1 chemometric methods for environmental pollution monitoring dmitry e. bykov samara state technical...
TRANSCRIPT
1
ChemometriChemometri
c Methods c Methods
for for
EnvironmentEnvironment
al Pollution al Pollution
MonitoringMonitoring
Dmitry E. Bykov
Samara State Technical University Samara, Russia
2
OutlinesOutlines
I. Introduction
II. Wastes recovering
III. Wastes conversion
IV. Wastes cancellation
V. Wastes management
VI. Landfills management
VII. Conclusions
3
The Goals The Goals
This lecture has two main objectives:
• To give information about our R & D
activities;
• To get your advices how to apply
chemometrics
4
Samara is a large industrial citySamara is a large industrial city
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Samara State Technical University SSTU
17 000 Students
Since 1914
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SSTU StructureSSTU Structure
SSTU
Research & Analysis
Center of
Industrial Ecology
FacultyFaculty
FacultyFaculty
FacultyFaculty
FacultyFaculty
FacultyFaculty
FacultyFaculty
FacultyFaculty
Faculty of
Chemical
Technology
InstituteInstitute
DepartmentDepartment
DepartmentDepartment
DepartmentDepartment
DepartmentDepartment
DepartmentDepartment
DepartmentDepartment
Department of
Industrial
Ecology
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Department of Industrial EcologyDepartment of Industrial Ecology
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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Reengineering of out-of-date technologies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
Department Research ActivitiesDepartment Research Activities
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Development activitiesDevelopment activities
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Public ActivitiesPublic Activities
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Research & Analysis Center ofIndustrial Ecology (RACIE)
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RACIERACIE ActivitiesActivities
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
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II. Wastes RecoveringII. Wastes Recovering
The goals are purification and regeneration
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Sleeper plant sewage purification
Waste emulsion regeneration
Copper contaminated sorbent
regeneration
Used enamel regeneration
Hydrolyzed salomass regeneration
High foul blowoff sewage purification
Sleeper plant sewage purification
High foul blowoff sewage purification
Tasks solvedTasks solved
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Sleeper plant sewage purificationSleeper plant sewage purification
Sleeper plants sewage water contains up to 10% of tars.
To purify it extraction with xylene is applied.
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Equilibrium in the water/tar/xylol Equilibrium in the water/tar/xylol systemsystem
Tar concentration in water, kg/m3
Extraction tie-line10
30
50
4.03.02.01.0
70
90
Pseudoequilibrium area
Tar
co
nc
entr
atio
n i
n x
ylen
e, k
g/m
3
Suspended matterconcentration
100 mg/l 300 mg/l 500 mg/l
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Tar extraction Tar extraction
Sewage water 100%
91%
9%
Tar
Water
Xylene
Sewage+Xylene 100%
91.2%
8.1%
0.7%
Emulsion 9%
79.1%
19.7%
1.2%
Extract 7%
2.4%
88.7%
8.8%
Refined water 84%
99.96%
0.02% 0.02%
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High foul blowoff sewage High foul blowoff sewage purificationpurification
Boiler blowoff
Purified water
Sludge
WaterCold reuse
water
K-2 & PAA H2SO4
Intake tank
Pump
Reactor
Acid storage volume
T
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Process parameters (Input)Process parameters (Input)
T – Temperature
Ph – Acidity
PAA – Flocculant (polyacrylamid) concentration
K-2 – Coagulant concentration
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Purified water quality (Output)Purified water quality (Output)
D – Optical density
Al – Concentration of aluminium ions Al3+
Fe – Concentration of ferric compounds
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Conventional univariate approach - Conventional univariate approach - II
0
1
2
3
4
5
6
7
8
5 6 7 8 9 10 11
Ph
Al
D
Fe
Output parameters versus acidity.
Other input parameters are constants
T = 20°C
[K-2] = 50 mg/l
[PAA] = 2 mg/l
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Conventional univariate approach - Conventional univariate approach - IIII
Output parameters versus temperature.
Other input parameters are constants
pH = 6
[K-2] = 40 mg/l
[PAA] = 2 mg/l0.0
0.1
0.2
0.3
0.4
0.5
30 35 40 45 50 55 60
T , °C
Al
D
Fe
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Conventional univariate approach - Conventional univariate approach - IIIIII
Output parameters versus PAA concentration.
