Combinatorial complexity, uncertainty, and emergent behavior in … · Computational challenges and...
Transcript of Combinatorial complexity, uncertainty, and emergent behavior in … · Computational challenges and...
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Catalysis Center for Energy Innovation www.efrc.udel.edu
Combinatorial complexity, uncertainty, and emergent behavior in the design of catalytic materials
Department of Chemical Engineering Center for Catalytic Science and Technology (CCST)
Catalysis Center for Energy Innovation (CCEI), an EFRCUniversity of Delaware
Coarse-graining of many-body systems
Catalysis Center for Energy Innovation www.efrc.udel.edu
Overview of energy concepts and select emerging technologies
Computational challenges and developments
Examples of emerging technologies
Coupled thermoelectric/catalytic microburners
Catalytic partial oxidation
Microreformers
Biomass processing
Outline
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Catalysis Center for Energy Innovation www.efrc.udel.edu
Overview of energy concepts and select emerging technologies
Computational challenges and developments
Topics discussed
Process design: Coupled thermoelectric/catalytic microburners
Design of emergent materials
Catalyst dynamics
Structure sensitivity
Outline
Catalysis Center for Energy Innovation www.efrc.udel.edu
Overview of energy concepts and select emerging technologies
Computational challenges and developments
Topics discussed
Process design: Coupled thermoelectric/catalytic microburners
Design of emergent materials
Catalyst dynamics
Structure sensitivity
Outline
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Catalysis Center for Energy Innovation www.efrc.udel.edu
The Future of Our Society is Not Sustainable with Current Practices
Global warming and increased (anthropogenic)CO2 emissions are a reality
Water quality is of concern
Our (fossil fuel) energy reserves are declining
Our energy demand is increasing, especially in developing countries
Our energy security is at risk Strong U.S. dependence on foreign oil and natural gas
Our energy reliability is problematic Widespread blackouts (Aug. 2003) affected 50 million customers
from Ohio to New York and Canada
Catalysis Center for Energy Innovation www.efrc.udel.edu
No single solution
‘Fossil fuels will be a major part of the world’s energy portfolio for decades to come…’
‘One thing is certain: There will be no single “silver bullet” solution to our energy needs’
* http://www.nap.edu/openbook.php?record_id=12204&page=R1
National Academy Press, 2008
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Environment: Capture and sequestration of CO2 Particulates, NOx from diesel engines
Efficiency Economics, atom economy, energy savings, reduced emissions Improved selectivity, process intensification
Hydrogen [economy] Production, storage, utilization (e.g., PEM FCs, biomass upgrade)
Increase energy supply Renewables (wind, solar, biomass, water split, CO2 utilization…) Underutilized resources, e.g., remote and offshore natural gas Clean coal technologies; Nuclear
Reliability, expensive or impossible transportation, process intensification and efficiency, portable electronics, H2 production
Key Concepts and Technologies
D. Vlachos & S. Caratzoulas, Chem. Eng. Sci. 65, 18 (2010)
Catalysis Center for Energy Innovation www.efrc.udel.edu
Environment: Capture and sequestration of CO2 Particulates, NOx from diesel engines
Efficiency Economics, atom economy, energy savings, reduced emissions Improved selectivity, process intensification
Hydrogen [economy] Production, storage, utilization (e.g., PEM FCs, biomass upgrade)
Increase energy supply Renewables (wind, solar, biomass, water split, CO2 utilization…) Underutilized resources, e.g., remote and offshore natural gas Clean coal technologies; Nuclear
Reliability, expensive or impossible transportation, process intensification and efficiency, portable electronics, H2 production
D. Vlachos & S. Caratzoulas, Chem. Eng. Sci. 65, 18 (2010)
Key Concepts and Technologies
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Total energy growth vs. energy savings from 2005 to 2030The projected most important ‘fuel’ is energy savings
OECD - Organization for Economic Cooperation and Development
Energy Savings from Enhanced Efficiency
