The CCSI, Modeling Carbon Capture

7/26/2019 The CCSI, Modeling Carbon Capture

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Modeling Activities Related to Carbon Capture

Michael Matuszewski

14 June 2016



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

LaboratoryDevelopment10-15 years

Process Scale Up20-30 years

Accelerated deployment timeline

Process Scale Up15 years

1 M

W e

1 0 M W e

5 0 0

M W e

1 0 0

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For Accelerating Technology Development

al LabsAcademia Industry

ly synthesizeed processes to

omising concepts

Better understand internalbehavior to reduce time for

troubleshooting

Quantify sources and effects ofuncertainty to guide testing &

reach larger scales faster

Stabilize the c

commercial d

3



elop new computational tools and models to enable industry to more rapidly dev

deploy new advanced energy technologiesase development on industry needs/constraints

onstrate the capabilities of the CCSI Toolset on non-proprietary case studiesxamples of how new capabilities improve ability to develop capture technology

loy the CCSI Toolset to industry&E licenses, CRADAommercialization activities

Goals & Objectives of CCSI



anced Computational Tools to Accelerate Carbon Capt

Technology Development

Lab & Pilot ScaleExperiments & Data

Device Scale ModelsValidated 3-D, CFD

Process SystemsDesign, Optimization & Control

Physical PropertiesKinetics

Thermodynamics



Data ManagementFramework

odels

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Uncertainty

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CFD Device Models

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Full Scale Systems

Unit

Problems

Laboratory Scale

Subsystem

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Pilot Scale

Systems



Solid Sorbent 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

y b ( k m o l / k m o l )

CO2

H2O

0 0.5 1200

400

600

800

1000

k m o l / h r

z/L

Solid Component Flow Profile of MB Regenerator

Bicarb. Carb. H2O



Device Scale (CFD) Models for Solid Sorbents

Particle: 118 micron

Particle clusters: mm

Bench top scale: ~10cm

Actual device scale: ~10m



• Isothermal

• Shell Feed

•

Perfectly cylindricalfibers

• Shell flow evenlydistributed

• Counter-current flow

•

Dense skin layer facesthe shell side

1D Hollow Fiber Membrane Model



Membrane System Decision Variables

M-1 M-2

M-3

Three stages configuration with sweep air to 2nd stage membrane and CO2 recycling to

1st stage membrane (Merkel et al.,2010)

To Boiler

LiquefactionTemperature and

Pressure

CO2Sequestration

To Stack

Vacuum pumps

Multistage compression

Flue Gas

Multistagecompression

Vacuum inlet

pressure

Feed pressure priorto M1

M-3 Capture Fraction

M-2 Capture FractionM-1 Capture Fraction



How to develop a gold standard solvent model?

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Test Runs at National Carbon Capture

Center, AL: Steady-State Runs

Operating Conditions Range

Solvent Flow (lb/hr) 7,000-26,000

Inlet Flue Gas (lb/hr) 5,000-6,500

Reboiler Steam Flow (lb/hr) 600-2,500

Inlet FG CO2 vol% 9-11%

# of beds 1-3

Intercooler no - yes

! All possible combinations of

different operating conditions

tested

0.0

2.0

4.0

6.0

0 1000 2000 3000

L / G

Reboiler Steam Flow (lb/hr)

Steady-State Test Matrix



Steady State Regenerator ValidationLean Loading Comparison

Lean Solvent Temperature Comparison

100

105

110

115

120

125

100 105 110 115 120 125

M o d e l S t r i p p e r B o t t o

m

T e m p e r a t u r e ( ° C )

Data Stripper Bottom Temperature (°C)

Sample Temperature Profiles

Case K1 Case K9 Case K10



Absorber Validation with DDR

4900

5300

5700

6100

6500

0.0 0.5 1.0 1.5 2.0 L e a n S o l v e n t t o a b s o r b e r (

k g / h )

Time (h)

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70.0

75.0

80.0

85.0

90.0

0.0 0.5 1.0 1.5 2.0

C O 2 c a p t u r e d ( %

)

Time (h)

1900

2100

2300

2500

2700

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w r a t e t o a b s o r b e r

( k g / h )

Time (h)

86

88

90

92

94

96

98

0 0.2 0.4 0.6 0.8 1

C O 2 c a p t u r e d ( % )

Time (h)



