Embed Size (px)
Citation preview
Solutions for Today | Options for Tomorrow
SYSTEMS ASPECTS OF GHG MITIGATION: OPPORTUNITIES AND CHALLENGESDavid C. Miller, Ph.D.Senior Fellow, Process Systems Engineering5 April 2017
2
• Materials Performance• Alloy Development/Manufacture• Geospatial Data Analysis
• Process Systems Engineering• Decision Science • Functional Materials• Environmental Sciences
• Energy Conversion Devices• Simulation-Based Engineering• In-Situ Materials Characterization• Supercomputer Infrastructure
Oil and Gas Strategic Office
Oil and Gas Strategic Office
NETL StructureMultiple Sites Operating as 1 LAB System
OREGON
ALASKA
TEXAS
WEST VIRGINIA
PENNSYLVANIA
3
NETL Core Competencies
Materials Engineering & Manufacturing
• Structural & Functional
• Design, Synthesis, & Performance
Geological & Environmental
Systems
• Air, Water & Geology
• Understanding & Mitigation
Energy Conversion Engineering
• Component & Device
• Design & Validation
• Process Systems• Optimization• Validation & Uncertainty• Economics• Energy Market Modeling• Grid• Life Cycle Analysis
Effective Resource Development • Efficient Energy Conversion • Environmental Sustainability
ComputationalScience &
Engineering• High Performance
Computing
• Data Analytics
Program Execution & Integration
• Technical ProjectManagement
• Market & Regulatory Analysis
Systems Engineering & Analysis
4
Systems Engineering & Analysis (SEA)Teams and Scope
Process Systems Engineering Research
• Process synthesis, design, optimization, intensification
• Steady state and dynamic process model development
• Uncertainty quantification• Advanced process control
Design, optimization, and modeling framework to be expanded to all SEA “systems”
Energy Systems Analysis Energy Process Analysis
Energy Markets Analysis
Energy Economy Modeling and Impact Assessment• Enhanced fossil energy representation• Multi-model scenario/policy analysis• Infrastructure, energy-water
Resource Availability and Cost Modeling• CO2 storage (saline and EOR)• Fossil fuel extraction• Rare earth elements• General subsurface technology
evaluation and supportGrid modeling and analysis
Environmental Life Cycle Analysis
Energy Process Design, Analysis, and Cost Estimation• Plant-level modeling, performance assessment• Cost estimation for
plant-level systems• General plant-level
technology evaluation and support
• Economic impact assessment• General regulatory, market and
financial expertise
5
From process to energy market
NETL Cost and Performance Baseline for Fossil Energy Plants
NETL CO2 Capture, Transport, Utilization and Storage - National Energy Modeling System (CTUS-NEMS)
• Detailed, transparent account of plant information
• Key resource for government, academia and industry
• Adopted by EIA; first incorporated into AEO 2014
NETL Carbon Capture Retrofits Database (CCRD)
NETL CO2 Saline Storage Cost Model
6
STACK GAS
STACK
CANSOLV
3132
33
22
23
CO2 PRODUCT
REBOILERSTEAM
REBOILER CONDENSATE
PULVERIZEDCOAL
BOILER
SCR
BAGHOUSE FGD
GYPSUMLIMESTONESLURRY
OXIDATIONAIR
MAKEUP WATER
BOTTOM ASH
COAL FEED
INFILTRATION AIR
1
4
8
7
9
14
FLY ASH
15 16
18 19
17 20
21
HP TURBINE
34
35
36
IPTURBINE LP TURBINE
CONDENSER
38FEEDWATER
HEATER SYSTEM
Note: Block Flow Diagram is not intended to represent a complete material balance. Only major process streams and equipment are shown.
