Optimization of CO2 Capture from Simulated Flue Gas Using K2CO3

Jun 8, 2018 - ABSTRACT: The cost-effective dry regenerable K2CO3/Al2O3 ... plants.1 Absorption using amine solutions is a matured .... (1. ( )) d t t ...
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Optimization of CO2 Capture from Simulated Flue Gas Using K2CO3/Al2O3 in a Micro Fluidized Bed Reactor Mohsen Amiri, and Shahrokh Shahhosseini Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b00789 • Publication Date (Web): 08 Jun 2018 Downloaded from http://pubs.acs.org on June 13, 2018

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Energy & Fuels

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Optimization of CO2 Capture from Simulated Flue Gas

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Using K2CO3/Al2O3 in a Micro Fluidized Bed Reactor

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Mohsen Amiri, and Shahrokh Shahhosseini*

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School of Chemical, Petroleum, and Gas Engineering, Iran University of Science and Technology,

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P.O. Box 16765163, Tehran, 16846-13114, Iran.

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ABSTRACT

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The cost effective dry regenerable K2CO3/Al2O3 seems to be a promising sorbent for CO2 removal

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from the flue gas of fossil fuel power plants. In this work, the characterization of carbonation

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reaction and process optimization were performed in a so-called micro fluidized bed reactor

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(MFBR), which has recently been applied to study gas–solid reactions. The sorbent was also

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characterized by BET and SEM techniques. In addition, the most important gas-solid heterogeneous

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models were evaluated and the kinetic parameters were determined by the model fitting approach.

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Based on the kinetic study results, the homogeneous model (HM) and the shrinking core model

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(SCM) were selected as the reaction models. Also, the effects of the independent variables including

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temperature, gas flow rate, and vapor pretreatment amount on the responses (adsorption capacity and

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reaction rate constant) were investigated by the response surface methodology (RSM) coupled with

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Box-Behnken-Design (BBD). Regarding to the analysis of variance (ANOVA) results, the

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temperature and gas flow rate are the most important factors affecting the adsorption capacity and

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the reaction rate constant, respectively. In addition, the semiempirical polynomials were developed

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to find the optimum condition corresponding to the highest adsorption capacity and reaction rate.

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Consequently, the optimum independent variables were 60 °C, 562 CCM and 22.2 mg H2O condition

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for the temperature, gas flow rate, and vapor pretreatment amount. The best response values of 65.29

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mg CO2/g sorbent and 0.3402 (min-1) were predicted for the adsorption capacity and reaction rate

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constant at the optimum conditions which were verified experimentally. The presented results are

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applicable and essential for future simulation and modeling CO2 capture in the fluidized bed reactor.

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Keywords: CO2 capture; Micro fluidized bed reactor (MFBR); Optimization; Response surface

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methodology (RSM); Adsorption; Kinetic.

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1. INTRODUCTION

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Global warming and climate change resulting from the greenhouse gas (GHG) emissions has

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become a widespread concern in the last two decades.1 According to the prediction of

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Intergovernmental Panel on Climate Change (IPCC) the temperature increases between 1.0 to 3.7 °C

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through the 21 century.2, 3 Among the greenhouse gases, carbon dioxide (CO2) is the most significant

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contributor to global warming.4, 5 The main source of anthropogenic CO2 emissions is the flue gas

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from power plants burning fossil fuels.6, 7 CO2 capture and storage (CCS) is known as an imperative

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option for decreasing CO2 emissions in recent years.8 Postcombustion is a key CCS technology

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option for the conventional power plants.9

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Postcombustion processes include mainly physical and chemical absorption, adsorption, cryogenic

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and membrane processes.5 Among these technologies chemical absorption and adsorption are the

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most appropriate ones for current power plants.1 Absorption using amine solutions is a matured

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technology in petroleum, natural gas and power plants, although it has several issues including

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solvent losses, corrosion, hazardous byproducts, and high-energy requirement for regeneration.5, 8, 10

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Therefore, adsorption process using solid sorbents is an encouraging alternative in CO2 capture from

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flue gas and also have a good potential in the future9.

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Recently, multiple research groups have been involved in the development of solid physisorbent

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for CO2 capture including activated carbonaceous materials, microporous/mesoporous silica or

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zeolites and MOFs.

