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For Accelerating Technology Development
National Labs Academia Industry Advisory Board
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 fasterStabilize the cost during commercial deployment
3
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 Emphasis on supporting post-combustion capture technologies
• Limited work on oxycombustion system• General tools capable of supporting all types of carbon capture technology development
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
Collaborate with industry to use the tools to support technology development & scale up
Goals & Objectives of CCSI (2011-2016)
4
Maximize the learning at each stage of technology development
Early stage R&D Screening concepts Identify conditions to focus development Prioritize data collection & test conditions
Pilot scale Ensure the right data is collected Support scale-up design
Demo scale Design the right process Support deployment with reduced risk
CCSI Toolset: New Capabilities for Modeling & Simulation
4
2016 R&D 100 Award
5
CCSI Toolset Module Connectivity
Process Models
Solid In
Solid Out
Gas In
Gas Out
Utility In
Utility Out
Basic Data Submodels
Carbon Capture Process
GHX-001CPR-001
ADS-001
RGN-001
SHX-001
SHX-002
CPR-002
CPP-002ELE-002
ELE-001
Flue GasClean Gas
Rich Sorbent
LP/IP SteamHX 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
21
24
2022
23
CYC-001
Process Synthesis, Design & Optimization
CFD Device Models
Process Dynamics and Control
6
High Fidelity Process Models for Carbon CaptureBubbling Fluidized Bed (BFB) Model Variable solids inlet and outlet location Modular for multiple bed configurations
Moving Bed (MB) Model Unified steady-state and dynamic Heat recovery system
Compression System Model Integral-gear and inline compressors Determines stage required stages, intercoolers Based on impeller speed limitations Estimates stage efficiency Off-design and surge control
Solvent System Model Predictive, rate-based models
Membrane System Model Hollow fiber Supports multiple configurations
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
7
Framework for Optimization, Quantification of Uncertainty and Surrogates
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
SimulationAspen
gPROMSExcel
SimSinter Config GUI
Res
ults
FOQUSFramework for Optimization Quantification of Uncertainty and Surrogates
Meta-flowsheet: Links simulations, parallel execution, heat integration
Sam
ples
SimulationBased
OptimizationUQ
ALAMO Surrogate
Models
TurbineParallel simulation execution management
systemDesktop – Cloud – Cluster
iREVEALSurrogate
ModelsOptimization
Under UncertaintyD-RM
BuilderHeat Integration Data ManagementFramework
8
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 Superstructure Using Surrogate Models
Surrogate models for each reactor and technology used
9
Sim
ulta
neou
s Pr
oces
s O
ptim
izat
ion
& H
eat I
nteg
ratio
n
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 80.4Base 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
• Industrial Collaborations• 7 CO2 Capture Program projects $40MM+ in total project value (TRL 3-7)• 6 additional external industrial agreements (executed or in progress)
- Cooperative R&D Agreement: GE, ADA-ES, Ion, TCM, SINTEF- Contributed Funds Agreement: COSIA ($500k)
• Includes enabling capture technology support:- Aerosol, dynamic characterization, turndown, advanced process control
• Optimal Design of Experiments• Improves model while optimizing experimental data generation• Applicable to lab through large pilot scale
• Solvent modeling framework• Fundamental characterization of solvent, device and system• Collaboration with International Test Center Network (ITCN) & SINTEF
CCSI2 Industrial Collaboration & Contributions
10
11
What was missing in the previous DoE? Mainly designed using a space-filling approach without considering the
output space When designed considering the output space, feedback from the
experimental data are not leveraged to update the DOE How to solve these issues? Develop DOE by taking into consideration the output space by using a
preliminary process model Use a sequential approach to improve DOE (through improvement of
process model, etc.-more on this later) as experimental data are obtained Being employed at NCCC and TCM for Test Campaigns in 2017
CCSI using Optimal Design of Experiments to inform test plans
11
12
Typical Steady-State DOE
Abso
rbe
rR
egen
erat
or
CCSI DOE Approach and Comparison with CCSI Model
13
Developing Detailed, Predictive Models of Solvent-Based Capture Processes
Steady-State and DynamicProcess Model
Process Sub-Models
Kinetic model
Hydrodynamic Models
Mass Transfer Models
Properties Package
Chemistry Model
Thermodynamic Models
Transport Models
UQ
Pilot/Commercia
l Scale Data
WWC/Bench/Pilot Scale Data
Lab Scale Data
UQ
Process UQ
