On-line and data fusion strategies for NIR-based industrial batch...

Department of Chemical Engineering and Analytical Chemistry

Faculty of Chemistry, University of Barcelona, Spain

Rodrigo R. de Oliveira and Dr. Anna de Juan

May 2017, Potsdam, Germany

On-line and data fusion strategies for NIR-based

industrial batch process monitoring and control

▪ Introduction

•Process description and related data

•Process modelling

▪ Data fusion strategies.

▪ On-line process control strategies

Outline

EuroPACT 2017 - Rodrigo R. de Oliveira 1

Introduction


Introduction


- Modern industrial processes requires monitoring of many process parameters.

- Univariate and multivariate sensors can be used to control product composition and physical properties.

Univariate sensor (e.g. temperature, viscosity)

Multivariate sensor (e.g. NIR, Raman)

Introduction


- Modern industrial processes requires monitoring of many process parameters.

- Univariate and multivariate sensors can be used to control product composition and physical properties.

Univariate sensor (e.g. temperature, viscosity)

Multivariate sensor (e.g. NIR, Raman)T

- Multivariate sensor data can be arranged in matrix- Univariate data, in a vector- To perform integral process control, data fusion is needed

λ

Pro

cess

ob

s. 1

K

Single batch process data

How to perform integral process control???

+

Process description


Gasoline quality control

• Gasoline (Mixture of HC’s from petroleum refinery and used vehiclefuel)

• Brazilian regulation for gasoline (ANP)• Type C gasoline (Gasoline + 27% ethanol)

• Adulteration with excess of ethanol, solvents, etc.

• Quality control based on gasoline distillation


Gasoline quality control

• Systematic and synchronized observations of distillation temperature readings and related volumes of condensate.

EuroPACT 2017 - Rodrigo R. de Oliveira

Classical distillation method (ASTM D86)

%recovered [%(v/v)]0 20 40 60 80

tem

pe

ratu

re [

oC

]

50

100

150

200

max. 65 oC

max. 80 oC

max. 190 oC

Type C gasoline - Distillation Curve

Dis

tilla

tio

n t

em

pera

ture

[ºC

]

7

▫ specification points

Distillation curves provides insufficient information on gasoline composition for quality control.

Near-Infrared (NIR) spectroscopy may be coupled to this method.

Automatic distillation device - Design


Thermocouple

Heater

NIRsource

Flowcell

FT-NIROpticalfibers

balance

PID

T[ºC]

Dataacquisition&controller

8

Automatic distillation device - Features


Thermocouple

Heater

NIRsource

Flowcell

FT-NIROpticalfibers

balance

PID

T[ºC]


Continuous and synchronized measurements of temperature,%recovered distillate weight and NIR spectra.

9

Automatic distillation device - Features


Thermocouple

Heater

NIRsource

Flowcell

FT-NIROpticalfibers

balance

PID

T[ºC]


Communication and control through PC software and storage ofdata in MATLAB format.

10


Synchronized measurements of boiling temperatures,%recovered distillate weight and NIR spectra.

10080

%Recovered [wt%]

6040

2001000

1500

wavelength [nm]

2000

0

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

NIR

sig

nal [a

.u.]

%recovered [wt%]0 20 40 60 80

tem

pera

ture

[oC

]

50

100

150

200Distillation Curve

Distillation curve (Temperatures) Distillation process NIR data

Batch distillation process - Output


%recovered [wt%]0 20 40 60 80

tem

pe

ratu

re [

oC

]

50

100

150

200

58.6 oC

75.1 oC

140.9 oC

184 oC

Distillation Curve of batch B07

wavelength [nm]1200 1400 1600 1800 2000 2200

sig

nal

[a.u

.]

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

10%

50%

70%

90%

raw NIR spectra at 4 observation points of batch B07

O-H O-HC-HC-H C-H C-HAromatic

compounds

Systematic variation of boiling temperatures and NIR spectra with process evolution

HC’s/Ethanol azeotropes

Light HC’s

Visual interpretation of process data

Process modelling


Gasoline batches analyzed


23 synthetic gasoline batches (type C)(Pure gasoline + Ethanol)

11 on-specification

12 off-specification

All batches were distilledcollecting distillation curvesand related NIR spectra

14

Process data preprocessing


NIR spectra preprocessing:(1) Baseline corrections using Savitzky-Golay derivative.(2) Signal intensity fluctuations by spectral normalization.

Raw-data Preprocessed NIR data

Distillation process modeling – PCA

Principal Component Analysis (PCA)


- Reduce original data dimensionality.- Score plots allow displaying process trajectories.

