D

JB

a

ARRAA

KPADSO

1

atghsiiriairTeaI

sai

1d

International Journal of Greenhouse Gas Control 8 (2012) 45–55

Contents lists available at SciVerse ScienceDirect

International Journal of Greenhouse Gas Control

j ourna l ho mepage: www.elsev ier .com/ locate / i jggc

ynamic modeling and absorption capacity assessment of CO2 capture process

ozsef Gaspar, Ana-Maria Cormos ∗

abes-Bolyai University, Faculty of Chemistry and Chemical Engineering, Arany Janos, No. 11, RO-400028 Cluj-Napoca, Romania

r t i c l e i n f o

rticle history:eceived 2 August 2011eceived in revised form 19 January 2012ccepted 28 January 2012vailable online 28 February 2012

eywords:ost-combustion CO2 capturebsorption

a b s t r a c t

One of the most promising carbon dioxide capture technology is based on chemical gas–liquid absorp-tion using alkanolamine solutions. In order to optimize the operating conditions of absorption–desorptioncycle, the development of a detailed mathematical model is necessary. Several mass transfer and hydrauliccorrelation models are published in the literature, but study of predictive capacity for absorption processis required. The aims of this work are to check the mass transfer and hydraulic correlation models pre-dictive capacity and to investigate the dynamic behavior and absorption performance of four differenttype alkanolamine. The study shows that the implemented mathematical model reveals a good qualityprediction of the absorption process for all alkanolamines.

ynamic modelingimulationptimization

The analysis of simulation results highlight that the mass transfer correlation model proposed by Wanget al. predicts well the effective mass transfer area and the mass transfer coefficient correlation. Modelproposed by Billet and Schultes predicts well in case of MEA and DEA. And correlation model proposed byRocha et al., predicts well in case of AMP and MDEA. The comparison of three hold up models show thatthe model proposed by Bravo et al. is capable to predict well this parameter in a wide column loadingrange.

. Introduction

Today the climate change issue has become a major concern,nd it is perhaps one of the most severe challenges of the modernime. Climate change and global warming is caused by increasedreenhouse gas emissions, mainly carbon dioxide resulted fromuman activity. Power generation sector, which use intensively fos-il fuels (coal, lignite, natural gas etc.), is a major contributor to thencrease of carbon dioxide concentration in the atmosphere andt consequently leads to global warming (IPCC, 2007). From thiseason it becomes obvious that advanced techniques for captur-ng and storing of CO2 – commonly referred to as carbon capturend storage CCS – may become an important part of the solutionn short time. A substantial research and development efforts areequired in order to deem CCS viable on techno-economic terms.here are several alternative technologies for capturing CO2, forxample: adsorption, cryogenic methods, membrane separationnd biological fixation (Kohl and Nielsen, 2005; White et al., 2003;PPC, 2005).

Carbon dioxide capture by gas–liquid absorption has been con-

idered to be the most viable solution, compared to the othervailable processes. The carbon dioxide absorption can be dividednto chemical absorption (using solvents which chemically interact

∗ Corresponding author. Tel.: +40 264 593833, fax: +40 264 590818.E-mail address: cani@chem.ubbcluj.ro (A.-M. Cormos).

750-5836/$ – see front matter © 2012 Elsevier Ltd. All rights reserved.oi:10.1016/j.ijggc.2012.01.016

© 2012 Elsevier Ltd. All rights reserved.

with CO2) and physical absorption (using solvents in which CO2 issoluble but without chemical reaction). There are three main con-ceptual methods for carbon dioxide capture generated from thecombustion of fossil fuel conversions: post-combustion capture,pre-combustion capture and oxy-combustion (Kohl and Nielsen,2005).

As potential technical and economical mature carbon capturetechnologies from the flue gases produced by the power gener-ation sector is post-combustion capture, the chemical gas–liquidabsorption being one CCS method which has the highest probabil-ity to be implemented in large scale applications practice in thefollowing years (International Energy Agency, 2004a, 2004b). Inpost-combustion capture case, the carbon dioxide concentrationsare relatively low, e.g. 4–8 vol.% in natural gas-fired and 12–15 vol.%in coal-fired power plants and the flue gas pressure is close to atmo-spheric. From the point of view of electric power consumption, incase of chemical solvents, the solvent flow rate is lower than inthe case of physical solvents, which means a lower electric powerconsumption for solvent circulation. In term of heat consumptionthe situation is totally reversed, the chemical solvents require muchmore heat for regeneration (CO2 desorption) than physical solventsdue to the chemical reactions involved in the process. These issuesrequire an integration analysis in terms of heat and electric power

for the carbon dioxide capture plant (Aronu et al., 2009; Cheng andTan, 2009; Descamps et al., 2008).

Amine-based post-combustion carbon dioxide capture and stor-age method (CCS) is the most viable pathway that allow to

46 J. Gaspar, A.-M. Cormos / International Journal o

Nomenclature

ae effective mass-transfer area (m2/m3)a packing specific mass transfer area (m2/m3)Ci molar concentration (kmol/m3)CL, CV, Ch, CE packing specific constantD diffusion coefficient (m2/s)ECO2 enhancement factorf friction factorF volumetric flow rate (m3/s)FSE surface correction factorFr Froude numberFt correction factor for total holdup due to effective

wetted areah heat transfer coefficient (kW/(m2 K))hL liquid hold upHa Hatta numberHCO2 Henry’s law constantkr reaction rate constant (m3/(kmol s))kG, kL gas or liquid film mass-transfer coefficient (m/s)Mi the molecular mass of component i (kg/kmol)N interfacial molar flux (kmol/(m2 s))Re Reynolds numberS the cross-sectional area (m2)t time (s)U velocity (m/s)q internal source termWe Weber numberx, y model parameter

Greek symbolsε void fraction� corrugation angle (◦)� viscosity (Pa s)� contact angle (◦)� thermal conductivity (W/(m2 K))� density (kg/m3)� resistance coefficient

Subscripts/superscriptsG gas phaseL liquid phasei, j CO2, amine, H2O

scmpti(

tpuotsaoFep

Int interface

ignificantly reduce the CO2 emissions associated with fossil fuelsombustion while enjoy the benefits of energy consumption. Thisethod is compatible and can be retrofitted to existing power

lants infrastructure with minimal changes in plant construc-ion. However, the major challenge remains the equipment designmprovement and minimizing of the operating cost and investmentPellegrini et al., 2010; Oyenekan and Rochelle, 2007).

