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Transcript of M.N. Pons, S. Le Bonté, O. Potier Laboratoire des Sciences du Génie Chimique, CNRS-ENSIC-INPL,...
![Page 1: M.N. Pons, S. Le Bonté, O. Potier Laboratoire des Sciences du Génie Chimique, CNRS-ENSIC-INPL, Nancy Adaptive Principal Component Analysis for toxic event.](https://reader036.fdocuments.us/reader036/viewer/2022062714/56649d175503460f949ed848/html5/thumbnails/1.jpg)
M.N. Pons, S. Le Bonté, O. Potier
Laboratoire des Sciences du Génie Chimique, CNRS-ENSIC-INPL, Nancy
Adaptive Principal Component Analysis for toxic event detection
![Page 2: M.N. Pons, S. Le Bonté, O. Potier Laboratoire des Sciences du Génie Chimique, CNRS-ENSIC-INPL, Nancy Adaptive Principal Component Analysis for toxic event.](https://reader036.fdocuments.us/reader036/viewer/2022062714/56649d175503460f949ed848/html5/thumbnails/2.jpg)
Introduction
New regulations:
treatment in adequate facilities of all incoming waters
stricter limits on effluent quality, on sludge
Crisis:
rainstorm
accidental release of toxic components
some may be forecast (fire water)
other not
![Page 3: M.N. Pons, S. Le Bonté, O. Potier Laboratoire des Sciences du Génie Chimique, CNRS-ENSIC-INPL, Nancy Adaptive Principal Component Analysis for toxic event.](https://reader036.fdocuments.us/reader036/viewer/2022062714/56649d175503460f949ed848/html5/thumbnails/3.jpg)
A short selection of potential toxics
Heavy metals: Hg, Cr, Pb, Cd, Zn, Cu ...
Solvents: white spirit, ...
Pesticides
Herbicides
Motor fuels: diesel oil, ...
Detergents
Dyes
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Introduction
New regulations:
treatment in adequate facilities of all incoming waters
stricter limits on effluent quality
Crisis:
rainstorm
accidental release of toxic components
some may be forecast (fire water)
other not
Improvement of plant control strategy
New scenarios
![Page 5: M.N. Pons, S. Le Bonté, O. Potier Laboratoire des Sciences du Génie Chimique, CNRS-ENSIC-INPL, Nancy Adaptive Principal Component Analysis for toxic event.](https://reader036.fdocuments.us/reader036/viewer/2022062714/56649d175503460f949ed848/html5/thumbnails/5.jpg)
Introduction
Characterisation of wastewater composition COD, BOD5, SS NT, NH4
+, NO3-
PT, PO4-
K, Ca, Mg, ... Heavy metals (Cu, Zn, Cd, Hg, Cr, …) Micropolluants
Some are time-consuming Some are very specific
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Introduction
Global (and faster) measurements temperature, conductivity, pH, redox turbidity light absorbance
fixed wavelength spectra
respirometry buffer capacity ...