16

X (I x J)NIR spectra

=

scores

loadingsE (I x J)+

T (I x A)

PT (A x J)

A PC’s

A P

C’s

𝜆 𝜆

ob

serv

atio

ns

(%re

cove

red

)

ob

serv

atio

ns

𝜆

Compressed abstract representationof process evolution

0.80.6

PC1 (84.64%)

0.40.2

0-0.2

-0.4-0.2PC2 (13.11%)

0

0.2

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.4

PC

3 (

1.2

3%

)

5 wt%

90 wt%

Distillation process modeling – PCA

Score plot of distillation batches.


Trajectories shapes indicates on-spec and off-spec. batches

17

Batch B11 on-spec (27% ethanol)

0.80.6

PC1 (84.64%)

0.40.2

0-0.2

-0.4-0.2PC2 (13.11%)

0

0.2

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.4

PC

3 (

1.2

3%

)

on-spec. B01-09 (model)on-spec. B11 (test)off-spec. B13 (test)

5 wt%

90 wt%

0.80.6

PC1 (84.64%)

0.40.2

0-0.2

-0.4-0.2PC2 (13.11%)

0

0.2

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.4

PC

3 (

1.2

3%

)


5 wt%

90 wt%

0.80.6

PC1 (84.64%)

0.40.2

0-0.2

-0.4-0.2PC2 (13.11%)

0

0.2

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.4

PC

3 (

1.2

3%

)


5 wt%

90 wt%

Batches B01-09 on-spec (27% ethanol)

Batch B13 off-spec (15% ethanol)

scoresT (I x A)

A PC’s

ob

serv

atio

ns

X = T PT + E

Distillation process modeling – MCR-ALS

Multivariate Curve Resolution – Alternating Least Squares- Multiwavelength extension of the Lambert-Beer’s law.

- Pure component with chemical meaning and easier to interpret than PCA.


X (I x J)NIR spectra

= E (I x J)+

C (I x NC)

ST (A x J)

NC

NC

𝜆 𝜆

ob

serv

atio

ns

(%re

cove

red

)

ob

serv

atio

ns

𝜆

Distillation profiles with chemicalmeaningful representation ofprocess evolution

ST -> pure spectral signatures of distilled fractions


Distillation process modeling – MCR-ALS

Evolution of C profiles indicates on-and off-specification batches

Pure spectral signatures ST indicatesqualitative characterization ofgasoline distilled fraction.

wavelength [nm]1200 1400 1600 1800 2000 2200

sig

nal [a

.u.]

-0.2

-0.1

0

0.1

0.2

0.3

%recovered [wt%]0 10 20 30 40 50 60 70 80 90

Rela

tive c

on

cen

trati

on

0

0.2

0.4

0.6

0.8

1

%recovered [wt%]0 10 20 30 40 50 60 70 80 90

Rela

tive c

on

cen

tra

tio

n

0

0.2

0.4

0.6

0.8

1off-spec. batch B13 (15% ethanol)

light HC’sethanolmid HC’sheavy HC’s

ST (pure spectral signatures)

C-H O-H

C-H. Aromatics

19

X = C ST + E

C (I x NC)

NC

%re

cove

red

% distilled wt.

Wavelength [nm]

% distilled wt.

C (distillation process profiles) on-spec.

Data fusion strategies


Batch process information


λ

%re

cove

red

T

Distillation curve (T)

%re

cove

red

C (I x NC)

NC

%re

cove

red

PCA-scores

T (I x A)

A PC’s

NIR information

%re

cove

red

MCR C-profiles

Data fusion incorporates different kinds of information.

Data fusion


- Raw sensor outputs are concatenated in a single data structure.

- Sensor information are unbalanced.%

reco

vere

d

T NIR/C-profiles

%re

cove

red

T NIR/PCA-scores

- Relevant features and univariate sensors are concatenated in a single data structure.

- Sensor information are balanced.

λT

%re

cove

red

Single batch data

Single batch dataSingle batch data

Mid-Level data fusion

Low-Level data fusion

On-line process controlMSPC strategies


Multivariate Statistical Process Control - MSPC

• MSPC process control uses multivariate measurements from temperature readings, spectra or data fusion of both sensors.

• MSPC are based on PCA models built with multivariate process information from normal operating conditions (NOC) batches.

• New batches are tested on these models to see whether they are on- or off-specification


𝐗𝐍𝐎𝐂 = 𝐓𝐍𝐎𝐂 𝐏𝐍𝐎𝐂𝐓 + 𝐄𝐍𝐎𝐂

Building MSPC model


variablesN

OC

bat

ches

PCA model

Dstat. Chart Qstat. Chart

(Variation within model) (Variation outside the model)

MSPC chart control limits

On-spec.