The environmental restrictions and economic aspects requirehe development of an accurate and general dynamic model whichermits the study of the absorption process’ behavior and the eval-ation of all operational challenges (including part-load operationf the power plants to match with grid demand). Studies of absorp-ion column using commercial simulation software are presented ineveral works (Cormos, 2009; Kothandaraman et al., 2009; Pacheond Rochelle, 1998; Puxty et al., 2009, 2010). Rate based models

f CO2 absorption in alkanolamine are also reported: (De Leye androment, 1986; McCann et al., 2008; Simon et al., 2011; Padureant al., 2011). A dynamic model of an absorber column for CO2ost-combustion capture, using mono-ethanol amine was present

f Greenhouse Gas Control 8 (2012) 45–55

by Cormos et al. (2009), Gaspar and Cormos (2011), Kvamsdalet al. (2009), Lawal et al. (2009). Versteeg et al. (1996) describethe kinetics of CO2 absorption and desorption in aqueous alka-nolamine solutions. For second-order irreversible reactions Welleket al. (1978) derived a correlation to calculate the enhancementfactor.

Large scale technology deployment, minimization of the capi-tal and operating costs and required investments are needed forimplementation of CCS technologies. Several articles have beenpublished on this topic (Abu-Zahra et al., 2007a, 2007b; Oi, 2007;Suppapitnarm et al., 2000), but detailed modeling and optimizationof a post-combustion capture process remain a complex task. Thefinding of the global-optimal solution of a multivariable system isdifficult because of the non-linearity and non-convex constraintsin the mathematical model.

This work presents the validation of a rate based model, devel-oped by authors, against experimental data, published in literatureby Aroonwilas and Veawab (2004) for four types of alkanolamines(MEA, DEA, AMP and MDEA) and by Aroonwilas et al. (2001),Tontiwachwuthikuls et al. (1992) and Tobiesen and Svendsen(2007) for primary alkanolamine (MEA). This work also meansimproving the knowledge about the post-combustion modeling bycomparing several mass transfer and hydraulic models, publishedin the literature. The present paper also investigate the dynamicbehavior, absorption performance and the effect of the liquid–gasphase ratio on the CO2 absorption efficiency, in case of case ofprimary (MEA), secondary (DEA), tertiary (MDEA) and stericallyhindered (AMP) amines. These amines were chosen because theyare the most representative of their class. Some of the basic proper-ties are presented in Table 1. (Padurean et al., 2011) The developedmodel is used for the absorption performance evaluation of thesealkanolamines from technical point of view. The developed modelhas been implemented in Matlab/Simulink 2008 (MAT Users Guide,2008).

2. Rate based modeling

2.1. Generic model. Assumptions.

The model of CO2 absorption process in alkanolamine solutionconsists of partial differential equations and constitutive relations.The present model is based on the two film theory and uses thefollowing assumptions (Gaspar and Cormos, 2011):

• Plug flow regime of both phases.• Model parameters are constant in the radial cross section of the

column.• Heat transfer by conduction and radiation is negligible.• The gas and liquid phase is considered to be an ideal mixture.• The solubility of carbon dioxide in liquid phase is described by

Henry’s law.• Chemical reaction, vaporization of the liquid takes place only

in the liquid phase and the vapors condense at the gas–liquidinterface.

• CO2, H2O and Amine fluxes are allowed in both directions(gas ↔ liquid).

• The absorption columns are operated adiabatically.

The phenomena which take place in an absorption columncan be described by mathematical equations (Gaspar and Cormos,2011), as follows.

The total mass balance for the gas and liquid phases are:

∂FL

∂t= −∂(ULFL)

∂z+ ULSae

�L

∑(Mi · Ni) (1)

J. Gaspar, A.-M. Cormos / International Journal of Greenhouse Gas Control 8 (2012) 45–55 47

Table 1Alkanolamines basic properties.

Properties MEA DEA AMP MDEA

Chemical formula C2H7NO C4H11NO2 C4H11NO C5H13NO2

Molecular weight [g/mol] 61.08 105.14 89.14 119.16Vapor pressure [mmHg at 20 ◦C] 0.20 0.28 1.00 0.01Density [kg/m3] 1012 1097 934 1038Boiling point [◦C] 170 268.8 165 247Flash point [◦C] 86 138 68 127Viscosity [cP at 20 ◦C] 19.37 351 147 101Solution conc. [wt%] <30 <35 <30 <50Solution loading <0.45 <0.8 <0.47 <0.8Ecotoxicity [mg/L] 250 500 100 200Toxicity [mg/kg] 1720 710 2900 1945

wcuaFd

wm

wit

hrsb

ud

2

ba

N

Biodegradability [% ThOD] 27

Safety Danger

Hazard code Corrosive

∂FG

∂t= −∂(UGFL)

∂z− UGSae

�G

∑(Mi · Ni), i = CO2, H2O, amine

(2)

here FL, FG are the liquid, respectively gas phase flow, S is theross-sectional area, �L, �G are the phases density, Mi is the molec-lar mass of component i and Ni is the flux of component i. UL, UGre the gas and liquid velocities: UL = FL/(S · ε · hL · sin(�)) and UG =G/(S · ε · (1 − hL) · sin(�)) (Rocha et al., 1993). The sign ± shows theirection of mass transfer in the absorption columns.

The component mass balance for the liquid and gas phase is:

∂CLi

∂t= −∂(ULCL

i)

∂z− ae · Ni − NR (3)

∂CGi

∂t= −∂(UGCG

i)

∂z+ ae · Ni (4)

here Ci is the concentration of component i, ae is the interfacialass transfer area and NR is the reaction term.The heat balance for the liquid and gas phase is:

∂TL

∂t= −∂(ULTL)

∂z− NR · RH

�L · cpL− ae ·

∑(Ni · i

VH

�L · cpL

)

+ h · ae

�L · cpL· (TG − TL) (5)

∂TG

∂t= −∂(UGTG)

∂z− h · ae

�G · cpG· (TG − TL) (6)

here cpL, cpG are the heat capacity, RH is the reaction heat, VHs the vaporization/condensation heat and h is the interfacial heatransfer coefficient.

