design and analysis of physical and computational experiments … · 2016. 3. 11. · design and...

Design and Analysis of Physical and Computational Experiments in

Chemical Engineering

Marco Reis

([email protected])

SAS® Fórum PORTUGAL 2015 Lisboa, 10 de Novembro de 2015

Outline

• I. Introduction

• II. Applications: JMP-SAS – Laboratory

– Industry

– Computer experiments

• III. Discussion & Conclusion

2

Introduction

3

3

Big Data

Data

Technology Analytics

Introduction

4

data + computation power = success ?

Applications

5

Laboratory Industry In silico

JMP-SAS in …

6

Laboratory

Laboratory: the “before” and the “after”…

“before”

• Time-consuming methods

• Affordable equipment

• Direct manipulation of samples, equipment and data

• Few data per experiment

• Few replicates

“after”

• Fast methods

• Expensive equipment

• Interaction through computer interfaces and electronics

• High volumes of data per experiment

• More replicates and samples can be processed

7

8


“Hyphenated instruments” (GC-MS,emission-excitation fluorescence,…)

Cromatograma (Vinho)

NIR

GC-MS

250 300 350 400 450 500 550 600 650 700 7500

20

40

60

Absorv

ance

250 300 350 400 450 500 550 600 650 700 750-10

-5

0

5

Absorv

ance

250 300 350 400 450 500 550 600 650 700 750-4

-2

0

2

Absorv

ance

Espectro UV-VIS


9

Univariate data Multivariate / Megavariate data

λ1 λ2 λ3 λ4 (…)


10


• “Data rich but information poor!” …

11

Design of

Experiments

(DOE)

Chemometrics: PCA, PLS, …

Visualization

Multivariate

Statistics

Applications: Laboratory

• Optimal operation of advanced analytical instrumentation

– Vicinal Diketones (VDK’s) are responsible for off-flavours in beer

– Optimize quantification of VDK’s:

• Dyacetil (DC)

• Pentanedione (PN)

– Head space solid-phase microextraction (HS-SPME)

– Gas Chromatography coupled with Mass Spectrometry detection (GC–MS)

12

GC-MS


• Current Practice

– Best guess & experience

– Empirical approaches

– Change-one-factor-at-a-time

• Approach followed

– D-Optimal Design of Experiments

– SAS-JMP®: Custom Design

13

Y Fβ ε

T

DMax F

F F

1

2ˆ TCov

F Fεβ

100

90

80

70

60


14

Preliminary screening

of factors and definition of experimental conditions

Optimal-DOE on selected factors

Processing of samples in randomized order

Data Analysis and model fitting

Validation and complete characterization of the

analytical procedure conducted at the optimal

factor levels

• Factors

15

Factor Qualitative/ Quantitative Levels

Type of Fiber Qualitative L1-DVB/PDMS, L2-Car/PDMS, L3-DVB/Car/PDMS

Sample volume Qualitative {5, 10}

Pre-incubation time (min) Quantitative [0,10]

Extraction time (min) Quantitative [5, 25]

Extraction temperature (°C) Quantitative [30, 50]

Agitation Qualitative {L1-Yes, L2-No}


16

Fib

er

co

ati

ng

(L

1)

Fib

er

co

ati

ng

(L

2)

Inc

ub

ati

on

tim

e (

t1),

min

Ex

tra

cti

on

te

mp

era

ture

, ºC

Ex

tra

cti

on

tim

e (

t2),

min

Ag

ita

tio

n

Sa

mp

le v

olu

me

Fib

er

co

ati

ng

[L

1]*

Inc

ub

ati

on

tim

e,m

in

Fib

er

co

ati

ng

[L

2]*

Inc

ub

ati

on

tim

e,m

in

Fib

er

co

ati

ng

[L

1]*

Ex

tra

cti

on

te

mp

era

ture

,ºC

Fib

er

co

ati

ng

[L

2]*

Ex

tra

cti

on

te

mp

era

ture

,ºC

Fib

er

co

ati

ng

[L

1]*

Ex

tra

cti

on

tim

e,

min

(1

)F

ibe

r c

oa

tin

g [

L2

]*E

xtr

ac

tio

n t

ime

, m

in (

2)