Dstat. Chart

DCL

Qstat. Chart

QCL

25

Var

iab

le 3

02

46

2

4

6

6

0

2

4

8

First PC

Second PC

NOC boundaries

on-spec

off-spec

on-spec

off-spec

XNOC

Batch 1

Batch 2Batch 3

…

Batch I

Testing new batches


𝐄𝐍𝐄𝐖 = 𝐗𝐍𝐄𝐖 − 𝐓𝐍𝐄𝐖𝐏𝐍𝐎𝐂𝐓

𝐷𝑠𝑡𝑎𝑡. = 𝐭TΘ−1𝐭

XNEW

𝑄𝑠𝑡𝑎𝑡. = 𝒆𝑖𝑇𝒆𝑖

New batches variables areprojected on to the model toextract Dstat. and Qstat. MSPCparameters.

𝐓𝐍𝐄𝐖 = 𝐗𝐍𝐄𝐖 𝐏𝐍𝐎𝐂

Qstat. Chart

QCL

Dstat. Chart

DCL

26

Projection on MSPC model

MSPC charts (New Batches)

Var

iab

le 3

02

46

2

4

6

6

0

2

4

8Large Qstat.

First PC

Second PC

NOC boundaries

Large Dstat.

MSPC - Model construction and testing

Gasoline distillation batches


Modelbuilding

Modeltesting

NO

C b

atches

On-spec

27

On-spec

Off-spec

Input data to build on-line MSPC models

On-line MSPC strategies were tested using:

NIR data (only)Preprocessed NIR data was the input to build and test the on-line MSPC

Mid-Level data fusionDistillation curves (temperature readings) were fused with scores from PCA or C-profiles from MCR-ALS and used a the input to build and test the on-line MSPC

%re

cove

red

T NIR/C-profiles

%re

cove

red

T NIR/PCA-scores

λ

%re

cove

red

Single batch data

Single batch data


On-line MSPC strategies

- On-line MSPC may be based on NIR data (only), or distillation curve/NIR info fused data.

- Input data from NOC batches are rearranged into suitable matrix to build on-line MSPC models.

- MSPC can be used to assess each new observation in a process.Batch (1)

NO

C b

atch

es

1

…

K

1 J

Batch (2)

…

Batch (I)

2 x K

I x K Input data:- NIR data only- T/NIR(PCA-scores)- T/NIR(MCR C-profiles)

Obs. (1) Obs.(2) … Obs.(w) Obs.(w+1) Obs.(w+2) …... Obs.(K)

NO

C b

atch

es

1

…

I

1 J 2 J (w-1)J w J (w+1)J (w+2)J (K-1)J K J

Variables



(T1 , P1) (T2 , P2) … (Tw , Pw) (Tw+1 , Pw+1) (Tw+2 , Pw+2) …... (TK , PK)a)

Obs (1) Obs.(2) … Obs.(w) Obs.(w+1) Obs.(w+2) …... Obs.(K)

bat

ch N

OC

1

…

I

1 J 2 J (w-1)J w J (w+1)J (w+2)J (K-1)J K J

(Tw+2 , Pw+2)(Tw+1 , Pw+1)

(Tw , Pw)…

(T2 , P2)(T1 , P1)

c)

…

(Tw+2 , Pw+2)(Tw+1 , Pw+1)

(Tw , Pw)b)

…

PCA-based MSPC models

Process evolution





bat

ch N

OC

1

…

I

1 J 2 J (w-1)J w J (w+1)J (w+2)J (K-1)J K J

(Tw+2 , Pw+2)(Tw+1 , Pw+1)

(Tw , Pw)…

(T2 , P2)(T1 , P1)

c)

…

(Tw+2 , Pw+2)(Tw+1 , Pw+1)

(Tw , Pw)b)

…


Process data structure

a) Individual Process Obs. Models

MSPC





bat

ch N

OC

1

…

I

1 J 2 J (w-1)J w J (w+1)J (w+2)J (K-1)J K J

(Tw+2 , Pw+2)(Tw+1 , Pw+1)

(Tw , Pw)…

(T2 , P2)(T1 , P1)

c)

…

(Tw+2 , Pw+2)(Tw+1 , Pw+1)

(Tw , Pw)b)

…



…

…

b) Fixed Size Moving Window (FSMW) -MSPC Models

MSPC





bat

ch N

OC

1

…

I

1 J 2 J (w-1)J w J (w+1)J (w+2)J (K-1)J K J

(Tw+2 , Pw+2)(Tw+1 , Pw+1)

(Tw , Pw)…

(T2 , P2)(T1 , P1)

c)

…

(Tw+2 , Pw+2)(Tw+1 , Pw+1)

