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A Model-Based Approach for the Evaluation of Materials and Processes for Post-Combustion Carbon Dioxide Capture from Flue Gas by PSA/VSA Processes George N. Nikolaidis, Eustathios S. Kikkinides, and Michael C. Georgiadis Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.5b02845 • Publication Date (Web): 05 Jan 2016 Downloaded from http://pubs.acs.org on January 8, 2016

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Industrial & Engineering Chemistry Research

A Model-Based Approach for the Evaluation of Materials and Processes for Post-Combustion Carbon Dioxide Capture from Flue Gas by PSA/VSA Processes George N. Nikolaidis†,‡, Eustathios S. Kikkinides†,‡,*, Michael C. Georgiadis†,‡,* †

Department of Chemical Engineering, Aristotle University of Thessaloniki, University Campus,

54124 Thessaloniki, Greece ‡

Chemical Process and Energy Resources Institute (CPERI), Centre for Research and Technology

Hellas (CERTH), 6th km Charilaou Thermi Rd., 57001 Thermi-Thessaloniki, Greece

*

Corresponding author email: [email protected]

*

Corresponding author email: [email protected]

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ABSTRACT This work presents a mathematical modeling framework for the simulation and optimization of PSA/VSA processes for post-combustion CO2 capture from dry flue gas (85% N2, 15% CO2). The modeling framework is first validated against literature data, illustrating good agreement in terms of several process performance indicators. Accordingly, the model is used to evaluate three available potential adsorbents for CO2 capture, namely, zeolite 13X, activated carbon and metal organic framework (MOF), Mg-MOF-74. A two-bed configuration (six-step VSA cycle) with light product pressurization has been employed in all simulations. The results from systematic comparative simulations demonstrate that zeolite 13X has the best process performance among the three adsorbents, in terms of CO2 purity and CO2 recovery. On the other hand, Mg-MOF-74 appears to be a promising adsorbent for CO2 capture, as it has considerably higher CO2 productivity compared to the other two adsorbents. Furthermore process optimization studies using zeolite 13X and Mg-MOF-74, have been performed to minimize energy consumption for specified minimum requirements in CO2 purity and in CO2 recovery at nearly atmospheric feed pressures. The optimization results indicate that the minimum target of 90% in CO2 purity and 90% in CO2 recovery is met for the VSA process under consideration for both adsorbents at different operating conditions resulting in different energy requirements. Thus, there is a complex relationship between optimal process performance indicators and operating conditions that varies among the different adsorbents and cannot be quantified by simple comparison of CO2/N2 adsorption isotherms and selectivity data. Evidently, detailed process modeling, simulation and optimization strategies, provide the most reliable way to evaluate both qualitatively and quantitatively potential adsorbents for CO2 capture.

KEYWORDS Pressure swing adsorption (PSA); Modeling; Simulation; Optimization; CO2 capture; Mg-MOF-74


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1. Introduction Anthropogenic carbon dioxide (CO2) emissions are considered as a great threat to the environment because of their contribution to the greenhouse effect and global warming. CO2 is considered to be responsible for 60% of the global warming caused by greenhouse gases (GHGs).1 Today, fossil fuels provide about 85% of the global energy demand and the outlook is that they will remain the dominant source of energy for the next decades. Consequently, global energy-related CO2 emissions, especially those from power plants that burn fossil fuels, have increased, thereby increasing CO2 concentration levels in the atmosphere.2 However, post-combustion CO2 capture is still required to avoid excess emissions of CO2 from existing power plants. The process of CO2 capture and sequestration (CCS), involves CO2 separation followed by pressurization, transportation, and sequestration. CO2 capture is the most expensive part of CCS, accounting for more than 75% of the estimated overall CCS cost.3 The capture units are expected to concentrate the CO2 from flue gas with CO2 purity and CO2 recovery exceeding 95% and 90%, respectively according to the Department of Energy requirements.4 According to the International Energy Agency’s roadmap, 20% of the total CO2 emissions should be removed by CCS by the year 2050.5 Thus it is important to develop energy-efficient industrial technologies for CO2 capture. There are several commercial technologies available for post-combustion CO2 capture: chemical absorption, membranes separation, cryogenic processes and adsorption. Chemical absorption using an amine-based aqueous solution is known as a promising CO2 capture method. The capture of CO2 in industrial streams is actually performed mainly by absorption in mono-ethanol-amine (MEA) and solvent extraction. Extending this technology to CO2 sequestration from flue gases has several drawbacks: the global economics of the process (absorption technology is not very suitable for low molar fractions of CO2), the extensive corrosion rate for the equipment and also the high energy


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requirement for the regeneration of the solvent.6 While membrane technology has a smaller footprint, most membranes are incapable of extracting CO2 with high purity and recovery from flue gases.7 Pressure swing adsorption (PSA) is a promising technology for CO2 capture due to its relatively better separation performance, higher productivity, lower energy consumption and lower capital investment cost in comparison to the traditional separation processes, such as cryogenic processes.8,

9

In an

adsorption process one or more components of a gas mixture are preferentially adsorbed in the pores of an adsorbent material. In a PSA process, the regeneration of the adsorbent is performed by reducing the total pressure, so the total pressure of the system changes (swings) between high and low pressure in a cyclic manner. A PSA process like all adsorption separation processes requires the use of an adsorption column packed with a microporous-mesoporous adsorbent material that selectively adsorbs one component (or a group of related components) from a gas mixture. The effluent stream during the adsorption step that no-longer contains the preferentially adsorbed species is called the light product or “raffinate”, while the effluent stream during the desorption step that contains the strongly adsorbed species in larger proportions compared to the feed stream, is often called the heavy product or “extract”. While a PSA process carries out adsorption at super-ambient pressure and desorption at nearambient pressure level, a Vacuum Swing Adsorption (VSA) process undergoes adsorption at nearly atmospheric pressure, while desorption is achieved under vacuum and a Vacuum Pressure Swing Adsorption (VPSA) process refers to cycling between adsorption step at pressures above atmospheric and desorption step under vacuum. The PSA process is dynamic in nature, operating in a cyclic manner with a fixed cycle time, and each adsorption column undergoing the same sequence of steps. After a number of cycles, the adsorption column approaches a “cyclic steady state” (CSS) in which the conditions at the end of each cycle are identical to those at the start of the same cycle. The typical operating steps of a PSA/VSA process are10: pressurization (FP, LPP, HPP), adsorption (F), co-current