CFD Hierarchy for Solvent-based Capture

Enable predictive understanding and prediction at scale

by developing a hierarchical multi-scale modeling

framework

Micro/Meso-ScaleVOF model Replace structure by an effective stationary

porous phase (porous media)

Macro-scaleTwo Fluid model

Use closure models (interfacial area; interphaseinteraction terms) developed from VOF

simulations Fernandes et al., JSF, 2009

Raynal et al., Workshop, 2012

Effective mass transfercoefficient for different flow

regimes



2015 2020 2025 2030 2035 2040 2045 2050

1 M W e

1 0

M W e

5 0 0

M W e

1 0 0

M W e

Carbon Capture Simulation for Industry Impact

Application to Pilot-Scale Post-Combustion C

erated deployment timeline

Process Scale Up15 years

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Carbon Capture Simulation for Industry Impact

ith industry partners on pilot projects

nsure success & maximize learning at this scale

• Data collection & experimental design

•

Develop & Validate models

• UQ to identify critical data

evelop demonstration plant design

• Utilize optimization tools (OUU, Heat Integration)

• Quantitative confidence on predictedperformance

•

Predict dynamic performance

, LANL, PNNL, WVU, U of Texas

FY20: $2.2M/year (Carbon Capture)

Carbon Capture Simulation Initiative Toolset Support

• Supports ongoing CCSI users

• Supports CCSI2

• Ensures CCSI capabilities continue to be avail

until they are commercially supported

• Limited development of new capabilities to suspecific needs encountered by CCSI2 & other

• Toolset licensing

• Continuation of Industry Advisory Board

– August 8-12 with NETL CO2 Capture Mtg

•

NETL, LBNL, LLNL, Princeton, CMU

• FY16-FY18: $2.1M/year (Crosscutting, CUS)



Board of Trustees of the University of Illinois (Champaign, IL)

–

Abbott Power Plant on the campus of the University of Illinois – Linde/BASF’s amine-based advanced CO2 capture absorption system – Partners are the Linde Group, BASF, Burns & McDonnell, and Affiliated Engineers Inc.niversity of Kentucky Research Foundation (Lexington, KY)

– Partners are EPRI, Koch Modular Process Systems, WorleyParsons, Smith Management Group, andCMTA Consulting Engineers.

RG Energy Inc. (Princeton, NJ)

–

Inventys’s VeloxoTherm™lstom Power Inc. (Windsor, CT) – Alstom’s chilled ammonia process (CAP) CO2 capture technology. – Partners are Technology Centre Mongstad, Georgia Institute of Tech, GE Power & Water—Purecowa

and ElectroSep Inc.outhern Company Services (SCS) (Birmingham, AL)

– Amine-based CO2 capture process at SCS’s Plant Barry

–

Partners are AECOM and Mitsubishi Heavy Industries America.eneral Electric Company—GE Global Research (Oklahoma City, OK) – Aminosilicone CO2 capture system, a non-aqueous chemical solvent – Partner is Test Centre Mongstad.

arge Pilot-Scale Post-Combustion Capture Project



AcknowledgementsorbentFit• David Mebane (NETL/ORISE, West Virginia University)

•

Joel Kress (LANL)rocess Models• Solid sorbents: Debangsu Bhattacharyya, Srinivasarao Modekurti, Ben Omell (West Virginia University), Andrew Lee, Hosoo Kim, Juan Morinelly, Yang Chen• Solvents: Joshua Morgan, Anderson Soares Chinen, Benjamin Omell, Debangsu Bhattacharyya (WVU), Gary Rochelle and Brent Sherman (UT, Austin)

• MEA validation data: NCCC staff (John Wheeldon and his team)

OQUS• 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, Jeremey Ou (LLNL)• OUU: DFO Team, UQ Team, Alex Dowling (CMU)• D-RM Builder: Jinliang Ma (NETL)• Turbine: Josh Boverhof, Deb Agarwal (LBNL)• SimSinter: Jim Leek (LLNL), John Eslick (CMU)

ata Management• Tom Epperly (LLNL), Deb Agarwal, You-Wei Cheah (LBNL)

FD 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. Sundar

Ozel (Princeton), Janine Carney, Rajesh Singh, Jeff Dietiker, Tingwen Li (NETL) Emily Ryan, William Lane (Boston University)

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The CCSI, Modeling Carbon Capture

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