optimal process design of mdea co2 capture …...*results taken from an mdea plant optimization...

| | 6/17/2019 Cristina Antonini 1

Optimal Process design of MDEA CO2 Capture Plant for Low-Carbon Hydrogen Production Cristina Antonini, José Francisco Pérez Calvo, Mijndert van der Spek, Marco Mazzotti

| |

• Enabling a Low-Carbon Economy via Hydrogen and CCS

• State-of-the-art low carbon H2 production Steam Methane Reforming with pre-combustion carbon capture

(solvent: Methyl diethanolamine, MDEA)

• Goals developing a methodology to optimize H2 production with CCS

testing on a case study with existing technologies

applying this methodology to new technologies (e.g. Vacuum Pressure Swing Adsorption)

6/17/2019 Cristina Antonini 2

Hydrogen production with CCS

https://blog.sintef.com/wp-content/uploads/2018/08/Elegancy-logo.png


Low-Carbon Hydrogen Production

Pure hydrogen Raw

Hydrogen SMR with

HT-WGS

Natural Gas Syngas CO2

capture

Hydrogen

purification

(PSA)

Flue gas CO2 to storage

PSA tail gas

Furnace

H2O


MDEA capture process: benchmark

Pure hydrogen Raw

Hydrogen SMR with

HT-WGS


capture

Hydrogen

purification

(PSA)


PSA tail gas

Furnace

H2O

Ab

sorb

er lean stream

rich stream

semi-lean stream

Syngas

Raw H2

CO2 to dehydration and compression

Stri

pp

er

CO2

HP flash

LP flash

gas recycle

1 Romano, M. C., Chiesa, P., & Lozza, G. (2010). Pre-combustion CO2 capture from natural gas power plants, with ATR and MDEA processes. International Journal of Greenhouse Gas Control, 4(5), 785-797.


This study: advanced MDEA process configuration

Pure hydrogen Raw

Hydrogen SMR with

HT-WGS


capture

Hydrogen

purification

(PSA)


PSA tail gas

Furnace

H2O

Ab

sorb

er lean stream

rich stream

semi-lean stream

Syngas

Raw H2


Stri

pp

er

CO2

HP flash

LP flash

gas recycle

Ab

sorb

er lean stream

rich stream

semi-lean stream

gas recycle

Syngas

Raw H2


Stri

pp

er

CO2

HP flash

LP flash

1 Romano, M. C., Chiesa, P., & Lozza, G. (2010). Pre-combustion CO2 capture from natural gas power plants, with ATR and MDEA processes. International Journal of Greenhouse Gas Control, 4(5), 785-797.


MDEA process simulation

Ab

sorb

er lean stream

rich stream

semi-lean stream

gas recycle

Syngas

Raw H2


Stri

pp

er

CO2

HP flash

LP flash

Mole flow [kmol/hr] Syngas

H2 4985

CO2 1070

CO 304

CH4 200

N2 13

Total flow [kmol/hr] 6572

• The process is simulated in Aspen Plus®

‒ RadFrac model with equilibrium stage calculations used for the columns

• The liquid phase is described by the Electrolyte NRTL model, while for the vapour phase Redlich-Kwong equation of state is used.

for CO2 compression the Peng-Robinson equation of state is selected

CO2 capture rate: 90%

Purity 88.8% 99.9%

T=308 K P=26 bar

Raw H2 Pure CO2

4985 0.0003

107 963

304 ppm

200 ppm

13 ppm

5609 963


MDEA process simulation

Ab

sorb

er lean stream

rich stream

semi-lean stream

gas recycle

Syngas

Raw H2


Stri

pp

er

CO2

HP flash

LP flash

Mole flow [kmol/hr] Syngas

H2 4985

CO2 1070

CO 304

CH4 200

N2 13

Total flow [kmol/hr] 6572

• The process is simulated in Aspen Plus®

‒ RadFrac model with equilibrium stage calculations used for the columns

• The liquid phase is described by the Electrolyte NRTL model, while for the vapour phase Redlich-Kwong equation of state is used.

for CO2 compression the Peng-Robinson equation of state is selected

CO2 capture rate: 97%

Raw H2 Pure CO2

4985 0.0003

32 1038

304 ppm

200 ppm

13 ppm

5534 1038

Purity 89.9% 99.9%

T=308 K P=26 bar

| |

𝑤 = 𝑊tot

𝑚 CO2 captured


Description of the optimization problem

Ab

sorb

er lean stream

rich stream

gas recycle

Syngas

Raw H2


Stri

pp

er

CO2

HP flash

LP flash

𝑇amb =

282 K, Δ𝑇min = 10 K

• Multi-objective optimization problem

• To minimize the total specific exergy w while maximizing the capture rate Ψ:

• All process variables, such as flowrates and column conditions, could be tuned to optimize the process

time demanding

• Faster systematic approach

define the Key Process Variables, which will then become the decision variables in the optimization problem

T=308 K P=26 bar

semi-lean stream

𝑊tot = 𝜂P 𝑊pumpi +

𝑖

𝜂C 𝑊comprj + 𝑄R 1 −𝑇amb

𝑇reb + Δ𝑇min

𝑗

min 𝑤,1

Ψ

| |

c/m

• Pressure and temperature of the units • Size of the columns • Split fractions

• Reboiler duty and feed stages

• Liquid to gas mass flow ratio (L/G)

CO2 to MDEA molar ratio (c/m)

c/m depends on the MDEA concentration (here: 40 wt%, CO2 free)