32
6
5
11
37
10
HYDRATED LIME
12
13
ACTIVATED CARBON
FD FANS
PA FANS
ID FAN
CO2 COMPRESSORS
BOILER FEEDWATER
29 RECLAIMER STEAM30
RECLAIMER CONDENSATE
DRYER
27
CO2 COMPRESSORS
28
2625
24VENT
DRYER STEAMDRYER
CONDENSATE
Supercritical Pulverized Coal Power PlantConventional Coal with CO2 Capture
7
• Pre-treatment• Lowers SOx to ~ 1 ppmv from ~40 ppmv out of FGD
• Cansolv CO2 Capture Process Details• 90 % CO2 capture• Steam extraction from crossover pipe between IP and LP sections of steam turbine• Product CO2 ~ 30 psia
• CO2 Compression System• CO2 compressed to 2,200 psig• 8 stages (2.23 to 1.48 stage pressure ratios)• Intercooling in each stage
• Water knockout in first 3 stages• TEG dehydration unit between stages 4 and 5
• 300 ppmw H2O in CO2 product
Supercritical Pulverized Coal Power PlantCO2 Capture and Compression
8
0
20
40
60
80
100
120
140
160
COE,
$/M
Wh
(201
1$)
CO₂ T&S FuelVariableFixedCapital
$143
$127
$99
$82$87
$58
$101
$70
$43
$71$6.13 MMBTU
$4 MMBTU
$8MMBTU
1
2
Plant Type NGCC Supercritical PC PlantCapture Rate 0% 90% 0% 16% 35% 90%
CO2 Emissions3 (lb/MWh-gross) 773 82 1,618 1,400 1,100 183Efficiency (HHV) 51.5% 45.7% 40.7% 39.2% 37.4% 32.5%
Cost of Capture4 ($/tonne) $71 $124 $87 $58
NETL’s “Cost and Performance Baseline for Fossil Energy Plants” Updates Projections for Today’s Technology Revision 3 released July 2015
• With state-of-the-art technology, adding 90% CO2 capture and storage (CCS) significantly increases the cost of electricity (COE)
– 45-65% for NGCC– ~75% for pulverized coal (PC)
• Lower capture rates for PC plants decrease the COE penalty, but result in a higher cost of capture
– e.g. $87/tonne versus $58/tonne for 35% and 90% capture, respectively
– Due in part to diseconomies of scale
• RD&D is needed to reduce the costs of advanced coal power with CCS to support an “all of the above” strategy
1T&S = transport (100 km) and storage in a Midwest saline formation 2+30%/-15% uncertainty range; different finance structure utilized for non-capture and capture plants3Fully-loaded design rates; does not account for start-up, shutdown, performance degradation between maintenance, part-load operation, etc.4Excludes CO2 T&S; relative to non-capture NGCC and non-capture supercritical PC design for NGCC and PC capture designs, respectively
9
Fossil Energy – Coal Research Program GoalsDriving Down the Cost of Electricity of Coal Power with CCS
0% Reduction
20% Reduction
30% Reduction
40
50
60
70
80
90
100
110
State-of-the-Art 2025 Demo 2030 Demo
Goals are for greenfield plants. Costs include compression to 2,215 psia, but exclude CO2 transport and storage costs.
Cost of Electricity Reduction Targets
Transformational Technology
IGCC orSupercritical PC
2nd-Generation Technology
COE
Rela
tive
to T
oday
’s
Coal
with
Cap
ture
(%)
10
Cost of Capturing CO2 from Industrial Sources
“Cost of Capturing CO2 from Industrial Sources” January 2014 https://www.netl.doe.gov/research/energy-analysis/search-publications/vuedetails?id=1836
11
Cost of Capturing CO2 from Industrial SourcesCost Breakdown
$0
$20
$40
$60
$80
$100
$120
$140
Ethanol Ammonia Natural GasProcessing
EthyleneOxide
Coal-to-Liquids
Gas-to-Liquids
RefineryHydrogen
Steel/Iron Cement
Firs
t-ye
ar "B
reak
even
" Req
uire
d CO
2Se
lling
Pric
e (C
onst
ant 2
011
USD
)Purchased Natural Gas
Purchased Power
Consumables
Variable O&M
Fixed O&M
CAPEX
High Purity CO2 Low Purity CO2
“Cost of Capturing CO2 from Industrial Sources” January 2014 https://www.netl.doe.gov/research/energy-analysis/search-publications/vuedetails?id=1836
12
0
20
40
60
80
100
120
0 50 100 150 200 250 300
Brea
keve
n Se
lling
Pric
e, $
/ton
ne
CO2 Available, Mt/yr
CTL GTL NGP Ethylene Oxide Ammonia Ethanol Steel Cement Ref H2
Capturing CO2 from Industrial SourcesIncremental CO2 Supply versus Breakeven Selling Priceon Greenfield Prices except for Steel Process
13
US DOE Office of Fossil Energy Industrial CCS Projects
Air Products Industrial Capture; EOR• Port Arthur, TX (Hydrogen plant at Valero Refinery)• 90%+ CO2 capture (Vacuum Swing Adsorption); ~925,000 tonnes CO2/year• EOR: Denbury West Hastings oil field• Total Project: $431 million; DOE share: $284 million• Operations: December 2012• 2,562,000 tonnes delivered as of 12/31/15
Air Products Industrial Capture to EOR
Archer Daniels Midland (ADM) Biofuel; Geologic Storage• Decatur, IL• CO2 >99% purity from fermentation reactors (dehydration & compression);
~900,000 tonnes CO2/year• Geologic Storage: Mt Simon saline reservoir• Plant ~97% complete• Operations: Expected 2017• Total Project: $208 million; DOE share: $141 million
Archer Daniels Midland
14
Industrial Process Heating in U.S.2010 Data
U.S. Department of Energy, Quadrennial Technology Review 2015, Technology Assessment 6I: Industrial Process Heating Systems; Washington, DC, 2015.