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depressurization (CoD), counter-current depressurization or blowdown (CnD), light reflux or purge (LR), heavy reflux or rinse (HR), pressure equalization (PE). With the development of novel adsorbent materials, adsorption technology has become a potential tool for CO2 capture from flue gases. There is a great variety of potential adsorbents currently considered for post-combustion CO2 capture: zeolites, activated carbons, metal organic frameworks (MOFs), metal oxides, hydrotalcites, organic-inorganic hybrids, ZIFs, COFs, silica and alumina based materials. Recent studies suggest that adsorption processes using zeolites and MOFs with large internal surfaces are promising alternatives for CO2 capture.11,

12

The adsorption mechanism for these types of

adsorbents is physisorption, where there is an interaction between the quadrupole moment of the gas and the polar adsorbent. CO2 has a strong quadrupole moment while N2 has a smaller but still significant quadrupole moment and therefore any adsorbent which adsorbs CO2 will also adsorb appreciable amounts of N2.13 Most zeolites have a very strong adsorption affinity for CO2 and therefore have nonlinear adsorption isotherms, relatively high CO2 adsorption capacities and high CO2 selectivity at low partial pressures. Commercial zeolite adsorbents are always bound together with clay or alumina to form a pellet or bead, which is a composite structure that contains both macropores and micropores. Hence, the overall mass transfer in zeolites is controlled by a combination of different diffusion mechanisms. Nevertheless, for the case of zeolite 13X, which is typically considered as a potential adsorbent for CO2 capture, gas diffusion appears to be macropore controlled.14 An effective screening method for selecting the most cost-effective zeolites for CO2 capture using a computational framework that effectively combines material selection and process optimization has been recently reported.7 In the last decade, MOFs have emerged as a new class of porous materials for CO2 capture because of their unique features, such as large specific surface area and the corresponding high CO2 adsorption capacity as well as regular pore size distributions. MOFs are assembled from metal clusters (e.g. square shaped, trigonal, tetrahedral ACS Paragon Plus Environment

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and octahedral) and organic linkers (e.g. carboxylates, imidazolates and tetrazolates). According to various combinations of metal clusters and organic linkers, we can synthesize an unlimited number MOFs with different structures and functionalities.15 The transport of CO2 and N2 in the MOF particles can be safely considered as macropore controlled.15 Water reaches saturation levels on hydrophilic adsorbents, such as zeolites and MOFs, preventing them from adsorbing CO2 and therefore a pre-layer is required to protect these adsorbents from the humidity. This reduces the percentage of the adsorption column containing the CO2 adsorbent and additional vacuum work is required to regenerate the prelayer. On the other hand, hydrophobic adsorbents, such as activated carbons, adsorb much smaller amounts of water and therefore humid flue gas can be processed using these adsorbents without any additional operating cost. Activated carbon beads are spherical and no binder material is used in their production. The spherical nature and hardness of activated carbon bead minimizes dust formation and attrition losses during adsorption and regeneration processes. Activated carbon beads also exhibit excellent fluidization properties both in gas and liquid applications. These characteristics make activated carbon bead the material of choice for higher performance in carbonaceous materials application.16 However, activated carbons have typically lower CO2 adsorption capacity and poorer selectivity than zeolites and MOFs. The growing interest in efficient CO2 capture has resulted in an increasing number of studies on CO2 removal from various flue gas mixtures employing PSA/VSA processes. Due to the relatively low flue gas pressure and high concentration of N2 in flue gas streams, VSA is the most efficient adsorption process to deal with the flue gases containing CO2 concentrations ranging from 15% to 55%.11 Various PSA/VSA cycle configurations for post-combustion CO2 capture from dry flue gas have been reported in the literature to investigate the effect of the incorporation of a heavy reflux (HR) step, a light reflux (LR) step and a light-end pressure equalization (PE) step on CO2 purity and CO2 recovery.10, 12, 16-41 These studies highlight the difficulties associated with choosing one PSA/VSA cycle configuration


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over another for a given application. However, it is difficult to capture CO2 with both high CO2 purity and high CO2 recovery in a single-stage PSA/VPSA process when the CO2 concentration in the flue gas is low, such as 3−20 % from coal-fired power plants without deep vacuum desorption. Most of the previous mentioned studies demonstrate that deep vacuum levels ( 1 − #|| = 150 + 1.75 D E , = 2.! ?2. A !

∀ ∈ (0, )

(5)

where P is the total pressure, > is the viscosity of bulk gas and .! is the particle radius. An equation of state is necessary to link concentration with temperature and pressure in the gas mixture. In the present study, the ideal gas law holds for the gas mixture. The physical properties (density, viscosity, thermal conductivity, specific heat capacity) of the gas mixture are functions of temperature, pressure and composition and can be assumed constant or calculated using some of the ACS Paragon Plus Environment

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available correlations or thermo-physical properties packages. The mass axial dispersion coefficient of

component i , and the heat axial dispersion coefficient / are calculated using Wakao correlations.47,

48

The overall uptake rate in a particle is expressed as a function of the bulk gas flow concentration through a linear driving force (LDF) model: $ 15, ∗ ($ − $ ), = .!=

∀ ∈ H0, I, = 1, … ,

!

(6)

where $ is the adsorbed amount of component i per unit mass of adsorbent, $ ∗ is adsorbed amount of component i per unit mass of adsorbent in equilibrium with the gas phase and , is the effective

diffusivity of component i. The effective diffusivity of component i , (for macropore controlled

transport mechanism) can be calculated by using Bosanquet equation: , =

! , K, , J! , + K,

∀L ∈ H0, .! I, = 1, … ,

!

(7)

where ! is the porosity of the particle, J! is the tortuosity factor of the particle, K, is the Knudsen diffusivity of component i and

,

is the molecular diffusivity of component i.

Adsorption equilibrium is described by the dual-site Langmuir isotherm: $ ∗ =

'

1+

where '

(:) M (:) ; + 45678 ∑ 9: M (:) ; 1

(O) and

'

+

(=) M (=) ; 45678 ∑ 9: M (=) ;

(8)

M (O) are the isotherm parameters and ; is the partial pressure of component i which

is a function of the molar fraction of component i in gas phase P and the total pressure P given by the Dalton’s law.


