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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
EuroPACT 2017 - Rodrigo R. de Oliveira 2
Introduction
EuroPACT 2017 - Rodrigo R. de Oliveira 3
- 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
EuroPACT 2017 - Rodrigo R. de Oliveira 4
- 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
EuroPACT 2017 - Rodrigo R. de Oliveira 5
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
EuroPACT 2017 - Rodrigo R. de Oliveira 6
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
EuroPACT 2017 - Rodrigo R. de Oliveira
Thermocouple
Heater
NIRsource
Flowcell
FT-NIROpticalfibers
balance
PID
T[ºC]
Dataacquisition&controller
8
Automatic distillation device - Features
EuroPACT 2017 - Rodrigo R. de Oliveira
Thermocouple
Heater
NIRsource
Flowcell
FT-NIROpticalfibers
balance
PID
T[ºC]
Dataacquisition&controller
Continuous and synchronized measurements of temperature,%recovered distillate weight and NIR spectra.
9
Automatic distillation device - Features
EuroPACT 2017 - Rodrigo R. de Oliveira
Thermocouple
Heater
NIRsource
Flowcell
FT-NIROpticalfibers
balance
PID
T[ºC]
Dataacquisition&controller
Communication and control through PC software and storage ofdata in MATLAB format.
10
EuroPACT 2017 - Rodrigo R. de Oliveira 11
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
EuroPACT 2017 - Rodrigo R. de Oliveira 12
%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
EuroPACT 2017 - Rodrigo R. de Oliveira 13
Gasoline batches analyzed
EuroPACT 2017 - Rodrigo R. de Oliveira
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
EuroPACT 2017 - Rodrigo R. de Oliveira 15
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)
EuroPACT 2017 - Rodrigo R. de Oliveira
- 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.
EuroPACT 2017 - Rodrigo R. de Oliveira
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%
)
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%
)
on-spec. B01-09 (model)on-spec. B11 (test)off-spec. B13 (test)
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.
EuroPACT 2017 - Rodrigo R. de Oliveira 18
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
EuroPACT 2017 - Rodrigo R. de Oliveira
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
EuroPACT 2017 - Rodrigo R. de Oliveira 20
Batch process information
EuroPACT 2017 - Rodrigo R. de Oliveira 21
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
EuroPACT 2017 - Rodrigo R. de Oliveira 22
- 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
EuroPACT 2017 - Rodrigo R. de Oliveira 23
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
EuroPACT 2017 - Rodrigo R. de Oliveira 24
𝐗𝐍𝐎𝐂 = 𝐓𝐍𝐎𝐂 𝐏𝐍𝐎𝐂𝐓 + 𝐄𝐍𝐎𝐂
Building MSPC model
EuroPACT 2017 - Rodrigo R. de Oliveira
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
EuroPACT 2017 - Rodrigo R. de Oliveira
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
EuroPACT 2017 - Rodrigo R. de Oliveira
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
EuroPACT 2017 - Rodrigo R. de Oliveira 29
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
On-line MSPC strategies
EuroPACT 2017 - Rodrigo R. de Oliveira 30
(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
On-line MSPC strategies
EuroPACT 2017 - Rodrigo R. de Oliveira 31
(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 data structure
a) Individual Process Obs. Models
MSPC
On-line MSPC strategies
EuroPACT 2017 - Rodrigo R. de Oliveira 32
(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 data structure
…
…
b) Fixed Size Moving Window (FSMW) -MSPC Models
MSPC
On-line MSPC strategies
EuroPACT 2017 - Rodrigo R. de Oliveira 33
(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 data structure
…
…
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
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
35
- on-spec: Batch below control limits- Qstat.: Fault detected in Qstat. chart- Dstat.: Fault detected in Dstat. Chart
NIR data (only)
Prone to false alarms
* Number of false alarm observations
*
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
36
- on-spec: Batch below control limits- Qstat.: Fault detected in Qstat. chart- Dstat.: Fault detected in Dstat. Chart
NIR data (only)
Prone to false alarms
Low sensitivity for fault detection
* Number of false alarms observations
*
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
37
- on-spec: Batch below control limits- Qstat.: Fault detected in Qstat. chart- Dstat.: Fault detected in Dstat. Chart
NIR data (only)
Prone to false alarms
Satisfactory performance
Low sensitivity for fault detection
* Number of false alarms observations
*
EuroPACT 2017 - Rodrigo R. de Oliveira 38
NIR data (only)
Testing on-line batch MSPC strategies
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
EuroPACT 2017 - Rodrigo R. de Oliveira 39
Test Batch Method
Ind. Obs. FSMW Evolving
on-spec
B10 on-spec. on-spec. on-spec. B11 Qstat. (2) 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. , 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
Ind. Obs. FSMW Evolving
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
EuroPACT 2017 - Rodrigo R. de Oliveira 40
Testing on-line batch MSPC strategies – Data Fusion
EuroPACT 2017 - Rodrigo R. de Oliveira 41
Testing on-line batch MSPC strategies – Data Fusion
Conclusions
EuroPACT 2017 - Rodrigo R. de Oliveira 42
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.
EuroPACT 2017 - Rodrigo R. de Oliveira 43
Acknowledgements
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The research project receives funding from the EuropeanCommunity‘s Framework programme for Research andInnovation Horizon 2020 (2014-2020) under grant agreementnumber 637232