16
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 (2011-2016)
16
Current licenseesProjects with industry
17
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
17
2016 R&D 100 Award
18
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 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
19
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
19
Data ManagementFramework
20
Simultaneous Simulation Based Optimization & Heat Integration
20
21
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 80.4Base 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
Objective: Max. Net efficiencyConstraint: CO2 removal ratio ≥ 90% Decision Variables (17): Bed length, diameter, sorbent and steam feed rate
22
Oxy-combustion process synthesis
1. Air Separation Unit2. Boiler3. Steam Turbine
4. Pollution Controls5. CO2 Compression Train
1 2
3
4
5
Closed
Open
Variables/Constraints102 104 106
Black Box
Flowsheet with Modular Models
Superstructure withSurrogates
Equation Based Formulation
100
ComputeEfficiency SQP
rSQP
Barrier/Interior Point
DFO
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.
23
Air Separation Unit Optimization Results
N2enriched
reflux
O2enriched recycle
10 stages3.5 bar
21 stages1.1 bar
N2waste
O2product
ΔTmin = 1.5 K196 kWh/tonne CO2
FeedAir
FeedAir
1.3 K 2.9 K
Tight Heat Integration
24
Double Reheat Regenerative Rankine Cycle & Detailed Boiler
Zone 1
Zone 2
Zone 3
Zone 4
Zone 5
Zone 6
Zone 7
Zone 8
Zone 9
Flue GasExit Plane
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.
David C. Miller, Ph.D.Senior Fellow, Process Systems EngineeringNational Energy Technology LaboratoryMarch 13, 2017
27
• Challenge: Develop and utilize multi-scale, simulation-based computational tools and models to support the design, analysis, optimization, scale-up and troubleshooting of innovative, advanced fossil energy systems with carbon capture.
• Next generation modeling and optimization platform– Current tools insufficient to address demands of integrated
advanced fossil energy systems. Needs a more flexible and open modeling environment
– Complete provenance information– Supports advanced solvers and computer architecture– Intrusive UQ– Process Synthesis, Integration, and Intensification– Process Control and Dynamics– Link to larger systems– Couple with energy market models– Open source
• Apply to development of new & novel energy systems
Development Of Innovative Advanced Energy Systems Through Advanced Process Systems Engineering
• Advanced computational tools and simulation techniques enable innovation and the more rapid development of advanced highly efficient, low-emission power plants
• Assess new concepts using computational simulations to enable prioritization of research areas
28
Integrated Multi-Scale Models
0 500 1000 1500 2000 2500 3000 3500 4000
CO
2 C
ap
tu
re [
%]
50
55
60
65CO
2 Capture SP
linear MPC
NMPC w/ equation based D-RMNMPC w/ data-driven D-RM
5
D-RM
uk
APC
d2
y1
Process
u1
dk-1 yk
rk
uk-1
d1
d3
SetpointsMeasured Process Variables(inputs, states, outputs) Controller Outputs(manipulated inputs)
Com
mer
cial
Sim
ulat
or /
DA
E So
lver
/ R
eal P
lant
MA
TLA
B
r1
Process Model Library
Tools for Properties Models
Process Optimization & Integration
Conceptual Design & Process Intensification
Process Dynamics & Control Grid and Dispatch
Energy Market ModelTools for Materials Optimization
29
Components of IDAES Toolset
29
Laboratory and/or Literature Data
Automated Fitting Algorithms for Physical
Properties, Thermodynamics and
Kinetics
Process Model Library
Conceptual Design Process SynthesisProcess Integration
Process DynamicsProcess Control
Process Optimization
Grid & Dispatch Models
Energy Market ModelsModel Customization
for Specific Applications and
Innovative Concepts
Identify New Advanced Energy Concepts
Analysis of Energy Systems
Understand Data Requirements
Investigate Multiple Scenarios across Time and Length Scales
Incorporation and Assessment of Uncertainty Across Models/Scales
Inherently Dynamic Systems – Design & Control
Solvers and Computational Platform to Enable Solution of Large Scale Problems
Data Management to Maintain Provenance, Organize Models, Enable Links Among Scales/Tasks
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.
