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

5

Samara State Technical University SSTU

17 000 Students

Since 1914

6

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

7

Department of Industrial EcologyDepartment of Industrial Ecology

8

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Reengineering of out-of-date technologies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

9

Development activitiesDevelopment activities

10

Public ActivitiesPublic Activities

11

Research & Analysis Center ofIndustrial Ecology (RACIE)

12

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

13

II. Wastes RecoveringII. Wastes Recovering

The goals are purification and regeneration

14

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

15

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.

16

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

17

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%

18

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

19

Process parameters (Input)Process parameters (Input)

T – Temperature

Ph – Acidity

PAA – Flocculant (polyacrylamid) concentration

K-2 – Coagulant concentration

20

Purified water quality (Output)Purified water quality (Output)

D – Optical density

Al – Concentration of aluminium ions Al3+

Fe – Concentration of ferric compounds

21

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

22

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

23

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

24

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

25

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

26

Chemometrics related problemChemometrics related problem

Would MSPC approach be useful there?

27

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

28

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

29

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

30

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

31

III. Wastes conversionIII. Wastes conversion

The goal is utilization

32

Tasks solvedTasks solved

Soap stock utilization

Conversion of plastic-insulated cable scraps

1,2-dichlorpropane processing

Polychlorethanes processing

33

Soap stock utilizationSoap stock utilization

Soap stock is a waste of oils and fats refining

This is a valuable product, which should

utilized

34

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

35

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

36

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

37

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

38

Chemometrics related problemChemometrics related problem

Will MSPC approach be useful in this case?

39

IV. Wastes cancellationIV. Wastes cancellation

The goal is wastes annihilation

40

Tasks solvedTasks solved

Oil polluted lands reclamation

Sewage sludge utilization

41

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!

42

Oil polluted lands reclamationOil polluted lands reclamation

Mixture

Oil polluted soil Activated sludge

43

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

44

Chemometrics related problemChemometrics related problem

The problem looks similar to biofuel

production.

Will this similarity be helpful?

45

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

46

V. Wastes managementV. Wastes management

The goal is collection and sorting

47

Wastes sourcesWastes sources

Municipal10%

Others12%

Agriculture11%Transport

3%

Industry64%

48

Wastes distribution within Wastes distribution within industryindustry

Metal works23%

Food6%Energy

4%

Construction8%

Metallurgy8%

Fuel9%

Textile3%

Chemistry39%

49

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

50

Domestic refuse break up

Total (100 %) Collected (83 %) Disposed (76 %)Recycled (7 %)

51

Waste collection system in Samara Waste collection system in Samara

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Wastes traverser stationWastes traverser station

53

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%

54

Chemometrics related problemChemometrics related problem

How to automate the wastes sorting?

Will NIR spectroscopy be helpful there?

55

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

56

VI. Landfills managementVI. Landfills management

The goal is ecological risk assessment

57

Well-run landfill KinelWell-run landfill Kinel

58

Illegal dump BezenchukIllegal dump Bezenchuk

59

How to estimate a landfill state?How to estimate a landfill state?

measured evaluated

ash content

age

density peculiarities

temperature

depth

humidity

pH

60

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

61

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

62

Chemometrics related problemChemometrics related problem

How to perform sampling on landfills?

Will sampling theory be helpful there?

1

2 3

4

5

6

7

8

9 11

12

13

14

15

16

1819

20

21

63

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

64

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!