The second term in Eq. (5) represents the released/consumedeat from the reaction between CO2 and MEA, the third termepresents the released/absorbed heat at water and MEA conden-ation/vaporization and the fourth term represents the heat changeetween the two phases due to temperature difference.

The phenomena which take place in a control balance vol-me are represented below, in Fig. 1. More details of the absorberynamic model are described in Gaspar and Cormos (2011).

.2. Mass transfer models

The theory of coupled chemical reaction and mass transfer can

e developed on the basis of the two film theory. The molar fluxcross the mass transfer area is expressed by the Eq. (7):

i = ki × (ci,G − ci,Gint ) (7)

42 2 2Danger Warning WarningHarmful Irritant Irritant

where ki represents mass transfer coefficient, ci,G represents theconcentration of component i in gas phase and ci,Gint representsthe concentration of component i at the interface.

It is assumed no liquid side resistance for the mass transfer ofwater, so the global mass transfer coefficient for water is numericequal with gas side resistance for water. The gas side resistance forwater is calculated with a correlation method proposed by Rochaet al. (1996).

The carbon dioxide mass transfer coefficient as the effectivemass transfer area is calculated with several correlation meth-ods. The methods assessed in the present paper are summarizedin Table 3.

2.3. Chemical reaction

The reaction of CO2 with various alkanolamines can proceed invarying absorption regime, depending on the column loading andamine type. The reaction mechanism implemented in the presentmodel is based on data revised by Versteeg et al. (1996). The reac-tion between CO2 and primary/secondary amines can be describedwith zwitterion mechanism. According to this mechanism for CO2-MEA system the overall reaction order has a value of two, and firstorder with respect to MEA. The overall reaction order for CO2-DEAsystem varies between 2 and 3. However it was concluded that thepseudo first order rate constant is in satisfactory agreement withexperimental data at 298 K. For sterically hindered amine (AMP)Bosch et al. (1990) concluded that the zwitterion mechanism isable to explain that the reaction rate and AMP shows an identicalbehavior to the conventional amines. A characteristics property ofsterically hindered amines is that they have a very low value ofthe equilibrium constant. The reaction between CO2 and tertiaryamines can be described with the base catalysis of the CO2 hydra-tion as proposed by Donaldson and Nguyen (1980). For MDEA theyreported an overall reaction order equal to two.

In case, when 2 < Ha � ECO2,∞ relation is satisfied, the reactiontake place in the so-called pseudo first order regime. According tothe above mentioned the reaction rate equation for aqueous solu-tions of amine is: rCO2 = k · [CO2] · [amine] (kmol m−3 s−1), wherek is the reaction rate constant. The rate constant for the reactionof alkanolamine and CO2 is presented in Table 2 (Versteeg et al.,1996). The chemical reaction in the absorption system has two dif-ferent effects on the overall behavior of the system. First, when acomponent is absorbed in the liquid, the component is consumedby the reaction with amine and the second effect is the driving

force may significantly increases (having positive effect on processintensification). The effect of the chemical reaction on the transferrate is built-in in the transfer equations by the enhancement factor(Wellek et al., 1978; Versteeg et al., 1996).

48 J. Gaspar, A.-M. Cormos / International Journal of Greenhouse Gas Control 8 (2012) 45–55

Table 2The reaction rate constant for CO2–amine systems.

Amine Expression Source k298◦ (m3/(mol s))

MEA k = 4.4 · 108 exp(

−5400T

)Versteeg et al. (1996) 5.9386

DEA log(k) = 10.4493 − 2274.5T Danckwerts and Sharma (1966) 0.6558( )

2

ptplbodr

2

aieseapT

MDEA k = 3.1 × 105 exp −5080T

AMP k = 1.943 × 107 exp(

−5174.49T

)

.4. Hydraulic models

Other essential parts of the model are the liquid hold up andressure drop calculation methods. These parameters depend onhe operating conditions and packing characteristics. The accuraterediction of the pressure drop is essential in the liquid–vapor equi-

ibrium determination, the gas phase properties calculation andetter prediction of the mass transfer rate. The exact knowledgef the liquid hold up variation is required to compute the pressurerop. The evaluated correlation methods in this paper are summa-ized in Table 3.

.5. Physical and chemical properties

Physical and chemical properties of four different CO2-lkanolamine systems were estimated using correlations publishedn literature. The absorption rate calculation requires the knowl-dge of alkanolamine properties: densities, CO2 solubility in amineolution, diffusion coefficients, viscosities, specific heat capacities,

tc. These parameters are strongly dependent on temperature,mine type and solution concentration. A list of the chemical andhysical properties, indicating the literature source, is presented inable 4.

Fig. 1. Absorption column and the mass transf

Saha et al. (1995) 0.0122

Versteeg et al. (1996) 0.5552

2.6. Influence of the phase ratio on absorption efficiency

The main challenge for post-combustion capture technologyis to find the optimal conditions for capturing CO2 (with respectto design and operation). The absorption rate is influenced by:column’s operating conditions, column’s packing type and liquidrespectively gas phase properties like dynamic viscosity, diffusioncoefficient, density which are determined by its composition (Khanet al., 2011).

According to the mass transfer model, the absorption rate ishighly dependent not only on the physical and chemical proper-ties of the system but it is also influenced by the liquid–gas ratio.Decreasing of this ratio can significantly reduce the required of col-umn height, without decreasing the absorption efficiency. So theliquid–gas ratio influences the working capital and the fixed capitaltoo (Gaspar and Cormos, 2011; Billet and Schultes, 1997). The aminetype and its concentration determine the liquid phase properties,the global mass transfer coefficient and the effective mass trans-fer area. A higher alkanolamine concentration favors the reactionrate but it involves a significant increase in the solution viscosityand the energy requirement in the alkanolamine regeneration step

(Cormos et al., 2010; Gaspar and Cormos, 2011; Nestor et al., 2010).Analysis of the absorption capacity of different alkanolamine solu-tions is important to provide preliminary designs and knowledgeabout the process.

er processes in an infinite small element.

