dynamic modeling of co 2 absorption plants for post ... · pdf filepost combustion capture ....
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NTNU
1st Post Combustion Capture Conference (PCCC1) 17th -19th May 2011, Abu Dhabi
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Andrew Tobiesena*
Magne Hillestadb, Hanne M. Kvamsdala, Actor Chikukwaa
aSINTEFMaterials and Chemistry, Postbox 4760 Sluppen,7494, Trondheim, Norway bNorwegianUniversity of Science and Technology, NTNU, SemSælandsvei 4, Trondheim, 7491, Norway
Dynamic modeling of CO2 absorption plants for post combustion capture Development, validation and example case study
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Rationale• The objective of this work is to develop and implement dynamic models in CO2SIM in
order to simulate a “closed loop transient CO2 absorption system”
• Focus is placed on competence building through use of simulation models that can be:
– verified against experiment and plant data where feedback to the work is done fairly quickly
• by developing dynamic simulation models based on already established computer architecture
• In general, to assist in understanding CO2 capture processes (or any chemical process), we believe process simulation is beneficial to all phases in a projects lifetime
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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• Large and frequent load changes of power plant requires understanding of dynamic operation– How shall the capture plant be operated and controlled?– What are the real consequences of varying loads at the CO2 removal plant?– How do we handle unsteady behavior, shut down, start up?
We are now moving towards building full scale plants
Reasons for developing a dynamic simulator Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
Exhaust: CO2,N2, O2 ,H2O
GT
ST
GeneratorAir
Pressurized NG
HRSG
LP steam(3.8 bar, 140 °C)
N2,
O2,
H2OAmine absorption
Amine stripper
CO2-rich 30 wt % MEA/H2O
CO2-lean 30 wt % MEA/H2O
Condenser
CO2 to compression
40 °C
45 °C
Re-boiler
1.9 bar
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– Difficult to foresee dynamic behavior of complex chemical processes
– Especially integrated processes such as that of a CO2 capture plant downstream from the power plant
• A dynamic simulator will help us understanding such process dynamics
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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Dynamic simulation of the capture plant• Existing tools like UniSimDesign and Aspen Hysys
– Have dynamic simulation options– Columns are modeled as equilibrium stages– No in-depth control over models and assumptions (black box)
• CO2SIM– A general purpose steady-state flow sheet simulator particularly developed for CO2
absorption processes – Have been developing this software for several years and includes advanced object-
oriented programming techniques for efficient and fast development• Reuse of class methods • Existing thermodynamic and hydraulic models as well as solvers are re-used • Existing use of simulator architecture with GUI and visualization methods
– Exact understanding as to how the models are developed, w.r.t. assumptions: mathematics, physics, algorithms/numerics
– Research organization: We develop and test own solvents and it is therefore necessary to have insight to our modeling methods, at all layers of the code
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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What dynamic simulator features we would like to have• Sub models describing thermodynamics and mass transfer with sufficient detail to
yield predictive power (rate based, etc)• Implement solvent systems based on established routines from CO2SIM (kinetics, VLE
etc)• Ability to visualize results
– General plotting functionality– All state variables as well as derived variables should be made available in the integration time
horizon • Efficient validation of models by use of data standardized extraction routines
– Streamlined information flow from pilot plant to simulation– Batch testing of all available campaign data
• Robust numerics when using the simulator (we want to converge the system..)– Ability to handle significant changes in load conditions (use of stiff DAE solver)
• Be able to execute in real time to give a virtual response close to the actual system
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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Development Strategy:
• Task 1: Development of the dynamic CO2SIM column model– Development of the model for absorption and desorption – Test model and validate towards pilot plant data (steady state and dynamic)
• Task 2: Implementation of the connected unit operations, flash tanks, mixers, storage tanks and heat exchangers– Definition of unit operations built into CO2SIM
• Task3: Implementation of the dynamic Network solver– Flow sheet model– Programming techniques: information handling– Information structure
• relationship between an event (the cause) and a second event ( the effect) -> causality
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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Task 1: The transient column model
• Based on first principle conservation laws for energy and mass
• Adaptability of the code for different chemical systems and process configurations has been emphasized– Uses CO2SIM architecture
• The subprograms and unit operations within the main module are developed using standardized syntax
• Handling of events• Data extraction routine from pilot plant to
simulator
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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Outline• Dynamic modeling of CO2 absorption plants• Project description
• Simulator requirements/capabilities • Short description of simulation model
• Verification/Validation (Preliminary) • Experimental validation using pilot plant data
• Simulation studies• Ramp behavior• Robustness of code
• Summary• Further work• Acknowledgements
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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Experimental validation of dynamic column model
• Tested towards the VOCC rig• Two time series cases (case A and B)• 30wt%MEA• Logging of input and output data every 5
seconds– The cases give about 500 updates
during simulation
• CO2SIM handles all these “events” automatically during integration to reflect process changes. – The events are collected and
systematically handled from log files (excel).