Measurement Uncertainty
Measurement Uncertainty
Measurement Uncertainty
14
CCSI Solvent Model is More Predictive Because It Utilizes Better Multiscale Models Developed with a Holistic Approach
20
40
60
80
100
120
0 0.2 0.4 0.6 0.8
Neg
ativ
e H
eat o
f A
bsor
ptio
n (k
J/m
ol
CO
2)
CO2 Loading (mol CO2/mol MEA)
Heat of Absorption Data* (30 wt% MEA and 40,80, and 120°C)
40°C, 30 wt% MEA
0.0001
0.01
1
100
0 0.2 0.4 0.6CO2
Part
ial P
ress
ure
(kPa
)
CO2 Loading (mol CO2/mol MEA)
80°C, 30 wt% MEA
0.0001
0.01
1
100
0 0.2 0.4 0.6
CO
2Pa
rtia
l Pr
essu
re (k
Pa)
CO2 Loading (mol CO2/mol …
Deterministic ModelStochastic Model
(Prior Parameter Distribution)Posterior Parameter
Distribution
Bayesian inference
15
In conjunction with a dynamic model, a greater amount of data can be collected within the same amount of time during pilot plant testing
Demonstrated at NCCC Dynamic data reconciliation
enables model validation
Increasing Learning at Pilot Scale with Dynamic Data Collection
4900
5300
5700
6100
6500
0.0 0.5 1.0 1.5 2.0
Lean
Sol
vent
to
abso
rber
(kg/
h)Time (h)
65.0
70.0
75.0
80.0
85.0
90.0
0.0 0.5 1.0 1.5 2.0
CO
2ca
ptur
ed
(%)
Time (h)
1900
2100
2300
2500
2700
0.0 0.5 1.0
Flue
gas
flow
rate
to
abso
rber
(kg/
h)
Time (h)
86889092949698
0 0.5 1CO
2ca
ptur
ed (%
)
Time (h)
Absorber Stripper
16
CCSI Toolset Support Advanced Process Control For Carbon Capture
Initial Flue Gas Ramp
Double-Pole Result (New)
Fast Response Slow Response
Flue
Gas
Flo
w(k
mol
/hr)
Time (sec)
17
CCSI Toolset supports oxy-combustion processes & optimization
4. Pollution Controls5. CO2 Compression Train
1 2
3
4
5
Dowling, A. W.; Eason, J. P.; Ma, J.; Miller, D. C.; Biegler, L. T., Equation-Based Design, Integration, and Optimization of Oxycombustion Power Systems. In Alternative Energy Sources and Technologies: Process Design and Operation, Martín, M., Ed. Springer International Publishing: Switzerland, 2016; pp 119-158.Ma, J.; Eason, J.; Dowling, A. W.; Biegler, L. T.; Miller, D. C., Development of a First-Principles Hybrid Boiler Model for Oxy-Combustion Power Generation System. International Journal of Greenhouse Gas Control 2016, 46, 136-157.
Zone 1
Zone 2
Zone 3
Zone 4
Zone 5
Zone 6
Zone 7
Zone 8
Zone 9
Flue GasExit Plane
18
CFD Models
Superstructure Optimization
Oxycombustion System Optimization Framework
Solvent Blend Model
CCSI TOOLSET PRODUCTSAVAILABLE AS OPEN SOURCE SUMMER 2017
19
CCSI2 employs a multi-scale modeling framework (materials through systems) based on fundamental, scientific principles, providing “glass-box” understanding of capture technology
Interconnectivity of scale, physics and chemistry permits well-informed modeling framework with full quantification of uncertainty
UQ leveraged to improve model prediction and data generation for lower risk CCS scale up, demo and commercialization
High throughput, intelligent computational screening informs most effective R&D pathways
Multiple active collaborations with world-class industrial partners and test centers
CCSI2 supports the full commercialization pathway for the following CCS technology platforms: Post combustion solvents, sorbents & membranes; oxycombustion; & compression
Open source suite of tools and methodologies can help all capture technology development projects maximize their learning and decrease technical risk
CCSI2 and Toolset Summary
For more informationhttps://www.acceleratecarboncapture.org/
David C. Miller, Ph.D., Technical [email protected]
Michael S. Matuszewski, Associate Technical [email protected]
21
Legend: 5 (Highest capability), 1 (Low capability), - (No capability) Competition
Description CCSI Aspen ANSYS Mode Frontier
CCSI Advantage
Cos
t Annual cost for a single user within a company. $10k $25k+ $25k+ $10k+ The CCSI Toolset is available for a flat fee of $10k/year, whereas the competition is licensed per concurrent user. Competition cost is estimated based on market research. The CCSI Toolset licensing structure is designed to help companies get the maximum benefits across the entire organization.
Annual cost for unlimited use within a company. $10k $1,000k+ $1,000k+ $500k+
Key
Fe
atur
es
Specifically designed to support development and scale up of new technology 5 2 2 -The CCSI Toolset is the only fully integrated and validated suite of computational tools and models that are specifically designed to support the development and scale up of carbon capture technologies.
Specifically designed for carbon capture 5 - - -Advanced machine learning tool for determining simple algebraic functional form and parameters of data 5 - - -
Applicable to full range of chemical processes 4 3 3 3
Dat
a M
anag
emen
t Graphical workflows for setting up and analyzing results 5 3 3 3
The CCSI Toolset has a fully integrated data management system to enable provenance tracking (i.e, dependencies) from experimental data to fully optimized process.