J. Gaspar, A.-M. Cormos / International Journal of Greenhouse Gas Control 8 (2012) 45–55 49

Table 3A summary of the implemented correlation methods for mass transfer coefficient, mass transfer area, liquid hold up and pressure drop calculation.

Model Description

Mass transfer coefficientBillet and Schultes (1999)

kL = CL · 121/6 · UL1/2 ·

(DLdh

)1/2Physically proven model for arranged and dumped packing

kG = CV · (ε − hL)−1/2 ·(

adh

)1/2· DG ·

(UG

a·�G

)3/4·(

�GDG

)1/3Based on vertical flow channel model

Rocha et al. (1996)

kL = 2 ·(

DL ·CE ·UL�·S

)1/2Assume the complete wetting of the wall

kG = 0.054 · DGS ·

((UL+UG)·�G ·S

�G

)0.8·(

�GDG ·�G

)0.33kG is based on the interphase transport model and kL on penetration model

Mass transfer areaDe Brito et al. (1994)aea = 0.465 · Re0.3

L Empirical model for Mellapack packingBillet and Schultes (1999)

aea = Cp

(UL ·�La·�L

)0.25·(

U2L

·ag

)0.1

Based on vertical flow channel modelCorrection factor need to be determined experimental for each packing type

Rocha et al. (1996)aea = FSE · 29.12·(WeL ·FrL)0.15 ·S0.359

Re0.2L

·ε0.6 ·(1−0.93·cos(�))·sin(�)0.3 Based on work of Shi and Mersmann (1985)Analytically determination of surface enhancement factor

Wang et al. (2006)aea = aeBRF · 0.17 ·

(�L�G

)x· exp

(− y

UG

)Based on work of Bravo, Rocha and FairTake into account the gas loading of the column

Liquid hold upSuess and Spiegel (1992)

hL = c · a0.83 · UxL ·

(�L

�L,0

)0.25Empirical correlation method for Mellapack structured packing

Billet and Schultes (1999)

hL =(

12 · �L ·UL ·a2

g·�L

)1/3Based on vertical flow channel modelDepends on the liquid phase properties, packing’s characteristics and column’s loading

Rocha et al. (1993)

hL =(

4 · FtS

)2/3·(

3·�L ·UL�L ·sin(�)·ε·gef

)1/3Take into consideration the surface tension and the contact angle between liquid and packing

Pressure dropBillet and Schultes (1999)(

pz

)= �L · a

(ε−hL)3 · F2v

2·KwGeneralized pressure drop calculation method

Rocha et al. (1993)( )e dropeterm

3

3

aAape

m(mervta

TP

(pz

)= f ·�G ·U2

GS·gc

· (1 − K · hL)−5 Dry pressurAllows the d

. Results and discussions

.1. Rate-based model validation

The model of the carbon dioxide absorption process, describedbove has been validated against experimental data, published byroonwilas et al. (2001); Aroonwilas and Veawab (2004), Tobiesennd Svendsen (2007) and Tontiwachwuthikuls et al. (1992). Theilot plant characteristics and operation condition for the selectedxperimental studies are presented in Table 5.

The present mathematical model has been validated for pri-ary, secondary, tertiary and sterically hindered alkanolamines

MEA, DEA, MDEA and AMP). This paper also presents the assess-ent of several mass transfer coefficient correlation models,

ffective mass transfer area correlation models, liquid hold up cor-

elation models and pressure drop correlations models. For modelsalidation purpose the model combinations has been assessed atwo different CO2 loading (zero and 0.25) for MEA, DEA, MDEAnd AMP. These cases were selected not only to validate the

able 4hysico-chemical properties.

Property MEA D

CO2 solubility Maceiras et al. (2008)

CO2 diffusion coefficient Maceiras et al. (2008)

Solution viscosity Maceiras et al. (2008) ChHeat of reaction & Heat of vaporizationSurface tension Li

correlation methodination of the maximum hydraulic capacity

absorber model but also to evaluate and compare different aminesabsorption performance at typical process conditions (Aroonwilasand Veawab, 2004). In order to check the predictive capacity ofthe absorber model for various type of packing and operatingconditions the authors compared the simulation results with exper-imental data for primary amine, MEA (Aroonwilas et al., 2001;Tontiwachwuthikuls et al., 1992; Tobiesen and Svendsen, 2007).

In order to evaluate the models prediction capacity for primary,secondary, tertiary and sterically hindered alkanolamines (MEA,DEA, MDEA, and AMP) the experimental CO2 concentration profile,along the column’s height, was compared with CO2 concentrationprofile obtained by simulation. The coefficient of multiple correla-tion (multiple R) statistical indicator was calculated for the modelsprediction accuracy assessment.

Figs. 2 and 3 show the comparison between the predicted and

measured gas phase carbon dioxide molar fraction along the col-umn’s height. The legend for the models codification is presentedin Table 6. As can be seen, the overall agreement between mea-sured and predicted values varies in a large domain. Where the

EA AMP MDEA

Versteeg et al. (1996)Versteeg et al. (1996)

owdhury et al. (2010) Xu et al. (1991) Cadours (1998)Perry and Green (1999); ChemCAD

u et al. (2006); ChemCAD; Perry and Green (1999)

50 J. Gaspar, A.-M. Cormos / International Journal of Greenhouse Gas Control 8 (2012) 45–55

Table 5Column characteristics and operating conditions.

Parameter Packing type

Sulzer DXa Gempack4Ab Berl saddlec Mellapackd

Column height (m) 2 2.21 6.6 4.35Column diameter (m) 0.02 0.1 0.1 0.15Packing surface area (m2/m3) 900 466 647 250Packing corrugation angle (◦) 30 30 30 30Liquid load (m3/(m2 h)) 10 15.9 13.5 13.6Gas flow rate (m3/h) 0.37 7.24 10 150CO2 lean loading 0.00, 0.25 0.00 0.00 0.21CO2 fraction in feed gas (%) 10 15 11.5 1.5

a Aroonwilas and Veawab (2004).b Aroonwilas et al. (2001).c Tontiwachwuthikuls et al. (1992).d Tobiesen and Svendsen (2007).