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The isotherm parameters are calculated by the following equations (9.a-9.d): '

(:)

= ):, (:) + )=, (:) &

M (:) = )Q, (:) exp ( '

(=)

(9.a)

)U, (:) ) &

(9.b)

= ):, (=) + )=, (=) &

M (=) = )Q, (=) exp (

(9.c)

)U, (=) ) &

(9.d)

The selectivity of component i over component j of an adsorbent V O (for a gas mixture with two

components where i is the strongly adsorbed component and j is the weakly adsorbed component) is provided by the following equation: $ PO V O = D E $O P

(10)

where P and PO represent the mole fractions of component i and component j in the feed gas,

respectively and $ and $O represent the adsorbed amounts (solid loadings) of component i and component j per unit mass of adsorbent, respectively.

For the inlet stream of the adsorption column Danckwert’s boundary conditions are applied: ? − W A = ,

,

#%! ?& − & W A = /

& ,

= 0 () , = 1, … , = 0 ()

!

= W , = 0 ()


(11)

(12)

(13)

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For the outlet stream or for the closed end of the adsorption column Danckwert’s boundary conditions are applied: = 0 ,

= (0), = 1, … ,

& = 0, = (0)

!

(14)

(15)

= 0 , = (0)

(16)

where W , W and & W are the interstitial velocity, the concentration of component i and the

temperature of the inlet stream of the adsorption column, respectively.

The molar flowrate of the gas stream entering or leaving the adsorption column is calculated by a gas valve equation recommended by the Fluids Control Institute Inc.49 provided by the following equations: X = Y,-Y Z;; W [\ X = Y,-Y Z;; W [\

1 − ( ]6^_)= ]

\, 45678 ∑ 9: P bc & 1 − (]

`a

:

5i`_

)=

5678 ∑ 9: P bc &

4

\,

d ;ef > ( d ;ef ≤ (

1

;h f 1

;h f

); W

(17)

); W

(18)

where X is the molar flowrate, Y,-Y is the gas valve constant, Z; is the stem position of the gas valve, ; W is the pressure at the inlet of the gas valve, ;ef is the pressure at the outlet of the gas valve,

P is the molar fraction of component i in gas phase and bc is the molecular weight of component i. The critical pressure ;h f is provided by the following equation:


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;h f = (

l 2 )mnl 1+k

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(19)

where the specific heat capacity ratio k is provided by the following equation:

k=

%! %Y

(20)

where %! is the specific heat capacity of bulk gas referring to constant pressure and %Y is specific heat

capacity of bulk gas referring to constant volume.

The compressor/vacuum pump power needed for compression/evacuation of a real gas in a single stage compressor/vacuum pump is provided by the following equation: ;* * 1 k ;opqL = ( ).& W s r k−1 ;-+

lnm l

− 1t X W

(21)

where & W is the temperature of the feed stream of the compressor/vacuum pump, ;-+ is the lower

pressure of the compressor/vacuum pump unit, ;* * is the higher pressure of the compressor/vacuum

pump, X W is the molar flowrate of the feed stream of the compressor/vacuum pump unit and r is the isentropic compression/evacuation efficiency of the compressor/vacuum pump, respectively. In this study r=0.72 and k =1.4 in all energy calculations.

2.3. Process performance indicators The comparison and evaluation of various process alternatives and design options for PSA/VSA processes is performed by employing several important performance indicators such as, product purity, product recovery, adsorbent productivity and energy consumption defined in equations 22, 23, 24 and 25, respectively.50 These process performance indicators have been extensively used to benchmark different PSA/VSA processes.


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Product purity =

Amount of component in the product stream Total amount of product stream

Product recovery =

Amount of component in the product stream Amount of component in the feed stream

Adsorbent productivity =

Amount of component in the product stream (Amount of adsorbent used)(PSA cycle time)

Energy consumption = Sum of all compression and vacuum sources used

(22)

(23)

(24)

(25)

Finally it is important to note that all process performance indicators (product purity, product recovery, adsorbent productivity and energy consumption) are calculated at cyclic steady state (CSS) conditions. Furthermore, in the present study, the term product refers to the heavy product, which is CO2 in the specific application. 2.4. Formulation of the optimization problem Optimization is normally performed to improve the performance of a PSA/VSA process by locating the optimal values of several important process variables that control the process. Based on parametric studies and sensitivity analysis, the most significant process variables in this optimization study are, feed pressure and flowrate, blowdown and evacuation pressure, while feed temperature, cycle time, operating step durations and column geometry are fixed in all cases under consideration at different feed temperatures. Optimization studies have been performed at different feed temperatures. The main objective is to investigate the effect of feed temperature, feed pressure, feed flow rate, blowdown pressure and evacuation pressure on the overall process performance. The minimization of energy consumption for specified minimum requirements in CO2 purity and in CO2 recovery is employed as the objective function.


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Hence, the optimization problem can be formulated as the minimization of energy consumption for specified minimum requirements in CO2 purity and in CO2 recovery, while optimizing the feed pressure, feed flow rate, blowdown pressure and evacuation pressure. Cycle time, operating step durations, column geometry and gas valve constants are kept constant in all cases under consideration at different feed temperatures: Min. Energy consumption

(26)

. . boq 'V or

CO2 purity ≥ min. CO2 purity

CO2 recovery ≥ min. CO2 recovery & = HT: , T= , TQ , TU I ≤ ; ≤ ≤ X ≤

≤ ;-+ ≤

≤ ;Y, ≤

where LB and UB denote the lower and upper bounds of optimization variables, respectively. 2.5. Numerical solution The modeling equations comprise a system of non-linear partial differential and algebraic equations (PDAEs) and detailed PSA modeling is computationally intensive, requiring the solution of a system of PDAEs with cyclic boundary conditions. The modeling equations have been implemented in the


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gPROMS™ modeling environment51 and the simulation is performed until cyclic steady state is achieved. The optimization problem has been solved using the gPROMS™/gOPT tool.51 The latter employs the SRQPD non-linear programming code, implementing a reduced sequential quadratic programming algorithm. In the present study the axial domain is discretized using centered finite difference method of second order with 40 discretization intervals although other discretization schemes have been tested showing nearly identical results. The solvers employed in the simulations used a value of 1x10-5 for absolute tolerance. All computations reported were carried out on a desktop workstation with Intel Core i5 3.10 GHz processor and 4.0 GB RAM.

