimensionless outlet CO2 concentration (C/Ci),

169

dimensionless length of bed, bed porosity, time scale of flow through the bed (min), axial coordinate

170

in fixed bed (m), total length of bed (m), superficial velocity of gas flow (m/min), inlet and outlet

171

CO2 concentration (%vol) and reaction rate constant (min-1), respectively. The activity (a) in the

172

above equation is the ratio of the reaction rate for the adsorbent at a time, to the reaction rate for the

173

fresh adsorbent. According to the DM, the variation of the activity over time is expressed as Eq (4):

da = − k d a nC mψ m dt I .C . t = 0 ⇒ a = 1

(4)

174

Where a, t, kd, n and m are sorbent activity, reaction time (min), deactivation rate constant (min-1)

175

and corresponding exponents. The outlet concentration profile of CO2 was obtained from numerical

176

solution of Eqs (3, 4) (n=1 and m=0) with corresponding boundary and initial conditions using the

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fourth-order Runge-Kutta method. In addition, the two unknown parameters in the equations (K and

178

kd) were determined by using nonlinear least-squares technique using experimental data.

179


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4. Experimental Design

181

4.1.

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Methodology

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Experimental design of the process for optimization of CO2 adsorption from the simulated flue gas

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was carried out using the response surface method (RSM). RSM is a collection of statistical and

184

mathematical techniques advantageous for developing, improving and optimizing the processes36. It

185

clarifies the effects of independent variables, alone or in combination, on the processes. In addition

186

to analyzing the effects of independent variables, this experimental methodology generates an

187

empirical model, which describes the corresponding quantity of the process37. Response surface

188

methodology (RSM) is the most prevalent optimization method used in recent years. The

189

experimental design and statistical analysis were performed using Stat-Ease software (Design-Expert

190

7.0 trial). A three-level three-factor Box-Behnken design (BBD) consisting of 17 experimental runs

191

was employed including five replicates at the center paint. The effects of unexplained inconsistency

192

in the observed response due to inessential factors were minimized by randomizing the order of the

193

experiments.

194

Three variables considered in this study were (X1) bed temperature (°C), (X2) H2O/CO2 mole ratio

195

in feed and (X3) vapor pretreatment time, which are presented in Table (1). Each variable was varied


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over three levels -1,0,+1 at the determined ranges based on some preliminary experiments. The

197

generalized quadratic polynomial model used in the RSM is given as Eq (5): 3

3

3

Y = β 0 + ∑ βi X i2 + ∑∑ βij X i X j + Ε i =1

(5)

i =1 j < i

198

Where Y is the predicted response (i.e. Initial capacity and Rate constant), 0, i, ii and ij are

199

regression coefficients for intercept, linear, quadratic and interaction terms, respectively. Xi and Xj

200

are independent variables, and Ε is the unanticipated error.

201

Table 1. Independent variables and their coded and actual values for optimization Independent variable

Unit

Symbol

Coded Levels -1

0

+1

Temperature

°C

X1

50

65

80

H2O/CO2

-

X2

0.5

1

1.5

Vapor pretreatment Time

min

X3

3

6

9

202

The analysis of variance (ANOVA) table was generated and the effect and regression coefficients

203

of the individual linear, quadratic and interaction terms were determined. The significances of all

204

terms in the polynomial model were judged statistically by calculating F-value at a probability (P-

205

value) of (0.001, 0.01, and 0.05).

206

5. Results and Discussion


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5.1.

Characterization of K2CO3/Al2O3 sorbent

208

5.1.1.

Porous structure performances

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It is generally verified that the microstructure of the sorbents play an important role in their CO2

210

capture processes. Therefore, the porous structure of the K2CO3/Al2O3 (KAL) sorbents were

211

characterized using N2 adsorption-desorption analysis. The specific surface areas and pore volumes

212

of the KAL sorbents (before and after the carbonation reaction) and fresh Al2O3 were calculated

213

using the Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH) methods, respectively.

214

The results are presented in Table (2).

215

Table 2. Structural characteristic of γ-Al2O3 and K2CO3/Al2O3 (before and after carbonation reaction) Sorbent

Surface area (m2/g)

Pore volume (cm3/g)

γ-Al2O3

173.9

0.49

K2CO3/Al2O3 (fresh)

73.4

0.24

K2CO3/Al2O3 (used)

52.1

0.13

216

Based on the results of Table (2), both surface area and pore volume of fresh KAl sorbent are

217

much less than those of the pure support (γ-Al2O3). In addition, pore size distributions (PSDs) of γ-

218

Al2O3 and KAL before and after reaction are illustrated in Figure 2. The results confirm that support

219

γ-Al2O3 is a mesoporous material (with pore diameter of in the range of 2-50 nm) and about 80% of

220

pore diameters are in the range of 2-10 nm with a peak point at 4.3 nm. After loading of K2CO3 on 16 ACS Paragon Plus Environment

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γ-Al2O3 (fresh KAL) the amount of pore volume decreased and also the curve peak shifted towards

222

larger pore diameter (5 nm), based on Figure 2 and Table (2). These variations could be explained by

223

K2CO3 partial filling or blocking of the smaller γ-Al2O3 pores and by hydrothermal treatment of

224

support during preparation of the sorbent. Similarly, both surface area and pore volume of the used

225

KAl are less than those of the fresh KAL, which shows that CO2 could penetrate well through the

226

pores of the sorbent and react with K2CO3 to produce KHCO3.