Table 6Legend for the models codification.

Cod Source

Mass transfer areaA1 De Brito et al. (1994)A2 Billet and Schultes (1999)A3 Rocha et al. (1996)A4 Wang et al. (2006)

Mass transfer coefficientM1 Billet and Schultes (1999)M2 Rocha et al. (1996)

Liquid hold upH1 Billet and Schultes (1999)H2 Rocha et al. (1993)H3 Suess and Spiegel (1992)

Fig. 2. CO2 molar fraction along the column height (A

absorption efficiency is almost 100% (Figs. 2a, b, d and 3a, c) thedeviations between measured and predicted values are higher atthe bottom of the column and in case of smaller absorption effi-ciency (Figs. 2c and 3b, d) the deviations are higher at the top of thecolumn.

In order to quantify the model accuracy, the multiple R factorwas calculated and for the best fit model is indicated on these fig-ures (Figs. 2 and 3). Based on multiple R statistical factor analysis,it is concluded that the mass transfer coefficient correlation modelproposed by Billet and Schultes (model M1) predicts well in case of

MEA and DEA. And the correlation model proposed by Rocha, Bravoand Fair (model M2) gives good results in case of AMP and MDEA.This may be due to the reaction rate, which decreases in order:MEA � DEA ≈ AMP � MDEA. Based on this statistical analysis, the

roonwilas and Veawab, 2004, CO2 loading = 0).

J. Gaspar, A.-M. Cormos / International Journal of Greenhouse Gas Control 8 (2012) 45–55 51

ht (Ar

u(rWatu(fasSs

atTa

debdsa

tp(aF

The level of agreement (indicated by the multiple R statisti-cal indicator) between simulation and experimental data is alsoreflected in Figs. 7 and 8. These figures show the liquid phase’stemperatures variation along the column’s height. The difference

Fig. 3. CO2 molar fraction along the column heig

se of effective mass transfer area model proposed by Wang et al.model A4) or Bravo, Rocha and Fair (model A3) gives the bestesults. Generally, the effective mass transfer model developed by

ang et al. (model A4) predicts better the effective mass transferrea, due to the fact that it take into account the gas loading ofhe column coupled with gas phase’s velocity and density. The liq-id hold up correlation model developed by Bravo, Rocha and Fairmodel H2) or by Billet and Schultes (model H1) predicts betteror alkanolamine solutions with smaller viscosity, like MEA, AMPnd MDEA or for amine solution with high viscosity and high CO2olution loading. The liquid hold up model proposed by Suess andpiegel (model H3), in case of high viscosity (like DEA) at small CO2olution loading gives the best results.

The mass transfer and liquid hold up models developed by Billetnd Schultes has the disadvantage that they require the experimen-al determination of some correction factor for each packing type.he knowledge of these correction factors is essentially for a goodgreement between measured and simulation data.

The discrepancy between measured and predicted values mayue to use of empirical correlations for physical and chemical prop-rties estimation and the CO2 concentration measurement maye a source of error. The difference between measured and pre-icted values is smaller at nil CO2 solution loading than at 0.25 CO2olution loading. This may be due to use of the pseudo first orderssumption.

For detailed validation of the above presented best fit modelhe predictions are compared with pilot plant experimental data

ublished by Aroonwilas et al. (2001); Tontiwachwuthikuls et al.1992) and Tobiesen and Svendsen, 2007. The stationary profilesnd experimental data, along the column’s height are presented inigs. 4 and 6. The simulation results highlight that the developed

oonwilas and Veawab, 2004, CO2 loading = 0.25).

model can predict with accuracy the CO2 absorption process fordifferent column height, packing type, column CO2 loading and thegas phase’s CO2 concentration. Like in the case of the data publishedby Aroonwilas et al. (2001) the deviations from the experimentalvalues are higher at the bottom of the column. The good predic-tion capacity of the best fit model (A4–M1–H2) is suggested bythe multiple R statistical indicator (Figs. 4 and 5) and relative error(Fig. 6).

Fig. 4. CO2 molar fraction along the column height (Aroonwilas et al., 2001).

52 J. Gaspar, A.-M. Cormos / International Journal of Greenhouse Gas Control 8 (2012) 45–55

Fig. 5. CO2 molar fraction along the column height (Tontiwachwuthikuls et al.,1992).

F

bflhscu

Fe

ig. 6. CO2 molar fraction along the column height (Tobiesen and Svendsen, 2007).

etween the two temperature profiles consists in the liquid feedow temperature. Fig. 8 shows a protuberance of the profiles atigher input temperatures, due the water condensation. Figs. 2–8

how the fact that the developed model is capable to predict both,oncentration and temperatures variation along the absorption col-mn’s height.

ig. 7. Liquid phase’s temperature along the column height (Tontiwachwuthikulst al., 1992).

Fig. 8. Liquid phase’s temperature along the column height (Tobiesen and Svendsen,2007).

3.2. Amine absorption performance assessment

In the evaluation of solvent absorption efficiency, a majorrole plays solvent: absorption capacity, solubility, vapor pressure,molecular weight, degradation and corrosion properties, reactionkinetics, environmental and cost factors (Table 1).

In order to evaluate the effect of the column loading on theamine absorption performance the authors compared the absorp-tion efficiency at eight different, L/V ratio (Fig. 9). This figure showsthat the amine absorption efficiency exponentially increases withthe L/V ratio increasing (especially in case of MEA and AMP) and theCO2 absorption performance of the studied amines can be rankedin the following order: MEA > AMP > DEA � MDEA. The same con-clusion is reached also from the analyses of Figs. 2 and 3. As can beseen, it is apparent that the required column height, to achieve thesame CO2 absorption efficiency, is the smallest in case of MEA and itincreases similar to the rate constant order of the amines (Table 2).Generally an absorption efficiency close to 90% is the most efficientway to explore the column, on an economic viewpoint.

