Simulation and Optimization of a Dual-Adsorbent, Two-Bed Vacuum

Article pubs.acs.org/IECR

Simulation and Optimization of a Dual-Adsorbent, Two-Bed Vacuum Swing Adsorption Process for CO2 Capture from Wet Flue Gas Shreenath Krishnamurthy,† Reza Haghpanah,‡ Arvind Rajendran,§ and Shamsuzzaman Farooq*,† †

Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585 Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Stanford, California 94305, United States § Department of Chemical and Materials Engineering, University of Alberta, 9107-116 Street, Edmonton, AB, Canada T6G2 V4 ‡

S Supporting Information *

ABSTRACT: Various options for the capture and concentration of CO2 from a wet flue gas at 25 °C containing 15% CO2 in 82% N2 and 3% moisture have been analyzed through detailed simulation and optimization. First, a proven cycle for dry flue gas, consisting of four steps including light product pressurization in a column packed with zeolite 13X established in an earlier communication (Haghpanah et al. AIChE J. 2013, 59, 4735) and demonstrated at the pilot scale (Krishnamurthy et al. AIChE J. 2014, 60, 1830), was applied to the wet flue gas. Detailed optimization studies using a nondominated sorting genetic algorithm (NSGA-II) in MATLAB were carried out first to maximize purity and recovery. Further optimization was carried out to obtain the operating conditions corresponding to minimum energy consumption subject to 95% purity and 90% recovery constraints. The minimum energy consumption in this process required to achieve 95% purity (dry basis) and 90% recovery was 230 kWh (t of CO2 captured)−1 with a productivity of 1.03 t of CO2 (m3 of 13X)−1 day−1. This energy consumption was considerably higher and the productivity was considerably lower than those reported for dry flue gas (Haghpanah et al. AIChE J. 2013, 59, 4735). Next, to improve the performance, a new dual-adsorbent, four-step vacuum swing adsorption (VSA) process with silica gel and zeolite 13X packed separately in two beds was proposed. By separating the two adsorbents in two beds, instead of layering them in the same column, it was possible to avoid rewetting of the concentrated CO2. The process optimization of the new cycle revealed that the 95% purity and 90% recovery target could be achieved at a lower energy penalty [177 kWh (t of CO2 captured)−1] while also improving the productivity [1.82 t of CO2 (m3 of 13X)−1 day−1, 1.29 t of CO2 (m3 of total adsorbent)−1 day−1].

1. INTRODUCTION Adsorption separation processes have been extensively investigated for the capture of CO2 from flue gas. These include laboratory-scale experimental studies,1,2 modeling and optimization studies,3−6 and pilot-scale demonstrations of CO2 capture from flue gas.7−11 However, most of these studies have considered a dry flue gas for CO2 capture. The underlying assumption in these studies is that moisture will be removed first and then the dry flue gas will be sent for CO2 capture. Real flue gas, however, contains various components such as SOx, NOx, and water vapor along with CO2 and N2. Prior to the capture process, the flue gas might be sent to selective catalytic reduction (SCR) and flue gas desulfurization (FGD) units for removal of NOx and SOx, and the resultant flue gas is a mixture of CO2 with a large amount of N2 that is saturated with moisture.12 The moisture content increases with increasing temperature (for example, the saturation moisture content is 3.2 mol% at 25 °C and 12 mol% at 50 °C13). The cost of drying using another separation process can be high. There are only a few published studies in which CO2 capture from wet flue gas using adsorption processes has been addressed. Li et al.14 carried out an experimental study of a three-step vacuum swing adsorption (VSA) cycle for capturing CO2 from a flue gas containing 12 mol % CO2, 3.4 mol % H2O, and 84.6 mol % air in a single bed with zeolite 13X as the adsorbent, at a pressure of 118 kPa and a temperature of 30 °C. The steps include pressurization, high-pressure adsorption, and © XXXX American Chemical Society

countercurrent evacuation; the durations of these steps were 3, 45, and 112 s, respectively. The CO2 purity achieved in the process was 72% on a wet basis, and the recovery was 60%. Under the same operating conditions, a purity of 69% and recovery of 78% were achieved for a dry flue gas. The performance deterioration was due to the coadsorption of H2O along with CO2 in 13X, thereby reducing its adsorption capacity. In a follow-up work, Li et al.15 studied the aforementioned three-step VSA process in a layered bed with CDX alumina and zeolite 13X as adsorbents. The objective was to trap the moisture in the CDX alumina layer and to capture and concentrate the CO2 in the 13X layer. Using the layered bed, CO2 was recovered along with water vapor at a purity of 67% on a wet basis, and the recovery was 76.9%. The effect of moisture on the performance of an adsorption process for capturing CO2 from flue gas employing zeolite 13X as the adsorbent was studied by Su and Lu.16 Using two columns, they studied the capture of CO2 from a humid flue gas by a temperature vacuum swing adsorption (TVSA) cycle comprising adsorption, heating, desorption under a vacuum, and cooling. The high-pressure adsorption steps were carried out at 30 and 40 °C, and the column was heated to 140 °C Received: June 19, 2014 Revised: August 22, 2014 Accepted: August 22, 2014

A

dx.doi.org/10.1021/ie5024723 | Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Industrial & Engineering Chemistry Research

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Table 1. Dual-Site Langmuir Isotherm Parameters for CO2, N2, and H2O Adsorption on Zeolite 13X and Silica Gel qsb (mol/m3)

a

b0 (m3/mol)

CO2a N2a H2Ob CO2b,c

3489.44 6613.55 10468.60 3125.7

8.65 × 10−7 2.50 × 10−6 2.35 × 10−7 8.8 × 10−8

CO2d N2d H2Oe

2692.30 918.90 718.13

3.2 × 10−7 1.2 × 10−5 5.6 × 10−13

ΔUb (kJ/mol) zeolite 13X −36.64 −15.82 −55.72 −38.15 silica gel −27.12 −12.12 −89.20

qsd (mol/m3)

d0 (m3/mol)

ΔUd (kJ/mol)

2872.35 0 6434.50 1897.5

2.63 × 10−8 0 7.99 × 10−8 1.18 × 10−9

−35.70 0 −45.48 −34.44

0 0 52632.60

0 0 7.74 × 10−8

0 0 −39.90

Zeochem 13X. bGrace Davison 13X. cObtained by fitting binary data. dZeochem silica gel. eGrace Davison silica gel.