On-lineIn-line (sampling)
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Introduction
Three methods under test Respirometry Absorbance spectra Buffer capacity
Multivariate data analysis method
Validation on simulation
Experimental validation
Conclusions
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Respirometry test: experimental set-up
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Respirometry test
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Time (sec)
Dis
solv
ed o
xyg
en (
mg
/L)
-0,1
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
OU
Rex
(m
g/L
.min
)
DO probe
sludge + substrate
Typical response curves
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Characteristic parameters
OUR curve
4 parametersMaximal value of Oxygen Uptake Rate
Oxygen volume (VO2) (5 or 15min)
Peak width
Initial slope
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Experimental results
0123456789
0 500 1000 1500
temps (s)
[O2
]d (
mg
/l)
toxique
témoin
% de réduction de VO2 5 minutes
-20
0
20
40
60
80
0,00
20,
005
0,01
20,
024
0,23
50,
294
0,58
81,
176
1,17
61,
471
1,76
52,
353
2,94
13,
529
5,88
27,
353
7,35
38,
824
14,7
0629
,412
concentration en toxique en mg/l
% d
e ré
duct
ion
+ CuSO4
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Experimental results
+ dye
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2 respirometers in parallel
toxics added in one respirometer
VO2(mg/l)
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
5
7/6/ 4:00 7/6/ 8:00 7/6/ 12:00 7/6/ 16:00 7/6/ 20:00 8/6/ 0:00 8/6/ 4:00 8/6/ 8:00 8/6/ 12:00
témoin
avec toxique
CuSO4 NaOHHCl
White Spirit
javel
Gasoil
Experimental results
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1
1,5
2
2,5
3
3,5
4
4,5
5
07/06/07:00
07/06/11:00
07/06/15:00
07/06/19:00
07/06/23:00
08/06/03:00
08/06/07:00
08/06/11:00
VO
2 (
mg
/l)
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
Ab
so
rba
nc
e
VO2 (mg/l)
A254nm
Experimental results
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UV-visible spectrometry
0,00E+00
5,00E-01
1,00E+00
1,50E+00
2,00E+00
2,50E+00
3,00E+00
3,50E+00
200 250 300 350 400 450 500 550 600
Longueur d'onde (nm)
Ab
sorb
ance
220nm
254nm
270nm
546nm
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0
1
2
3
4
5
6
7
8
9
200 250 300 350 400
Wavelength (nm)
No
rm.
ab
s
urea
nitrate
creatinine
urine
210
nm
220
nm
254
nm
270
nm
UV-visible spectrometry
Anthropogenic substances
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0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
200 250 300 350 400
Wawelength (nm)
No
rm. a
bs
orb
an
ce
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
UV-visible spectrometry
210
nm
220
nm
254
nm
270
nm Detergents
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UV-visible spectrometry
0
0,5
1
1,5
2
2,5
3
3,5
200 300 400 500 600 700 800 900
longueur d'onde (nm)
Ab
sorb
ance
ABS (vert)
ABS(violet)
ABS(bleu)
ABS(jaune)
Dyes
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UV-visible spectrometry
Norm. Abs0
0.5
1
1.5
2
2.5
3
3.5
4
21/3
/01
0:00
21/3
/01
12:0
0
22/3
/01
0:00
22/3
/01
12:0
0
23/3
/01
0:00
23/3
/01
12:0
0
24/3
/01
0:00
24/3
/01
12:0
0
25/3
/01
0:00
25/3
/01
12:0
0
26/3
/01
0:00
26/3
/01
12:0
0
210
220
254
270
546
Wednesday Thursday Friday Saturday Sunday
Abs. Abs
0.000
0.100
0.200
0.300
0.400
0.500
21/3
/01
0:00
21/3
/01
12:0
0
22/3
/01
0:00
22/3
/01
12:0
0
23/3
/01
0:00
23/3
/01
12:0
0
24/3
/01
0:00
24/3
/01
12:0
0
25/3
/01
0:00
25/3
/01
12:0
0
26/3
/01
0:00
26/3
/01
12:0
0
254
Wednesday Thursday Friday Saturday Sunday
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Buffer capacity