227

228 229

Figure 2. BJH pore size distributions for γ-Al2O3 and K2CO3/Al2O3 (before and after carbonation

230

reaction)

231

5.1.2.

SEM Analysis


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Another key factor for the CO2 capture process is the distribution type of K2CO3 on the AL2O3

233

support before and after the reaction. The effect of carbonation reaction (1) on the morphology of

234

sorbent and the characteristics of the active-sorbent distributions over the support particles were

235

evaluated using SEM analysis. The SEM images were taken at a magnification of 5000 which is

236

shown in Figure 3.

237 238 239 (a)

(b)

240

Figure 3. SEM images of K2CO3/Al2O3 sorbent, (a) before carbonation reaction, (b) after carbonation

241

reaction

242

As exhibited in Figure 3a, the porous surface of Al2O3 where well covered by rod-shaped

243

crystallites of K2CO3 in the range of 1-5 µm before the carbonation reaction, similar result is 18 ACS Paragon Plus Environment

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reported by Qin et al 2014 11; whereas after carbonation reaction some aggregate is created over the

245

surface of support (shown in Figure 3b). This aggregates imply the creation of KHCO3 crystals,

246

which block the porous sorbent and results in a reduction in active sites for CO2 adsorption.

247

5.1.3.

XRD Analysis

248

In order to find out the reaction paths of K2CO3/Al2O3, the phase composition change of sorbent

249

before and after the reaction was studied by XRD analysis. XRD patterns of γ-Al2O3 support and

250

KAl sorbent samples before and after the carbonation reaction are shown in Figure 4.

251 252

Figure 4. XRD patterns for: (a) fresh γ-Al2O3 support, (b) K2CO3/Al2O3 before carbonation reaction,

253

(c) K2CO3/Al2O3 after carbonation reaction in 8% CO2 + 8% H2O +84% N2 at 65 °C. Crystalline

254

phases: (■) γ-Al2O3, (▲) K2CO3, (♦) KHCO3, (✯) K4H2(CO3)3.1.5H2O


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255

As shown in Figure 4a, there are mainly six peaks attributed to γ-Al2O3 [ICDD PDF-2 #10-0425],

256

which selected as support material. The diffraction peaks present in Figure 4b are assigned to the

257

monoclinic crystalline phase of potassium carbonate [K2CO3, ICDD PDF-2 #71-1466] and γ-Al2O3,

258

which are consistent with the K2CO3/Al2O3 design samples before reaction. Based on the peak

259

intensity of XRD pattern (Figure 4c) the main product of carbonation reaction at the condition of 8%

260

CO2 + 8% H2O +84% N2 at 65 °C was KHCO3 and implies the nearly complete carbonation

261

conversion of K2CO3. However, the XRD pattern (Figure 4c) not only shows two phases of γ-Al2O3

262

and the monoclinic crystalline phase of potassium bicarbonate [KHCO3, ICDD PDF-2 # 1-0976] but

263

also smaller values of the mixed potassium hydrogen carbonate hydrate phase [K4H2(CO3)3.1.5H2O,

264

ICDD PDF-2 # 20-0886]. This indicates that K2CO3 could be converted to KHCO3 and

265

K4H2(CO3)3.1.5H2O in the presence of CO2 and H2O. Similar results already have been declared by

266

other researchers

267

concentration of 5-9% at 60 °C

268

reaction temperature and high H2O concentration as reported by Zhao et al. 25. In addition, Luo et al.

269

presented the following reaction mechanism instead of reaction (1) based on the XRD pattern tests.

16, 24, 25, 38, 39

. It was stated that K4H2(CO3)3.1.5H2O was produced in H2O 16

. More K4H2(CO3)3.1.5H2O is formed in the condition of low

K2CO3(s) + 1.5H2O(g)  K2CO3·1.5H2O(s)

(2)

2K2CO3·1.5H2O(s) + CO2(g)  K4H2(CO3)3·1.5H2O(s) + 0.5H2O(g)

(3)

K4H2(CO3)3·1.5H2O(s) + CO2(g)  4KHCO3(s) + 0.5H2O(g)

(4)


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270

They claimed that the carbonation reaction process of K2CO3 could be separated into three

271

reactions, including the formation of K2CO3·1.5H2O from K2CO3, the subsequent production of

272

K4H2(CO3)3·1.5H2O from K2CO3·1.5H2O, and the production of KHCO3 from K4H2(CO3)3·1.5H2O.

273

Moreover, they reported that the production of KHCO3 from K4H2(CO3)3·1.5H2O was

274

thermodynamically promising upon the increase of the CO2 concentration. As a result, the

275

concentration of H2O and CO2 (or the mole ratio of H2O/CO2) and temperature could be the key

276

variables that affect the reaction path, which is discussed more in the next section.