2. MODELING AND SIMULATION OF THE VSA CYCLES FOR CO2 CAPTURE FROM WET FLUE GAS A nonisothermal, nonisobaric model was used to simulate the capture of CO2 from a wet flue gas. The model equations were discussed earlier4 and were validated for dry flue gas using pilotplant experiments, as reported previously.7 These equations were extended to accommodate a third component, with the addition of a second component mass-balance equation and a third adsorption rate equation. The model equations were discretized using weighted essential non oscillatory (WENO)finite volume method, and the resultant system of coupled ordinary differential equations (ODEs) was solved with 30 finite volumes in MATLAB.3,4 The model equations, boundary conditions, and cyclic-steady-state criterion are detailed in the Supporting Information. The adsorption equilibrium of CO2 and N2 in zeolite 13X was obtained using a gravimetric apparatus, and these results were presented in the publication on the pilot-plant study.7 The adsorption data for water vapor on 13X used in this study were taken from the work of Wang and LeVan.18 Although there might be some differences in moisture adsorption between our 13X sample and the sample used in the published study, any such differences will not impair our immediate objective to understand the effect of wet flue gas on CO2 capture by VSA on zeolite 13X. The adsorption isotherms of water vapor on zeolite 13X were fitted to the dual-site Langmuir isotherm model, which is of the form

before vacuum was applied. It was shown that the adsorbent was less sensitive to moisture when the feed temperature was below 30 °C and the performance deterioration was higher at 40 °C because of the higher moisture content in the feed. In another work, Xu et al.17 simulated the capture of CO2 from a flue gas mixture containing 12% CO2, 10% H2O, and 78% air by employing a layered bed containing a desiccant and zeolite 13X, similar to the study carried out by Li et al.14 In this case, BASF Sorbead WS and F-200 alumina were the desiccants. The cycle used in this study was a nine-step VSA cycle with adsorption, equalization, desorption, product purge, and repressurization steps. It was possible to achieve 93% purity on a wet basis and 80% recovery when the bed was packed with a 1:3 ratio of Sorbead and zeolite 13X. A higher purity of 94.3% on a wet basis and a lower recovery of 79% were achieved when the bed was packed with F-200 activated alumina. The advantage of adding a layer of a desiccant before the 13X bed is that the desiccant can remove moisture more efficiently than 13X. The disadvantage of layering the two adsorbents in the same bed is that the concentrated CO2 product from the evacuation step is collected with the moisture through the same outlet, thus making it humid again after it has been separated and concentrated. Although it can be argued that most of the moisture will condense out in the compression step necessary to sequester CO2, the resulting increased moisture and twophase flow will affect the compressor performance, which can easily be avoided. A new cycle is proposed to eliminate the unnecessary rewetting of concentrated CO2. This cycle, detailed later, is a dual-adsorbent, two-bed, four-step VSA process in which the desiccant and 13X are packed in two separate columns. This arrangement, similar to a layered bed, allows the desiccant bed to trap moisture and the moisture-free CO2/N2 mixture to pass to the 13X bed. However, unlike the layered bed, this arrangement allows separate evacuation of the two beds to extract concentrated CO2 that is practically dry. The proposed new cycle was used to study the capture of CO2 from a wet flue gas containing 15% CO2 in 82% N2 and 3% moisture at 25 °C. Silica gel was chosen as the desiccant, and the reasons for this preference are discussed in section 4. The cycle was first analyzed to understand the effect of the length of the silica gel bed with respect to that of the 13X bed on the process performance. The dual-adsorbent system was then optimized to obtain the length of the silica gel bed (for a fixed length of the 13X bed) and the operating conditions for minimum energy consumption. The energy advantages of the new cycle over the four-step cycle with light product pressurization using only 13X are also addressed.

qi* =

qsb, ibiCi 1 + biCi

+

qsd, idiCi 1 + diCi

(1)

where bi = b0i e−ΔUb,i / RT

(2)

and di = d0i e−ΔUd,i / RT

(3)

(See the Notation section for the definitions of the remaining variables.) The data and fits are shown in Figure S1 of the Supporting Information, and the isotherm parameters are reported in Table 1. For completeness, the adsorption isotherm parameters of CO2 and N2 are also reported in Table 1. Two different approaches (called cases 1 and 2) were employed for predicting mixture equilibrium, as detailed next. 2.1. Case 1. The perfect positive (PP) form19 of the extended dual-site Langmuir isotherm was used B


Industrial & Engineering Chemistry Research qi* =

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qsb, ibiCi

* qCO = ,ter

1 + bH2OC H2O + bCO2CCO2 + bN2C N2 +

2

qsd, idiCi 1 + d H2OC H2O + dCO2CCO2 + d N2C N2

2

(4)

qN* ,ter = 2

2

1 + dCO2,binCCO2 + d H2OC H2O + d N2C N2

qsb,N bN2CCO2 2

1 + bCO2,binCCO2 + bH2OC H2O + bN2C N2 +

qsd,N d N2CCO2 2

1 + dCO2,binCCO2 + d H2OC H2O + d N2C N2

(7)