Fib

er

co

ati

ng

[L

1]*

Ag

ita

tio

n

Fib

er

co

ati

ng

[L

2]*

Ag

ita

tio

n

Fib

er

co

ati

ng

[L

1]*

Sa

mp

le v

olu

me

Fib

er

co

ati

ng

[L

2]*

Sa

mp

le v

olu

me

Inc

ub

ati

on

tim

e (

t1)*

Ex

tra

cti

on

te

mp

era

ture

Inc

ub

ati

on

tim

e (

t1)*

Ex

tra

cti

on

tim

e (

t2)

Inc

ub

ati

on

tim

e (

t1)*

Ag

ita

tio

n

Inc

ub

ati

on

tim

e (

t1)*

Sa

mp

leV

olu

me

Ex

tra

tio

n t

em

pe

ratu

re*

Ex

tra

cti

on

tim

eE

xtr

ati

on

te

mp

era

ture

*A

git

ati

on

Ex

tra

tio

n t

em

pe

ratu

re*

Sa

mp

le v

olu

me

Ex

tra

tio

n t

ime

(t2

)*A

git

ati

on

Ex

tra

tio

n t

ime

(t2

)*S

am

ple

vo

lum

e

Ag

ita

tio

n*

Sa

mp

le v

olu

me

• Preliminary analysis

of the design


• Results

17

Samples

volume

10 ml

5 ml

4500000

4000000

3500000

2500000

3000000

2000000

1500000

1000000

Scla

ed

Est

ima

te

500000

CarPDMS DVBCarPDMS DVBPDMS

Fiber Coating

Important few:

• Fiber coating

• Sample volume


• Profiler: optimal solution

18

500000040000003000000

20000001000000

0

10

.50

Som

a100ppb 4944465

[4361217,

552713]

Desi

rab

ilty

0.883729

L1

L2

L3

5m

l

10

ml

0 1 2 3 4 5 30

35

40

45

50 5 10

15

20

35

5 10

15

20

35

0

0.2

5

0.5

0.7

5 1

L2 5 ml 5 30 25

Fiber coating Sample Volume Incubation time

(t1), min

Extraction

temperature

ºC

Extraction time

(t2), min

Desirability


• Solution:

19

Factor Optimal

Level

Type of Fiber L2-Car/PDMS

Sample volume 5 ml

Pre-incubation time

(min) 5 min

Extraction time (min) 25 min

Extraction temperature

(°C) 30 ºC

Agitation L1 - Yes


• Validation

20

Parameter Diacetyl Pentanedione

Linear regression

(y=mx+b) 0.0026x + 0.0538

0,0057x - 0,0316

Linear concentration

range 10-300 μg L-1

10-200 μg L-1

R² 0.9999 0.9997

LOD (μg L-1) 0.9 3.3

LOQ (μg L-1) 2.8 10.0

Recovery %

LB + 50 μg L-1 SA 91 102

LB + 100 μg L-1 SA 97 99

LB + 200 μg L-1 SA 94 91


• Application

21

Sample Diacetyl Pentanedione

Cc (μg L−1) SD Cc (μg L−1) SD

LB1 n.d. --- n.q. ---

LB 2 40,7 2,4 17,0 0,6

LB 3 207,8 3,3 27,7 0,2

LB 4 294,9 21,4 30,6 3,5

LB 5 34,5 0,7 20,6 1,0

LB 6 83,8 3,2 23,8 0,1

LB 7 61,6 3,6 17,6 0,6

LB 8 n.d. --- 17,8 0,7

LB 9 6,5 1,6 17,8 0,2

LB 10 n.d. --- 15,4 0,7

LB 11 34,7 0,6 52,9 3,0

LB 12 74,9 4,0 76,4 3,0

LB 13 118,7 1,4 141,0 3,9

LB 14 n.d. --- 20,4 1,0

LB 15 n.d. --- 18,3 0,4

LB 16 n.d. --- 18,6 0,1

LB – lager beer; Cc – Concentration; SD - standard deviation; n.q. - not quantified; n.d. – not detected

Conclusions

“Before” “After”

22

Conclusions

• Planning

– MSA, R&R

– Design of experiments:

• Factorial, Fractional Factorial

• Plackett-Burman

• Mixture designs

• D-optimal, A-optimal, …

• Response surface

• Split-plot designs

• Tagushi robust designs

• Super-saturated designs

(Definitive Screening Designs)

23

JMP-SAS in …

24

Industry

Applications: Industry

• Identify the most influential variables for the partition of phenolic byproducts in a binary mixture at equilibrium

• Mixture

– Organic phase (org): MNB

– Acid phase (ac): H2SO4

– Sub-products to analyze: 2,4-DNF, TNF

25


26


• Approach: First Principles / Mechanistic

– 2+ PhD’s

– European project: 5.6+ M€

– Several Industry-University projects

… reaction network still unknown!...