2. Conceptual Design, Optimization, UQ, and Intensification of Advanced Energy Systems2.1. Advanced Optimization Strategies for Bubbling Fluidized Bed Processes in Pyomo
Larry Biegler, Mingzhao Yu, David Molina Thierry2.2 Advanced Oxycombustion Systems Optimization
Larry Biegler, John Eason, Jinliang Ma, Tony Burgard, Dehao Zhu2.3 Chemical Looping Systems Optimization
Andrew Lee, Larry Biegler, Mingzhao Yu, David Molina Thierry, TBD2.4 Molecular design of oxygen carriers for chemical looping
Chrysanthos Gounaris, Chris Hanselman2.5 Tools for Kinetics and Thermophysical Properties
Nick Sahinidis, Zach Wilson, Marissa Engle, John Eslick, TBD 2.6 Advanced Solvent System Optimization
John Eslick, Debangsu Bhattacharyya, Paul Akula, TBD2.7 Conceptual Design Tools
Ignacio Grossmann, Qi Chen, John Siirola, Tony Burgard, Jaffer Ghouse2.8 Optimal Planning of Electric Power Infrastructures
Ignacio Grossmann, Cristiana Lara, Ben Omell, Joel Theis, Omar Guerra3. Software Architecture, Algorithms, and Distributed Computing
3.1 System Architecture John Siirola, Dan Gunter
3.2 Optimization Algorithms and Parallel Computing Nick Sahinidis, Benjamin Sauk, Dan Gunter, John Siirola
3.3 Data Management and Workflow Deb Agarwal, You-Wei Cheah
4. PSE Support for Advanced Combustion Systems4.1 Model Development to Support ACS
Andrew Lee, Chinedu Okali, Debangsu Bhattacharyya, Anca Ostace, Jinliang Ma
www.acceleratecarboncapture.org– SorbentFit
• David Mebane, Brian Logsdon, Kuijun Li (West Virginia University)• Joel Kress (LANL)
– Process Models• Solid sorbents: Debangsu Bhattacharyya, Srinivasarao Modekurti, Ben Omell (West Virginia
University), Andrew Lee, Hosoo Kim, Juan Morinelly, Yang Chen (NETL)• 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 others)
– FOQUS• ALAMO: Nick Sahinidis, Alison Cozad, Zach Wilson (CMU)• Superstructure: Nick Sahinidis, Zhihong Yuan (CMU)• DFO: John Eslick (CMU), Qianwen Gao (NETL)• Heat Integration: Yang Chen, Ignacio Grossmann (CMU)• 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, Abdelrahman Elbashandy, Deb Agarwal (LBNL)• SimSinter: Jim Leek (LLNL), John Eslick (CMU)
– APC Framework• Priyam Mahapatra, Steve Zitney (NETL)
– Data Management• Tom Epperly (LLNL), Deb Agarwal, You-Wei Cheah (LBNL)
– Oxy-combustion• Alex Dowling, Larry Biegler, John Eason (CMU), Jinliang Ma (NETL)
Funding provided by U.S. Department of Energy, Office of Fossil EnergyBaseline & Industrial CCS Slides: Kristin Gerdes, Jeff Hoffman