It was assumed that CO2 and N2 did not compete with water vapor and therefore the adsorption of water vapor was described by eq 1 in case 2. The two cases represent two extreme assumptions to account for the presence of water. Case 1 represents underestimation of water and overestimation of CO2 adsorption. Case 2 most likely represents overestimation of water and improved estimation of CO2 adsorption. The results from these two cases are expected to bracket the true behavior. Detailed cycle synthesis study by Haghpanah et al.3 showed that the four-step VSA cycle with light product pressurization (LPP) was the most efficient cycle among six different cycle configurations investigated, and this conclusion was validated with pilot-plant experiments.7 Therefore, in our study, a fourstep VSA cycle with LPP was first simulated to establish a benchmark for the capture of CO2 from wet flue gas. The four steps were pressurization with light product from the product end, high-pressure adsorption, forward blowdown, and reverse evacuation, as shown in Figure 1. The column and the

2

1 + bCO2,binCCO2 + bH2OC H2O qsd,CO ,bindCO2,binCCO2 2

1 + dCO2,binCCO2 + d H2OC H2O

qsd,CO ,bindCO2,binCCO2 (6)

qsb,CO ,binbCO2,binCCO2

+

2

1 + bCO2,binCCO2 + bH2OC H2O + bN2C N2 +

with parameters from the single-component isotherm fits (data in the first, second and third rows of Table 1 under zeolite 13X), to describe the competitive adsorption in the ternary system. 2.2. Case 2. Binary adsorption equilibrium data for CO2 and H2O in zeolite 13X were reported by Wang and LeVan.20 In their study, the binary adsorption of CO2 was measured for three different water loadings, namely, 1, 3.4, and 9.4 mol/kg. The water loadings were held constant, and at each water loading, the CO2 concentration was increased in steps to obtain the adsorption isotherm data at three different temperatures (0, 25, and 50 °C). In their study, Wang and LeVan found that the experimental equilibrium isotherm of CO2 in the presence of moisture was lower than the predictions of the competitive dual-site Langmuir isotherm model using the single-component isotherm parameters. To reduce the difference, the measured mixture data were fitted to the competitive dual-site Langmuir model represented by the equation * qCO = ,bin

qsb,CO ,binbCO2,binCCO2

(5)

In eq 5, bCO2,bin and dCO2,bin also follow Arrhenius-type temperature dependence, given by eqs 2 and 3 with the preexponential constants and internal energy changes on the righthand sides of the equations represented by b0,CO2,bin, d0,CO2,bin, ΔUb,CO2,bin, and ΔUd,CO2,bin. In the experimental adsorption study, it was observed that moisture loading was unaffected by the introduction of CO2. Given the H2O loadings (1, 3.4, and 9.4 mol/kg) and the temperatures (0, 25, and 50 °C), the corresponding partial pressures were estimated according to the single-component dual-site Langmuir isotherm model. Using the H2O partial pressure obtained from the above step and the singlecomponent isotherm parameters of H2O (third row in Table 1 under zeolite 13X) in eq 5, the CO2 isotherm parameters qsb,CO2 ,bin, qsd,CO2,bin, b0,CO 2,bin, d0,CO2,bin, ΔU b,CO2 ,bin, and ΔUd,CO2,bin were fitted to the H2O/CO2 mixture data to empirically account for the reduced affinity of CO2 for 13X in the presence of moisture. The fitted CO2 parameters are also listed in Table 1 (fourth row under zeolite 13X). It is important to note that the maximum partial pressure of water vapor in the work of Wang and LeVan20 was 0.01 bar, and hence, in this study, we used extrapolated information. Equation 5 was then extended to accommodate the presence of N2 in flue gas, and the adsorption isotherms for CO2 and N2 under ternary conditions in case 2 were described by the equations

Figure 1. Schematic of a four-step VSA process with light product pressurization (LPP).

adsorbent properties used in the simulations were similar to those used in the work of Haghpanah et al.,3 and they are reproduced in Table S2 of the Supporting Information. The macropore molecular diffusion mechanism was assumed for the transport of CO2, N2, and H2O in zeolite 13X. As mentioned earlier, real flue gas can contain up to 12% moisture at 50 °C. However, the isotherm data in the literature are limited to 0.018 bar in zeolite 13X and 0.03 bar in silica gel. To obtain isotherm data at higher partial pressures, significant C



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that the CO2 front was pushed farther into the column in the presence of moisture, which results in the increased loss of CO2 in the high-pressure adsorption and blowdown steps, causing a drop in recovery. This also reduced the residual N2 content in the bed, which increased the dry-basis CO2 purity somewhat. Deeper penetration of the CO2 front was due to the higher adsorption capacity of water vapor, which affected the adsorption of CO2 in the zone closest to the column inlet. As expected, the bed profiles of water vapor in both cases, shown in Figure 2b, confirm that the moisture front was concentrated in a small mass-transfer zone close to the column inlet. The increased energy consumption in the wet cycle is due to the additional energy needed to remove the strongly adsorbed moisture from the column, which was concentrated close to the feed end. 3.2. Optimization of the Cycle for Wet Flue Gas. The optimization of the four-step VSA process with LPP was performed using a non-dominated sorting genetic algorithm (NSGA-II) in MATLAB. This approach was adapted from the work of Haghpanah et al., 3 because of its relatively straightforward implementation and greater computational speed through parallelization. Although genetic algorithms (GAs) do not theoretically guarantee that the global minimum is identified they have been shown to escape local minima; a key limitation of gradient-based optimization techniques. Genetic algorithms have been extensively used in the chemical engineering literature to optimize a variety of processes such as catalytic reactions,21 simulated moving bed chromatography,22 and pressure swing adsorption.3,4,23,24 Optimization was first performed to maximize purity and recovery. The decision variables were the step durations (tads, tbd, tevac), the vacuum pressure levels (PI, PL), and the inlet feed velocity (V0). The high pressure (PH) was kept fixed at 1 atm. The duration of the light product pressurization (LPP) step was the duration required by the column to attain high pressure (PH) and was dependent on the exit flow rate from the highpressure adsorption step of the previous cycle. The duration of this step can, at most, be equal to the duration of the adsorption step. The bounds for the decision variables are listed in Table 2.

