Capabilities of the CCSI ToolsetDavid C. Miller, Ph.D.

9 August 2016

For Accelerating Technology Development

National LabsAcademia 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

2

• 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

Goals & Objectives of CCSI

3

Current licenseesProjects with industry

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 Properties

Kinetics

Thermodynamics

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

5

CCSI Toolset to accelerate development and scale-upProcess 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 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

21

24

2022

23

CYC-001

Process Synthesis, Design & Optimization

CFD Device ModelsCCSI CFD Validation Hierarchy for Sorbent Capture

NETLCarbonCaptureUnit(C2U)ReactingUnit

Upscaling

(heat transfer

filtering)

Coupled

adsorption

reaction, heat

transfer and flow

Non-reactive

flow and heat

transfer of

cooling tubes

Hydrodynamics

of Bubbling

Fluidized Bed

25MWe,100MWe,650MWe

SolidSorbentSystems

Upscaling

(flow filtering)

Upscaling

(energy

filtering)

Intermediate

Validation

(Adsorber without

reactions and heat

transfer)

1MWeCarbonCaptureSystem

Demonstration and

Full Scale Systems

Unit

Problems

Laboratory Scale

Subsystem

(Decoupled

benchmark cases)

Pilot Scale

Systems

Process Dynamics

and Control

Basic Data Requirements for CCSI Analyses

Sorbents– Adsorption equilibrium, f(py,i, T, xi)

• All species over relevant conditions

– Heat of Adsorption for all species, f(T, xi)

• CO2 and H2O minimum

– Heat Capacity, f(T, xi)

– Adsorption/Desorption Kinetics, f(py,i, T, xi)

• All species over relevant conditions

– Thermal Conductivity, f(T, xi)

– Density, f(T, xi)

– Particle Size Distribution

– Sphericity

Solvents– Vapor-Liquid Equilibrium Data

• over relevant py,i,T, xi ranges

– Heat of Absorption, f(T, xi)

– Kinetic Data, f(py,i, T, xi)• Including speciation

– Mass Transfer Data • from wetted wall column, bench scale system

– Viscosity, f(T, xi)

– Heat Capacity, f(T, xi)

– Density, f(T, xi)

– Surface Tension, f(T, xi)

– Vapor Pressure, f(T, xi)

– Thermal Conductivity, f(T, xi)

– Hydraulic Data for specific packing

• Challenge:

– Large number of parameters in bench scale models and properties submodels

– Limited data = full calibration conceptually and computationally difficult.

• New approach:

– Multi-scale calibration

– Propagate uncertainty from properties models during bench scale calibration

CCSI Approach: Multi-scale Calibration

Bench Scale Model

PQ1

PQ2

PM1

PM2

Physical Inputs (e.g. T, Q)

Properties parameters

Properties parameters

Bench scale parameters

Physical Inputs (e.g. gas flow)

Output (%CO2 capture)

Y2

Y1

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/Commercial Scale Data

WWC/Bench/Pilot Scale Data

Lab Scale Data

UQ

Process UQ

Measurement Uncertainty

Measurement Uncertainty

Measurement Uncertainty

10

Optimization, Uncertainty Quantification, Surrogate Models

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

gPROMS

Excel

SimSinter Config GUI

Resu

lts

FOQUS

Framework for Optimization Quantification of Uncertainty and Surrogates

Meta-flowsheet: Links simulations, parallel execution, heat integration

Sa

mp

les

Simulation

Based

Optimization

UQ

Surrogate Models

(ALAMO, iREVEAL,

BSS-ANOVA, etc.)

TurbineParallel simulation execution management

system

Desktop – Cloud – Cluster

Basic Data

(SolventFit)

Optimization

Under

Uncertainty

D-RM

BuilderHeat

Integration Data Management

Framework

Highly Resolved Models for Solvent-based Capture

Predictive understanding at scale

Hierarchical multi-scale modeling framework

Micro/Meso-Scale VOF model Macro-scale

Two Fluid model

Fernandes et al., JSF, 2009

Raynal et al., Workshop, 2012

Effective mass transfer

coefficient for different flow

regimes

11

12

CCSI Toolset Products

Process Models

Basic Data Fitting Tools

log10(k0_1)

