ccsi ieaghg pccc3 2015 (final) - carbon capture … · basic+data+model ... •...
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Multi-scale modeling of carbon capture systemsDavid C. Miller,a,* Joel D. Kress,e David S. Mebane,g Xin Sun,f Curtis B. Storlie,e Sankaran Sundaresan,h Debangsu Bhattacharyya,g Nikolaos V. Sahinidis,b John C. Eslick,b Charles H. Tong,d Deb A. AgarwalcaNational Energy Technology Laboratory, Pittsburgh, PA USA, bCarnegie Mellon University, Pittsburgh, PA USA, cLawrence Berkeley National Laboratory, Berkeley, CA USA, dLawrence Livermore National Laboratory, Livermore, CA USA, eLos Alamos National Laboratory, Los Alamos, NM USA, fPacificNorthwest National Laboratory, Richland, WA USA, gWest Virginia University, Morgantown, WV USA, hPrinceton University, Princeton, NJ USA
8-9 September 2015
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Challenge: Accelerate Development/Scale Up
2010 2015 2020 2025 2030 2035 2040 2045 2050
1 MWe1 kWe 10 MWe 100 MWe 500 MWe
Traditional time to deploy new technology in the power industry
Laboratory Development10-15 years
Process Scale Up20-30 years
Accelerated deployment timeline
Process Scale Up15 years
1 MWe
10 MWe
500 MWe
100 MWe
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For Accelerating Technology Development
National Labs Academia Industry
Rapidly synthesize optimized processes to identify promising
concepts
Better understand internal behavior to reduce time for troubleshooting
Quantify sources and effects of uncertainty to guide testing & reach larger scales faster
Stabilize the cost during commercial
deployment
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• Develop new computational tools and models to enable industry to more rapidly develop and deploy new advanced energy technologies– Base development on industry needs/constraints
• Demonstrate the capabilities of the CCSI Toolset on non-proprietary case studies– Examples of how new capabilities improve ability to develop capture technology
• Deploy the CCSI Toolset to industry– Initial licensees, CRADA
Goals & Objectives of CCSI
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Advanced Computational Tools to Accelerate Carbon Capture Technology Development
Lab & Pilot ScaleExperiments & Data
Device Scale Models Validated 3-D, CFD
Process SystemsDesign, Optimization & Control
Physical PropertiesKinetics
Thermodynamics
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Data ManagementFramework
Process Models
Solid In
Solid Out
Gas In
Gas Out
Utility In
Utility Out
CCSI Toolset Workflow and Connections
Basic Data Submodels
Optimized Process
GHX-‐001CPR-‐001
ADS-‐001
RGN-‐001
SHX-‐001
SHX-‐002
CPR-‐002
CPP-‐002ELE-‐002
ELE-‐001
Flue Gas
Clean Gas
Rich Sorbent
LP/IP Steam
HX Fluid
Legend
Rich CO2 Gas
Lean Sorbent
Parallel ADS Units
GHX-‐002
Injected Steam
Cooling Water
CPT-‐001
1
2
4
7
8
5 3
6
9
10
11
S1
S2
S3
S4
S5
S6
12
13
14
15
16
17
18
19
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24
2022
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CYC-‐001
Algebraic Surrogate Models
Uncertainty
Uncertainty
Uncertainty
Uncertainty
Superstructure Optimization
Uncertainty
Simulation-Based Optimization
Uncertainty
Process Dynamics and
Control
CFD Device Models
Uncertainty Quantification
Framework forOptimization
Quantification of Uncertainty & Sensitivity
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Solid Sorbent: Process Systems Example
Lab & Pilot ScaleExperiments & Data
Device Scale Models Validated 3-D, CFD
Process SystemsDesign, Optimization & Control
Physical PropertiesKinetics
Thermodynamics
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Basic Data Model
Alkylammoniumcarbamate formed by two molecules of tetraethylenepentamine (TEPA) and CO2.