Other input parameters are constants
T = 20°C
pH = 6
[K-2] = 40 mg/l0
0.1
0.2
0.3
0.4
1 2 3 4 5
PAA, mg/l
Al
D
Fe
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Conventional univariate approach - Conventional univariate approach - IVIV
Output parameters versus K-2 concentration.
Other input parameters are constants
T = 20°C
pH = 6
[PAA] = 2 mg/l0
0.1
0.2
0.3
0.4
0.5
0.6
20 25 30 35 40 45 50
K-2, mg/l
Al
D
Fe
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Optimal process setupOptimal process setup
Temperature T=35°C
Acidity Ph= 6
PAA concentration [PAA]=2 mg/l
K-2 concentration [K-2]= 40 mg/l
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Chemometrics related problemChemometrics related problem
Would MSPC approach be useful there?
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PLS2 ModelPLS2 Model
K2
pH
PAA
T
AlD
Fe
-0.8
-0.4
0.0
0.4
0.8
-0.5 0.0 0.5 1.0
PC1
PC2Loadings Plot
Inputparameters
Output parameters
T , pH, PAA, K-2
Fe , D, Al
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Predicted optical densityPredicted optical density
R2 = 0.96
0.0
0.2
0.4
0.0 0.2 0.4Measured D
Pre
dic
ted
D
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Predicted concentration of Predicted concentration of aluminium ions aluminium ions
R2 = 0.54
0.0
0.3
0.6
0.0 0.3 0.6Measured Al
Pre
dic
ted
Al
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Predicted concentration of ferric Predicted concentration of ferric compounds compounds
R2 = 0.93
0.0
0.4
0.8
1.2
0.0 0.4 0.8 1.2
Measured Fe
Pre
dic
ted
Fe
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III. Wastes conversionIII. Wastes conversion
The goal is utilization
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Tasks solvedTasks solved
Soap stock utilization
Conversion of plastic-insulated cable scraps
1,2-dichlorpropane processing
Polychlorethanes processing
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Soap stock utilizationSoap stock utilization
Soap stock is a waste of oils and fats refining
This is a valuable product, which should
utilized
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Conventional method of utilizationConventional method of utilization
H2SO4
Oil refining
Fat refining
Stock gathering
Soap stock
Soap stock
Deoxidation
Mixed soap stock
Fat separation
Laundry soap
Mixture of saturated and unsaturated fatty acids, neutral fat
Waste is utilized into not valuable soap
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Soap stock compositionSoap stock composition
Water 74%
Neutral fat5%
Unsaturated fatty acids
solts1%
Saturated fatty acids
solts19%
Catalyst residiums
1%
Water85%
Neutral fat2%
Unsaturated fatty acids
solts10%
Saturated fatty acids
solts1%
Cellulose, slime1%
Phosphatide1%
Vegetable oil production wastes
Fat production wastes
Stock composition is different for oil and fat
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Oil production wastes utilizationOil production wastes utilization
Waste is utilized into valuable dry oil
Oil refining
Soap stock
DeoxidationFat
separation
Etherificationpolymerization
oxidization
Mixture of saturated fatty
acids and neutral fat
Desiccant GlycerinО2
Compounding
Oxidized oil
Dry oil
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Fat production wastes utilizationFat production wastes utilization
Waste is utilized into valuable products
Soap stock
Hydro-genation
Fat separation
Mixture of saturated
fatty acids and
neutral fat
Neutralization
Calcium stearate
Fat production
Commercialstearin
СаО
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Chemometrics related problemChemometrics related problem
Will MSPC approach be useful in this case?
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IV. Wastes cancellationIV. Wastes cancellation
The goal is wastes annihilation
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Tasks solvedTasks solved
Oil polluted lands reclamation
Sewage sludge utilization
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Oil polluted lands reclamationOil 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!
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Oil polluted lands reclamationOil polluted lands reclamation
Mixture
Oil polluted soil Activated sludge
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Oil conversionOil conversion
ES is enzyme-substrate complex E is enzyme (catalase)
S is substrate (oil) Р is oil decomposition product
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 50 100 150 200
Time, day
Oil
co
nv
ers
ion
S0=1
S0=2
S0=3
S0=5
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Chemometrics related problemChemometrics related problem
The problem looks similar to biofuel
production.