3. Results and discussion 3.1. Model validation The above detailed modeling framework has been validated against previously reported PSA/VSA models as well as experiments. The simulation results of this study in terms of process performance indicators (CO2 purity and CO2 recovery) are in good agreement with the results of work of Ko et al.26, 52

and summarized in Table S1 in the supporting information material.

It is illustrated that the proposed modeling framework predicts satisfactorily the behavior of the process. The small differences with the results of Ko et al.26, 52 are attributed to the use of a gas valve equation employed to calculate the flowrate and pressure at the end of the adsorption column during pressure changing steps, as opposed to a linear change of velocity at the feed end used in the original work of Ko et al.26,

52

It should be emphasized that the gas valve equation results in exponential

pressure histories during the pressure changing steps. A smaller effect on the differences may be also due to the different number of intervals and different order of approximation of the discretization method as well as due to different physical properties of the gas mixture that have been calculated using an available thermo-physical properties package compared to the constant values of physical properties of the work of Ko et al.26, 52 ACS Paragon Plus Environment

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3.2. Comparison and evaluation of adsorbents As a next step, we study the effect of the type of adsorbent on the PSA/VSA process performance for CO2 removal from dry flue gas. Three different types of adsorbents have been selected: traditional ones, such as zeolite and activated carbon, and a novel adsorbent from the family of MOF’s. The proposed modeling framework has been applied to compare these adsorbents for post-combustion CO2 capture over a range of operating conditions (298 K, 313 K and 323 K) in terms of several process performance indicators. More specifically, we consider zeolite 13X as a representative of the zeolite group, which has been studied for more than two decades and is the current benchmark commercial adsorbent for CO2 capture. The diffusion of CO2 in commercial zeolite 13X beads is controlled by mass transport in the macropores, both under Knudsen and molecular diffusion regimes.14 The parameters of the dual-site Langmuir adsorption isotherm for CO2 and N2 on zeolite 13X have been adopted from the work of Ko et al.26 and summarized in Table S2 in the supporting information material. Activated carbons are promising adsorbents, since they have large specific surface area, they are water tolerant and can be produced with novel morphologies (monolith, bead, fiber, granular). Additionally, they are less expensive than other adsorbents like zeolites.16 However, activated carbons have typically lower CO2 adsorption capacity and poorer selectivity than zeolites and MOFs. The parameters of the dual-site Langmuir adsorption isotherm for CO2 and N2 on activated carbon (AC) have been adopted from the work of Maring et al.53 and summarized in Table S2 in the supporting information material. Mg-MOF-74 [Mg2(dobdc), (dobdc=1,4-dioxido-2,5-benzenedicarboxylate), (Mg(C4HO3)(H2O).4H2O), CPO-27-Mg] has received significant attention recently, since it has both high CO2 adsorption capacity and high CO2 adsorption affinity and is considered as a potential adsorbent in the present study. The parameters of the dual-site Langmuir adsorption isotherm for CO2 and N2 on Mg-MOF-74 have been


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adopted from the work of Mason et al.54 and summarized in Table S2 in the supporting information material. The mixture selectivity of CO2 over N2 at different temperatures and total pressure of 1 bar for the three potential adsorbents (13X, AC, Mg-MOF-74), is presented in Figures 1, 2, 3, respectively. It is important to note that when the temperature is increased the selectivity of CO2 over N2 decreases and the decrease is more pronounced for adsorbent Mg-MOF-74, at low mole fractions. Other physical properties (density and heat capacity) of the adsorbents required for the process modeling and simulation have been adopted from the works of Ko et al.26, of Chue et al.18, of Wu et al.55 and summarized in Table S3 in the supporting information material. The parameters of the adsorption column model, have been adopted from the work of Ko et al.26 and summarized in Table S4 in the supporting information material. The adsorption column porosity is assumed to be 0.348 (porosity of randomly packed spherical beads), while the particle/bead porosity is assumed to be 0.38 for all adsorbents in the present study. In the present study, a two-bed configuration (six-step VSA cycle) with light product pressurization and one pressure equalization step is considered. The use of one pressure equalization step improves primarily CO2 recovery and CO2 productivity from 1.2 up to 19% for the case of zeolite 13X as the feed flowrate increases from 0.056 to 0.185 lt/s_STP. A similar trend is observed in energy savings ranging from 2.8 up to 15%, respectively. Similar results are observed for the case of Mg-MOF-74, where the improvement is slightly smaller than for the case of zeolite 13X. The sequence of the operating steps for the two-bed configuration (six-step VSA cycle) is illustrated in Figure 4 and consist of: pressurization with the light product counter-currently (CC), adsorption (Ads), pressure equalization (PED) (co-current depressurization to the other column), co-current depressurization or blowdown (CoD) to intermediate pressure, counter-current depressurization or evacuation (Evac) to low pressure


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and pressure equalization (PER) (counter-current re-pressurization from the other column). The interaction between the beds during each operating step for the two-bed configuration is illustrated in Figure 5. More specifically, the dry flue gas mixture (85% N2, 15% CO2) is considered to be available at 1.10 bar pressure and at different temperatures 298 K, 313 K and 323 K while the blowdown pressure is set at 0.20 bar and the evacuation pressure is set at 0.02bar. At the beginning of the process, the adsorption column is saturated with pure N2 at 1.10bar. The cycle time of the process is fixed at 240s and the operating time steps are fixed as follows: tCC=20s, tAds=80s, tPED=20s, tCoD=20s, tEvac=80s, tPER=20s. It is important to note that the operating time steps are not independent variables, and in a typical multibed configuration, only the duration of one or two operating steps can be independently varied. When we use two-bed or multi-bed configurations the operating time steps must be correlated due to the need for synchronization of the beds, although it was recently demonstrated that this constraint can be relaxed using idle/holding times for each bed.40 The proposed modeling framework has been applied to estimate process performance indicators using each of the three potential adsorbents to objectively compare them over a range of feed flowrates at different feed temperatures. Simulation results in terms of various process performance indicators, for the three adsorbents, are illustrated in Figures 6, 7 at Tfeed=298K, Figures 8, 9 at Tfeed=313K and Figures 10, 11 at Tfeed=323K. The results clearly indicate that zeolite 13X yields the highest CO2 purity, followed by Mg-MOF-74 and activated carbon over the full range of feed flowrates as illustrated in Figures 6, 8, 10. On the other hand, Mg-MOF-74 yields the highest CO2 recovery followed by zeolite 13X and activated carbon over the full range of feed flowrates as shown in these figures. Mg-MOF-74, while showing higher CO2 productivity than the other adsorbents as illustrated in Figures 7, 9, 11 has considerably lower energy requirement than activated carbon as shown in these figures and produces a much lower CO2 purity than 13X zeolite due to significant N2 adsorption and poor CO2 desorption. Mg-MOF-74 has the ACS Paragon Plus Environment