(Tw , Pw)b)

…



…

…

c) Evolving MSPC Models

MSPC

Test Batch Method

Ind. Obs. FSMW Evolving

on-spec

B10 on-spec. on-spec. on-spec. B11 Qstat. (9) on-spec. on-spec.

off-spec

B12 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B13 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B14 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B15 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B16 Qstat., Dstat. Qstat., Dstat. Qstat. , Dstat. B17 Qstat., Dstat. Qstat., Dstat. Qstat. B18 Qstat., Dstat. Qstat., Dstat. Qstat. B19 Qstat., Dstat. Qstat., Dstat. Qstat. B20 Qstat., Dstat. Qstat., Dstat. Qstat. B21 Qstat., Dstat. Qstat., Dstat. Qstat. B22 Qstat., Dstat. Qstat., Dstat. Qstat. B23 Qstat., Dstat. Qstat., Dstat. Qstat.

Testing on-line batch MSPC strategies

34

- on-spec: Batch below control limits- Qstat.: Fault detected in Qstat. chart- Dstat.: Fault detected in Dstat. Chart

NIR data (only)

Test Batch Method


on-spec


off-spec



35


NIR data (only)

Prone to false alarms

* Number of false alarm observations

*

Test Batch Method


on-spec


off-spec



36


NIR data (only)


Low sensitivity for fault detection

* Number of false alarms observations

*

Test Batch Method


on-spec


off-spec



37


NIR data (only)


Satisfactory performance

Low sensitivity for fault detection

* Number of false alarms observations

*


NIR data (only)


Individual Observation MSPC

✓ Qstat charts prone to show false alarms (insufficient information is considered)

FSMW - MSPC

✓ Sensitive Dstat. charts (less weight of past NOC observations in models)

✓ No false alarms on Qstat. charts

Evolving MSPC

✓ Less sensitive for Dstat chart (high weight of past NOC observations in models)

-·- B11 (on-spec)-·- B21 (off-spec)

Dstat. MSPC chart Qstat. MSPC chart


Test Batch Method


on-spec


off-spec

B12 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B13 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B14 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B15 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B16 Qstat., Dstat. Qstat., Dstat. Qstat. , Dstat. B17 Qstat., Dstat. Qstat., Dstat. Qstat. B18 Qstat., Dstat. Qstat., Dstat. Qstat. B19 Qstat., Dstat. Qstat., Dstat. Qstat. , Dstat B20 Qstat., Dstat. Qstat., Dstat. Qstat. B21 Qstat., Dstat. Qstat., Dstat. Qstat. , Dstat. B22 Qstat., Dstat. Qstat., Dstat. Qstat. , Dstat. B23 Qstat., Dstat. Qstat., Dstat. Qstat. , Dstat.

Test Batch Method


on-spec

B10 Qstat. (1) on-spec. on-spec. B11 Qstat. (1), Dstat. (1) on-spec. on-spec.

off-spec

B12 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B13 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B14 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B15 Qstat., Dstat. Qstat., Dstat. Qstat., Dstat. B16 Qstat., Dstat. Qstat., Dstat. Qstat. , Dstat. B17 Qstat., Dstat. Qstat., Dstat. Qstat. B18 Qstat., Dstat. Qstat., Dstat. Qstat. B19 Qstat., Dstat. Qstat., Dstat. Qstat. B20 Qstat., Dstat. Qstat., Dstat. Qstat. B21 Qstat., Dstat. Qstat., Dstat. Qstat. , Dstat. B22 Qstat., Dstat. Qstat., Dstat. Qstat. , Dstat. B23 Qstat., Dstat. Qstat., Dstat. Qstat. , Dstat.

Temperature/NIR (PCA-scores) Temperature/NIR (MCR C-profiles)

- The best on-line MSPC strategy is FSMW-based method.- Data fusion increases generally the correct detection of off-spec batches

Testing on-line batch MSPC strategies – Data Fusion

Conclusions


Conclusions

• PCA and MCR-ALS process modeling tools were useful to understandprocess evolution from a global point of view and component pointof view giving a qualitative description of gasoline composition andits distillation process evolution.

• NIR data information (compressed as PCA scores or C MCR-ALSprofiles) and Temperature from distillation curves were used as inputfor data fusion strategies.

• The best on-line batch MSPC strategy was obtained by using modelsbased on moving windows including a limited number of past processobservations (FSMW).

• Data fusion strategies improved on-line MSPC performance based onsole NIR information.


Acknowledgements

44

The research project receives funding from the EuropeanCommunity‘s Framework programme for Research andInnovation Horizon 2020 (2014-2020) under grant agreementnumber 637232

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