Chemometric
Methods for
Environmental
Pollution
Monitoring
Dmitry E. Bykov
Samara State Technical University
Samara, Russia
1
Outlines
I.
Introduction
II. Wastes recovering
III. Wastes conversion
IV. Wastes cancellation
V.
Wastes management
VI. Landfills management
VII. Conclusions
2
The Goals
This lecture has two main objectives:
• To give information about our R & D activities;
• To get your advices how to apply chemometrics
3
Samara is a large industrial city
4
Samara State Technical University SSTU
Since 1914
17 000 Students
5
SSTU Structure
SSTU
Faculty
Faculty
Faculty
Faculty
Faculty
Faculty
Faculty
Faculty
Faculty
Faculty
Faculty
Faculty
Faculty
Faculty
Faculty of
Chemical
Institute
Institute
Technology
Department
Department
Department
Department
Department
Department
Department
Department
Department
Department
Department
Department
Department of
Research & Analysis
Industrial
Center of
Ecology
Industrial Ecology
6
Department of Industrial Ecology
7
Department Research Activities
Design the processes and equipment
• for waste treatment
• industrial sewage cleaning
Reengineering of out-of-date technologies
Ecological auditing and improvement of
ecological management in industry
8
Development activities
9
Public Activities
10
Research & Analysis Center of
Industrial Ecology (RACIE)
11
RACIE Activities
Chemical analysis of topsoil, wastes, sewage,
and ground water
Development of standards that regulate the
pressure on the environment by human activities
Designing the up-to-date landfills for industrial
and domestic wastes
12
II. Wastes Recovering
The goals are purification and regeneration
13
Tasks solved
Sleeper plant sewage purification
Sleeper plant sewage purification
Waste emulsion regeneration
High foul blowoff sewage purification
Copper contaminated sorbent regeneration
Used enamel regeneration
Hydrolyzed salomass regeneration
High foul blowoff sewage purification
14
Sleeper plant sewage purification
Sleeper plants sewage water contains up to 10% of tars.
To purify it extraction with xylene is applied.
15
Equilibrium in the water/tar/xylol system
Suspended matter
concentration
Tar concentration in xylene, kg/m3
100 mg/l 300 mg/l 500 mg/l
90
70
Pseudoequilibrium
area
50
30
10
1.0
2.0
3.0
4.0
Tar concentration in water, kg/m3
16
Tar extraction
Emulsion 9%
Extract 7%
79.1%
Sewage+Xylene
Sewage water 100%
100%
8.8%
19.7%
2.4%
88.7%
1.2%
91.2%
91%
Water
Refined water 84%
0.02%
0.02%
9%
8.1%
0.7%
Xylene
99.96%
Tar
17
High foul blowoff sewage purification
T
Reactor
K-2 & PAA
H2SO4
Acid storage
volume
Water
Cold reuse
water
Sludge
Intake tank
Purified
water
Pump
Boiler
blowoff
18
Process parameters (Input)
T
–
Temperature
 Ph
–
Acidity
 PAA – Flocculant (polyacrylamid) concentration
 K-2
– Coagulant concentration
19
Purified water quality (Output)
 D
–
Optical density
 Al
–
Concentration of aluminium ions Al3+
 Fe – Concentration of ferric compounds
20
Conventional univariate approach - I
8
Output
parameters
versus acidity.
Al
7
D
6
Fe
5
3
Other input
parameters
are constants
2
T = 20°C
1
[K-2] = 50 mg/l
4
Ph
0
5
6
7
8
9
10
[PAA] = 2 mg/l
11
21
Conventional univariate approach - II
0.5
Output
parameters
versus
temperature.
Al
D
Fe
0.4
0.3
0.2
Other input
parameters
are constants
0.1
pH = 6
T , °C
[K-2] = 40 mg/l
0.0
30
35
40
45
50
55
60
[PAA] = 2 mg/l
22
Conventional univariate approach - III
Output
parameters
versus PAA
concentration.
0.4
Al
0.3
D
Fe
Other input
parameters
are constants
0.2
0.1
T = 20°C
PAA, mg/l
0
1
2
3
4
5
pH = 6
[K-2] = 40 mg/l
23
Conventional univariate approach - IV
0.6
Output
parameters
versus K-2
concentration.
