Combinatorial complexity, uncertainty, and emergent behavior in … · Computational challenges and developments Examples of emerging technologies Coupled thermoelectric/catalytic

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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


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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Topics discussed

Process design: Coupled thermoelectric/catalytic microburners

Design of emergent materials

Catalyst dynamics

Structure sensitivity

Outline




Topics discussed



Catalyst dynamics


Outline

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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


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)









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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









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Down-scaling: Reliability, expensive or impossible transportation, process

intensification and efficiency, portable electronics, H2 production




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


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




Topics discussed



Catalyst dynamics


Outline

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Combinatorial problemLarge mechanismsComplex feedstocks

Challenges in Catalytic Process Modeling

Salciccioli et al, Chem. Eng. Sci. (In press)


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


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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Topics discussed



Catalyst dynamics


Outline


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)


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


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


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


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


0.9 eV

1.1 eV

1.7 eV

1.4 eV

12

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


Clusters of exposed Pt form; no mixing happensClusters of exposed Pt form; diffusion of Ni to sub-surface onlyDiffusion of Ni into Pt bulk


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


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



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


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

Documents

Combinatorial complexity, uncertainty, and emergent behavior in … · Computational challenges and developments Examples of emerging technologies Coupled thermoelectric/catalytic