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13% CO2, 9% H2O) due to the high attraction to the water vapor, low thermal and mechanical

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stability, and low CO2/N2 selectivity or low CO2 adsorption capacities at moderately low CO2 partial

9, 11

However, these sorbents are not applicable under flue gas condition (78 N2,

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pressure.

An especially promising opportunity is the usage of dry alkali metal-based sorbents to

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capture CO2 from flue gas stream. 8, 12, 13 Dry alkali metal carbonates like K2CO3 and Na2CO3 could

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react with CO2 in the presence of water vapor and produce the alkali metal hydrogen carbonates salt

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(KHCO3 or NaHCO3) at low temperatures through the reaction R1 14, 15:

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M2CO3 + CO2 + H2O  2MHCO3

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∆H = -141 kJmol-1 ⸫ M=K

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

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temperature range of (50-100 °C). MHCO3 decomposes at a moderate temperature change of 120-

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200 °C and produce CO2/H2O mixture that could be converted into CO2 stream ready for

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transportation and storage by condensing the water vapor.4, 12, 16 In addition, it is reported that K2CO3

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shows more CO2 capture capacities than Na2CO3.16, 17 Researchers suggested several porous matrixes

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as support including Al2O3, TiO2, Ac, SiO2, ZrO2, CaO and zeolites.18-23 Among them K2CO3/Al2O3,

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appeared to possess a high porosity, mechanical strength and attrition resistance. It has a well-

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developed microstructure and seems to be an almost perfect sorbent for fluidized bed CO2 capture

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process.8, 17, 24

(R1) ∆H = -135 kJmol-1 ⸫ M=Na

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According to the literature, most of the studies on potassium based sorbent have been conducted

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using thermogravimetric analyzer (TGA)20, 25-30 or in a fixed bed reactor.15, 17-19, 23, 31-36 These studies

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provide a good comprehension of carbonation reaction behavior and the effect of operating factors.

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The carbonation reaction of K2CO3/Al2O3 in a fixed bed reactor and the effect of operating condition

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have been reported by Amiri et al.15 They found that the temperature and vapor pretreatment time are

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the most significant carbonation process variables.

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The so-called micro fluidized bed reactor (MFBR) has been successfully developed to study gas–

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solid reactions characteristics and reaction kinetics.37-49 In comparison to TGA and fixed bed, MFBR

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results in quicker heat and mass transfer between gas and particles, faster reaction heat removal and

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less external interfacial diffusion limitations. MFBR also gives the capability of real-time

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measurement of the gaseous product through an on-line gas analyzer.40, 49 Although few studies have

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been performed on the CO2 capture using K2CO3/Al2O3 in fluidized bed reactors in bench scale,21, 50-

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under different operating conditions in MFBR. The final goal for this technology is to develop a CO2

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capture process with dual fluidized-bed reactors under optimum conditions to achieve maximum CO2

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capture capacity and reaction rate. Performance studies describing the effect of different operating

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variables on the adsorption capacity and reaction kinetics are essential to commercially apply CO2

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capture with the fluidized bed technology.53

there is no systematic statistical study on carbonation reaction characteristics and reaction kinetics

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Recently, response surface methodology (RSM) is successfully applied to estimate the individual

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and interactive effects of multiple process variables and find the optimum conditions.15,

54-56

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Therefore, in the present study the micro fluidized bed reactor (MFBR) was employed to evaluate the

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carbonation characteristics of K2CO3/Al2O3 sorbent in the simulated flue gas. RSM with

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Box−Behnken design (BBD) has been applied for the experimental design, proposing empirical

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correlation and specification of the optimum values of the process variable (Temperature, gas flow

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rate and vapor pretreatment time) for the desirable response variables (adsorption capacity and

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reaction rate constant). Kinetic parameters were investigated based on the model-fitting approach.

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The main objectives were to optimize the carbonation process in MFBR and evaluate the effect of

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the process variables on adsorption capacity and reaction rate by proposing two semiempirical

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correlations.

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2. EXPERIMENTAL SECTION 2.1.