Al
0.5
D
Fe
0.4
Other input
parameters
are constants
0.3
0.2
T = 20°C
0.1
K-2, mg/l
pH = 6
0
20
25
30
35
40
45
50
[PAA] = 2 mg/l
24
Optimal process setup
 Temperature
T=35°C
 Acidity
Ph= 6
 PAA concentration
[PAA]=2 mg/l
 K-2 concentration
[K-2]= 40 mg/l
25
Chemometrics related problem
Would MSPC approach be useful
there?
26
PLS2 Model
Loadings Plot
0.8
PC2
T
Fe
D
Al
0.4
pH
PAA
Input
parameters
0.0
T , pH, PAA, K-2
-0.4
K2
Output
parameters
PC1
-0.8
-0.5
0.0
0.5
1.0
Fe ,
D,
Al
27
Predicted optical density
0.4
Predicted D
R2 = 0.96
0.2
0.0
0.0
0.2
0.4
Measured D
28
Predicted concentration of aluminium ions
0.6
Predicted Al
R2 = 0.54
0.3
0.0
0.0
0.3
Measured Al
0.6
29
Predicted concentration of ferric compounds
Predicted Fe
1.2
R2 = 0.93
0.8
0.4
0.0
0.0
0.4
0.8
1.2
Measured Fe
30
III. Wastes conversion
The goal is utilization
31
Tasks solved
Soap stock utilization
Conversion of plastic-insulated cable scraps
1,2-dichlorpropane processing
Polychlorethanes processing
32
Soap stock utilization
Soap stock is a waste of oils and fats refining
This is a valuable product, which should utilized
33
Conventional method of utilization
H2SO4
Oil
refining
Soap stock
Stock
gathering
Fat
refining
Mixed
soap
stock
Deoxidation
Fat
separation
Soap stock
Laundry
soap
Mixture of
saturated and
unsaturated
fatty acids,
neutral fat
Waste is utilized into not valuable soap
34
Soap stock composition
Vegetable oil
production wastes
Fat production wastes
Water
74%
Neutral fat
5%
Unsaturated
fatty acids
solts
1%
Water
85%
Saturated
fatty acids
solts
19%
Catalyst
residiums
1%
Cellulose
slime
1%
Neutral fat
2%
Unsaturated
fatty acids
solts
10%
Saturated
fatty acids
solts
1%
Phosphatide
1%
Stock composition is different for oil and fat
35
Oil production wastes utilization
Oil
refining
Soap
stock
Deoxidation
Desiccant
Glycerin
О2
Oxidized
oil
Compounding
Fat
separation
Mixture of
saturated fatty
acids and
neutral fat
Etherification
polymerization
oxidization
Dry
oil
Waste is utilized into valuable dry oil
36
Fat production wastes utilization
Soap
stock
Fat
production
Hydrogenation
Са
О
Neutralization
Fat
separation
Mixture of
saturated
fatty acids
and
neutral fat
Commercial
stearin
Calcium
stearate
Waste is utilized into valuable products
37
Chemometrics related problem
Will MSPC approach be useful in this case?
38
IV. Wastes cancellation
The goal is wastes annihilation
39
Tasks solved
Oil polluted lands reclamation
Sewage sludge utilization
40
Oil polluted lands reclamation
We have:
Oil polluted lands that should be reclamated
A lot of activated sludge that should be utilized
Let’s mix them up!
41
Oil polluted lands reclamation
Activated sludge
Oil polluted soil
Mixture
42
Oil conversion
100%
90%
S0=5
Oil conversion
80%
70%
60%
S0=3
50%
40%
S0=2
30%
S0=1
20%
10%
0%
0
50
100
150
200
Time, day
ES is enzyme-substrate complex
E is enzyme (catalase)
S
Р is oil decomposition product
is substrate (oil)
43
Chemometrics related problem
The problem looks similar to biofuel production.
Will this similarity be helpful?