Normally measured Wastewater pH Alkalinity
Here Acidification (pH 3) Titration to pH 11 Buffer capacity versus pH
pH = f(Vol.NaOH)
2
4
6
8
10
12
0 1 2 3 4
NaOH 0,1N (ml)
pH
Capacité tampon = f(pH)
0
0,2
0,4
0,6
0,8
1
3 5 7 9 11
pHC
apac
ité
tam
pon
(m
eq/
l/pH
)
dpHdCNaOH
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Buffer capacity
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
3 4 5 6 7 8 9 10 11 12
pH
cap
ac
ité
tam
po
n
(mé
q/l/
pH
)
0h
2h
4h
6h
8h
10h
12h
14h
16h
18h
20h
22h
Bu
ffer
cap
aci
ty
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Buffer capacity
Initial pH
7.17.2
7.37.47.57.6
7.77.87.9
25/2
0:0
0
25/2
12:
00
26/2
0:0
0
26/2
12:
00
27/2
0:0
0
27/2
12:
00
28/2
0:0
0
28/2
12:
00
1/3
0:00
1/3
12:0
0
2/3
0:00
2/3
12:0
0
3/3
0:00
3/3
12:0
0
4/3
0:00
4/3
12:0
0
pH=7.21 (phosphates)
0
0.02
0.04
0.06
0.08
0.1
0.12
25/2
0:0
0
25/2
12:
00
26/2
0:0
0
26/2
12:
00
27/2
0:0
0
27/2
12:
00
28/2
0:0
0
28/2
12:
00
1/3
0:00
1/3
12:0
0
2/3
0:00
2/3
12:0
0
3/3
0:00
3/3
12:0
0
4/3
0:00
4/3
12:0
0
Monday Tuesday W_day FridayThursday Sat_day Sunday Monday
pH=9.25 (ammonium)
0.2
0.30.4
0.5
0.60.7
0.8
0.91
1.1
25/2
0:0
0
25/2
12:
00
26/2
0:0
0
26/2
12:
00
27/2
0:0
0
27/2
12:
00
28/2
0:0
0
28/2
12:
00
1/3
0:00
1/3
12:0
0
2/3
0:00
2/3
12:0
0
3/3
0:00
3/3
12:0
0
4/3
0:00
4/3
12:0
0
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Fault detection background
Univariate SPC MultivariateSPC
Overload of data
PLS
Partial Least Squares
Projection to Latent Structures
PCA
Principal Component Analysis
Continuous process (steady state)
Kresta et al. (1991): fluidized bed and extractive distillation column
Batch and Fedbatch
Lennox et al. (1999): Fermentation processes
?? Wastewater treatment plant = continuous process but not at steady state
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Adaptive PCA
Diurnal cycle
1 sample / 30 min (48 samples / day) or / 1hr (24 samples / day)
4 Principal Variables (PVi) : Ourex max, Ourex T, Slope, Width ( 15 min)
In the case of 1 sample / 1 hr, the samples j to j+23 are used and 2 PCs are considered:
PC1 = 1PV1 + 1PV2 + 1PV3 + 1PV4
PC2 = 2PV1 + 2PV2 + 2PV3 + 2PV4
At sample j+24: prediction
PC1 (j+24) = 1PV1 (j) + 1PV2 (j) + 1PV3 (j) + 1PV4 (j)
PC2 (j+24) = 2PV1 (j) + 2PV2 (j) + 2PV3 (j) + 2PV4 (j)
At sample j+24: actual
PC ’1 (j+24) = 1PV1 (j+24) + 1PV2 (j+24) + 1PV3 (j+24) + 1PV4 (j+24)
PC ’2 (j+24) = 2PV1 (j+24) + 2PV2 (j+24) + 2PV3 (j+24) + 2PV4 (j+24)
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Adaptive PCA
Prediction error = Detection (Q statistic)
SPE = [PC1(j+24) - PC ’1(j+24)]2 + [PC2(j+24) - PC ’2(j+24)]2
Update of i, i, i, and i using samples j+1 to j+24
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Adaptive PCA
CP1 CP2
σ1, μ1
h
h+1
h+2
h+3
h+4
.
.
.
h+23
h+24
σ2, μ2
h+25
σ3, μ3 …etc
.
.
.
![Page 27: M.N. Pons, S. Le Bonté, O. Potier Laboratoire des Sciences du Génie Chimique, CNRS-ENSIC-INPL, Nancy Adaptive Principal Component Analysis for toxic event.](https://reader036.fdocuments.us/reader036/viewer/2022062714/56649d175503460f949ed848/html5/thumbnails/27.jpg)
Effect of slow change in plant state
PCA on 24 previous samples (1 sample/hr),
estimation of actual sample
0,00
5,00
10,00
15,00
20,00
25,00
70 80 90 100 110 120
Time (hr)
Ou
rmax
, W
idth
-1,0
-0,5
0,0
0,5
1,0
1,5
2,0
2,5
3,0
VO
2, I
nit
ial
slo
pe
VO2
Width
Initial slope
Ourmax
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3 4
0 a.m. d+1
1 a.m. d+1
2 a.m. d+1
12 a.m. d+1
1 a.m. d+2
f1
f2
night morning
noon
afternoon
evening
no toxic
toxic event
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Why simulating ?
