Workshop Data Collection and Mining toward Virtual Chemistry of Smart Energy Carriers | Napoli, April 5th-6th 2016

Collaboration of experiments and simulations towards the development of

predictive models

Salvatore Iavarone, Sean T. Smith, Philip J. Smith, Alessandro Parente

Predictive models are needed for new technological breakthroughs

2

Predict the futureExplaining the past

Foresee unobserved outcomes

Analyze collected data

Mathematical models

Validation and

Uncertainty Quantification

Model development and

simulations

A tight coupling of simulation and experiments is needed to ensure predictivity with uncertainty quantification

3

Both deterministic and bayesian approaches have been proposed to make inference from experimental observations for an underlying model

Both approaches use a two-step process: a surrogate is built as a representation of the full simulation and used for the subsequent analysis

B2B employs deterministic upper and lower bounds rather than probability distributions (KOH)

Bound-to-Bound* Data Collaboration (B2B) defines a deterministic framework, while Kennedy & O’Hagan** (KOH) is statistical and bayesian

* T. Russi, A. Packard, M. Frenklach, Chemical Physics Letters 499 (2010) 1–8 ** M. C. Kennedy and A. O’Hagan, Journal of the Royal Statistical Society B 63 (2001) 425–464

A framework for uncertainty quantification must account for all sources of uncertainty, with a hierarchical procedure

4

ye = ym (x) + � + ✏

experiment

model prediction

discrepancy between ye and ym(x)

experimental error

Procedure* 1. Identify potential uncertain parameters, x 2. Definition of the quantity of interest (QoI), y 3. Most sensitive parameters/design of experiments 4. Develop a surrogate model 5. Consistency (B2B)/Calibration (KOH) 6. Hierarchical prediction

* Bayarri M.J., Berger J.O., Paulo R., Sacks J., Cafeo J.A., Cavendish J., Lin C.-H., Tu J., Technometrics 49 (2007) 138-154

Unit

Lab

Pilot

Prediction

A model is an approximation to the truth and the truth comes from the measured data

The inverse problem

5

Bound-To-Bound Data Collaboration (B2B-DC)

le |ym (x)� ye| ue for each e 2 E↵i xi �i for all i = 1, . . . , n

Initial bounds on uncertain parameters and experiments are combined. New uncertainty bounds for the uncertain parameters are determined, to be consistent with the data

Deterministic uncertainty bounds for predictions can be estimated

l |ym (x)| u

The inverse problem

6

Kennedy & O’Hagan (KOH)

Gaussian Process (GP) for ym and δ

Unpractical with more than 8 active variables

Developed for computer code optimization (focus is not on predictivity)

ye = ym (x) + � + ✏

01

23

45

01

23

45−6

−4

−2

0

2

Parameter 2Parameter 1

POD

coef

ficie

nt #

1

−5

−4

−3

−2

−1

0

1

ym = f (x) +N�0,�2

n

�f (x) = � (x)T w

7

Both B2B and KOH require an infrastructure to make experimental data available for UQ analysis

Efficient management of direct and indirect combustion data, reaction mechanisms and computer codes for the mechanism analysis and uncertainty quantification

PrIMe: data repository as well as all the algorithms needed to quantify prediction uncertainties, data set consistency, to discriminate between multiple models (model forms) and to conduct sensitivity analyses.