Kaisare and Vlachos (submitted),ExxonMobil report,
www.exxonmobil.com
Catalysis Center for Energy Innovation www.efrc.udel.edu
Environment: Capture and sequestration of CO2 Particulates, NOx from diesel engines
Efficiency Economics, atom economy, energy savings, reduced emissions Improved selectivity, process intensification
Hydrogen [economy] Production, storage, utilization (e.g., PEM FCs, biomass upgrade)
Increase energy supply Renewables (wind, solar, biomass, water split, CO2 utilization…) Underutilized resources, e.g., remote and offshore natural gas Clean coal technologies; Nuclear
Reliability, expensive or impossible transportation, process intensification and efficiency, portable electronics, H2 production
D. Vlachos & S. Caratzoulas, Chem. Eng. Sci. 65, 18 (2010)
Key Concepts and Technologies
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Catalysis Center for Energy Innovation www.efrc.udel.edu
Environment: Capture and sequestration of CO2 Particulates, NOx from diesel engines
Efficiency Economics, atom economy, energy savings, reduced emissions Improved selectivity, process intensification
Hydrogen [economy] Production, storage, utilization (e.g., PEM FCs, biomass upgrade)
Increase energy supply Renewables (wind, solar, biomass, water split, CO2 utilization…) Underutilized resources, e.g., remote and offshore natural gas Clean coal technologies; Nuclear
Down-scaling: Reliability, expensive or impossible transportation, process
intensification and efficiency, portable electronics, H2 production
D. Vlachos & S. Caratzoulas, Chem. Eng. Sci. 65, 18 (2010)
Key Concepts and Technologies
Catalysis Center for Energy Innovation www.efrc.udel.edu
SR: CH4 + H2O = CO + 3H2 + 206 kJ/molWGS: CO + H2O = CO2 + H2 - 41 kJ/mol
Current Syngas Production Route
Steam reforming: endothermic
Fixed bed catalytic reactors with Ni catalyst
Heat transfer controlledFlames supply the heat to multitubular reformers
Slow (~1s);
Sehested, Cat. Today 111 (2006) 103
GM: Chevrolet S-10
Need for
more efficient,
less bulky processes
via better catalysts and
process intensification
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use localized Solar/distributed need Biomass/transportation cost
Future Energy Will be Associatedwith Down-Scaling
Onboard Gas-stations
Vlachos and S. Caratzoulas, Chem. Eng. Sci. 65, 18 (2010)
Courtesy: Ballard Power Systems
Smart Car
Catalysis Center for Energy Innovation www.efrc.udel.edu
Courtesy of the British Geological Survey
Down-Scaling: Untapped Reserves in Remote and Offshore Locations
Offshore GTLLarge reservoirs of unutilized natural gasRestrictions on flaring require stranded gas to be re-injected (costly)
Kaisare and Vlachos (submitted),ExxonMobil report, www.exxonmobil.com
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Electronics
Down-Scaling: Portable Power
System Energy Density W-Hr/Kg
Li batteries 75
LiSOCl2 300Methanol 6300/5600Ammonia 6200/5200JP-8 13200
Kaisare and Vlachos (submitted),ExxonMobil report, www.exxonmobil.com
Catalysis Center for Energy Innovation www.efrc.udel.edu
Overview of energy concepts and select emerging technologies
Computational challenges and developments
Topics discussed
Process design: Coupled thermoelectric/catalytic microburners
Design of emergent materials
Catalyst dynamics
Structure sensitivity
Outline
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Combinatorial problemLarge mechanismsComplex feedstocks
Challenges in Catalytic Process Modeling
Salciccioli et al, Chem. Eng. Sci. (In press)
Catalysis Center for Energy Innovation www.efrc.udel.edu
Bottom-up and Top-down Modeling:Process Design and Catalyst Screening
Reviews: Chem. Eng. J., 90, 3 (2002); Chem. Eng. Sci., 59, 5559 (2004); Adv. Chem. Eng., 30, 1 (2005)
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Overarching Concept:Hierarchical Multiscale Modeling
Start with a sufficiently simple but physically relevant model at each scaleLink all models Perform a sensitivity analysisIdentify important scale and parameter(s)Use higher level theory for this scale and parameter(s)Iterate
Catalysis Center for Energy Innovation www.efrc.udel.edu
Hierarchy Enables Rapid Screening of Chemistry, Fuels, and Catalysts
Semiempirical: UBI-QEP, TST
ab initio:DFT, TST, DFT-
MD
Continuum: MF-ODEs
Discrete: KMC
Ideal: PFR, CSTR, etc.