dx
dt= k
x
hs2p
c
� xw/x
i(1)
db
dt= k
b
hsap
c
� bw/b
i(2)
da
dt= k
a
hph
� a/a
i� k
b
hsap
c
� bw/b
i(3)
1 = s+ x+ w (4)
w = x+ b/nv
(5)
TGA wt. % =⇥Mn
v
(x+ b) +H(b+ a)⇤/⇢ (6)
x ⌘ carbamate site fraction (7)
s ⌘ free amine site fraction (8)
w ⌘ alkylammonium site fraction (9)
b ⌘ bicarbonate concentration (10)
a ⌘ adsorbed water concentration (11)
pc
⌘ partial pressure CO2 (12)
ph
⌘ partial pressure H2O (13)
nv
⌘ site density of amines (14)
kx
, kb
, ka
⌘ rate constants (15)
x
, b
, a
⌘ equilibrium constants (16)
x
= exph S
x
RT
iexp
h�H
x
RT
i/P (17)
b
= exph S
b
RT
iexp
h� H
b
RT
i/P (18)
a
= exph S
a
RT
iexp
h�H
a
RT
i/P (19)
kx
= ⇣x
exph�H‡
x
RT
i(20)
kb
= ⇣b
exph�H‡
b
RT
i(21)
ka
= ⇣a
exph�H‡
a
RT
i(22)
1
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Process ModelsBubbling Fluidized Bed (BFB) Model• 1-D, nonisothermal with heat exchange• Unified steady-state and dynamic• Adsorber and Regenerator• Variable solids inlet and outlet location• Modular for multiple bed configurations
Moving Bed (MB) Model• 1-D, nonisothermal with heat exchange• Unified steady-state and dynamic• Adsorber and Regenerator• Heat recovery system
Compression System Model• Integral-gear and inline compressors• Determines stage required stages, intercoolers• Based on impeller speed limitations• Estimates stage efficiency• CO2 drying (TEG absorption system)• Off-design performance.• Includes surge control algorithm
0 0.2 0.4 0.6 0.8 10.04
0.06
0.08
0.1
0.12
0.14
z/L
yb (k
mol
/km
ol)
CO2H2O
0 0.5 1200
400
600
800
1000
kmol
/hr
z/L
Solid Component Flow Profile of MB Regenerator
Bicarb. Carb. H2O
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• Discrete decisions: How many units? Parallel trains? What technology used for each reactor?
• Continuous decisions: Unit geometries• Operating conditions: Vessel temperature and pressure, flow rates,
compositions
Carbon Capture System Configuration
Surrogate models for each reactor and technology used
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Superstructure Optimization
Optimal layout
Mixed-integer nonlinear programming model in GAMS• Parameters• Variables• Equations
• Economic modules• Process modules
• Material balances• Hydrodynamic/Energy balances
• Reactor surrogate models• Link between economic modules and process modules
• Binary variable constraints• Bounds for variables
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Optimization &Heat Integration
w/o heat integration Sequential Simultaneous
Net power efficiency (%) 31.0 32.7 35.7Net power output (MWe) 479.7 505.4 552.4Electricity consumption b (MWe) 67.0 67.0 80.4
Base case w/o CCS: 650 MWe, 42.1 %Chen, Y., J. C. Eslick, I. E. Grossmann and D. C. Miller (2015). "Simultaneous Process Optimization and Heat Integration Based on Rigorous Process Simulations." Computers & Chemical Engineering. doi:10.1016/j.compchemeng.2015.04.033
Objective: Max. Net efficiencyConstraint: CO2 removal ratio ≥ 90% Decision Variables (17): Bed length, diameter, sorbent and steam feed rate
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Uncertainty Quantification for Prediction Confidence§ Now that we have
• A chemical kinetics model with quantified uncertainty• A process model with other sources of uncertainty• Surrogates with approximation errors• An optimized process based on the above
§ UQ questions• How do these errors and uncertainties affect our prediction confidence (e.g. operating cost) for the optimized process?
• Can the optimized system maintain >= 90% CO2 capture in the presence of these uncertainties?
• Which sources of uncertainty have the most impact on our prediction uncertainty?
• What additional experiments need to be performed to give acceptable uncertainty bounds?