27


• Sequential Data-Driven Approach

– Establish a research question

– Gather knowledge

– Design experiment

– Analyze data

– Act on the results

28


• Repeatability & Reproducibility (R&R) study

29


• Design of experiments

– Randomized complete factorial

– No replicates

30


• Experiments

31


• Analysis

32

2,4-DNF TNF

Efeitos mais significativos

Temperatura do equilíbrio não

exerce um efeito significativo na

resposta .


• Modeling

33


• Conclusions

– Concentrations of sub-products in the acid phase depend on just two parameters

– Processes provide you with the answers you need, if you ask them politely!

– Do the right questions = design the right experiments

34 George E.P. Box

JMP-SAS in …

35

In silico

I

J

K

XIxJxK I

R + P

Quantitative and

Qualitative Variables Sensor data Spectra

Images

Quality parameters

+ U(P)

ig Data

atch Processes B

36

Scope & Motivation

• Batch processes

– Widely used in industry (high added-value specialties, but also commodities)

• Semiconductor (~s, min)

• Chemical and Petrochemical (~hr)

• Pharmaceutical (~days)

• Food & Drinks (~hr, weeks, years)

• (…)

– Flexible (multipurpose, many degrees of freedom for intervention, scalable to different production ranges)

37

Scope & Motivation

• Many Batch Process Monitoring (BPM) methods and variants have been proposed: – 2-Way

• Batch-Wise unfolding (Nomikos & MacGregor, 1994, 1995)

• Variable-Wise unfolding (Wold et al., 1987, 1998)

– 3-Way • PARAFAC (Bro, 1997; Westerhuis et al., 1999)

• TUCKER3 (Geladi, 1989; Louwerse & Smilde, 2000)

– Dynamic • ARPCA (Choi et al., 2008)

• BDPCA (Chen & Liu, 2002)

– Hierarchical (Rännar, MacGregor & Wold, 1998)

– Local, Evolving (Ramaker et al., 2005)

– Kernel methods (Lee, J.-M. et al., 2004; Jia et al., 2010)

– Multiscale (Rato et al., 2015)

– (…)

39

Proposed Methodology

• Number of independently generated simulated testing scenarios

5×3×50 + 3×6×50 = 750+900 =1650

Proposed Methodology

• Methods and variants – Synchronization

• None, Indicator variable (IV), dynamic time warping (DTW)

– Modelling • 2-way – Batch-wise unfolding

– Infilling : zero deviation, current deviation, projection (missing data)

– Window size (Q statistics): 1, 3, 5

• 3-way – PARAFAC, Tucker3 – Infilling : zero deviation, current deviation, projection (missing

data)

• Dynamic – ARPCA, BDPCA – ARPCA: without (1) /with (2) normalization

– BDPCA: without (1) /with (2) normalization and using DPCA-DR (3)

60 different methods / versions

Analysis of Results

(…) DYN3W2W

90

80

70

60

50

40

30

20

Models

Sco

re

NONE

IV

DTW

Sync

Multi-Vari Chart for Score by Sync - Models (SEMIEX)

Conclusions

• Conclusions

– The results are case-dependent to a great extent

– More consistent methods:

• 2-Way (Synchronization NONE or DTW / Window 1 or 3)

• Dynamic: ARPCA (ORIG or NORM)

– Synchronization

• IV tends to present a comparatively worse performance

– Infilling

• 2-Way: MD

• 3-Way: CD

44

data + computation power + science = success

[science = domain knowledge + scientific method]

Conclusions & Discussion

O futuro …

45

http://www.eq.uc.pt/~marco/research/pclab/

SAS-JMP, Jos van der Velden, Volker Kraft

Ana Cristina Pereira

José Carlos Marques

Ana Leonor

Cristina Gaudêncio

Tiago Rato

Ricardo Rendall

Veronique Medeiros

Acknowledgements

http://www.enbis.org/

European Network for

Business and Industrial

Statistics

design and analysis of physical and computational experiments … · 2016. 3. 11. · design and...

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