The disadvantage of the use of MEA, compared to DEA, is that iscorrosive and presents a medium biodegrability and relatively hightoxicity. MDEA and AMP show high toxicity and lowest biodegrabil-

ity, so they are the most dangerous on an environmental viewpoint(Table 1; Eide-Huagmo et al., 2009; Padurean et al., 2011).

Fig. 9. Dependency of the CO2 absorption efficiency by L/V ratio, CO2 column load-ing: 0.25.

J. Gaspar, A.-M. Cormos / International Journal of Greenhouse Gas Control 8 (2012) 45–55 53

F

tlvdcsm

st(

3

ubpAlFa

d

F

ig. 10. CO2 molar fraction profile along the absorption column height (MEA).

Fig. 9 also indicates that the operation conditions can influencehe order of the amine absorption performance. If thermodynamicimitation is reached (L/V < 4.5) AMP become the most efficient sol-ent. This may be due because the mass transfer become the rateetermining step, in case of thermodynamic limitation. This is inorrelation with the Henry’s law constant value (HCO2 ) which ismaller in case of MEA than for AMP and constant decreasing isore pronounced for MEA at L/V < 4.5.The evaluation of the simulation results show that there is no

ignificant difference between the results obtained by the use ofhe pressure drop correlation method proposed by Billet–Schultes1999) and Rocha–Bravo–Fair (1993).

.3. Dynamic behavior analyses

The dynamic behavior was analyzed, by increasing of the col-mn’s feed stream loading. In order to obtain a reliable dynamicehavior analyses, the initial-steady state operating conditions forrimary, secondary and tertiary amines (MEA, DEA, MDEA andMP) are the same (see Table 5) and the column’s feed stream

oading is increased after 1000 s from 0 (red line) to 0.25 (blue line),igs. 10–13. These figures represent the CO molar fraction at initial

2nd final steady-state and at transient moments.

At first sight it is obvious that the effect of the same perturbationiffers from amine to amine. The magnitude of the perturbation’s

ig. 11. CO2 molar fraction profile along the absorption column height (DEA).

Fig. 12. CO2 molar fraction profile along the absorption column height (MDEA).

effect is quantified by calculating the difference of the sweet gasstream CO2 content between 0 and 0.25 column’s loading reportedto the final CO2 content.

The magnitude of the perturbation’s effect is in concordancewith the amines reaction rate order: the sweet gas stream CO2 con-centration variation is the most significant in case of CO2 absorptionby MEA, which is followed by DEA and AMP. As it is showed fromthese figures the liquid feed stream CO2 concentration is less sig-nificant in case of CO2 absorption by MDEA, which has a reactionrate constant much lower than the other amines (MEA, DEA andAMP).

Another concordance, that reflects these figures, is the depen-dence between the time needed to achieve the new steady stateand the amine’s reaction rate. The MEA-CO2 system reaches fastestthe new steady state in approximately 17 min, followed by DEAand AMP (25 min) and by the slowest of the studied systems is theMDEA-CO2 system (50 min), which presents a similar behavior tothe physical absorption systems.

Generally it can be concluded that the absorption efficiencydecrease with the column’s feed stream loading increasing. MEA-CO2 and DEA-CO2 systems after the absorption process’ efficiencyincreasing present a decay. This phenomenon does not take place

when using AMP or MDEA. This may be due to the reaction rateorder or to the CO2 solubility dependency of the column loading.

Fig. 13. CO2 molar fraction profile along the absorption column height (AMP).

5 rnal o

4

cpmphRemmdrd

ttcStMmrpp

artmnciC

vcmbtbtsrct

A

Ablc

R

A

A

A

4 J. Gaspar, A.-M. Cormos / International Jou

. Conclusions

A rate based model has been developed for the CO2 post-ombustion process simulation using amine based solutions in aacked bed absorption column. The authors also assessed severalass transfer and liquid hold up correlation models and check their

rediction capacity for primary, secondary, tertiary and stericallyindered alkanolamines (MEA, DEA, MDEA, and AMP). The multiple

statistical indicator was calculated in order to compare the differ-nt correlation models prediction capacity and to choose the best fitodel for the above mentioned amines. The best fit model, based onultiple R value, was validated against a wide range experimental

ata of gas phase CO2 concentration and liquid phase temperature,eported in the literature. The results reveal the accuracy of theeveloped dynamic model.

The analysis of the simulation results highlight that the massransfer correlation model proposed by Wang et al. predicts wellhe effective mass transfer area and the mass transfer coefficientorrelation for all alkanolamines. Model proposed by Billet andchultes predicts well in case of MEA and DEA and the correla-ion model proposed by Rocha, Bravo and Fair in case of AMP and

DEA. The prediction of the liquid hold up is essential to deter-ine the hydraulic capacity of the column and the mass transfer

ate. The comparison of three hold up model shows that the modelroposed by Bravo, Rocha and Fair is capable to predict well thisarameter in a wide column loading range.

The evaluation of the absorption performance reveals that thebsorption efficiency of the amine based solvents depends on theate determining step (reaction rate or mass transfer rate). Dueo the highest reaction rate at L/V > 4.5, monoethanolamine is the

ost efficiency solvent (from absorption point of view) and alka-olamines performance decrease according to their reaction rateonstant (MEA > AMP > DEA � MDEA). If thermodynamic limitations reached AMP become the most effective solvent, due to higherO2 solubility in its solution.

