MODELLING AND SIMULATION OF INTENSIFIED

REGENERATOR FOR

POST-COMBUSTION CO2 CAPTURE

By

Atuman S. Joel and Meihong Wang Process/Energy Systems Engineering Group, School of Engineering,

University of Hull

10th European Conference on Coal Research and its Applications

Background

What is process intensification

Motivations

Aim and Objectives

Rotating Packed Bed Stripper

Methodology

Model Validation

Process analysis

Conclusion and future work

Courtesy: IPCC

Micromixing controlled

processes

Nanoparticles

Syntheses

Fast

reaction Polymerization

Mass transfer limited

processes

CO2 H2S/CO2 SO2

Micromixing and mass transfer controlled process

A desulfurization site

(Qian et al., 2012)

BERR (2006)

500 MWe supercritical coal fired power plant 46% efficiency 8,000 tonnes of CO2 per day.

Lawal et al. (2012)

Size of regenerator required will be 17m in packing height and

9m in diameter

Kothandaraman et al. (2009)

Majority (approximately 62%) of the energy that was consumed during the CO2 capture process was required for

the regeneration of absorbent

To develop a model of intensified post-combustion

carbon capture (PCC) process for capture of CO2 from a fossil fuel-fired power plant

Modelling and simulation of intensified absorber

Modelling and simulation of intensified regenerator

Modelling and simulation of intensified heat exchanger

Modelling and simulation of intensified PCC process

Schematic diagram of a rotating packed

bed regenerator

Liquid phase mass transfer coefficient Chen et al. (2006)

Gas phase mass transfer coefficient Chen, (2011)

Effective interfacial area Luo et al. (2012)

Liquid hold-up Burns et al. (2000)

Pressure drop Llerena-Chavez and Larachi (2009)

Heat transfer coefficient Chilton and Colburn analogy

Parameters for equilibrium constant Austgen et al. 1989

Kinetic parameters Aspen, 2008

Variable Runs

Run 1 Run 2 Run 3 Run 4

Rotor speed (RPM) 800 800 800 800

Rich-MEA temperature (oC) 67.1 68 69 70

Rich-MEA pressure (atm) 1 1 1 1

Boil-up ratio 0.9 0.2 1.0 0.5

Rich-MEA flow rate (kg/s) 0.2 0.5 0.2 0.4

Rich-MEA composition (wt %)

H2O

CO2

MEA

58.116

8.984

32.9

54.117

10.383

35.5

54.013

10.287

35.7

61.536

7.664

30.8

Input process conditions (Jassim et al., 2007)

0

0.1

0.2

0.3

0.4

0.5

0 0.1 0.2 0.3 0.4 0.5

Lean-M

EA

lo

ad

ing

Mo

de

l

Lean-MEA loading Experimental

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

KGa

Mo

de

l

KGa Experimental

The agreement of simulation and experimental KGa

The agreement of simulation and experimental lean-MEA loading

From the graph the error prediction of the model for KGa is

less than 30% and for lean-MEA loading is less than 11%

The 30% error of the model might be from error in

measuring the amount of desorbed CO2 from the

experimental study

Error due to contamination of sample content by

atmospheric air and that was unavoidable because it

happened very fast (Jassim et al., 2007)

The model has predicted the experimental data

reasonably, then the authors decides to carry out process

analysis

It should be noted that the experimental study by Jassim et

al. (2007) was done using steam as the stripping agent.

The Authors modify the validated model to include a

reboiler and then study the following parameters

Regeneration efficiency = 1 −Lean CO2 loading

Rich CO2 loading× 100

Regeneration energy =Reboiler duty

Mass of CO2 desorbed

Effect of Rich-MEA flow-rate on regeneration

efficiency

Process input condition

Rotor speed = 1000 rpm

Rich-MEA temp = 82 oC

Rich-MEA pres. = 1 atm

Rich-MEA comp. (wt. %)

H2O = 58.116

CO2 = 8.984

MEA= 32.900

Reboiler temp. = 105 oC

Effect of Rich-MEA flow rate on

regeneration efficiency

25

26

27

28

29

30

31

32

0.2 0.4 0.6 0.8 1

Regenera

tion e

ffic

iency (

%)

Rich MEA flow rate (kg/s)

The figure shows a decrease in regeneration efficiency as

the rich-MEA solvent flow rate increases.

This is so because of decrease in resident time of the

solvent in the regenerator

Which means less CO2 will be stripped off from the rich-MEA stream

The regeneration efficiency reduced by 7.6% as the rich-

MEA solvent flow rate increase from 0.4 kg/s to 0.9 kg/s

Effect of Rich-MEA flow-rate on regeneration

energy

Effect of Rich-MEA flow rate on

regeneration efficiency

Process input condition

Rotor speed = 1000 rpm

Rich-MEA temp = 82 oC

Rich-MEA pres.= 1 atm

Rich-MEA comp. (wt. %)

H2O = 58.116

CO2 = 8.984

MEA= 32.900

Reboiler temp. = 105 oC 3.50

3.70

3.90

4.10

4.30

4.50

4.70

0.2 0.4 0.6 0.8 1Regenera

tion E

nerg

y (

GJ/ton C

O2)

Rich MEA flow rate (kg/s)

It can be observed from the figure above that the

regeneration energy increases with increase in rich-MEA

flow rate.

There is 4.7% percentage increase in the regeneration

energy as the rich-MEA solvent flow rate increases from

0.4 kg/s to 0.9 kg/s.