extrapolation is required, and hence, our study is limited to 25 °C only.

3. FOUR-STEP VSA CYCLE WITH LPP 3.1. Effect of Moisture. The four-step VSA cycle with LPP was simulated for the following operating conditions for flue gas containing 15% CO2 in 82% N2 and 3% moisture at 25 °C: tads = 78.4 s, tbd = 37.9 s, tevac = 111.6 s, PI = 0.08 bar, PL = 0.03 bar, and V0 = 0.45 m/s. Under these operating conditions with a dry flue gas, it was possible to attain 95.2% purity and 90.1% recovery with an energy consumption of 161.2 kWh (t of CO2 captured)−1 and a productivity of 1.8 t of CO2 (m3 of 13X)−1 day−1. For wet flue gas, using case 1 equilibrium parameters for the ternary interaction, the purity was 78.8% on a wet basis (95.2% on a moisture-free basis), and the recovery decreased to 88.6%. The productivity was 1.73 t of CO2 (m3 of 13X)−1 day−1. However, the energy consumption increased significantly to 188.8 kWh (t of CO2)−1. Under the same conditions, 80% purity (96.2% on a dry basis), 85.4% recovery, an energy consumption of 192.4 kWh (t of CO2 captured)−1, and a productivity of 1.64 t of CO2 (m3 of 13X)−1 day−1 were achieved using case 2 parameters. The bed profiles of CO2 for the wet and dry cycles are compared in Figure 2a. It is evident

Table 2. Bounds for Optimization of a Four-Step VSA Process with LPP variable

lower bound

upper bound

variable

lower bound

upper bound

tads (s) tbd (s) tevac (s)

20 30 30

150 200 200

PI (bar) PL (bar) V0 (m/s)

0.03 0.03 0.1

0.5 0.5 2

Because the blowdown pressure must be higher than the evacuation pressure for the two steps to be physically distinct, the following inequality constraint was used PI ≥ PL + 0.02 (bar)

(8)

In the optimization, the population size was 10 times the number of decision variables, and the genetic algorithm was run for 60 generations. The purity, recovery, energy, and productivity values were taken at cyclic steady-state (CSS) conditions. The criterion for cyclic steady state was less than 0.5% error in the mass balance of five consecutive cycles. Most configurations reached a cyclic steady state in ∼300 cycles.

Figure 2. Bed profiles of (a) CO2 and (b) H2O in a four-step VSA process with LPP. Solid lines correspond to dry flue gas, and dashed and dotted lines correspond to cases 1 and 2, respectively, for wet flue gas. Operating conditions: tads = 78.4 s, tbd = 37.9 s, tevac = 111.6 s, PI = 0.08 bar, PL = 0.03 bar, and V0 = 0.45 m/s. D



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Figure 3. Purity−recovery Pareto curves for four-step VSA with light product pressurization (LPP) for wet (cases 1 and 2) and dry cycles. The lower bound on the evacuation pressure was PL = 0.03 bar. The Pareto curve for dry flue gas was taken from Haghpanah et al.3

Figure 4. Energy−productivity Pareto curves for four-step VSA with light product pressurization (LPP) for wet (cases 1 and 2) and dry flue gases and the proposed dual-adsorbent, two-bed, four-step cycle for wet flue gas. All of the Pareto curves satisfy 95% purity and 90% recovery constraints. Open symbols denote productivity in tonnes of CO2 (m3 of 13X)−1 day−1, whereas solid symbols denote productivity in tonnes of CO2 (m3 of total adsorbent)−1 day−1. Productivity based on total adsorbent volume was calculated by appropriately modifying eq 14. The lower bound on the evacuation pressure was PL = 0.03 bar. The Pareto curve for dry flue gas was taken from Haghpanah et al.3

comparison with that of the four-step LPP cycle for dry flue gas. As discussed earlier, the additional energy consumption was due to the energy required to desorb water from the column. The minimum energy consumption for 95%−90% purity−recovery constraints in the LPP cycle with 15% CO2 and 3% H2O in a balance of nitrogen as the feed was 195.1 kWh (t of CO2 captured)−1 with a productivity of 1.32 t of CO2 (m3 of 13X)−1 day−1 for case 1 and 230 kWh (t of CO2 captured)−1 with a productivity of 1.03 t of CO2 (m3 of 13X)−1 day−1 for case 2. In the case of the LPP cycle with dry flue gas, the minimum energy consumption was 154 kWh (t of CO2 captured)−1, and the productivity was 1.52 t of CO2 (m3 of 13X)−1 day−1.3

The purity−recovery Pareto curves for the wet flue gas cycle for both cases 1 and 2 are shown along with the Pareto curve for dry flue gas in Figure 3. The Pareto curve for dry flue gas was taken from the work of Haghpanah et al.3 Both the wet and dry cycles were able to achieve the desired purity−recovery target of 95%−90%. Therefore, the next objective was to identify operating conditions with the minimum energy penalty in the four-step cycle with LPP for CO2 capture from wet flue gas subject to the constraints of purity ≥ 95% and recovery ≥ 90%. The energy−productivity Pareto curves for the four-step cycle with LPP in Figure 4 clearly show that the energy consumption increased in the presence of moisture in E



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In case 2, the amount of CO2 adsorbed was lower, which, in turn, increased N2 adsorption. Thus, there was a decrease in the effective selectivity of CO2 over N2 is less in case 2 compared to that in case 1, which increased the energy consumption in the former case.