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highest CO2 productivity, but most of the CO2 is removed at a deeper vacuum level because of strong thermal effects and very nonlinear adsorption isotherm and therefore the energy requirement is at the same level as the 13X zeolite. The comparison of the three adsorbents shows that zeolite 13X has the best performance among the three adsorbents, in terms of CO2 purity even though Mg-MOF-74 shows considerably higher CO2 adsorption capacity (but in the meantime higher N2 adsorption capacity and poor regeneration). On the other hand Mg-MOF-74 appears to be a promising adsorbent for CO2 capture as it has considerably higher CO2 productivity compared to the other two adsorbents. Although it is quite common to compare adsorbents based on physicochemical properties such as equilibrium adsorption capacity and selectivity, the results from process simulations do not seem to support such a simple practice. Therefore, a detailed process modeling and simulation strategy such as the one described in this work, appears to be the most reliable way to assess the effectiveness of potential adsorbents for CO2 capture and concentration. 3.3. Optimization case studies Since zeolite 13X and Mg-MOF-74 adsorbents have shown some promising features as potential candidates for CO2 removal from dry flue gas, we wanted to further explore their capabilities and improve their process performance. Hence, in addition to the simulation studies, we performed process optimization studies using the same VSA process described in section 3.2 above. Two different optimization case studies have been performed: (I) optimization using zeolite 13X as an adsorbent and (II) optimization using Mg-MOF-74 as an adsorbent both at nearly atmospheric feed pressures. The objective of these case studies is to minimize energy consumption for specified minimum requirements in CO2 purity (90 %) and in CO2 recovery (90 %) at nearly atmospheric feed pressures. We have relaxed the minimum requirements for CO2 purity and CO2 recovery as a single stage VSA process is considered. The optimization variables are: feed pressure (1.1-1.5 bar), feed flowrate (0.048-0.48 ACS Paragon Plus Environment

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lt/s_stp), blowdown pressure (0.11-0.70 bar) and evacuation pressure (0.01-0.10 bar). Note that the values in parenthesis after each optimization variable indicate lower and upper bounds of each variable, respectively. The optimization study has been performed at different fixed feed temperatures. All studies have been carried out at a constant column geometry, cycle time, duration of operating steps and gas valve parameters. The optimization results for case studies I and II are summarized in Table S5 and Table S6, respectively in the supporting information material and are illustrated in Figure 12 and Figure 13, respectively. The results of these case studies reveal that, the minimum target of 90% in CO2 purity and 90% in CO2 recovery is met for the VSA process under consideration at nearly atmospheric pressures for both adsorbents. It is important to note that zeolite 13X has lower energy requirements than MgMOF-74, as can be seen from Figures 12 and 13, respectively, and can be attributed mainly to the need for lower blowdown and evacuation pressures when working with Mg-MOF-74 (around 0.11 and 0.015 bar, respectively) as opposed to zeolite 13X (around 0.215 and 0.025 bar, respectively). These differences in the optimal desorption pressures can be related to differences in CO2 adsorption isotherm steepness and CO2/N2 selectivity at low pressures for each adsorbent, which may vary at different temperatures due to the different heat of adsorption between zeolite 13X and Mg-MOF-74. Thus, there is a complex relationship between optimal process performance indicators and operating conditions that varies between the two studied adsorbents. Evidently, further work must be done to explore in detail the effect of other parameters including the duration of operating steps and overall cycle time, column geometry, more complex bed configurations, etc.

4. Conclusions This work presents a mathematical modeling framework for the simulation and optimization of PSA/VSA processes for post-combustion CO2 capture from dry flue gas (85% N2, 15% CO2). The


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model is first validated against literature data, illustrating good agreement in terms of several process performance indicators. Accordingly, the developed modeling framework is employed for a comparative evaluation of three available potential adsorbents for CO2 capture, zeolite 13X, activated carbon and metal organic framework (MOF), Mg-MOF-74. A two-bed configuration (six-step VSA cycle) with light product pressurization has been employed in all simulations in accord with recent pilot plant studies. Systematic comparative simulations demonstrate that zeolite 13X has the best process performance among the three adsorbents, in terms of CO2 purity and CO2 recovery, for the VSA process under consideration. On the other hand, Mg-MOF-74 appears to be a promising adsorbent for CO2 capture, as it has considerably higher CO2 productivity compared to the other two adsorbents. Before we generalize this conclusion, further work must be done to explore in detail the effect of other parameters including the duration of operating steps and overall cycle time, column geometry, more complex bed configurations, etc. This is part of an ongoing study that will be presented in a future publication. As a next step, zeolite 13X and Mg-MOF-74 have been selected for process optimization to minimize energy consumption for specified minimum requirements in CO2 purity and in CO2 recovery at nearly atmospheric feed pressures. The optimization results indicate that the minimum target of 90% in CO2 purity and 90% in CO2 recovery is met for the VSA process under consideration for both adsorbents. However zeolite 13X shows lower energy requirements than Mg-MOF-74 and this can be attributed mainly to the need for lower desorption (blowdown and evacuation) pressures when working with MgMOF-74 as opposed to zeolite 13X. The differences in the optimal desorption pressures can be related to differences in the structure of CO2/N2 adsorption isotherms at low pressures for each adsorbent, at different temperatures, revealing a complex relationship between optimal process performance indicators and operating conditions that varies among different adsorbents. Therefore, detailed process


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modeling, simulation and optimization strategies, provide the most reliable way to evaluate both qualitatively and quantitatively potential adsorbents for CO2 capture.