The assessment of the absorption capacity, on an economicaliewpoint, shows that MEA is the cheaper solution to capturearbon dioxide, but it has major disadvantages: his corrosivity,edium biodegrability and a relatively high toxicity. The second

est choice on an economical viewpoint is the use of MDEA solu-ion to capture the CO2, but MDEA has high toxicity and lowestiodegrability, so she is the most dangerous on an environmen-al viewpoint. In conclusion, the concentration of the alkanolamineolution does not influence the absorption capacity. The simulationesults have shown that, in order to operate the process at lowestost, but at sufficient high absorption efficiency it is need to exploithe column at efficiency close to 90%.

cknowledgment

This work was supported by a grant of the Romanian Nationaluthority for Scientific Research, CNCS - UEFISCDI, project num-er PN-II-ID-PCE-2011-3-0028: “Innovative methods for chemical

ooping carbon dioxide capture applied to energy conversion pro-esses for decarbonized energy vectors poly-generation”.

eferences

bu-Zahra, M.R.M., Schneiders, L.H.J., Niederer, J.P.M., Feron, P.H.M., Versteeg, G.F.,2007a. CO2 capture from power plants: Part I. A parametric study of the technicalperformance based on monoethanolamine. International Journal of GreenhouseGas Control 1, 37–46.

bu-Zahra, M.R.M., Schneiders, L.H.J., Niederer, J.P.M., Feron, P.H.M., Versteeg, G.F.,

2007b. CO2 capture from power plants: Part II. A parametric study of the eco-nomical performance based on mono-ethanolamine. International Journal ofGreenhouse Gas Control 1, 135–142.

ronu, U.E., Svendsen, H.F., Hoff, K.A., Juliussen, O., 2009. Solvent selection for carbondioxide absorption. Energy Procedia I, 1051–1057.

f Greenhouse Gas Control 8 (2012) 45–55

Aroonwilas, A., Tontiwachwuthikul, P., Chakma, A., 2001. Effects of operating anddesign parameters on CO2 absorption in columns with structured packings.Separation and Purification Technology 24, 403–411.

Aroonwilas, A., Veawab, A., 2004. Characterization and comparison of the CO2

absorption performance into single and blended alkanolamines in a packedcolumn. Industrial & Engineering Chemistry Research 43, 2228–2237.

Billet, R., Schultes, M., 1997. Influence of phase ratio on packing efficiency in columnsfor mass transfer processes. Chinese Journal of Chemical Engineering 5 (2),117–126.

Billet, R., Schultes, M., 1999. Prediction of mass transfer columns with dumpedand arranged packings – updated summary of the calculation methodof billet and schultes. Chemical Engineering Research and Design 77,498–504.

Bosch, H., Versteeg, G.F., van Swaaij, W.P.M., 1990. Gas–liquid mass transfer withparallel reversible reactions. Kinetics of the reaction of CO2 with the steri-cally hindered amine AMP at 298 K. Chemical Engineering Science Journal 45,1167–1173.

Cadours, R., 1998. Absorption–desorption de gaz acides par des solutions aqueusesd’amines. PhD Thesis. Ecole des Mines de Paris.

Cheng, H.H., Tan, C.S., 2009. Carbon dioxide capture by blended alkanolamines inrotating packed bed. Energy Procedia I, 925–932.

Chowdhury, F.I., Akhtar, S., Saleh, M.A., 2010. Viscosities and excess viscosities ofaqueous solutions of some diethanolamines. Journal of Molecular Liquids 155,1–7.

Cormos, A.M., Gaspar, J., Padurean, A., 2009. Modeling and simulation of carbondioxide absorption in monoethanolamine in packed absorption columns. StudiaUniversitas, Seria Chemia LIV 3, 36–48.

Cormos, C.C., 2009. Assessment of hydrogen and electricity co-production schemesbased on gasification process with carbon capture and storage. InternationalJournal of Hydrogen Energy 34, 6065–6077.

Cormos, C.C., Starr, F., Tzimas, E., 2010. Use of lower grade coals in IGCC plants withcarbon capture for the co-production of hydrogen and electricity. InternationalJournal of Hydrogen Energy 35 (2), 556–567.

Danckwerts, P.V., Sharma, M.M., 1966. Absoprtion of carbon dioxide into solutionsof alkalis and amines (with some notes on hydrogen sulphide and carbonylsulphide). Chemical Engineering Journal 10, 244–288.

De Brito, M.H., von Stockar, U., Bangerter, A.M., Bomio, P., Laso, M., 1994. Effectivemass-transfer area in a pilot plant column equipped with structured pack-ings and with ceramic rings. Industrial & Engineering Chemistry Research 33,647–656.

De Leye, L., Froment, G.F., 1986. Rigorous simulation and design of columns for gasabsorption and chemical reaction – I: Packed columns. Computers & ChemicalEngineering 10, 493–504.

Descamps, C., Bouallou, C., Kanniche, M., 2008. Efficiency of an Integrated Gasifi-cation Combined Cycle (IGCC) power plant including CO2 removal. Energy 33,874–881.

Donaldson, T.L., Nguyen, Y.N., 1980. Carbon dioxide reaction kinetics and trans-portation in aqueous amine membranes. Industrial & Engineering ChemistryFundamentals 19, 260–266.

Gaspar, J., Cormos, A.M., 2011. Dynamic modeling and validation of absorber anddesorber columns for post-combustion CO2 capture. Computers and ChemicalEngineering 35 (10), 2044–2052.

Eide-Huagmo, I., Brakstad, O.G., Hoff, K.A., Sorheim, K.R., Falck da Silva, E.,Svendsen, H.F., 2009. Environmental impact of amines. Energy Procedia I,1297–1304.

Intergovernmental Panel on Climate Change (IPPC), 2005, May. Reference Documenton Best Available Techniques for Large Combustion Plants. European Commis-sion, JRC, Seville, Spain, http://eippcb.jrc.es/pages/FActivities.htm.

Intergovernmental Panel on Climate Change (IPCC), 2007. 4-th Assessments Report,Climate Change, www.ipcc.ch.

International Energy Agency (IEA), Greenhouse Gas R&D Programme (GHG), 2004a.Impact of Impurities on CO2 Capture. Transport and Storage, Report no. PH4/32.

International Energy Agency (IEA), Greenhouse Gas R&D Programme (GHG), 2004b.Improvement in Power Generation With Post-Combustion Capture of CO2.Report No. PH4/33.

Kothandaraman, A., Nord, L., Bolland, O., Howard, J., Herzog, H.J., McRae, G.J., 2009.Comparison of solvents for post-combustion capture of CO2 by chemical absorp-tion. Energy Procedia 1, 1373–1380.

Khan, F.M., Krishnamoorthi, V., Mahmud, T., 2011. Modelling reactive absorptionof CO2 in packed columns for post-combustion carbon capture applications.Chemical Engineering Research and Design 89 (9), 1600–1608.