The reason for the increase in regeneration energy is

associated to greater quantity of solvent that will be

heated.

Effect of Rotor Speed on regeneration efficiency

Process input condition

Rich-MEA flow rate = 0.5 kg/s

Rich-MEA temp = 82 oC

Rich-MEA pres. = 1 atm

Rich-MEA comp. (wt. %)

H2O = 58.116

CO2 = 8.984

MEA= 32.900

Reboiler temp. = 105 oC

Rotor speed range from

400rpm to 1200rpm

Effect of rotor speed on regeneration

efficiency

29

29.1

29.2

29.3

29.4

29.5

29.6

29.7

29.8

29.9

30

0 500 1000 1500

Regenera

tion e

ffic

iency (

%)

Rotor speed (RPM)

Higher rotational speed creates smaller liquid droplets

and thin films in the packing regions of the bed

This leads to greater areas for mass and heat transfer.

Higher speed can also produce the opposite effect on

mass and heat transfer by decreasing the contact time

From the figure above we can see that the regeneration

efficiency increases with in increase in rotational speed.

This is so because more CO2 will be stripped off from

the rich MEA stream.

Effect of Rotor Speed on regeneration energy

Process input condition

Rich-MEA flow rate = 0.5 kg/s

Rich-MEA temp = 82 oC

Rich-MEA pres. = 1 atm

Rich-MEA comp. (wt. %)

H2O = 58.116

CO2 = 8.984

MEA= 32.900

Reboiler temp. = 105 oC

Rotor speed range from

400rpm to 1200rpm

Effect of rotor speed on regeneration

energy

4

4.05

4.1

4.15

4.2

4.25

4.3

4.35

4.4

0 500 1000 1500

Regenera

tion E

nerg

y (

GJ/ton C

O2)

Rotor speed (RPM)

The figure shows that as the rotational speed increases

the regeneration energy decreases

This is true because of the increase in mass and heat

transfer as the rotors speed increase since we have

more droplet and thin films in the bed

Also at higher rotational speed the problem of liquid

mal-distribution is overcome leading to higher wetted

area which subsequently contributes to improving mass

transfer.

Conclusion and future task

Model validation was performed and the model

outputs are in good agreement with experimental

results.

The effect of rich-MEA flow-rate on regeneration

efficiency and energy were studied.

The effect of rotor speed on regeneration

efficiency and regeneration energy were studied.

Finally it can be said that RPB process has great

potential application for thermal regeneration.

Validation of intensified regenerator process which has a

reboiler unit.

Modelling and simulation of intensified heat exchanger for

PCC process

Modelling and simulation of intensified PCC process

Scale-up of intensified PCC process

The authors would like to acknowledge financial

support from EU FP7 (Ref: PIRSES-GA-2013-

612230) and UK Research Councils’ Energy

Programme (Ref: NE/H013865/2).

AspenTech., 2010. Aspen Physical Properties System – Physical Property Methods. Available at:

http://support.aspentech.com/ (accessed in May 2012).

Austgen, D.M., Rochelle, G.T., Peng, X., Chen, C. C., 1988. "A Model of Vapor-Liquid Equilibria in the Aqueous Acid

Gas-Alkanolamine System Using the Electrolyte-NRTL Equation," Paper presented at the New Orleans AIChE

Meeting, March

BERR, (2006) Advanced power plant using high efficiency boiler/turbine. Report BPB010. BERR, Department for

Business Enterprise and Regulatory Reform; Available at: www.berr.gov.uk/files/file30703.pdf. (accessed 6/04/2012)

Burns, J. R., Jamil, J. N., Ramshaw, C., 2000. Process intensification: operating characteristics of rotating packed

beds — determination of liquid hold-up for a high-voidage structured packing. Chemical Engineering Science 55(13),

2401-2415.

Chen, Y. S., 2011. Correlations of Mass Transfer Coefficients in a Rotating Packed Bed. Ind. Eng. Chem. Res., 50,

1778.

Chen, Y. S., Lin, F. Y., Lin, C. C., Tai, C. Y., Liu, H. S., 2006. Packing Characteristics for Mass Transfer in a Rotating

Packed Bed. Ind. Eng. Chem. Res. 45, 6846.

Intergovernmental Panel on Climate Change, 2007. Contribution of Working Group III to the Fourth Assessment

Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United

Kingdom/New York, United States.

Jassim, M. S., Rochelle, G., Eimer, D., Ramshaw, C., 2007. Carbon dioxide absorption and desorption in aqueous

monoethanolamine solutions in a rotating packed bed. Industrial & Engineering Chemistry Research 46(9), 2823-

2833.

Lawal, A., Wang, M., Stephenson, P., Obi, O., 2012. Demonstrating full-scale post-combustion CO2 capture for coal-

fired power plants through dynamic modelling and simulation. Fuel 101, 115-128.

Llerena-Chavez, H., Larachi F., 2009. Analysis of flow in rotating packed beds via CFD simulations—Dry pressure

drop and gas flow maldistribution. Chemical Engineering Science 64, 2113-2126.

Luo, Y., Chu, G. W., Zou, H. K., Wang, F., Xiang, Y., Shao, L., Chen, J. F., 2012. Mass transfer studies in a rotating

packed bed with novel rotors: Chemisorption of CO2. Ind. Eng. Chem. Res. 51, 9164-9172.

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