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
Validation of CO2 Capture - The VOCC-project (2007)
Absorber:Packing height = 5.4mID = 0.5m
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• Case B: Stepwise variations in CO2gas concentration
• Inlet gas concentration of CO2 increased in two single step-changes
• then decreased in a large reverse single step-change
• All other process variables kept constant Stable liquid and gas flow over
the experiment.
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VOCC test caseDynamic modeling of CO2 absorption systems
Project descriptionValidation
Simulation studiesSummary
1000 1500 2000 2500 3000 35000.02
0.025
0.03
0.035
0.04
0.045
0.05
0.055Property logging of inputs (only at events): molfracCO2vap and phase:vap
time [s]
Pro
pert
y: m
olfr
acC
O2v
ap
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Simulated and measured:liquid solvent outlet temperature
Input co2 concentration (molfraction)
1000 1500 2000 2500 3000 3500311
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321Property logging of outputs (only at events): temp and phase:liq
time [s]
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rty:
te
mp
VOCC Case B
1000 1500 2000 2500 3000 35000.02
0.025
0.03
0.035
0.04
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0.055Property logging of inputs (only at events): molfracCO2vap and phase:vap
time [s]
Pro
pert
y: m
olfr
acC
O2v
ap
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
Blue line –SIMULATIONRed line - PILOT
Input data (Pilot and Simulation)
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Simulated and measured:Rich loading
VOCC Case B
1000 1500 2000 2500 3000 35000.3
0.32
0.34
0.36
0.38
0.4
0.42Property logging of outputs (only at events): loading and phase:liq
time [s]
Pro
pert
y: lo
adin
g
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
1000 1500 2000 2500 3000 35000.02
0.025
0.03
0.035
0.04
0.045
0.05
0.055Property logging of inputs (only at events): molfracCO2vap and phase:vap
time [s]
Pro
pert
y: m
olfr
acC
O2v
ap
Input CO2 concentration (molar fraction)
Blue line –SIMULATIONRed line - PILOT
Input data (Pilot and Simulation)
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0 500 1000 1500 2000 2500 3000 3500 400080
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125Property logging of inputs (only at events): flow and phase:vap
time [s]
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flo
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Simulation examples
• Increasing molar ratio between the gas and liquid flow rate– Varying the liquid and vapor input
flow rates by ~50%?• In a time frame of 50 seconds
– Initially running at steady state then increase gas flow with 50% over a short time interval
– Observe the transients, then run end situation to steady state again
• 20 meter packing, identical inlet concentrations , only vary flow rate
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
Blue: gas flow rate (kmol/h)Green: liquid flow rate (kmol/h)
Case A: Increasing gas load
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0 500 1000 1500 2000 2500 3000 3500 40000.39
0.4
0.41
0.42
0.43Property logging of outputs (only at events): loading and phase:liq
time [s]
Pro
per
ty:
load
ing
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Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
Green liquid flow rate (kmol/h)
Blue: gas flow rate (kmol/h)
0 500 1000 1500 2000 2500 3000 3500 400080
90
100
110
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130Property logging of inputs (only at events): flow and phase:vap
time [s]
Pro
per
ty:
flo
w
Case A: Increasing the flue gas loadEffect on loading
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Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
Green liquid flow rate (kmol/h)
Blue: gas flow rate (kmol/h)
0 500 1000 1500 2000 2500 3000 3500 400080
90
100
110
120
130Property logging of inputs (only at events): flow and phase:vap
time [s]
Pro
per
ty:
flo
w
0 500 1000 1500 2000 2500 3000 3500 4000
0.65
0.7
0.75
0.8
0.85Property logging of inputs (only at events): gasremoved and phase:vap
time [s]
Pro
pert
y: g
asre
mov
ed
Case A: Increasing the flue gas loadEffect on percent CO2 removed
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• Varying the molar ratio between the gas and liquid flow rate– In a time frame of ~300 seconds– Initially running at steady state
then increase/reduce gas flow with 50%
– Observe the transients, then run end situation to steady state again
• 20 meter packing, identical inlet concentrations , only vary flow rate
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
Blue: gas flow rate (kmol/h)Green: liquid flow rate (kmol/h)
0 50 100 150 200 250 300 350 400
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100
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115
120
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130Property logging of inputs (only at events): flow and phase:vap
time [s]
Pro
per
ty:
flo
w
Case B: Varying flue gas load
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Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
Green liquid flow rate (kmol/h)
Blue: gas flow rate (kmol/h)
0 500 1000 1500 2000 2500 3000 3500 40000.39
0.4
0.41
0.42Property logging of outputs (only at events): loading and phase:liq
time [s]
Pro
per
ty:
load
ing
0 500 1000 1500 2000 2500 3000 3500 400080
90
100
110
120
130Property logging of inputs (only at events): flow and phase:vap
time [s]
Pro
per
ty:
flo
w
Case B: Varying the flue gas loadEffect on loading
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Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
Green liquid flow rate (kmol/h)
Blue: gas flow rate (kmol/h)
0 500 1000 1500 2000 2500 3000 3500 400080
90
100
110
120
130Property logging of inputs (only at events): flow and phase:vap
time [s]
Pro
per
ty:
flo
w
0 500 1000 1500 2000 2500 3000 3500 40000.76
0.77
0.78
0.79
0.8
0.81
0.82
0.83
0.84Property logging of inputs (only at events): gasremoved and phase:vap
time [s]
Pro
pert
y: g
asre
mov
ed
Case B: Varying the flue gas loadEffect on CO2 removed
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Summary• At this point in the project a robust codebase is developed for dynamic simulation
– Absorber, desorber packing model (this presentation)– Verified numerics– Validated towards plant data (preliminary)
• The event updating procedures facilitates rapid simulation using plant data for validation• Current model gives acceptable match towards pilot plant data for MEA
– Both at dynamic and steady state operation
• The implementation methodology allows for efficient simulation of the units’ transient behavior for continuously changing input conditions or design parameters, part load operation, varying input conditions and ramping behavior.