Associated content Supporting Information Tables S1 to S6 as described in the text. This information is available free of charge via the Internet at http://pubs.acs.org. Acknowledgement Financial support from: (i) the co-financed by the European Union and the Greek State program FENCO-NET: Project MOFCCS (Contract No.13FENCO-13-940) and (ii) the EFENIS FP7 project (Contract No. ENER/FP7/296003) are gratefully acknowledged.

Nomenclature

M(:) = Langmuir isotherm parameter at first site, 1/Pa

M(=) = Langmuir isotherm parameter at second site, 1/Pa

= molar concentrations of gas phase in bulk gas, mol/m3

W = molar concentrations of gas phase at the inlet of adsorption column, mol/m3 %! = specific heat capacity of bulk gas referring to constant pressure, J/(kg·K)

%Y = specific heat capacity of bulk gas referring to constant volume, J/(kg·K) %!! = specific heat capacity of the particle, J/(kg·K) Y,-Y = gas valve constant


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


= effective diffusivity, m2/s

K = Knudsen diffusivity, m2/s

= molecular diffusivity, m2/s

= axial dispersion coefficient, m2/s

X = molar flowrate, mol/s

12,3 = isosteric heat of adsorption, J/mol

): = dual site Langmuir isotherm parameter, mol/kg )= = dual site Langmuir isotherm parameter, 1/K

)Q = dual site Langmuir isotherm parameter, 1/Pa )U = dual site Langmuir isotherm parameter, K

k , = heat transfer coefficient for the column wall, J/(m2·K·s)

= adsorption column length, m bc

= molecular weight, g/mol !

= number of components, -

= mass generation term, mol/(m3·s)

r = isentropic compression efficiency of the compressor, -

rY = isentropic evacuation efficiency of the vacuum pump, ACS Paragon Plus Environment

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; = total pressure, Pa

; = pressure of the feed stream, Pa

; W = pressure at the inlet of the gas valve, Pa

;ef = pressure at the outlet of the gas valve, Pa

$ = adsorbed amount per unit mass of adsorbent, mol/kg

$ ∗ = adsorbed amount per unit mass of adsorbent in equilibrium state, mol/kg

' = dual site Langmuir isotherm parameter, mol/kg ' = heat generation term, J/(m3·s)

. = adsorption column radius, m .! = particle radius, m .!h = pore radius, m

Z; = stem position of the gas valve

= time, s

& = temperature of the adsorption column, K & = temperature of the feed stream, K

& W = temperature of the fluid at the inlet of the adsorption column, K

&+,-- = temperature of the wall, K


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


= interstitial velocity, m/s

W = interstitial velocity at the inlet of the adsorption column, m/s

P = molar fraction in gas phase, = axial discretization domain, m Greek letters

V O = selectivity of component i over component j of an adsorbent,k = specific heat capacity ratio,-

= porosity of the adsorption column, -

! = porosity of the particle, -

/ = thermal conductivity of bulk gas, J/(m·K·s)

/ = heat axial dispersion coefficient, J/(m·K·s)

> = viscosity of bulk gas, Pa·s

# = density of bulk gas, kg/m3

#! = density of the particle, kg/m3

J! = tortuosity factor of the particle, -

J- = total cycle time, s Subscripts


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= component Superscripts = particle-pellet Abbreviations CFDM = Centered Finite Difference Method LDF = Linear Driving Force PSA = Pressure Swing Adsorption VSA = Vacuum Swing Adsorption VPSA = Vacuum Pressure Swing Adsorption RPSA = Rapid Pressure Swing Adsorption

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11. Hasan, M. M. F.; Baliban, R. C.; Elia, J. A.; Floudas, C. A., Modeling, simulation, and optimization of postcombustion CO2 capture for variable feed concentration and flow rate. 2. Pressure swing adsorption and vacuum swing adsorption processes. Industrial and Engineering Chemistry Research 2012, 51, (48), 1566515682. 12. Zhang, J.; Webley, P. A.; Xiao, P., Effect of process parameters on power requirements of vacuum swing adsorption technology for CO2 capture from flue gas. Energy Conversion and Management 2008, 49, (2), 346356. 13. D'Alessandro, D. M.; Smit, B.; Long, J. R., Carbon dioxide capture: Prospects for new materials. Angewandte Chemie - International Edition 2010, 49, (35), 6058-6082. 14. Hu, X.; Mangano, E.; Friedrich, D.; Ahn, H.; Brandani, S., Diffusion mechanism of CO2 in 13X zeolite beads. Adsorption 2014, 20, (1), 121-135. 15. Nalaparaju, A.; Khurana, M.; Farooq, S.; Karimi, I. A.; Jiang, J. W., CO2 capture in cation-exchanged metal–organic frameworks: Holistic modeling from molecular simulation to process optimization. Chemical Engineering Science 2015, 124, (0), 70-78. 16. Shen, C.; Yu, J.; Li, P.; Grande, C. A.; Rodrigues, A. E., Capture of CO2 from flue gas by vacuum pressure swing adsorption using activated carbon beads. Adsorption 2011, 17, (1), 179-188. 17. Kikkinides, E. S.; Yang, R. T.; Cho, S. H., Concentration and recovery of CO2 from flue gas by pressure swing adsorption. Industrial and Engineering Chemistry Research 1993, 32, (11), 2714-2720. 18. Chue, K. T.; Kim, J. N.; Yoo, Y. J.; Cho, S. H.; Yang, R. T., Comparison of activated carbon and zeolite 13X for CO2 recovery from flue gas by pressure swing adsorption. Industrial and Engineering Chemistry Research 1995, 34, (2), 591-598. 19. Takamura, Y.; Narita, S.; Aoki, J.; Hironaka, S.; Uchida, S., Evaluation of dual-bed pressure swing adsorption for CO2 recovery from boiler exhaust gas. Separation and Purification Technology 2001, 24, (3), 519528. 20. Na, B. K.; Koo, K. K.; Eum, H. M.; Lee, H.; Song, H. K., CO2 Recovery from Flue Gas by PSA Process using Activated Carbon. Korean Journal of Chemical Engineering 2001, 18, (2), 220-227. 21. Na, B. K.; Lee, H.; Koo, K. K.; Song, H. K., Effect of rinse and recycle methods on the pressure swing adsorption process to recover CO2 from power plant flue gas using activated carbon. Industrial and Engineering Chemistry Research 2002, 41, (22), 5498-5503. 22. Park, J. H.; Beum, H. T.; Kim, J. N.; Cho, S. H., Numerical analysis on the power consumption of the PSA process for recovering CO2 from flue gas. Industrial and Engineering Chemistry Research 2002, 41, (16), 41224131. 23. Gomes, V. G.; Yee, K. W. K., Pressure swing adsorption for carbon dioxide sequestration from exhaust gases. Separation and Purification Technology 2002, 28, (2), 161-171. 24. Choi, W. K.; Kwon, T. I.; Yeo, Y. K.; Lee, H.; Song, H. K.; Na, B. K., Optimal Operation of the Pressure Swing Adsorption (PSA) Process for CO2 Recovery. Korean Journal of Chemical Engineering 2003, 20, (4), 617623. 25. Chou, C. T.; Chen, C. Y., Carbon dioxide recovery by vacuum swing adsorption. Separation and Purification Technology 2004, 39, (1-2 SPEC. ISS.), 51-65. 26. Ko, D.; Siriwardane, R.; Biegler, L. T., Optimization of pressure swing adsorption and fractionated vacuum pressure swing adsorption processes for CO2 capture. Industrial and Engineering Chemistry Research 2005, 44, (21), 8084-8094. 27. Grande, C. A.; Cavenati, S.; Rodrigues, A. E., Pressure swing adsorption for carbon dioxide sequestration presented at ENPROMER. In 4th Mercosur Conference on Process Systems Engineering, Rio de Janeiro, Brasil 2005. 28. Reynolds, S. P.; Ebner, A. D.; Ritter, J. A., Stripping PSA cycles for CO2 recovery from flue gas at high temperature using a hydrotalcite-like adsorbent. Industrial and Engineering Chemistry Research 2006, 45, (12), 4278-4294. 29. Reynolds, S. P.; Mehrotra, A.; Ebner, A. D.; Ritter, J. A., Heavy reflux PSA cycles for CO2 recovery from flue gas: Part I. Performance evaluation. Adsorption 2008, 14, (2-3), 399-413. ACS Paragon Plus Environment