44
More on lands reclamation
Konstantin Chertes
Samara State Technical University ,
Samara, Russia
Possibilities of application
of multidimensional data
analysis methods to
substantiate directions of
degraded land
recultivation
45
V. Wastes management
The goal is collection and sorting
46
Wastes sources
Industry
64%
Others
12%
Municipal
10%
Transport
3%
Agriculture
11%
47
Wastes distribution within industry
Chemistry
39%
Construction
8%
Fuel
9%
Food
6%
Energy
4% Metallurgy
8%
Textile
3%
Metal works
23%
48
Domestic refuse composition
40
1994
1997
2000
2002
35
30
25
20
15
10
5
0
Paper
food
waste
Wood
Ferrous Nonmetals ferrous
metals
Textile
Bones
Glass
Leather Stones
& rubber
Plastics
Small
parts
Others
49
Domestic refuse break up
Total (100 %)
Collected (83 %)
Disposed (76 %) Recycled (7 %)
50
Waste collection system in Samara
51
Wastes traverser station
52
Polymer wastes composition
Other
5%
Rubber
10%
PE
10%
PVC
4%
EPS
6%
EU
8%
PAN
5%
PET
40%
PP
8%
PS
4%
Polymer wastes weight portion is 10 %
Polymer wastes cost portion is 60 %
53
Chemometrics related problem
How to automate the wastes sorting?
Will NIR spectroscopy be helpful there?
54
More on waste sorting and recycling
Nataliya Ryumina
Samara State Technical
University, Samara, Russia
Sorting of polymers
according to the types
by the method of near
infrared spectroscopy
55
VI. Landfills management
The goal is ecological risk assessment
56
Well-run landfill Kinel
57
Illegal dump Bezenchuk
58
How to estimate a landfill state?
measured
evaluated
ash content
age
density
peculiarities
temperature
depth
humidity
pH
59
Prediction of maturity (age)
X- a n d Y -l o a d in g s
S c ores
0.2
4
PC2
PC2
M a t u ri ty
0
2
A sh
0
-8 C
-0 . 2
W eight
De pth
H u m i d i ty
-0 . 4
-2
PC1
-4
-4
-2
0
2
4
-0 . 5
B e z e n c h u k 2 , X-e x p : 5 5 % , 2 9 % Y -e x p : 8 2 % , 4 %
RM SE
-0 .2 5
PC1
Le ns
+ 28 C
-0 . 6
0
0.25
0.5
B e z e n c h u k 2 , X-e x p : 5 5 % , 2 9 % Y -e x p : 8 2 % , 4 %
P re d i c t e d Y
R oo t M ea n S qu are E rror
0.09
1.2
E le m e n t s : 1 2 3
RMSEC
C o rre la ti o n : 0 . 9 2 5 0
RMSEP
0 .0 8 5
R M CE P : 0.07 75
0.9
0.08
0.6
0 .0 7 5
0.07
0.3
1
2
3
B e z e n c h u k 2 , V a ria b l e c . M a t u rit y v. M a t u rit y
4
PCs
5
0.4
0 .6
0.8
B e z e n c h u k 2 , (Y -va r, P C ): (M a t u rit y , 2 )
1
1.2
M e a s u re d Y
60
PCA-based classification
S co re s
S co res
1
1
PC2
PC2
0
0
-1
-1
Ash
W e ig ht
P C1
-2
P C1
-2
-4
-2
0
2
4
-4
K in e l 1 , X-e x p : 9 3 % , 5 %
-2
0
2
4
K i n e l 1 , X-e x p : 9 3 % , 5 %
S co res
S co re s
1
1
PC2
PC2
0
0
-1
-1
T e m p e rature
D e p th
P C1
-2
P C1
-2
-4
-2
K in e l 1 , X-e x p : 9 3 % , 5 %
0
2
4
-4
-2
0
2
4
K i n e l 1 , X-e x p : 9 3 % , 5 %
61
Chemometrics related problem
How to perform sampling on landfills?
Will sampling theory be helpful there?
2
3
4
5
16
14
6
20
15
1
7
9
11
18
19
21
8
12
13
62
More on landfill state evaluation
Olga Tupicina
Samara State Technical University , Samara, Russia
Chemometrics-based evaluation of mancaused formations’ stability
Evgeniy Michailov
Samara State Technical University , Samara, Russia
Ecological assessment of waste fields
with multivariate analysis - feasibility
study
63
VII. Conclusions
Numerous cases that are of interest in ecology and
waste management have been presented
Our first chemometric experience inspire us to use it
more and more
We are entirely open for co-operation in ecological
chemometrics
It is great to see so many outstanding scientists here!
64