De

nsity

3 4 5 6 7

0.0

1.0

2.0

log10(k0_2)

De

nsity

7 8 9 10 11

02

46

log10(k0_3)

De

nsity

7 8 9 10 11

0.0

0.4

0.8

EA_1

De

nsity

0 50 100 150 200

0.0

00.0

20.0

4

EA_2

De

nsity

0 50 100 150 200

0.0

00

.04

0.0

8

EA_3

De

nsity

0 50 100 150 200

0.0

00

0.0

10

log10(D0)

De

nsity

-11.0 -10.5 -10.0 -9.5 -9.0

01

23

45

a

De

nsity

-1.0 -0.8 -0.6 -0.4 -0.2

02

46

8

b

De

nsity

-3.0 -2.5 -2.0 -1.5 -1.0 -0.5

0.0

1.0

2.0

APC Framework

d1d3

y3

Process 1

Process 2

Process 3

D-RM 1

APC 3

D-RM 3

3uk-1

u3

3uk

3dk-1

y2

3yk

APC 2

D-RM 2

2yk

2uk

2uk-1

u2

2dk-1

d2

2rk

3rk

r2

r3

1uk

1uk-1

u1

1dk-1

1yk

y1

1rk

r1

APC 1

APC Framework

Process Simulator /

Real Plant

CFD Models

CCSI CFD Validation Hierarchy for Sorbent Capture

NETLCarbonCaptureUnit(C2U)ReactingUnit

Upscaling

(heat transfer

filtering)

Coupled

adsorption

reaction, heat

transfer and flow

Non-reactive

flow and heat

transfer of

cooling tubes

Hydrodynamics

of Bubbling

Fluidized Bed

25MWe,100MWe,650MWe

SolidSorbentSystems

Upscaling

(flow filtering)

Upscaling

(energy

filtering)

Intermediate

Validation

(Adsorber without

reactions and heat

transfer)

1MWeCarbonCaptureSystem

Demonstration and

Full Scale Systems

Unit

Problems

Laboratory Scale

Subsystem

(Decoupled

benchmark cases)

Pilot Scale

Systems

Gas In

Liquid In

Gas Out

Liquid Out

PackedBed

Film Flow: O (mm)

WWC:

O(mm)

Device Scale:

O(m)

Domain (60´50 mm2)

Superstructure Optimization

Oxycombustion System

Optimization Framework

Solvent Blend Model

1E-2

1E+0

1E+2

1E+4

1E+6

0.1 0.3 0.5 0.7 0.9

P*CO2

(Pa)

loading (mol CO2/mol alk)

40°C7m2° Amine

LeLi,2015

pKa =8

=9

=10

pKa =11

13

Major release November 2015

Updated June 2016

15

Tuesday, August 9 – Admiral Room1:30 2:50 Sub-Process Models

Baseline VLE Modeling

Modeling Improvements via Simultaneous Regression

Solvent-Based Model Development: Incorporating Uncertainty

FOQUS: A Computational Tool for Design Optimization and Uncertainty Quantification

3:05 4:05 Process ModelsApproximate Models

Rigorous/Predictive Models & Uncertainty Quantification

Deterministic Dynamics & Control

Innovative Processes

4:05 4:45 Unit Operation ModelsPredictive Device-Scale Performance for Sorbent- and Solvent-Based CO2 Capture with High Fidelity CFD Models

4:45 4:55 New Capabilities: Amine Aerosol Modeling4:55 5:15 Data and Simulation Management

16

Wednesday, August 10 – Admiral Room

9:00 9:10Welcome & Day 2 OverviewMichael Matuszewski, National Energy Technology Laboratory

9:10 9:45CCSI Toolset Commercialization & Long Term SupportAdekola Lawal, Process Systems Enterprise

9:45 10:15CCSI Toolset Licensing Status, Benefits & ProceduresSusan Sprake, Los Alamos National Laboratory

10:45 11:00The Future of CCSI2: Making an Impact on Industry John Shinn

1:00 5:00 CCSI Toolset Demonstrations – Discuss Tools/Models

National LabsAcademia 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

17

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.

18

Lab & Pilot ScaleExperiments & Data

Device Scale Models Validated 3-D, CFD

Process SystemsDesign, Optimization & Control

Physical Properties

Kinetics

Thermodynamics