Process variables

lean stream

rich stream

semi-lean stream

gas recycle

Syngas

Raw H2


CO2

b2

b1

1-b1

1-b2

QR

T Nabs

Pabs

PHP

PLP

T Nstrip

Pstrip

feed stage

feed stage

c

𝑐

𝑚 =

CO2 syngas

MDEA rich stream

m

| |

feed stage


Decision variables and ranges of investigation

Single-variable sensitivity analysis was performed on all process variables

Aspen Plus ® Flowsheet

Process initialization with literature data

Fixed value assigned

Key Process

Variables

No lean stream

rich stream

semi-lean stream

gas recycle

Syngas

Raw H2


CO2

T Nabs

Pabs

PHP

PLP

T Nstrip

Pstrip

feed stage

1-b1

1-b2

Yes

Key Process Variables (KPVs)

Range of investigation

Optimization problem

lean stream

rich stream

semi-lean stream

gas recycle

Syngas

Raw H2


CO2

c/m 1-b1

1-b2

8 stages 26 bar 35°C

5 stages 1.3bar

5bar

1.15 bar

b2

b1

QR


Specific optimization problem

• To minimize the total specific exergy w while maximizing the capture rate Ψ

• Genetic algorithm

lean stream

rich stream

semi-lean stream

gas recycle

Syngas

Raw H2


CO2

b2

b1

1-b1

1-b2

QR c/m

8 stages 26 bar 35°C

5 stages 1.3bar

5bar

1.15 bar

| |

c/m = 0.30 c/m = 0.30

Key process variables analysis – c/m


Capture rate Ψ

c/m = 0.30

| |

c/m = 0.30 c/m = 0.30

Key process variables analysis – c/m


feasible region

unfeasible region

Capture rate Ψ

Ψ = 90%

c/m = 0.50

+ 10%

c/m = 0.30

c/m = 0.40


Analysis of the optimization results

c/m 0.30 c/m = 0.30

Capture rate Ψ

QR [MJth/kgCO2]

b1

b2

Capture rate Ψ



c/m = 0.30

+ 8.8 %

• •

•

•

Benchmark*

QR [MJth/kgCO2]

b1

b2

Capture rate Ψ Capture rate Ψ

*Results taken from an MDEA plant optimization work1

1Romano, M. C., Chiesa, P., & Lozza, G. (2010). Pre-combustion CO2 capture from natural gas power plants, with ATR and MDEA processes. International Journal of Greenhouse Gas Control, 4(5), 785-797.

Ψ = 90%



• At Ψ > 97% the w exponentially increases

• The contribution of b2 becomes more important to reach higher capture rates more efficiently

+ 18 %

QR [MJth/kgCO2]

b1

b2 c/m = 0.30

Capture rate Ψ Capture rate Ψ


Conclusions

• A rigorous approach was developed with the goal of finding the optimal operating conditions of a MDEA CO2 capture plant

‒ multi-objective optimization was used as a tool to find the Pareto Optimum between the total specific exergy and the capture rate

‒ the decision variables were selected among the process variables by performing single-parameter sensitivity analysis

• The addition of a second splitter is advantageous especially while operating at high capture rates

• To decide how to operate the CO2 capture plant, we need to look at the entire process


Acknowledgment

ACT ELEGANCY, Project No 271498, has received funding from DETEC (CH), BMWi (DE), RVO (NL), Gassnova (NO), BEIS (UK), Gassco, Equinor and Total, and is cofunded by the European Commission under the Horizon 2020 programme, ACT Grant Agreement No 691712.


Conclusions

• A rigorous approach was developed with the goal of finding the optimal operating conditions of a MDEA CO2 capture plant

‒ multi-objective optimization was used as a tool to find the Pareto Optimum between the total specific exergy and the capture rate

‒ the decision variables were selected among the process variables by performing single-parameter sensitivity analysis

• The addition of a second splitter is advantageous especially while operating at high capture rates

• To decide how to operate the CO2 capture plant, we need to look at the entire process


Back-up slides


Design Improving Energy Consumption

H2 Raw H2 SMR with

HT-WGS

Natural Gas Syngas Hydrogen

purification

(PSA)


PSA tail gas

Furnace

H2O CO2 capture

H2O A

bso

rbe

r

lean stream

rich stream

semi-lean stream

gas recycle

(N2,H2, CO, CH4)

Syngas

Raw H2

CO2 to dehydration and

compression

Str

ipp

er

CO2

HP

flash

LP

flash

Water

Pre

-ref

orm

er

Re

form

er

Flue gas

Natural gas

Steam

PSA tail gas

Steam to CO2 capture

Syngas

Water

HT-WGS

Steam to CO2 capture


Modelling Framework Modelling framework

Input Data

Experimental and manufactures

data from literature

Case study data

1.First principle thermodynamic models

Aspen Plus® modelling

Equilibrium-based model

Rate-based model

2.Reduced order models for process optimization

Linear Piece-wise

affine or

3. MILP optimization

min𝒙,𝒚

𝒄𝑻𝒙 + 𝒅𝑻𝒚

subject to

𝑨𝒙 + 𝑩𝒚 ≤ 𝒃

𝒙 ≥ 𝟎 ∈ ℝ𝑁, 𝒚 ∈ 𝟎, 𝟏 𝑀

Implementation

1.

2.

3.

c c

Energy efficiency : Eoutput = ɳ Einput Cost linearization : Ci = miS + qi

optimal process design of mdea co2 capture …...*results taken from an mdea plant optimization...

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