Kohl, A., Nielsen, R., 2005. Gas Purification, 5th edition. Gulf Publishing Company,Houston (Chapter 2).

Kvamsdal, H.M., Jakobsen, J.P., Hoff, K.A., 2009. Dynamic modeling and simulation ofa CO2 absorber column for post-combustion CO2 capture. Chemical EngineeringProcess Journal 48, 135–144.

Lawal, A., Wang, M., Stephenson, P., Yeung, H., 2009. Dynamic modeling ofCO2 absorption for post combustion in coal-fired power plants. Fuel 88,2455–2462.

Liu, G.B., Yu, K.T., Yuan, X.G., Liu, C.J., Guo, Q.C., 2006. Simulations of chemical absorp-tion in pilot-scale and industrial-scale packed columns by computational mass

transfer. Chemical Engineering Science 61, 6511–6529.

Maceiras, R., Alves, S.S., Cancela, M.A., Alvarez, E., 2008. Effect of temperature on car-bon dioxide absorption in monoethanolamine solutions. Chemical EngineeringJournal 137, 422–427.

MATLAB Users Guide, 2008. The MathWorks Inc.

rnal o

M

O

O

P

P

P

PP

P

R

R

S

J. Gaspar, A.-M. Cormos / International Jou

cCann, N., Maeder, M., Attalla, M., 2008. Simulation of enthalpy and capacity ofCO2 absorption by aqueous amine systems. Industrial & Engineering ChemistryResearch Journal 47, 2002–2009.

i, L.E., 2007. Aspen HYSYS simulation of CO2 removal by amine absorption from agas based power plant. In: SIMS 2007 Conference, Goteborg.

yenekan, B.A., Rochelle, G.T., 2007. Alternative stripper configurations for CO2 cap-ture by aqueous amines. AIChE Journal 53 (12), 3144–3154.

acheo, M.A., Rochelle, G.T., 1998. Rate-base modeling of reactive absorption on CO2

and H2S into aqueous methyldiethanolamine. Industrial & Engineering Chem-istry Research Journal 37, 4107–4117.

adurean, A., Cormos, C.C., Cormos, A.M., Agachi, S.P., 2011. Multicriterial analysisof post-combustion carbon dioxide capture using alkanolamines. InternationalJournal of Greenhouse Gas Control 5 (4), 676–685.

ellegrini, G., Strube, R., Manfrida, G., 2010. Comparative study of chemicalabsorbents in post-combustion CO2 capture. Energy 35 (2), 851–857.

erry, R.H., Green, D.W., 1999. Perry’s Chemical Engineers’ Handbook, USA.uxty, G., Rowland, R., Allport, A., Yang, Q., Bown, M., Burns, R., Maeder, M., Attalla,

M., 2009. Carbon dioxide postcombustion capture: a novel screening study of thecarbon dioxide absorption performance of 76 amines. Environmental ScienceTechnology 43, 6427–6433.

uxty, G., Rowland, R., Attalla, M., 2010. Comparison of the rate of CO2 absorptioninto aqueous ammonia and monoethanolamine. Chemical Engineering Science65, 915–922.

ocha, J., Bravo, J., Fair, J., 1993. Distillation columns containing structured packings:a comprehensive model for their performance. 1. Hydraulic models. Industrial& Engineering Chemistry Research 32, 641–651.

ocha, J., Bravo, J., Fair, J., 1996. Distillation columns containing structured pack-

ings: a comprehensive model for their performance. 1. Mass-transfer models.Industrial & Engineering Chemistry Research 35, 1660–1667.

aha, A.K., Bandyopadhyay, S.S., Biswas, A.K., 1995. Kinetics of absorption of CO2

into aqueous solutions of 2-amino-2-methyl-1-propanol. Chemical EngineeringScience 50, 3587–3598.

f Greenhouse Gas Control 8 (2012) 45–55 55

Shi, M.G., Mersmann, A., 1985. Effective interfacial area in packed columns. GermanChemical Engineering Journal 8, 87.

Simon, L.L., Elias, Y., Puxty, G., Artanto, Y., Hungerbuhler, K., 2011. Rate basedmodeling and validation of a carbon-dioxide pilot plant absorption column oper-ating on monoethanolamine. Chemical Engineering Research and Design 89 (9),1684–1692.

Suess, P., Spiegel, L., 1992. Hold-up of Mellapak structured packings. Chemical Engi-neering and Processing 31, 119–124.

Suppapitnarm, A., Seffen, K.A., Parks, G.T., Clarkson, P.J., 2000. A simulated anneal-ing algorithm for multi-objective optimization. Engineering Optimization 33,59–85.

Tobiesen, F.A., Svendsen, H.F., 2007. Experimental validation of a rigor-ous absorber model for CO2 post-combustion capture. AlChE Journal 53,846–865.

Tontiwachwuthikuls, P., Meisen, A., Lim, C.J., 1992. CO2 absorption by NaOH,monoethanolamine and 2-amino-2-methyl-1-propanol solutions in a packedcolumn. Chemical Engineering Science Journal 47 (2), 381–390.

Versteeg, G.F., Van Dijck, L.A.J., Van Swaaij, W.P.M., 1996. On the kinetics betweenCO2 and alkanolamines both in aqueous and non-aqueous solutions. ChemicalEngineering Journal 144, 113–158.

Wang, G.Q., Yuan, X.G., Yu, K.T., 2006. A method for calculating effective interfa-cial area of structured packed distillation columns under elevated pressures.Chemical Engineering and Processing 45, 691–697.

Wellek, R.M., Brunson, R.J., Law, F.H., 1978. Enhancement factors for gas absorptionwith 2nd-order irreversible chemical-reaction. Canadian Journal of ChemicalEngineering 56, 181–186.

White, C.M., Strazisar, B.R., Granite, E.J., Hoffman, J.S., 2003. Separation and capture

of CO2 from large stationary sources and sequestration in geological formations– coalbeds and deep saline aquifers. Journal of the Air & Waste ManagementAssociation 53, 645–715.

Xu, S., Otto, F.D., Mather, A.E., 1991. Physical properties of aqueous AMP solutions.Journal of Chemical Engineering Data 36, 71–75.