multi-objective optimization of solid sorbent- based co2 ... · post-combustion carbon capture...
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Multi-Objective Optimization of Solid Sorbent-
based CO2 Capture Systems
Miguel Zamarripa, John Eslick, David Miller
National Energy Technology Laboratory (NETL)
CO2 Industrial, Engineering and R&D Approaches Session
AIChE Annual Meeting, Minneapolis, MN, USA.
October 31st , 2017
US power production in 2015:
• 2/3 from Fossil fuels.
Importance of Post-combustion Carbon Capture
2
- Schematic Diagram of Thermal Power Plant -
BOILER
Air
Fuel
TURBINESteam
Electricity
Flue Gas
Clean Gas
CO2
S
T
A
C
K
> 99% PURITY
Compression
Flue Gas:
• Coal Power plant, 650 MW
(~27 kmol/s) PCC: • Low CO2 concentration (~12 % vol)
• Multiple components (H2O, N2, CO, O2)
• Capture target 90%
STORAGECO2
Post Combustion
Capture (PCC)
Post-Combustion Carbon Capture Technologies
3
Liquid Solvents – absorption
Membranes – gas permeation
Solid Sorbents – adsorption
Current studies often do not rigorously optimize
complete systems considering
• multiple technology options
• process configurations
• operating conditions
Goals:
• Simultaneously optimize the process
configuration, process design and operating
conditions based on rigorous models.
• Explore changes in the optimal results (plant
design, configuration, and operation) as a function
of different capture rates (i.e., 40%, 60%, or
90%)
Solid Sorbents – adsorption
Gas – Solid contactors (adsorption and regeneration):
• Bubbling fluidized bed reactors:
– 1D model (3 regions: Emulsion, Cloud-Wake, Bubble)1.
– PDE’s + algebraic equations (~14,000 equations).
– Sorbent properties (Arrhenius constant & activation energy, heat of adsorption).
Solid Sorbent Technologies
4
[1] Lee, A., & Miller, D. C. (2012). Industrial &
Engineering Chemistry Research,52(1), 469-484.
Unit level:
H, D, LB
Gas – Solid contact
Pressure Drop
Costs: Operation + Investment
D
H
LB
System level:
Reactor design:
• Solids Feed (SF, top or bottoms)
• Overflow and underflow operation
• Diameter (D), height (H), solid bed depth (LB)
• Heat exchanger: # tubes and tube spacing
SF
SF
Superstructure Optimization Framework
5
Discrete Decisions: How many beds (Ads and Rgn)?
Operating conditions (T, P, F, z)
Flue
Gas
No. parallel
trains
Clean Gas
Adsorber
Train (beds)
d1
d2
dn
…
gas to
storage
CO2 & H2O
Flue Gas HX
a1
a2
an
…
coolant
Hot in
Solid HX
Solid HX
Solid
Gaseous
Cooler
Regeneration
Train (beds)
Steam
A B
A B
A B
A B
A B
A B
No. of Parallel trains?
What technology used for each reactor (A or B)?
Unit Dimensions (D, h, HX area) Continuous decisions:
Fixed
&
Operating Cost
Problem
Complexity
Increases with:
- # of technologies
- # of stages
- Non-linearities of
the problem
MINLP
Cost of Electricity
6
𝒔. 𝒕.
Quality Guidelines for Energy System Studies:
Performing a Techno-economic Analysis for Power
Generation Plants (DOE/NETL-2015/1726)
Capital cost levels and their elements
𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝐵𝑎𝑙𝑎𝑛𝑐𝑒𝑠
𝐸𝑛𝑒𝑟𝑔𝑦 𝐵𝑎𝑙𝑎𝑛𝑐𝑒𝑠
𝐸𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡 𝐷𝑒𝑠𝑖𝑔𝑛
min𝐶𝑂𝐸 =𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 + 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝑓𝑖𝑥 + 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝑣𝑎𝑟
𝑁𝑒𝑡 𝑃𝑜𝑤𝑒𝑟
𝑃𝑟𝑜𝑐𝑒𝑠𝑠 𝐶𝑜𝑛𝑓𝑖𝑔𝑢𝑟𝑎𝑡𝑖𝑜𝑛
Costing Methodology:
• Investment cost
– Sorbent, Power Plant, Capture
(ads, rgn, HX, cmp).
• Operating cost:
– Fixed: labor, maintenance,
others.
– Variable: utilities “coolant &
steam”, waste water, others.
• Net power:
– Power PP – (kW for compression,
blowers, pumps, etc).