Computational Fluid Dynamics
(CFD)
Mesoscopic:PDEs
Discrete:CGMC
Pseudo-homogeneous:Transport correlations
DFT-based correlations, BEPs, GA
Catalyst/adsorbed phase:
Reaction rate
Reactor scale:Performance
Electronic:Parameter estimation
Accuracy, costKaisare, Stefanidis, Federici, Deshmukh, Mettler
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Review: Salciccioli et al., Chem. Eng. Sci., Accepted; J. Phys. Chem. C 114, 20155 (2010)
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Catalysis Center for Energy Innovation www.efrc.udel.edu
Overview of energy concepts and select emerging technologies
Computational challenges and developments
Topics discussed
Process design: Coupled thermoelectric/catalytic microburners
Design of emergent materials
Catalyst dynamics
Structure sensitivity
Outline
Catalysis Center for Energy Innovation www.efrc.udel.edu
Predicting Novel Catalytic Materials
First principles methods (DFT) promise to deliver strategies for rational catalyst designCurrent studies limited tocases when:
Thermodynamics (heat ofadsorption) dominates1,2
Linear interpolation isemployed3
1 Strasser et al., J. Phys. Chem. B 107(40), 11013 (2003)2 Greeley and Mavrikakis, Nat. Materials 3(11), 810 (2004)3 Jacobsen et al., J. Am. Chem. Soc. 123, 8404 (2001)
This image cannot currently be displayed.
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Molecular Architecture Plays a Pivotal Role
Surface monolayer materials exhibit emergent behaviorMethods for predicting emergent materials are lacking
3.0 Langmuir NH3 at 350K at
UHV
Inte
nsity
(ar
b. u
nits
)
750700650600550500450400350
Temperature (K)
Thick Ni
Ni-Pt
Pt-Ni-Pt
Pt(111)
14 amu
Hansgen, Chen, and Vlachos, Nature Chem. 2, 484-489 (2010)
Catalysis Center for Energy Innovation www.efrc.udel.edu
NH3 Decomposition for COx-Free H2
Excellent hydrogen carrierHigh-energy density Liquid at 25 oC and 8 atmOne of the most widely
produced chemicals(>100 metric tones/yr)- Haber-Bosch Process- Infrastructure is already set up
Cat. decomposition of NH3 on RuSlightly endothermicMillisecond/portable1
Minimal downstream processing
Met
hano
lEt
hano
l
Met
hane
Prop
ane0
5
10
15
20
25
30
% w
t
17 .6%
Am
mon
ia
Oct
ane
1Deshmukh et al., Int. J. Multiscale Comp. Eng. 2, 221-238 (2004)
2NH3 = N2 + 3H2 + 46 kJ/mol
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High Throughput MultiscaleModel-based Catalyst Design
Search is done on atomic descriptors while running the full chemistry and reactor modelsOptimal catalyst properties are identified
350 oC1 atm
Prasad et al., Chem. Eng. Sci. 65, 240 (2010)
4550
5560
6570
110120
130140
0
0.1
0.2
0.3
0.4
0.5
QH
[kcal/mol]QN
[kcal/mol]
Con
vers
ion
NH3 decomposition
*NH*NH 33 *H*NH**NH 23
*H*NH**NH 2 *H*N**NH *2N*2N 2 *2H*2H 2
Catalysis Center for Energy Innovation www.efrc.udel.edu
Living in a Multiscale WorldFactors
Reactor conditions Temperature, composition, residence time, reactor location
Uncertainty in pre-exponentials and energeticsUncertainty in mappings of energeticsAdsorbate-adsorbate interactions
MethodologyA Monte Carlo search in each parameter is carried outFor every set of parameters, the optimal properties are computedA distribution of optimal properties is deduced
Uncertainn
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Parametric and Hierarchical Model Uncertainty
Effect of adsorbate-adsorbate interactions
Ulissi et al., J. Cat. In press
Catalysis Center for Energy Innovation www.efrc.udel.edu
Identifying Bimetallic Catalysts
Optimum heat of chemisorption of N of ~130 kcal/molNiPtPt is a good prospective bimetallic surface
Surface Sub-surface
Metals BEN (kcal/mol)
PtTiPt 56.5
PtVPt 59.5
PtCrPt 72.6
PtMnPt 84.9
PtFePt 83.9
PtCoPt 87.0
PtNiPt 89.8
NiPtPt 137.5
CoPtPt 159.9
FePtPt 169.9
MnPtPt 162.2
CrPtPt 166.5
VPtPt 184.1
TiPtPt 191.5
Pt 102.1 Ni 113.8
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Emergent Behavior Verified Experimentally
3.0 Langmuir NH3 at 350 K at
UHV
Ammonia decomposes on Ni-Pt-PtNo decomposition on other surfacesNi-Pt-Pt is the most active catalyst
Inte
nsity
(ar
b. u
nits
)
750700650600550500450400350
Temperature (K)
Thick Ni
Ni-Pt
Pt-Ni-Pt
Pt(111)
14 amu
Hansgen, Chen, and Vlachos, Nature Chem. 2, 484-489 (2010)
Ni-Pt-Pt
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Surface Characterization of Ni-Pt(111)
Ni deposited at 300 KLow Ts
Ni islands on Pt terraces and steps
No mixing
After annealing at 640 K
AES shows some Ni diffuses into Pt bulk
STM unable to distinguish Ni and Pt on the surface
Kitchin et al., Surf. Sci. 544, 295, (2003).