ReSpecTh: data repository as well as the computer codes related to the analysis and reduction of reaction mechanisms

Application: a scale-bridging model for coal devolatilization

8

courtesy of Phil Smith, University of Utah

Suited to large-scale simulations of coal-fired boilers

Able to predict ultimate volatile yield (thermodynamics) and rate (kinetics)

Reduced physical model with quantified model-form uncertainty

Model development using data collaboration and bayesian analysis

Demonstration-scaleprediction

Pilot-scale validation

Large-scale validation

TomSeanAndyDavidJeremyMichael

Jeremy

Phil

Jennifer

Bench-scalevalidation

1.5 MWth oxy-coal furnace

5 MWth oxy-gas furnace

char oxidation

ash transformation

multiphase flow

soot formation radiation

devolatilization

500 MWe oxy-AUSC

DesignBoiler

450 MWe oxy-USC

Demo.Boiler

980 MWe AUSC

Existing Boiler

15 MWth oxy-coal boiler

Demonstration-scaleprediction

Pilot-scale validation

Large-scale validation

TomSeanAndyDavidJeremyMichael

Jeremy

Phil

Jennifer

Bench-scalevalidation

1.5 MWth oxy-coal furnace

5 MWth oxy-gas furnace

char oxidation

ash transformation

multiphase flow

soot formation radiation

devolatilization

500 MWe oxy-AUSC

DesignBoiler

450 MWe oxy-USC

Demo.Boiler

980 MWe AUSC

Existing Boiler

15 MWth oxy-coal boiler

Demonstration-scaleprediction

Pilot-scale validation

Large-scale validation

TomSeanAndyDavidJeremyMichael

Jeremy

Phil

Jennifer

Bench-scalevalidation

1.5 MWth oxy-coal furnace

5 MWth oxy-gas furnace

char oxidation

ash transformation

multiphase flow

soot formation radiation

devolatilization

500 MWe oxy-AUSC

DesignBoiler

450 MWe oxy-USC

Demo.Boiler

980 MWe AUSC

Existing Boiler

15 MWth oxy-coal boiler

Demonstration-scaleprediction

Pilot-scale validation

Large-scale validation

TomSeanAndyDavidJeremyMichael

Jeremy

Phil

Jennifer

Bench-scalevalidation

1.5 MWth oxy-coal furnace

5 MWth oxy-gas furnace

char oxidation

ash transformation

multiphase flow

soot formation radiation

devolatilization

500 MWe oxy-AUSC

DesignBoiler

450 MWe oxy-USC

Demo.Boiler

980 MWe AUSC

Existing Boiler

15 MWth oxy-coal boiler

Demonstration-scaleprediction

Pilot-scale validation

Large-scale validation

TomSeanAndyDavidJeremyMichael

Jeremy

Phil

Jennifer

Bench-scalevalidation

1.5 MWth oxy-coal furnace

5 MWth oxy-gas furnace

char oxidation

ash transformation

multiphase flow

soot formation radiation

devolatilization

500 MWe oxy-AUSC

DesignBoiler

450 MWe oxy-USC

Demo.Boiler

980 MWe AUSC

Existing Boiler

15 MWth oxy-coal boiler

9

Properties: 1. Oxy-firing environment 2. Industrial-like conditions

• high temperature • high heating rates

T

TIC

T

N2

AIR

NG

O2

CO2T Temperature measured continuously

Preheating section

Vertical reactor

Quenching part

N2

Injection ports for probespressurecoalcompositiontemperature

Cooling loops

O2 CO2N2Particle

collection system

Injection port for particle collection probe

Electrical heating system

Coal feeding system K-tron

Coal

Uncertainty sources: 1. particle residence time 2. particle temperature 3. particle conversion

L=4.5 m, ID = 15 cm t = 5-1500 ms

HR = 103-105 K/s, Tmax = 1673 K

Representative data from the pyrolysis tests on the IFRF Isothermal Plug Flow Reactor (IPFR)

In PrIMe: ~800 tests (devol., char ox., …) of 270 fuels (coals, biomasses, …) performed in the IPFR via XML files

10

Preliminary CFD analyses using CPD allow identifying the sources of discrepancy

CPD model leads to an over-prediction of the devolatilization rates at low and medium temperatures, with a constant final conversion value for the three temperatures

The discrepancy, δ, can be associated to a lack of accuracy in the evaluation of the ultimate volatile yield at a given hold temperature