Solid Sorbent Preparation

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The solid sorbent (K2CO3/ γ-Al2O3) was prepared by coating the conventional wet impregnation of

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(K2CO3, Merck, 99% purity) on the porous particles (γ-Al2O3, Merck, 99% purity). The preparation

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process was composed of four stages: (1) mixing and impregnation of in the aqueous mixture of

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deionized water and 16.2 g K2CO3 with a magnetic stirrer at 25°C for 14 h, (2) drying at 100°C in an

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oven for 24 h, (3) calcination at 300°C for 4 h, and (4) graining and sieving of the particles for

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fluidized bed tests. Additionally, the desired loading of K2CO3 on γ-Al2O3 particles was 35 wt. %.

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2.2.

Sorbent Characterization

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A PHS-1020 (PHSCHINA) system with N2 adsorption-desorption was applied to analyse surface

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area and pore size distributions. The microscopic shape of the sorbent was examined by a Philips

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XL30 scanning electron microscope (SEM). The impregnated amount of the K2CO3 on Al2O3 was

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determined by a PHILIPS PW1480 X-ray fluorescence (XRF) system.

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2.3.

Apparatus and Procedure

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The cyclic carbonation reactions under isothermal condition were performed in a micro fluidized

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bed reactor (MFBR) unit, as illustrated in Figure 1. The experimental apparatus primarily composed

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of five segments: (1) gas supply section, (2) steam generator system, (3) temperature-controlled bath,

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(4) micro fluidized bed reactor (MFBR), and (5) online CO2 analysis in the outlet stream. Feed gases

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(N2 and CO2) were provided from high-purity (vol. 99.999%) cylinders and transmitted to the MFBR

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with separate mass flow controllers (MFC). In addition, water feed was prepared by a high-precision

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liquid pump and then preheated by heat tracing of the tube line to ensure full water vaporization

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before mixing with N2 and CO2. Isothermal condition of reactor was established with a circulating 7 ACS Paragon Plus Environment

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fluid through the MFBR jacket. The reactor temperature was measured by two thermocouples at the

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entrance and output of the MFBR. Dimensionally, the internal height and diameter of the bed zone

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were initially 20 mm and 12 mm, respectively. Moreover, CO2 composition in the treated outlet

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stream was continuously recorded by an online infrared (IR) analyzer (Vaisala, Finland,

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measurement limit vol. 0-20%). Furthermore, the carbonation and regeneration tests were performed

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at 60-80 and 300 °C, respectively. Based on the results of the previous study, H2O/CO2 mole ratio

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was kept as one with a balanced amount of N2 and the cyclic tests were performed by the same

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regenerated sorbent due to complete regeneration of the sorbent at 300 °C.15 The particle size of bed

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material was in the range 90−106 µm. According to the Ergun equation, the minimum fluidization

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velocity (Umf) and corresponding minimum fluidization flow rate of the sorbent particles were

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calculated as 1.91 cm/s and 129.8 mL/min, respectively. Therefore, the total gas flow rate of the inlet

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stream through MFBR was designed in the range of 450-650 mL/min to ensure fluidization of

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particles.

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Figure 1. Schematic diagram of the micro fluidized bed reactor (MFBR) apparatus

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At the beginning of each carbonation experiment about 2 g of solid adsorbent was loaded into the

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MFBR. Then, N2 gas was passed through the MFBR in order to reach the isothermal condition of the

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sorbet particles and to prevent water vapor condensation at low temperature. Thereafter, N2 stream

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with water vapor (mol. 10%) was passed through the bed till the amount of H2O during the vapor

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pretreatment period reached to 12-24 mg. Subsequently, CO2 was added to the gas mixture of N2 and

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H2O vapor and the carbonation reaction began through the sorbent fluidized bed and CO2 was

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removed. Finally, the sorbent regenerated at 300 °C and prepared for the next experimental run. The

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reaction characteristics including kinetic parameters were determined by analyzing the data collected

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using the online gas analyzer.

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3. THEORETICAL BASIS

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3.1.

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CO2 capture capacity during carbonation reaction, Ac (mg CO2/g sorbent), was calculated through

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Adsorption Capacity and Conversion

eq 1:

Ac =

1000C i w

t

∫ Q (1 −ψ (t) ) ρ.dt 0

 mg CO2     g sorbent 

(1)

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

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

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(1.55 g/cm3), respectively. Furthermore, CO2 removal conversion degree, X, was determined by eq

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2: t

X =

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∫0 (C i −C )Q dt tf

∫0 (C i −C )Q dt

t

=

∫ (1 −ψ (t ) ) dt ∫ (1 −ψ (t ) ) dt 0 tf

(2)

0

Where C, and tf are the outlet CO2 concentration (vol %) and final time (min), respectively.