277

Moreover, based on the XRD results in Figure (4), neither in the fresh sorbent nor in the reaction 17, 26

278

product the phase of KAl(CO3)2(OH)2 is found. Similar results were obtained by Zhao

279

According to the literature, this phenomenon could be related to two reasons. Firstly,

280

KAl(CO3)2(OH)2 could be generated by the reaction of the γ-Al2O3 support with K2CO3 during

281

calcination in the preparation of sorbent

282

completely converted into the K2CO3 phase during calcination at the temperature higher than 290

283

°C.15, 40 The second reason for the absence of KAl(CO3)2(OH)2 phase in reaction product could be

284

due to the water pretreatment effect. Lee et al. found that after water pretreatment the amount of

285

KAl(CO3)2(OH)2 phase id significantly reduced 38.

17, 38

.

. In addition, the KAl(CO3)2(OH)2 phase was


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286

287 288

5.2.

Page 22 of 48

CO2 Breakthrough Curve of K2CO3/Al2O3

The result of a typical test during carbonation reaction for K2CO3/Al2O3 (under the reaction condition of H2O/CO2=1.5, 65 °C with 9min vapor pretreatment) is shown in Figure 5.

289

Figure 5. Breakthrough curve of CO2 outlet concentration (reaction condition: of H2O/CO2=1.5, 65

290

°C with 9min vapor pretreatment), (a) comparison of the experimental and modelling results (R2

291

=0.99), (b) definition of adsorption zone (full and partial adsorption zones)

292

As is illustrated in Figure 5a, CO2 concentration of the outlet gas varies over the reaction time,

293

which is also called as “Breakthrough Curve (BC)”. Furthermore, the modelling result is compared

294

with the real experimental one and shows high accuracy (R2=0.99). Calculated kinetic parameters for

295

this test, K and kd were equal to 210452.817 (min-1) and 0.142 (min-1), respectively. Besides, the

296

CO2 concentration at the outlet is definitely zero for about 50 min, indicating 100% CO2 removal

297

(also called “Breakthrough Time (BT)”), then CO2 concentration at the outlet rapidly increases to

298

reach as same as the inlet concentration ( equals one). Based on this definition, the longer BT is, the 22 ACS Paragon Plus Environment

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Energy & Fuels

299

more efficient CO2 removal is. CO2 removal capacity could be evaluated by integrating the area

300

under the curve of (1-(t)), as displayed in Figure 5b. The adsorption zone is divided into full and

301

partial adsorption zones, FAZ and PAZ respectively, where FAZ corresponds to 100% removal. For

302

this run test, adsorption capacities are 82.55 and 27.61 (mg CO2/g sorbent) for FAZ and PAZ,

303

respectively. This result indicates that about 75% of total CO2 removal capacity (110.16 (mg CO2/g

304

sorbent)) belongs to FAZ, which is more important in terms of industrial consideration than PAZ.

305

Moreover, Fr value for this test is evaluated as 96.1%, (real K2CO3 ratio in sorbent was 35.5%

306

through XRF result), which shows the reaction practically reaches to the complete conversion.

307

Therefore, it is so useful to discover the optimum reaction condition in which the adsorption capacity

308

in the FAZ (Aci) reaches the highest possible value.

309

5.3.

Analysis of variance (ANOVA)

310

The designed experiments (17 runs) using BBD method and the responses obtained from the

311

experiments are shown in Table (3). The observed Aci and Kd vary between (35.15-82.55 (mg CO2/g

312

sorbent)) and (0.18-0.68 (min-1)), respectively.

313

Table 3. Corresponding experimental design and response values STD X1 X2

X3 Aci (mg CO2/g sorbent) Kd (min-1)

1

50 0.5 6

35.152

0.68

2

80 0.5 6

48.290

0.543


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Page 24 of 48

3

50 1.5 6

37.909

0.27

4

80 1.5 6

53.150

0.22

5

50 1

3

44.048

0.352

6

80 1

3

63.243

0.25

7

50 1

9

68.673

0.255

8

80 1

9

81.642

0.21

9

65 0.5 3

58.754

0.51

10

65 1.5 3

64.139

0.18

11

65 0.5 9

80.566

0.39

12

65 1.5 9

82.548

0.142

13

65 1

6

66.865

0.253

14

65 1

6

67.325

0.25

15

65 1

6

65.864

0.263

16

65 1

6

67.270

0.2535

17

65 1

6

68.420

0.2453

314

An acceptable fit of the model is needed to avoid poor or uncertain optimized results. This is

315

essential to confirm the accuracy of the model. Table (4) shows the analysis of variance (ANOVA)

316

of regression parameters of the predicted response surface quadratic model for Aci and kd using the

317

results of the experiments.

318 319

Table 4. ANOVA results for the RSM-BBD model of each responses Analysis of variance (Aci)


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Source

D.f.a

Sum of squares Mean square F-value

Pr>F

Model

9

3233.41

359.27

407.45

4), which clarify

330

that noise to ratio of the model is placed in the desirable range

331

well the model fits the data. Strong lack of fit (p

Optimization of CO2 Capture Process from Simulated Flue Gas by Dry

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