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30. Xiao, P.; Zhang, J.; Webley, P.; Li, G.; Singh, R.; Todd, R., Capture of CO2 from flue gas streams with zeolite 13X by vacuum-pressure swing adsorption. Adsorption 2008, 14, (4-5), 575-582. 31. Zhang, J.; Webley, P. A., Cycle development and design for CO2 capture from flue gas by vacuum swing adsorption. Environmental Science and Technology 2008, 42, (2), 563-569. 32. Agarwal, A.; Biegler, L. T.; Zitney, S. E., A superstructure-based optimal synthesis of PSA cycles for postcombustion CO2 capture. AIChE Journal 2010, 56, (7), 1813-1828. 33. Shen, C.; Liu, Z.; Li, P.; Yu, J., Two-Stage VPSA Process for CO2 Capture from Flue Gas Using Activated Carbon Beads. Industrial & Engineering Chemistry Research 2012, 51, (13), 5011-5021. 34. Wang, L.; Liu, Z.; Li, P.; Wang, J.; Yu, J., CO2 capture from flue gas by two successive VPSA units using 13XAPG. Adsorption 2012, 18, (5-6), 445-459. 35. Liu, Z.; Wang, L.; Kong, X.; Li, P.; Yu, J.; Rodrigues, A. E., Onsite CO2 Capture from Flue Gas by an Adsorption Process in a Coal-Fired Power Plant. Industrial & Engineering Chemistry Research 2012, 51, (21), 7355-7363. 36. Wang, L.; Yang, Y.; Shen, W.; Kong, X.; Li, P.; Yu, J.; Rodrigues, A. E., Experimental evaluation of adsorption technology for CO2 capture from flue gas in an existing coal-fired power plant. Chemical Engineering Science 2013, 101, 615-619. 37. Wang, L.; Yang, Y.; Shen, W.; Kong, X.; Li, P.; Yu, J.; Rodrigues, A. E., CO2 Capture from Flue Gas in an Existing Coal-Fired Power Plant by Two Successive Pilot-Scale VPSA Units. Industrial & Engineering Chemistry Research 2013, 52, (23), 7947-7955. 38. Haghpanah, R.; Majumder, A.; Nilam, R.; Rajendran, A.; Farooq, S.; Karimi, I. A.; Amanullah, M., Multiobjective optimization of a four-step adsorption process for postcombustion CO2 capture via finite volume simulation. Industrial and Engineering Chemistry Research 2013, 52, (11), 4249-4265. 39. Haghpanah, R.; Nilam, R.; Rajendran, A.; Farooq, S.; Karimi, I. A., Cycle synthesis and optimization of a VSA process for postcombustion CO2 capture. AIChE Journal 2013, 59, (12), 4735-4748. 40. Krishnamurthy, S.; Rao, V. R.; Guntuka, S.; Sharratt, P.; Haghpanah, R.; Rajendran, A.; Amanullah, M.; Karimi, I. A.; Farooq, S., CO2 capture from dry flue gas by vacuum swing adsorption: A pilot plant study. AIChE Journal 2014, 60, (5), 1830-1842. 41. Riboldi, L.; Bolland, O., Evaluating Pressure Swing Adsorption as a CO2 separation technique in coalfired power plants. International Journal of Greenhouse Gas Control 2015, 39, (0), 1-16. 42. Webley, P., Adsorption technology for CO2 separation and capture: a perspective. Adsorption 2014, 20, (2-3), 225-231. 43. Ishibashi, M.; Ota, H.; Akutsu, N.; Umeda, S.; Tajika, M.; Izumi, J.; Yasutake, A.; Kabata, T.; Kageyama, Y., Technology for removing carbon dioxide from power plant flue gas by the physical adsorption method. Energy Conversion and Management 1996, 37, (6–8), 929-933. 44. Kikkinides, E. S.; Nikolic, D.; Georgiadis, M. C., Modeling of Pressure Swing Adsorption Processes. In Dynamic Process Modeling, Engineering, P. S., Ed. 2011; Vol. 7 pp 137-172. 45. Nikolic, D.; Giovanoglou, A.; Georgiadis, M. C.; Kikkinides, E. S., Generic modeling framework for gas separations using multibed pressure swing adsorption processes. Industrial and Engineering Chemistry Research 2008, 47, (9), 3156-3169. 46. Nikolic, D.; Kikkinides, E. S.; Georgiadis, M. C., Optimization of multibed pressure swing adsorption processes. Industrial and Engineering Chemistry Research 2009, 48, (11), 5388-5398. 47. Wakao, N.; Funazkri, T., Effect of fluid dispersion coefficients on particle-to-fluid mass transfer coefficients in packed beds. Correlation of sherwood numbers. Chemical Engineering Science 1978, 33, (10), 1375-1384. 48. Wakao, N.; Kaguei, S.; Funazkri, T., Effect of fluid dispersion coefficients on particle-to-fluid heat transfer coefficients in packed beds. Correlation of nusselt numbers. Chemical Engineering Science 1979, 34, (3), 325-336. 49. Choi, C. T.; Wen-Chung, H., Incorporation of a valve equation into the simulation of a pressure swing adsorption process. Chemical Engineering Science 1994, 49, (1), 75-84.