4. PROPOSED DUAL-ADSORBENT, TWO-BED, FOUR-STEP VSA PROCESS FOR WET FLUE GAS As shown in the previous section, it was possible to obtain 95% purity and 90% recovery of CO2 for the capture and concentration of CO2 from a wet flue gas with 3% moisture. However, the energy consumption was higher, and it would increase further at higher moisture contents. In this section, the energy performance of the proposed new cycle is investigated. The choice of silica gel and the desiccant in the proposed new cycle is discussed first. Silica gel and alumina are wellknown adsorbents that exhibit high capacities for moisture,18,25,26 and the isotherms at 25 °C for the adsorption of water vapor on both of these adsorbents are shown in Figure S2 (Supporting Information). The moisture loading in alumina is initially favorable but shows an inflection at a pressure well within the range of moisture partial pressures in wet flue gas.25,26 Capillary condensation occurs at pressures beyond the inflection point. Desorption under these conditions follows a different pathway, giving rise to hysteresis. The presence of a hysteresis loop in an isotherm in the pressure range of interest is detrimental to the performance of the PSA process, which was the determining factor in the selection of silica gel over alumina as the adsorbent in this study. The sequence of operations in the proposed new dualadsorbent, four-step cycle in which the two adsorbents are separately packed in two columns is schematically shown in Figure 5. The cycle consists of the following steps: (1) Pressurization: In this step, the silica gel column is pressurized with the feed from the feed end, and the product end is closed. The 13X column is pressurized from the product end in the reverse direction with the light product obtained from the adsorption step of the previous cycle. The zeolite bed cannot be pressurized with the feed, as the moisture present in the feed would contaminate the feed end of the column. (2) Adsorption: In the high-pressure adsorption step, feeding to the silica gel column continues, and its product end is now opened. The exit stream from the high-pressure adsorption step in the first column, free of moisture, is the feed for the 13X column, which also undergoes the high-pressure adsorption step. (3) Blowdown: The silica gel column is then blown down to an intermediate pressure in the countercurrent direction, whereas cocurrent blowdown is carried out in the 13X bed. Haghpanah et al.4 explained that the objective of the cocurrent blowdown step in the 13X bed is to eliminate most of the nitrogen from the zeolite bed while conserving CO2 in the bed to increase its purity and recovery in the evacuation step. In case of the silica gel bed, the blowdown step serves the purpose of removing adsorbed moisture from the bed, and hence, the blowdown is done in the same direction as the evacuation. (4) Evacuation: The silica gel bed is evacuated in the reverse direction to further remove moisture from the bed. The 13X bed is also evacuated in the reverse direction to obtain the CO2 product. In the sequence of operation of the two-bed VSA process, the only coupled step is the high-pressure adsorption step, where the exit stream of the first column is the feed to the

Figure 5. Schematic of the proposed dual-adsorbent, two-bed, fourstep VSA process for CO2 capture and concentration from wet flue gas. Durations of the blowdown and evacuation steps, as well as PI and PL can be different in the two beds.

second column. Therefore, the duration of the high-pressure adsorption step in the two columns should be the same. The blowdown and evacuation steps in the two beds are uncoupled, and different durations in the two beds are possible. The duration of the light product pressurization step is dependent on the exit flow rate of the effluent stream from the adsorption step. At least two units of the two-bed scheme in Figure 4 will be necessary to conduct LPP directly using the product from another 13X bed. With only two units, the feeding will still remain intermittent. Multiples of the two columns packed with two different adsorbents will be necessary to make the feeding continuous. 4.1. Adsorption Equilibrium in Silica Gel Used in Process Simulation. The adsorption isotherms of CO2 and N2 on Zeochem silica gel were obtained using a RUBOTHERM magnetic suspension balance and circa 1 g of silica gel. The isotherms were obtained for temperatures ranging from 25 to 75 °C, and the data were fitted to a Langmuir isotherm. The results from gravimetry are shown in Figure 6. Clearly, the CO2 capacity was much higher than the nitrogen capacity in silica gel, and the single-site Langmuir model was able to describe the adsorption of CO2 and N2 in silica gel. Limited validation of the single-component isotherms was performed by dynamic column breakthrough experiments, and the results are also included in Figure 6. Binary adsorption of CO2 and N2 was studied by performing dynamic column breakthrough experiments in a bed saturated with nitrogen. The equilibrium loading was obtained by mass balance and was compared with the predictions of the extended Langmuir model. It can be seen in Figure 7 that the prediction from the extended Langmuir model using the singlecomponent adsorption isotherm parameters was in good F