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Energy & Fuels

3.2.

Kinetic Method

Generally, the overall kinetic rate of heterogeneous gas solid reactions under isothermal condition as a function of reaction temperature T (K) and conversion X, can be defined by eq 3:

dX = K(T) F(X) dt 155 156

(3)

Where F(X) is the differential reaction rate function depending on the mechanism and K(T) is the reaction rate constant determined by the Arrhenius equation in eq 4: K(T ) = K o e - E a

RT

(4)

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Where Ko, R, and Ea are pre-exponential factor (s-1), gas constant (8.314 Jmol-1K-1), and apparent

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activation energy (Jmol-1), respectively. In addition, the integral reaction model G(X) can be

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determined by integrating eq 3 in the form of eq 5. G(X ) =



X

o

dX = K(T) t F(X )

(5)

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Accordingly, based on the conventional model-fitting method57, the experimental conversion and

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time data can be used in eq 4 and K(T) is determined as the slop of linear curve G(X) ~ t. There are

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twenty mechanism functions that have been commonly used for the kinetic parameters specification

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of heterogeneous solid-state reactions38, 49, 53, 57-59, as illustrated in Table 1. In current study, the form

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of G(x) with the highest average correlation coefficient (R2) in all tests is selected to be the

165

mechanism function that characterizes the carbonation reaction kinetics.

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Table 1. Typical Mechanism Function Using in Gas-Solid Reactions No. Model Power law models 1 2 3 4 Phase interfacial reaction 5 Zero order (plate) 6 2D (cylindrical) 7 3D (spherical) Diffusion models 8 1D 9 2D Va lensi equation 10 3D(Jander) 11 3D(A-J) 12 3D(Ginstling–Broushtein) Reaction-order models 13 First order 14 3/2 order 15 Second order 16 Third order Nucleation models 17 n=1.5 18 n=2 19 n=3 20 n=4

F(X)

G(X)

Ref

4X3/4 3X2/3 2X1/2 2/3X-1/2

X1/4 X1/3 X1/2 X3/2

57

1 2(1-X)1/2 3(1-X)2/3

X 1-(1-X)1/2 1-(1-X)1/3

57

1/(2X) [-ln(1-X)]-1 3/2(1-X)2/3[1-(1-X)1/3]-1 3/2(1-X)(2/3)[(1+X)1/3-1]-1 3/2[(1-X)-1/3-1]-1

X2 X+(1-X)ln(1-X) [1-(1-X)1/3]2 [(1+X)1/3-1]2 (1-2/3X)-(1-X)2/3

1-X (1-X)3/2 (1-X)2 (1-X)3

-ln(1-X) 2[(1-X)-1/2-1] (1-X)-1-1 0.5[(1-X)-2-1]

1.5(1-X)[-ln(1-X)]1/3 2(1-X)[-ln(1-X)]1/2 3(1-X)[-ln(1-X)]2/3 4(1-X)[-ln(1-X)]3/4

[-ln(1-X)]2/3 [-ln(1-X)]1/2 [-ln(1-X)]1/3 [-ln(1-X)]1/4

57 57 57

57 57

57 38, 57 38, 57 38, 49 38, 57

57 57 57 57

38, 57 38, 57, 58 38, 57, 58 38, 57, 58

167 168

4. EXPERIMENTAL DESIGN: METHODOLOGY

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Response surface method (RSM) was used for experimental design and optimization of the

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carbonation process. RSM is a collection of statistic and arithmetical techniques beneficial for

171

optimizing the processes, which is used widely in recent years

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variables, alone or with their interactions, on the measured responses are explained by RSM.

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Furthermore, a multivariate empirical model based on the independent variables and their

174

interactions is generated as a quadratic polynomial equation shown in eq 6. 12 ACS Paragon Plus Environment

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. The effects of independent

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Energy & Fuels

3

3

3

y = α 0 + ∑ α i x i2 + ∑∑ α ij x i x j + e i =1

(6)

i =1 j