Particle conversion - Sebuku coal devolatilization

1173K 1373K 1573K

A yield model is needed, to account for yield variation with hold temperature

11

The simplest model which embodies an ultimate yield function of the particle temperature is the one by Biagini and Tognotti (BT) model*

* E. Biagini, L. Tognotti, Fuel Process. Technol. 126 (2014) 513–520.

dX

dt= A exp

✓� E

RTp

◆(Xf �X)

Xf = 1� exp

✓�DI

Tp

Tst

◆

SFOR equation

Ultimate volatile yield model

Devol. index

Particle temperature (from CFD)

T proximate

Introducing a ultimate yield model improves conversion predictions

12

BT predictions are deficient at temperatures lower than about 1300 K

The application of B2B to BT leads to global inconsistency, even increasing the experimental uncertainty artificially to 50%

The results confirm the need for a yield model, but suggest that other model forms could improve the model performances

Particle conversion1173K 1373K 1573K

A new model form for the yield model leads to consistency between experiments and simulations

13

600 800 1000 1200 1400 16000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

T [K]

X [−

]

@1173 K@1373 K@1573 Kmodel

Xf = 0.5

1 + erf

✓Tp � T1

T2

p2

◆�Novel yield model,

two active parameters

New yield curve & B2B analysis

14

Bayesian analysis is used to investigate the role of model-form uncertainty Prior knowledge: yield is zero at the underground temperature, and unity at the boiling point of graphite

Experimental data provide a posterior state with (increased) knowledge

15

The yield model found consistent using B2B is within the GP posterior credible range

Experimental data significantly narrow the range of model-form uncertainty

CPD and BT yield models are not in the “credible” range

0 0.05 0.1 0.15 0.2 0.25 0.3 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

t [s]

X [−

]

CFDExp

0 0.05 0.1 0.15 0.2 0.25 0.30

0.1

0.2

0.3

0.4

0.5

0.6

t [s]

X [−

]

CFD particlesCFD meanExp

The novel yield model is used to make new predictions of coal conversion

16

Generalised improvement of coal conversion at all hold temperatures

Particle conversions under-predicted for residence times lower than 0.05 s, at 1173 K and 1373 K, i.e. kinetically limited regime below 800 K

0 0.05 0.1 0.15 0.2 0.25 0.3 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

t [s]

X [−

]

CFDExp

0 0.05 0.1 0.15 0.2 0.25 0.30

0.1

0.2

0.3

0.4

0.5

0.6

t [s]

X [−

]

CFD particlesCFD meanExp

0 0.05 0.1 0.15 0.2 0.25 0.30

0.1

0.2

0.3

0.4

0.5

0.6

t [s]

X [−

]

CFD particlesCFD meanExp

0 0.05 0.1 0.15 0.2 0.25 0.3 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

t [s]

X [−

]

CFDExp

1173K 1373K 1573K

Conclusions

17

Two approaches based on collaborative experimental and numerical investigations were used to propose a scale-bridging model for coal devolatilization

A new yield model for coal devolatilization is proposed and validated using consistency analysis (B2B)

A Gaussian Process (GP) allows evaluating the credibility of the different model-forms for the coal volatile yield

CFD simulations carried out using the novel ultimate yield model show good agreement between predicted and experimental conversion for all cases

800 tests (devol., char ox., …) of 270 fuels (coals, biomasses, chars, blends) on the IFRF IPFR made available in PrIMe

Acknowledgments

18

This research was sponsored by the Department of Energy, National Nuclear Security Administration, under Award Number(s) DE-NA0002375

The support of the Chemistry of Smart Energy Carriers and Technologies (SMARTCATS), Chemistry and Molecular Sciences and Technologies COST Action CM1404, is acknowledged

Workshop Data Collection and Mining toward Virtual Chemistry of Smart Energy Carriers | Napoli, April 5th-6th 2016

Thank you for your attention! Questions, comments?

Alessandro.Parente@ulb.ac.be