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gases in silica gel is practically homogeneous and that the single-site Langmuir isotherm model can fit their equilibrium data very well. However, the dual-site Langmuir model, with zero values for the second-site parameters, was used to provide a general framework of the extended dual-site Langmuir model for mixture equilibrium predictions. In this part of the study, the perfect positive (PP) form of the extended dual-site Langmuir isotherm19 (eq 4) was used, along with the parameters from the single-component isotherm fits, to describe the competitive adsorption of the ternary system in both silica gel and 13X adsorbents. This is similar to case 1 described earlier for 13X. 4.2. Parametric Study of the Proposed New Process for Wet Flue Gas. In the simulations, the silica gel bed was assumed to be at low pressure (PL) initially, and the 13X bed was considered to be at high pressure (PH) saturated with the light component. The silica gel bed was pressurized with feed, and this step was followed by the high-pressure adsorption step, where the output from the first column was the input to the second column, which simultaneously underwent the highpressure adsorption step. The exit-stream data from the highpressure adsorption step of the second column were then stored in a buffer and used for the light product pressurization step in the subsequent cycle. The simulations were carried out using 30 finite volume elements in MATLAB. The model equations, boundary conditions, and cyclic steady-state criterion are detailed in the Supporting Information. In the dual-adsorbent, two-bed, four-step VSA process, the purity of CO2 was determined as the average CO2 content in the evacuation stream from the 13X bed

Figure 6. Adsorption equilibrium of CO2 (open symbols) and N2 (solid symbols) on Zeochem silica gel. Equilibrium capacities measured using dynamic column breakthrough (DCB) experiments are also shown. The lines are fits to the dual-site Langmuir isotherm model.

agreement with the equilibrium loading obtained from the binary breakthrough experiments. The CO2 adsorption isotherms on Zeochem silica gel from our laboratory-scale measurements and those obtained by Wang and LeVan18 on Grace Davison silica gel (grade 40) were compared, and comparable adsorption capacities were observed for the two materials in the pressure range investigated. The comparison is shown in Figure S3 of the Supporting Information. Therefore, it appeared reasonable to combine our CO2 data on silica gel with the moisture data from the work of Wang and LeVan.18 The adsorption equilibrium data for water vapor in silica gel are shown along with the fits obtained using the dual-site Langmuir model in Figure 8, and the parameters are reported in Table 1. For both CO2 and N2, the parameters for the second site were very small and were approximated as zero. This implies that adsorption of these two

purity =

P0v0εb|13X RT0

t

∫0 evac

P0v0εb|13X RT0

t

v ̅ yCO

∫0 evac

2,in

(t ) Pin̅ (t )

Tin̅ (t ) v ̅ (t ) Pin̅ (t ) Tin̅ (t )

dt

dt (9)

The recovery of CO2 was determined as the fraction of CO2 fed to the silica gel column during pressurization and high-pressure adsorption that was recovered from the 13X bed in the evacuation step

Figure 7. Validation of the extended Langmuir model for binary adsorption on silica gel. Symbols denote binary equilibrium capacities obtained from dynamic column breakthrough (DCB) experiments. The line denotes the prediction of the extended dual-site Langmuir model. G



Article

Figure 8. Adsorption equilibrium data for water vapor in Grace Davison silica gel taken from the work of Wang and LeVan.17 Symbols denote experimental measurements while the line shows the fit of the dual-site Langmuir isotherm.

recovery = P0v0εb|13X RT0 P0v0εb|SG RT0

t

∫0 press

v ̅ yCO

2,in

∫0

tevac v ̅ yCO2,in (t ) Pin̅ (t )

(t ) Pin̅ (t )

Tin̅ (t )

dt

Tin̅ (t )

dt +

P0v0εb|SG RT0

productivity =

t

∫0 ads

v ̅ yCO

2,in

(t ) Pin̅ (t )

Tin̅ (t )

̅ in

0

(11)

where Wbd and Wevac were obtained from the isentropic compression calculations

Wbd =

γ 1 εbπri 2 v0P0 η γ−1

tbd

∫ 0

γ−1 ⎡ ⎤ ⎢⎛ Patm ⎞ γ ⎥ ⎟⎟ − 1⎥ d t v ̅ P ̅ exit ⎢⎜⎜ ⎢⎝ P0P ̅ exit ⎠ ⎥ ⎣ ⎦

(12)

Wevac

γ 1 = εbπri 2 v0P0 η γ−1

tevac

∫ 0

v ̅ yCO

2,in

(t ) Pin̅ (t )

Tin̅ (t )

dt

(1 − εb|13X )πri 2L(tlpp + tads + tbd + tevac)

The following set of operating conditions from the Pareto curve of the four-step VSA with LPP was first chosen to understand the performance of the proposed dual-adsorbent, two-column, four-step VSA process: tads = 46.78 s, tbd = 42.83 s, tevac = 47.01s, PI = 0.07 bar, PL = 0.03 bar, and V0 = 0.59 m/s. For these operating conditions, 96.5% purity and 85.6% recovery were achieved with an energy consumption of 197.8 kWh (t of CO2 captured)−1. Initially, the lengths of both the silica gel and 13X beds were chosen to be 1 m. It can be seen in Figure 9a that the purity and recovery values were very low (61.7% purity and 12.7% recovery) when the length of the silica gel column was 1 m. This is due to the fact that most of the CO2 was retained in the desiccant bed, which resulted in a lower recovery from the 13X bed. The corresponding energy consumption, shown in Figure 9b, was also high, at 1355 kWh (t of CO2)−1. By decreasing the length of the silica gel bed, it was possible for CO2 to reach the 13X bed, which improved its purity and recovery in the evacuation step from that bed. It is further shown in Figure 9b that the energy consumption decreased with decreasing silica gel bed length. Under these operating conditions, the best results obtained were 94.6% purity and 92.7% recovery with an energy consumption of 193.6 kWh (t of CO2 captured)−1 when the silica gel bed length was 0.2 m. According to Figure 9b, decreasing the energy consumption in the silica gel bed by decreasing its length was the main reason for the decrease in the overall energy consumption. With a further reduction in the bed length, the performance deteriorated, and in the absence of a silica gel bed (i.e., bed length ≈ 0), the performance was similar to the fourstep VSA process with LPP using 13X alone. It was possible to achieve 95% purity and 91.6% recovery when the blowdown and evacuation pressures of the second column were changed to 0.072 and 0.033 bar, respectively (with the other operating conditions kept fixed at their previous