𝐶𝑎𝑝𝑡𝑢𝑟𝑒 𝑇𝑎𝑟𝑔𝑒𝑡
Product and Process Design Principles Synthesis
(Seider et al., 2009)
Purchase cost calculations
Multi-objective Analysis
7
Superstructure Opt. Model
Process Models
Solid In
Solid Out
Gas In
Gas Out
Utility In
Utility Out
Surrogate Models
(nonlinear models
suitable for optimization)
+First Principle Models
Surrogate Model
Generation and
Validation
B FB A D S
G a s_ In
G a s_ O u t
S o lid _ Ou tS o lid _ In
H X _ In H X _ O u t
Multi-objective Analysis
7
Superstructure Opt. Model
Process Models
Solid In
Solid Out
Gas In
Gas Out
Utility In
Utility Out
Surrogate Models
(nonlinear models
suitable for optimization)
+First Principle Models
Reactor Design
Dt – unit diameter
Heat Exchanger design
Solids bed depth
SolidIn {Fm, P, T,
w(Bic), w(Car), w(H2O)}
GasOut {F, P, T, z("CO2"),
z("H2O"), z("N2")}
SolidOut {Fm, P, T,
w(Bic), w(Car), w(H2O)}
HXOut {F, T}HXIn {F, T}
17 inputs vars
12 outputs varsGasIn {F, P, T,
z("CO2"),z("H2O"), z("N2")}
• BFB for Adsorption & Regeneration
• Detailed ACM simulation.
~14,000 equations
12 EQUATIONS
Multi-objective Analysis
7
Superstructure Opt. Model
Process Models
Solid In
Solid Out
Gas In
Gas Out
Utility In
Utility Out
Surrogate Models
(nonlinear models
suitable for optimization)
+First Principle Models
• BFB for Adsorption & Regeneration
• Detailed ACM simulation.
• Data Management
• Run ALAMO
• Validation
• Data Set:
• 2000 samples
• Latin Hypercube
Sampling method
• Cross-Validation
• 200 samples
• LHS methodRigorous Gas Outlet Flow rate
Fit data
Surr
og
ate
Ga
s O
utlet F
low
ra
te
R2= 0.99
Rigorous Gas Outlet Flow rate
Su
rro
ga
te G
as
Ou
tle
t F
low
ra
te
R2= 0.99
Solid Sorbent System – Case Study
8
Flue Gas
# Nu
4-12
SolidRichHX
SolidLeanHXClean Gas
GasMathematical Model
• First principle
• Surrogate models.
Adsorber
beds
Regeneration
beds
FG_HX
Rich CO2 Gas
to storage Adsorption system
Plant consists of:
Flue gas (650 MW power plant)
90 % capture needed
CO2 ~12% (molar fraction)
4 adsorber & regeneration beds
2 technologies (reactor configuration)
4 – 12 parallel units.
Summary:
• Superstructure optimization allow us to explore all the possible
plant layouts.
• Optimization problem (GAMS/Dicopt):
• 383 equations
• 588 variables (24 Discrete)
• 90% CO2 Capture.
Optimal Solutions
Optimal Case 1 Case 2 Case 4 Case 5 Case 6 Case 7
% COE increase - 0.347 0.766 3.689 3.68 4.536 6.23
Adsorber beds 3 3 3 3 2 3 3
Regeneration beds 3 3 2 1 3 2 2
Ads parallel units 6 6 6 6 6 6 7
Rgn parallel units 6 6 6 6 5 4 7
Fixed layoutDifferent initialization
9
65
70
75
80
85
90
95
100
105
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Re
lative
CO
E (
%,
$/M
Wh
)
Capture Target
• Cost of electricity due to capture
• Capture target (90% - Base Case)
COE vs Capture Target
0
1
2
3
4
5
6
7
Cap 40 Cap 60 Cap 90
Parallel Trains
NuAd NuRg
10
• Cost of electricity due to capture
• Capture target (90% - Base Case)
COE vs Capture Target
65
70
75
80
85
90
95
100
105
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Rela
tive C
OE
($/M
Wh)
Capture Target
10
0
50
100
150
40% 60% 90%
kg steam / tCO2 (% increase)
0
20
40
60
80
100
40% 60% 90%
Unit design cost (%)
Adsorption Cost
Regeneration Cost0
1
2
Cap 40 Cap 60 Cap 90
Mill
ions
Solid flowrate (kg/hr)
• Superstructure optimization is challenging
– PDE models replaced by surrogates
• Integrated conceptual design and process synthesis tools
– Facilitate rapid development
• Robust mathematical optimization framework
– Optimal process configuration changes with capture target
– Demonstrates importance of conceptual design
• Complements typical flowsheet optimization
• Potential extension for multiple technologies
Remarks
11
Disclaimer This presentation was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference hereinto any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
AcknowledgmentsNational Energy Technology Laboratory and Oak Ridge Institute for Science and Education (ORISE).
Thank you for your
attention
For more information
https://www.acceleratecarboncapture.org/
David C. Miller, Ph.D., Technical Director
Michael S. Matuszewski, Associate Technical Director