Data management framework that includes provenance relationships among models and experimental data 5 3 2 -
Simultaneous property and process parameter estimation 5 - - -Property estimation 5 3 - 2Solvent blends properties estimation tool that minimizes data requirements for new blends 5 3 - -Sorbent properties model estimation/fitting tool 5 - - -
Proc
ess
and
Dev
ice-
Scal
e M
odel
s
Dynamic moving bed reactor process model for gas/solid contacting 5 * - -All solid-gas process models are dynamic, non-isothermal, rigorous, flexible, capable of simulating embedded heat exchangers, and readily configurable for general absorption/ adsorption/ desorption processes. Highly accurate yet computationally efficient process models available in the CCSI Toolset can be readily used for UQ, process control, optimization and scale-up studies. Industry is already teaming with CCSI to utilize the Toolset.
#Aspen supports dynamic simulation of solvent-based capture systems, but does directly not support dynamic rate-based simulation.
*A user can develop these models using this software, but the software does not have pre-built, validated models available as part of its commercial offerings.
Dynamic bubbling fluidized bed process model for gas/solid contacting 5 * - -Dynamic compression process models tailored for CO2 5 * - -Membrane models 5 - - -Dynamic solvent models with rate-based mass transfer 5 # - -High resolution "filtered" models for hydrodynamics and heat transfer in a fluidized bed 5 - * -Hierarchically validated device-scale fluidized bed model at multiple scales 4 - * -
Oxycombustion boiler model 5 - * -
Adv
ance
d Pr
oces
s C
ontr
ol
Nonlinear Model Predictive Control 5 2 - -
The CCSI Toolset includes the ability to develop fast and accurate nonlinear dynamic reduced models from generic process models (not limited to specific simulation software) and include dynamic uncertainty quantification. Supports advanced process control using computationally efficient algorithms with the flexibility to use open-source nonlinear programming solvers and rigorous disturbance estimation methods. Aspen’s nonlinear control algorithms are non-generic and based on dated and inefficient sub-space identification methods.
Capabilities of the CCSI Toolset
22
Legend: 5 (Highest capability), 1 (Low capability), - (No capability) Competition
Description CCSI Aspen ANSYS Mode Frontier
CCSI Advantage
Adva
nced
Pro
cess
C
ontr
ol
Nonlinear Model Predictive Control 5 2 - - The CCSI Toolset includes the ability to develop fast and accurate nonlinear dynamic reduced models from generic process models (not limited to specific simulation software) and include dynamic uncertainty quantification. Supports advanced process control using computationally efficient algorithms with the flexibility to use open-source nonlinear programming solvers and rigorous disturbance estimation methods. Aspen’s nonlinear control algorithms are non-generic and based on dated and inefficient sub-space identification methods.
Multiple Model-based Predictive Control 5 4 - -Efficient State/Disturbance Estimation 5 2 - -
Automated tool for developing fast dynamic, nonlinear models 5 4 - -
Opt
imiz
atio
n
Derivative free optimization 5 - - 4
The CCSI Toolset includes the most advanced and flexible set of optimization tools. Aspen’s optimization solvers are dated, and modeFrontier does not natively link with process simulation software.
Process optimization 5 5 - 2Incorporates code from the world's most advanced algebraic deterministic MINLP optimization solver (BARON) 5 - - -
Support for optimization under uncertainty (OUU) 4 - - 4Support for external optimization solvers 5 - - 4Support for multi-objective optimization 4 - - 4
UQ
Simultaneous property and process parameter estimation with UQ 5 - - -Only the CCSI Toolset was created from the ground up
with uncertainty quantification as a central goal. As such, it is the only tool with integrated Bayesian statistics and uncertainty quantification that goes well beyond simple sensitivity analysis, enabling propagation of uncertainty across scales and enabling Bayesian calibration for unprecedented predictive model capability.
Support for Sensitivity analysis 5 4 4 4Support for Bayesian statistics to estimate uncertainty and/or parameters 5 - - -Propagation of uncertainty across scales from properties to device to equipment 5 - - -Hierarchical validation framework for CFD models to predict scale up performance with quantitative confidence 5 - 4 -
Wor
kflo
w
Integrated multiscale modeling framework that supports models from particle, to device, to process 4 - - -
Only the CCSI Toolset includes native support for validation using rigorous statistical methods.
The CCSI Toolset includes various tools to enable physics-based scale –bridging of multi-physics models across different length scales to allow model-based optimization and uncertainty propagation.
Workflow for developing & validating particle scale models 4 - - -Workflow for developing & validating device scale models 5 - 3 -Workflow for developing & validating process scale models 5 3 - -Ability to automatically manage 1000's of simulations in parallel on cloud-based computers 5 - - -Ability to exploit parallel computing architectures 5 - 5 3Support for interconnecting multiple software and simulation tools (Aspen, gPROMS, Thermoflow, Excel, MFIX, Fluent) 5 - - 3
Automated workflow to enable superstructure optimization using detailed models 4 - - -Automated workflow to enable detailed 2-D and 3-D models to be incorporated into systems models 4 - - -