The energy consumption was determined as the sum of the energy consumption in the blowdown and evacuation steps in both columns, normalized with respect to the amount of CO2 from the 13X bed in the evacuation step

0

t

∫0 evac

(14)

dt

(10)

energy consumption Wbd,SG + Wevac,SG + Wbd,13X + Wevac,13X = v ̅y (t ) Pin̅ (t ) t P0v0εb|13X dt ∫ evac CO2T,in (t ) RT

P0v0εb|13X RT0

γ−1 ⎡ ⎤ ⎢⎛ Patm ⎞ γ ⎥ ⎟⎟ − 1⎥ d t v ̅ P ̅ in ⎢⎜⎜ ⎢⎝ P0P ̅ in ⎠ ⎥ ⎣ ⎦

(13)

The energy consumption was calculated with an isentropic efficiency η of 72%.5 Finally, the productivity was calculated based on the amount of CO2 from the 13X bed in the evacuation step normalized by the total cycle time and adsorbent volume of the zeolite 13X H



Article

Table 3. Bounds on the Decision Variables Used in the Optimization of the Dual-Adsorbent, Two-Bed, Four-Step VSA Process tads (s)

tbd (s)

lower bound upper bound

20 150

30 200

lower bound upper bound

− −

30 200

tevac (s)

PI (bar)

silica gel bed 30 0.03 200 0.5 13X bed 30 0.03 200 0.5

PL (bar)

BL (m)

V0 (m/s)

0.03 0.5

0.1 1

0.1 2

0.03 0.05

− −

− −

moisture from the first bed during the high-pressure adsorption step exit yH O |SG < 100 ppm 2

(15)

As in the optimization of the single-column, four-step VSA with LPP, the population size was 10 times the number of decision variables, and the optimization was carried out for 60 generations. The input parameters are listed in Table S2 of the Supporting Information. The results from the optimization shown in Figure 4 suggest that the energy consumption of the dual-adsorbent, two-bed, four-step process was improved. The minimum energy consumption in the proposed new cycle for 95% purity and 90% recovery was 177 kWh (t of CO2 captured)−1, and the productivity was 1.82 t of CO2 (m3 of 13X)−1 day−1 [1.29 t of CO2 (m3 of total adsorbent)−1 day−1]. This is lower than the energy consumption values obtained in the four-step VSA process with LPP for capturing CO2 from a wet flue gas using only zeolite 13X. The operating conditions corresponding to the minimum energy for the proposed new cycle are tads = 46.20 s, tbd =42.21 s, tevac = 46.02s, PI = 0.48 bar, PL = 0.3 bar, and BL = 0.41 m for the silica gel column and tbd = 56.30 s, tevac = 101.20 s, PI = 0.07 bar, and PL = 0.03 bar for the 13X column. The length of the 13X bed was fixed at 1 m. The inlet feed velocity to the silica gel bed was 0.7 m/s. The bed profiles corresponding to the aforementioned operating conditions for the minimum energy consumption are provided in Figures 10 and 11. Most of the water was retained in the silica gel column, and the stream entering the 13X column had a very low moisture content (20 ppm). The LPP step in the 13X column sharpened the CO2 profile (see Figure 11a), which allowed for the use of a lower blowdown pressure to achieve high purity and recovery. Because the moisture content in the gas stream entering the 13X bed was very low and confined over a short length near the inlet, consideration of case 2 was not important. In the case of a conventional monoethanolamine (MEA) process for capturing CO2 from a coal-fired power plant containing 13.3% CO2, 6% moisture, and the rest N2, the values of energy consumption were reported to be between 320 and 550 kWh (t of CO2 captured)−1.27 Although the proposed novel dual-adsorbent process has been studied for a flue gas with lower (3%) moisture content, the results look promising to expect lower energy consumption over the MEA process at a comparable moisture content.

Figure 9. (a) Purity−recovery from the 13X bed and (b) energy consumption in the dual-adsorbent, two-bed, four-step VSA process as a function of silica gel bed length. Bd denotes blowdown, and Ev denotes evacuation.

values), with a further decrease in the total energy consumption to 186.6 kWh (t of CO2 captured)−1. Hence, detailed optimization of the dual-adsorbent, two-bed, four-step VSA process was considered necessary to reach its minimum energy limit. 4.3. Optimization of the Dual-Adsorbent, Two-Bed, Four-Step VSA Process. To identify an operating configuration with minimum energy consumption, multiobjective optimization was performed using a genetic algorithm (GA) in MATLAB. The duration of the adsorption step in both columns was considered to be the same. The duration of the pressurization step in the first column was kept fixed at 20 s, and the duration of the light product pressurization step in the second column was dependent on the effluent flow rate from the high-pressure adsorption step. Therefore, there were 11 decision variables in total: tads (same for both columns); tbd and tevac (different in the two columns); PI and PL (both different in the two columns); V0 (inlet feed velocity); and BL (length of the silica gel bed). The bounds for each of the decision variables are provided in Table 3. As mentioned before, the purpose of employing a desiccant bed is to trap the moisture from the flue gas, and the moisture-free gas is then sent to the 13X bed to capture and concentrate CO2. Thus, a constraint was also imposed on the maximum exit concentration of