CCSI UQ framework is designed to answer these questions
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Perform statistical analyses with FOQUSEnsemble AnalysesØUncertainty analysisØSensitivity analysisØCorrelation analysisØScatterplots for visualization
Response Surface (RS) AnalysesØRS validationØRS visualizationØRS-based uncertainty analysisØRS-based sensitivity analysisØRS-based Bayesian inference
Ensemble UA
RS-based UA
RS-based SA
RSvalidation
RS visualizationRS-based Bayesian inference
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Solid Sorbents: Validated CFD Model Example
Lab & Pilot ScaleExperiments & Data
Device Scale Models Validated 3-D, CFD
Process SystemsDesign, Optimization & Control
Physical PropertiesKinetics
Thermodynamics
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C2U BatchUnit
Building Predictive Confidence for Device-scale CO2 Capture with Multiphase CFD Models
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Quantitatively predicting scale up performance
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Advanced Computational Tools to Accelerate Carbon Capture Technology Development
Lab & Pilot ScaleExperiments & Data
Device Scale Models Validated 3-D, CFD
Process SystemsDesign, Optimization & Control
Physical PropertiesKinetics
Thermodynamics
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Carbon Capture Simulation for Industry Impact
• Work closely with industry partners to help scale up– Large scale pilots
• Help ensure success at this scale– Employ simulation to predict performance, potential issue– Help resolve issues using simulation tools
• Maximize learning at this scale– Data collection & experimental design– Develop & Validate models– UQ to identify critical data
• Help develop demonstration plant design– Utilize optimization tools (OUU, Heat Integration)– Quantitative confidence on predicted performance– Predict dynamic performance
– Partnership via CRADA
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Acknowledgements– SorbentFit
• David Mebane (NETL/ORISE, West Virginia University)• Joel Kress (LANL)
– Process Models• Solid sorbents: Debangsu Bhattacharyya, SrinivasaraoModekurti, Ben Omell (West Virginia University), Andrew Lee,
Hosoo Kim, Juan Morinelly, Yang Chen (NETL)• Solvents: Joshua Morgan, Anderson SoaresChinen, Benjamin Omell, Debangsu Bhattacharyya (WVU), Gary Rochelle
and Brent Sherman (UT, Austin)• MEA validation data: NCCC staff (John Wheeldon and his team)
– FOQUS• ALAMO: Nick Sahinidis, Alison Cozad, Zach Wilson (CMU), David Miller (NETL)• Superstructure: Nick Sahinidis, Zhihong Yuan (CMU), David Miller (NETL)• DFO: John Eslick (CMU), David Miller (NETL)• Heat Integration: Yang Chen, Ignacio Grossmann (CMU), David Miller (NETL)• UQ: Charles Tong, Brenda Ng, JeremeyOu (LLNL)• OUU: DFO Team, UQ Team, Alex Dowling (CMU)• D-RM Builder: JinliangMa (NETL)• Turbine: Josh Boverhof, Deb Agarwal (LBNL)• SimSinter: Jim Leek (LLNL), John Eslick (CMU)
– Data Management• Tom Epperly (LLNL), Deb Agarwal, You-Wei Cheah (LBNL)
– CFD Models and Validation• Xin Sun, Jay Xu, Kevin Lai, Wenxiao Pan, Wesley Xu, Greg Whyatt, Charlie Freeman (PNNL), Curt Storlie, Peter
Marcey, Brett Okhuysen (LANL), S. Sundaresan, Ali Ozel (Princeton), Janine Carney, Rajesh Singh, Jeff Dietiker, Tingwen Li (NETL) Emily Ryan, William Lane (Boston University)
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 herein to 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.
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Framework for Optimization, Quantification of Uncertainty and Sensitivity
D. C. Miller, B. Ng, J. C. Eslick, C. Tong and Y. Chen, 2014, Advanced Computational Tools for Optimization and Uncertainty Quantification of Carbon Capture Processes. In Proceedings of the 8th Foundations of Computer Aided Process Design Conference – FOCAPD 2014. M. R. Eden, J. D. Siirola and G. P. Towler Elsevier.
SimSinterStandardized interface for simulation software
Steady state & dynamic
SimulationAspengPROMSExcel
SimSinter ConfigGUI
Results
FOQUSFramework for Optimization Quantification of Uncertainty and Sensitivity
Meta-flowsheet: Links simulations, parallel execution, heat integration
Samples
SimulationBased
OptimizationUQ
ALAMO Surrogate Models
Data ManagementFramework
TurbineParallel simulation execution
management systemDesktop – Cloud – Cluster
iREVEALSurrogate Models
Optimization Under
Uncertainty
D-RMBuilder
Heat Integration
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• Organizational Meetings: March 2010 - October 2010• Technical work initiated: Feb. 1, 2011• Preliminary Release of CCSI Toolset: September 2012
– Initial licenses signed• CCSI Year 3 starts Feb. 1, 2013
– Began solvent modeling/demonstration component• 2013 Toolset Release: October 31, 2013
– Multiple tools and models released and being used by industry
• 2014 Toolset Release: October 31, 2014
• Future– Final IAB meeting: Sept. 23-24, 2015 (Reston, VA) – Final major release November 2015– Commercial licensing late 2015 or early 2016
CCSI Timeline
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