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List of Figure Captions Figure 1. Mixture selectivity of CO2/N2 at different temperatures and total pressure of 1 bar for 13X. Figure 2. Mixture selectivity of CO2/N2 at different temperatures and total pressure of 1 bar for AC. Figure 3. Mixture selectivity of CO2/N2 at different temperatures and total pressure of 1 bar for MgMOF-74. Figure 4. Sequence of operating steps for the two-bed configuration (six-step VSA cycle). Figure 5. Interaction between the beds during each operating step for the two-bed configuration. Figure 6. Effect of feed flow rate on CO2 purity and recovery at T=298 K. Figure 7. Effect of feed flow rate on CO2 productivity and energy requirement at T=298 K. Figure 8. Effect of feed flow rate on CO2 purity and recovery at T=313 K. Figure 9. Effect of feed flow rate on CO2 productivity and energy requirement at T=313 K. Figure 10. Effect of feed flowrate on CO2 purity and recovery at T=323 K. Figure 11. Effect of feed flowrate on CO2 productivity and energy requirement at T=323 K. Figure 12. Effect of feed temperature on optimal CO2 purity, CO2 recovery and energy requirements of case study I (adsorbent, zeolite13X). Figure 13. Effect of feed temperature on optimal CO2 purity, CO2 recovery and energy requirements of case study II (adsorbent, Mg-MOF-74).


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Selectivity of CO2/N2 for 13X 2000 1800 1600 1400

SCO2/N2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1200

298K

1000

313K

800

323K

600 370K

400 200 0 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

YCO2 Figure 1.


33


Selectivity of CO2/N2 for AC 200 180 160 140

SCO2/N2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 45

120

298K

100

313K

80

323K

60 370K

40 20 0 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

YCO2 Figure 2.


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Selectivity of CO2/N2 for Mg-MOF-74 4000 3500 3000

SCO2/N2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


2500

298K

2000

313K

1500

323K 370K

1000 500 0 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

YCO2 Figure 3.


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

z=L

Pressurisation with light product

z=0

z=L

Adsorption

z=0

z=L

Pressure Equalization (Depressurisation)

z=0

z=L

Blowdown

z=0

z=L

Evacuation

z=0

.

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z=L

Pressure Equalization (Repressurisation)

z=0

.

Figure 4.


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. z=L

z=L Blowdown

z=0

z=0 Adsorption

z=L

z=L Evacuation

z=0

z=0 P-equalisation (D)

z=L

z=L

z=0

z=0 Pressurisation with LP

P-equalisation (R)

z=L

z=L z=0 Pressurisation with LP

z=0 Evacuation

z=L

z=L

z=0 P-equalisation (R)

z=L

z=0 Blowdown

z=0

Adsorption

z=L P-equalisation (D)

z=0 .

Figure 5.


37


100

100 90

90 80

CO2 Purity, %

80 70

70

60 50

60

CO2 Recovery, %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 45

40 50

40 0.00

0.05

Mg-MOF-74

13X

A-C

Mg-MOF-74

13X

A-C

0.10

0.15

30 20 0.20

Ffeed (lt/s STP) Figure 6.


38

Page 39 of 45

CO2 Productivity, mol CO2/Kg.s

0.006

0.005

250

Mg-MOF-74 13X A-C Mg-MOF-74 13X A-C

200

0.004

0.003

150

0.002 100

Energy, KJ/mol CO2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.001

0.000 0.00

0.05

0.10

0.15

50 0.20



39


100

100 90

90

CO2 Purity, %

80 70

80

60

70

50

CO2 Recovery, %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 40 of 45

40 60

50 0.00

0.05

Mg-MOF-74

13X

A-C

Mg-MOF-74

13X

A-C

0.10

0.15

30 20 0.20



40

Page 41 of 45


0.006

0.005

250


200

0.004

0.003

150

0.002 100

Energy, KJ/mol CO2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.001

0.000 0.00

0.05

0.10

0.15

50 0.20



41


100

100 90

90

CO2 Purity, %

80 70

80

60

70

50

CO2 Recovery, %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 42 of 45

40 60

50 0.00

Mg-MOF-74 Mg-MOF-74

0.05

13X 13X

30

A-C A-C

0.10

0.15

20 0.20



42

Page 43 of 45


0.006

0.005

250


200

0.004

0.003

150

0.002 100

Energy, KJ/mol CO2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.001

0.000 0.00

0.05

0.10

0.15

50 0.20



43

100

Page 44 of 45

100 Purity

Recovery

Energy

95 80 90 60 85

80

Energy, KJ/mol CO2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

CO2 Purity, % - CO2 Recovery, %


40 298

313

323

333

Temperature, K

Figure 12.


44

Page 45 of 45

CO2 Purity, % - CO2 Recovery,%

100

120 Purity

Recovery

Energy

95

100

90

80

85

60

80

40 298

313

323

Energy, KJ/mol CO2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


333

Temperature, K

Figure 13.


45

Model-Based Approach for the Evaluation of ... - ACS Publications

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