Will this similarity be helpful?
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More on lands reclamationMore 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
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V. Wastes managementV. Wastes management
The goal is collection and sorting
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Wastes sourcesWastes sources
Municipal10%
Others12%
Agriculture11%Transport
3%
Industry64%
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Wastes distribution within Wastes distribution within industryindustry
Metal works23%
Food6%Energy
4%
Construction8%
Metallurgy8%
Fuel9%
Textile3%
Chemistry39%
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Domestic refuse composition Domestic refuse composition
0
5
10
15
20
25
30
35
40
Paper foodwaste
Wood Ferrousmetals
Non-ferrousmetals
Textile Bones Glass Leather& rubber
Stones Plastics Smallparts
Others
1994 1997 2000 2002
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Domestic refuse break up
Total (100 %) Collected (83 %) Disposed (76 %)Recycled (7 %)
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Waste collection system in Samara Waste collection system in Samara
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Wastes traverser stationWastes traverser station
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Polymer wastes compositionPolymer wastes composition
Polymer wastes weight portion is 10 %
Polymer wastes cost portion is 60 %
EPS6%
Rubber10%
Other5%
PE10% PVC
4%
PET40%
PS4%
PP8%
PAN5%
EU8%
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Chemometrics related problemChemometrics related problem
How to automate the wastes sorting?
Will NIR spectroscopy be helpful there?
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More on waste sorting and More on waste sorting and recyclingrecycling
Nataliya RyuminaSamara State Technical University, Samara, Russia
Sorting of polymers according to the typesby the method of near infrared spectroscopy
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VI. Landfills managementVI. Landfills management
The goal is ecological risk assessment
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Well-run landfill KinelWell-run landfill Kinel
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Illegal dump BezenchukIllegal dump Bezenchuk
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How to estimate a landfill state?How to estimate a landfill state?
measured evaluated
ash content
age
density peculiarities
temperature
depth
humidity
pH
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Prediction of maturity (age) Prediction of maturity (age)
Scores
-4
-2
0
2
4
-4 -2 0 2 4
PC1
PC2
Bezenchuk 2, X-exp: 55%, 29% Y-exp: 82%, 4%
X- and Y-loadings
Lens
Depth
Humidity
+28C
Maturity
Ash
Weight
-8C
-0.6
-0.4
-0.2
0
0.2
-0.5 -0.25 0 0.25 0.5
PC1
PC2
Bezenchuk 2, X-exp: 55%, 29% Y-exp: 82%, 4%
Root Mean Square Error
0.07
0.075
0.08
0.085
0.09
1 2 3 4 5PCs
RMSE
RMSEC
RMSEP
Bezenchuk 2, Variable c.Maturity v.Maturity
0.3
0.6
0.9
1.2
0.4 0.6 0.8 1 1.2Measured Y
Predicted Y
Bezenchuk 2, (Y-var, PC): (Maturity, 2)
Elements: 123Correlation: 0.9250RMCEP: 0.0775
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PCA-based classificationPCA-based classification
Scores
-2
-1
0
1
-4 -2 0 2 4
PC1
PC2
Kinel 1, X-exp: 93%, 5%
Ash
Scores
-2
-1
0
1
-4 -2 0 2 4
PC1
PC2
Kinel 1, X-exp: 93%, 5%
Weight
Scores
-2
-1
0
1
-4 -2 0 2 4
PC1
PC2
Kinel 1, X-exp: 93%, 5%
Depth
Scores
-2
-1
0
1
-4 -2 0 2 4
PC1
PC2
Kinel 1, X-exp: 93%, 5%
Temperature
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Chemometrics related problemChemometrics related problem
How to perform sampling on landfills?
Will sampling theory be helpful there?
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2 3
4
5
6
7
8
9 11
12
13
14
15
16
1819
20
21
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More on landfill state evaluationMore on landfill state evaluationOlga Tupicina Samara State Technical University , Samara, Russia
Chemometrics-based evaluation of man-caused formations’ stability
Evgeniy MichailovSamara State Technical University , Samara, Russia
Ecological assessment of waste fields with multivariate analysis - feasibility study
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VII. ConclusionsVII. 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!