5. CONCLUSIONS CO2 capture from a wet flue gas containing 15% CO2 in 82% N2 and 3% moisture was studied using a nonisothermal, nonisobaric model. Detailed optimization of the four-step VSA I



Article

Figure 10. Gas- and solid-phase composition profiles of (a) CO2 and (b) water vapor in the silica gel column of the dual-adsorbent, twobed, four-step process at cyclic steady state when operated under the conditions of minimum energy. Column 1: tads = 46.2 s, tbd = 42.2 s, tevac = 40 s, PI = 0.48 bar, and PL = 0.3 bar. Column 2: tads = 46.2 s, tbd = 56.3 s, tevac = 101.2 s, PI = 0.07 bar, and PL = 0.03 bar. The lengths of the silica gel bed and the inlet feed velocity (V0) were 0.41 and 0.70 m/s, respectively.

Figure 11. Gas- and solid-phase composition profiles of (a) CO2 and (b) water vapor in the 13X column for the same conditions as in Figure 10.

concentrated CO2. For this new cycle, the minimum energy consumption to capture 90% CO2 at 95% purity was 177 kWh (t of CO2 captured)−1 with a productivity of 1.82 t of CO2 (m3 of 13X)−1 day−1 [1.29 t of CO2 (m3 of total adsorbent)−1 day−1]. The evacuation pressure PL was 0.03 bar. The ratio of the length of the silica gel bed to that of the zeolite 13X bed was 0.41. The minimum energy for the proposed new cycle was lower than even the lower bound of the minimum energy for CO2 capture from wet flue gas using the four-step VSA process with LPP and only zeolite 13X. The proposed two-bed, dualadsorbent configuration also solves the problem of rewetting the concentrated CO2 encountered in a layered bed and is able to produce nearly dry concentrated CO2.

process with LPP was carried out first for wet flue gas using only a 13X bed, and it was found that 95% purity (on a dry basis and 78.4% on a wet basis) and 90% recovery were achieved with a minimum energy consumption of 195.1 kWh (t of CO2 captured)−1 and a productivity of 1.32 t of CO2 (m3 of 13X)−1 day−1 for case 1 and with a minimum energy consumption of 230 kWh (t of CO2 captured)−1 and a productivity of 1.03 t of CO2 (m3 of 13X)−1 day−1 for case 2, compared to a minimum energy consumption of 154 kWh (t of CO2 captured)−1 and a productivity of 1.52 t of CO2 (m3 of 13X)−1 day−1 for dry flue gas. The increase in energy consumption was due to additional energy requirements to remove moisture from the column during the evacuation step and a reduction in the effective selectivity of CO2 over N2. Clearly, there is an urgent need for more reliable CO2/N2/H2O mixture equilibrium data over a wider range to allow for a more definitive conclusion about handling wet flue gas in a 13X bed alone. A novel dual-adsorbent, two-bed, four-step VSA process has been proposed that employs silica gel and 13X packed in two separate columns. This cycle is able to achieve nearly dry

■

ASSOCIATED CONTENT

S Supporting Information *

Adsorption isotherm, model equations, and input parameters to the simulator. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*Tel.: +65-65166545. Fax: +65-67791936. E-mail: chesf@nus. edu.sg. J



Article

Notes

The authors declare no competing financial interest.

■ ■

■

ACKNOWLEDGMENTS We thank Zeochem AG, Uetikon, Switzerland, for providing the silica gel adsorbent samples.

SG = silica gel ter = ternary

REFERENCES

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NOTATION b = pre-exponential constant for site 1 in the dual-site Langmuir isotherm (m3 mol−1) BL = bed length of the silica gel bed in the dual-adsorbent, two-column VSA process (m) C = gas-phase composition (mol m−3) d = pre-exponential constant for site 2 in the dual-site Langmuir isotherm (m3 mol−1) hi = inside heat-transfer coefficient (W m−2 K−1) ho = outside heat-transfer coefficient (W m−2 K−1) Kw = wall thermal conductivity (W m−1 K−1) KZ = effective gas thermal conductivity (W m−1 K−1) L = bed length of the zeolite 13X bed in the dual-adsorbent, two-column VSA process (m) P = pressure (bar) P̅ = dimensionless pressure q* = equilibrium loading (mol m−3) qsb = saturation constant for site 1 in the dual-site Langmuir model (mol m−3) qsd = saturation constant for site 2 in the dual-site Langmuir model (mol m−3) R = universal gas constant (J mol−1 K−1) ri = column inner radius (m) T = temperature (K) t = step duration (s) T̅ = dimensionless temperature U = internal energy (J mol−1) v ̅ = dimensionless velocity v0 = interstitial inlet velocity (m s−1) W = isentropic work (J) xCO2 = q*CO2/(qsb, CO2 + qsb, CO2) xH20 = q*H2O/(qsb, H2O + qsb, H2O) yCO2 = mole fraction of carbon dioxide in 13X column yH2O = mole fraction of water vapor in silica gel column

Greek Symbols

εb = bed voidage η = efficiency factor γ = adiabatic constant

Subscripts

0 = pre-exponential constant/reference value 13X = zeolite 13X ads = adsorption step atm = atmospheric pressure b = site 1 in the dual-site Langmuir isotherm bd = blowdown step bin = binary d = site 2 in the dual-site Langmuir isotherm evac = evacuation step exit = column exit H = high pressure I = intermediate pressure i = component i in = column inlet L = low pressure LPP = light product pressurization K



Article

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