Unsteady state
Many factors to examine:
Location of sludge sampling
Ratio sludge / raw water
Quality of detection in function of the toxic conc. and nature, release time and type
….
Experiments on the real plant should be carefully selected
« Experiments » on a simulated plant
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Plant layout
Incoming water to be tested
Secondary settlerExternal recycle
Aeration tank
Biomass sample
Biomass samplePrimary settler
Wastage flow
River
Biomass sample
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Concentration of toxic
Release profile
0 0.07 0.17 0.11 0.16 0.23
0.1 0.07 0.58 0.42 0.34 0.22
0.5 0.07 1.49 1.37 1. 0.25
Concentration
De t
e cti
o n
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Toxic release time
De t
e cti
o n
Release time
Release profile
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Toxic release time
0,0
0,1
0,2
0,3
0,4
0,5
0,6
24 48 72 96 120 144 168
Time (hr)
Ou
r m
ax
0
0,1
0,2
0,3
0,4
0,5
0,6
To
xic
Tuesday Wednesday Thursday Friday Saturday Sunday
Detection = 1.49 (0.07) Detection = 2.77 (0.17)
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Normal situation
-2
-1,5
-1
-0,5
0
0,5
1
1,5
2
-4 -2 0 2 4
f1
f2
2 p.m.
3 p.m.
4 a.m.
11 a.m.
Normal 24hr cycle:
dry weather
normal activity
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Normal situation
5 initial variables : OURend, OURmax/A254, VO2/A254, width et A254
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Critical situation: heavy metals
-2
-1
0
1
2
3
-4 -3 -2 -1 0 1 2 3 4
f1
f2
-2
-1
0
1
2
3
-4 -3 -2 -1 0 1 2 3 4
f1
f2
-2
-1
0
1
2
3
-4 -3 -2 -1 0 1 2 3 4
f1
f2
HgSO4
6 mg/l
30 mg/l
K2Cr2O7
6 mg/l
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Critical situation: diesel oil
-2
-1,5
-1
-0,5
0
0,5
1
1,5
2
2,5
-6 -4 -2 0 2 4
f1
f2
4 a.m .
8 a.m .
12 a.m .
5 p.m .
5 a.m .
9 a.m .
10 a.m .
Addition of various amounts
of diesel oil
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Critical situation: white spirit
-5
0
5
10
15
20
25
30
-4 -2 0 2 4 6
f1
f2
4 a.m.
8 a.m.
12 a.m.
5 p.m.
5 a.m.5 p.m.
11 a.m.
Addition of various amounts
of white spirit
very strong inhibition
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Buffer capacity
4 initial variables : pH, β(pH=4,75),β(pH=7,21), β(pH=9,25)
SPE = [PC1(h) - PC’1(h+24)]2 + [PC2(h) - PC’2(h+24)]2
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Buffer capacity
5-6 Nov.2001, 14h : Wastewater + citrate
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UV-visible spectrophotometry
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Conclusions
Global (and rapid) characterization of the composition of wastewaters
Absorbance spectra - Buffer capacity - Respirometry
+ Classical measurements (T, pH, rH, …)
+ flowrate + rainfall
Combined with statistical methods
Community activity (design, control, critical situation)We wish to thank
the Grand Nancy Council for its help GEMCEA, LCPC, NANCIE the students and colleagues
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Plant model
2D models for the primary settler (Stokes) and the final clarifier (Takacs et al.)
Reactors in series with backmixing = f(flowrate, aeration rate)
Basic control on sludge wastage
IAWQ ASM 1 + inhibition :
growth rate of heterotrophs and autotrophs
death rate
degradation of toxic
Influent description
COST 624 Benchmark
Functions describing the Nancy WWTP effluent
Respirometer model FORTRAN code on PC