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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Currently under development• Network solver to handle sequential dynamic integration (finished but needs testing)
– Builds the network from a GUI (graphical user interface) – Including recycles
• Integrated handling of events (input changes) during simulation of networks with recycles• A few units implemented: Storage tank, dynamic flash, mixer tank and the column model
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
Acknowledgement This presentation forms a part of the BIGCO2 project, performed under the strategic Norwegian research program Climit. The authors acknowledge the partners: StatoilHydro, GE Global Research, Statkraft, Aker Clean Carbon, Shell, TOTAL, ConocoPhillips, ALSTOM, the Research Council of Norway (178004/I30 and 176059/I30) and Gassnova(182070) for their support.
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Example simulation case study • Changing the power plant load:
increasing/decreasing the volumetric ratio between gas flow rate and liquid flow rate
• What are the consequences of varying the liquid and vapor input flow rates by ~50%?– Running at steady state, vary
loads at a short time interval to see the transients, then run end situation to steady state again
• 20 meter packing, identical inlet concentrations , only vary flow rate
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
1 1.005 1.01 1.015 1.02 1.025 1.03 1.035
x 104
80
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130Property logging of inputs (only at events): flow and phase:vap
time [s]
Pro
pert
y: fl
ow
red = liquid flowblue = gas flow
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Simulation Case study
• Simulated outlet vapor and liquid temperatures at each event
1 1.005 1.01 1.015 1.02 1.025 1.03 1.035
x 104
321
321.5
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325.5Property logging of outputs (only at events): temp and phase:vap
time [s]
Pro
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y: te
mp
1 1.005 1.01 1.015 1.02 1.025 1.03 1.035
x 104
316.8
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317.1
317.15Property logging of outputs (only at events): temp and phase:liq
time [s]
Pro
pert
y: te
mp
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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Simulation Case study
• Simulated rich loadings and percent CO2 removed from gas at each event
1 1.005 1.01 1.015 1.02 1.025 1.03 1.035
x 104
0.775
0.78
0.785
0.79
0.795
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0.805
0.81
0.815
0.82Property logging of inputs (only at events): gasremoved and phase:vap
time [s] P
rope
rty:
gas
rem
oved
1 1.005 1.01 1.015 1.02 1.025 1.03 1.035
x 104
0.376
0.378
0.38
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0.39
0.392
0.394Property logging of outputs (only at events): loading and phase:liq
time [s]
Pro
pert
y: lo
adin
g
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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Simulation Case study
• Simulated outlet vapor and liquid temperatures, forward to steady state– at each event
1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08
x 104
0.78
0.785
0.79
0.795
0.8
0.805
0.81
Property logging of inputs (only at events): gasremoved and phase:vap
time [s]
Pro
pert
y: g
asre
mov
ed
1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1
x 104
0.378
0.38
0.382
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0.39
Property logging of outputs (only at events): loading and phase:liq
time [s]
Pro
pert
y: lo
adin
g
Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
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Dynamic modeling of CO2 absorption systemsProject description
ValidationSimulation studies
Summary
Green liquid flow rate (kmol/h)
Blue: gas flow rate (kmol/h)
0 500 1000 1500 2000 2500 3000 3500 400080
90
100
110
120
130Property logging of inputs (only at events): flow and phase:vap
time [s]
Pro
per
ty:
flo
w
0 500 1000 1500 2000 2500 3000 3500 4000317.69
317.7
317.71
317.72
317.73
317.74
317.75Property logging of outputs (only at events): temp and phase:liq
time [s]
Pro
pert
y: te
mp
Case B: Varying the flue gas loadEffect on outlet solvent temperature