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Coarse-graining Enables Simulation of Emergent Behavior
Simulation time~1 year: traditional KMC method~1 day with coarse-grained KMC
c0 = 0.25 0.56 0.67
Nanodiscs LabyrinthInverted
nanodiscs
c0 = 0.28 0.50 0.73
CGMC simls
Pb/Cu expts
Chatterjee and Vlachos, Chem. Eng. Sci., 62, 4852 (2007)
Pb/Cu(111):Nature 412, 875 (2001); J. Phys. Cond. Matter 14, 4227 (2002)
200-300 nm
Catalysis Center for Energy Innovation www.efrc.udel.edu
0.9 eV
1.1 eV
1.7 eV
1.4 eV
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3
4
5
Reaction coordinate
En
erg
y (e
V)
0 2 4 6 8 10 12
-6414
-6413.5
-6413
-6412.5
-6412
-6411.5
Multiscale Modeling of Ni-Pt Mixing
Timescale (s)10–12 10–9 10–6 10–3 100 103
1 ps 1 ns 1 s 1 ms 1 min 1 h 1 day
Molecular Dynamics ExperimentAccelerated MD
0 5 10 15‐6300
‐6200
‐6100
‐6000
‐5900
‐5800
‐5700
t (ns)
True Configu
rational Energy (eV
)
Vmin
Ebias
Simulated Annealing
Nudged Elastic Band (Ebarrier) & Transition State Theory
Wang et al., J. Chem. Phys. 133, 224503 pg. 1-11 (2010)
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Mixing of Ni and Pt vs. TemperatureTop View Side View
Tem
perature
Wang et al., J. Chem. Phys. 133, 224503 pg. 1-11 (2010)
Clusters of exposed Pt form; no mixing happensClusters of exposed Pt form; diffusion of Ni to sub-surface onlyDiffusion of Ni into Pt bulk
Catalysis Center for Energy Innovation www.efrc.udel.edu
0.25 ML Ni on Pt(111) at 600 KSimulation cell size: 8.3 nm X 8.7 nm, 1080 atoms in each layer
Random, t=0 10 ps 160 ps 2 ns Equilibrium
Wang et al., J. Chem. Phys. 133, 224503 pg. 1-11 (2010)
The 1 ML cartoon is too simplisticClusters restructure at ns time scaleStatistical distribution of sites
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Random, t=0 10 ps 160 ps 800 ps Equilibrium
0.5 ML Ni on Pt(111) at 600 K
Structures formed depend on Ni coverage
Wang et al., J. Chem. Phys. 133, 224503 pg. 1-11 (2010)
Catalysis Center for Energy Innovation www.efrc.udel.edu
Take Home MessagesA multitude of computational tools and UHV experiments employed to probe different time scalesCatalyst structure is very dynamicMixing entails multiple time scales:
Surface configuration is metastableMixing from the top layer to the second layer occurs firstNi starts to diffuse into Pt bulk at longer times
Ni diffusion happens via correlated hopping
Higher temperatures, smaller Ni coverages and Pt steps facilitate mixing between Ni and Pt
An ensemble of structures needed for kinetics
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Future energy needs, a hydrogen economy, and renewables require downscaling
News catalysts, media and reactor concepts are needed
Process intensification leads to increased efficiency, lower capital, lower emissions
Partial oxidation shows complex stratificationMicroreformers can also operate at millisecondsReactive extraction for HMF production
Process intensification may have to be accompanied by catalyst intensificationBiomass processing provides exiting research opportunities
Outlook
Catalysis Center for Energy Innovation www.efrc.udel.edu
Energy: Danielle Hansgen; Ying Chen;Mike Salciccioli; Zach Ulissi; Vinay Prasad; Matteo Maestri
Structure sensitivity: Michail Stamatakis
Microsystems: George Stefanidis, Matt Mettler, Dan Norton, Justin Federici, Stefano Tacchino
Biomass: Samir Mushrif
Jingguang Chen, Stavros Caratzoulas
NSF; CCEI/EFRC; DOE/BES
Acknowledgements