exciting real-time analytical techniques? imaging and ...1502764/attachment… · 10. wood 6 wood...

Applications of hyperspectral

imaging and machine learning

methods for real-time

classification of waste stream

components

Martin Sevcik1

Jan Skvaril1

Elena Tomas Aparicio2

1Future Energy Center, Mälardalen University, Västerås, Sweden2Mälarenergi AB, Västerås, Sweden

17th September 201919th International Council of Near Spectroscopy Conference, Gold Coast, Australia

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https://www.mdh.se/utbildning/livslangtla

rande/futuree/applied-spectroscopy-for-

future-energy-and-environmental-

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2

Introduction

● Combined heat and power production

● Municipal solid waste → Refuse-derived fuel (RDF)

● Fuel is highly variable: → Process instabilities

● Increase formation emissions

● Lower efficiency

CONTROL

SYSTEM

Steamto the turbine

Flue gasfor further treatment

Ashused bed material

Steam boilerCirculating

fluidized bed

Refuse derived fuelComposition

Moisture content,

Ash content,

Heating value,

Other properties

Paper and carbord

28%

Plastics24%

Organic matter24%

Others9%

Textiles9%

Cellulose6%

3

Introduction

CONTROL

SYSTEM

Steamto the turbine




fluidized bedSingle-point

NIR sensor

Real-time measurement

of fuel properties


Moisture content,

Ash content,

Heating value,

Other properties

● Previously installed single-point NIR sensor → Provide good conditions for feed-forward control

● Appropriate operational measures

4

CONTROL

SYSTEM

Steamto the turbine




fluidized bedSingle-point

NIR sensor

Automatic

pre-sorting?

Hyperspectral

imaging NIR

sensor?

Real-time identification

of components and

measurement of fuel

properties


Moisture content,

Ash content,

Heating value,

Other properties

← Further upstream

Introduction

● New interest in potential material pre-sorting

Spectroscopy workflow

5

Samples

(training and

internal validation)

Samples

(external

validation set)

NIR-HSI

acquisition

Hypercube

unfolding

and spectra

extraction

Data pre-treatment

(various methods)

Machine learning

classification

algoritms

PLS-DA, SVM,

RBNN

1000 pixels

training set

1000 pixels internal

validation set

Model

Identification of waste

material components by

reference method

NIR-HSI

acquisition

Hypercube

unfolding

and spectra

extraction

Data pre-treatment

(various methods)

Prediction on

external

validation data set

(whole image)

False colour

images

Model

performance

evaluation

Internal

validation set

SENSITIVITY

1 - SPECIFICITY

Overall

accuracy

Model

performance

evaluation

external

validation set

SENSITIVITY

1 - SPECIFICITY

Overall

accuracyPrediction

on internal

validation

data set

Computational

time

Selected pre-

treatment

Data pre-treatment

(various methods)

Training set - 1000 pixels

per material category

Internal validation set – 1000

pixels per material category

Sampling and sample praparation

CategoryNumber of

sample objectsExamples of the samples

1. Paper 22

Carboard boxes, paper tissues, napkins, printing

paper, notebooks, recycled paper, newspapers,

magazines

2. Incombustibles 12 Ceramics, cups, stones, glass bottles, metals

3. Food 7 Dried fruits, pasta, rice

4. PE 12HDPE – shampoo bottle, bottle caps

LDPE – plastic bags, food packages

5. PET 4 Plastic bottles, food containers

6. PP 5 Food containers, straws

7. PS 5 Expanded foam, plastic cutlery

8. PVC 2 Tubes

9. Textile 4 Old clothes, curtains

10. Wood 6 Wood chips

11. Background - Carbon black styrene-butadiene rubber

● Sampling from the RDF and other sources…

6

PET

HDPE

Textile

PS

PP PVC

Paper LDPE

Glass Paper hygienic

CeramicsCardboard

Wood Print recycled

Print white Food

Metal

● Samples identified by FT-IR Spectrometer in ATR mode, followed by library search:

● Common materials

● Polymers, Polymer Additives & Plasticizers

● NIR-HSI Spectral data push-broom camera

● 900 - 1700 nm, InGaAs detector,

● 224 spectral bands, 640 spatial pixels

● Framerate 300 fps, 5 ms exposure time

● Approx. 1200 frames per image

● Two linear halogen light sources

● Laboratory scanning table 20 cm x 40 cm

● 90 mm/s, translational movement

Scanner table

translational

movement

Sensing area

width approx.

18 cm

Hyperspectral

imaging camera

(900 – 1700 nm)

Illumination

source 2

Illumination

source 1

NIR-HSI DATA

ACQUISTION

Samples

Data acquisition

7

ATR

Mid IR spectra

Data processing and chemometrics

8

● Decomposition of the hyperspectral cube, calculations, pre-processing and

modelling were done using scripts programmed in MATLAB

● Removing bands based on signal-to-noise ratio → 192 bands

● The training data set and internal validation set included different randomly

selected data points (i.e. pixels), 1000 per material category

● Relative absorbance - converted to relative absorbance based on raw reflectance

image from the sample, dark reference image and white reference image.

● Spectral data pre-processing

● Mean centering (MC),

● Standard normal variate (SNV),

● Savitzky-Golay smoothening (SGS, 2nd order polynomial, 11 smoothing points)

● Savitzky-Golay 1st derivative (SG1, 2nd order polynomial, 11 smoothing points)

● Savitzky-Golay 2nd derivative (SG2, 2nd order polynomial, 11 smoothing points)

Chemometrics and model evaluation

9

● Classification algorithms used:

● Partial least-squares discriminant analysis (PLS-DA),

● Support vector machine (SVM),

● Radial-basis neural network (RBNN),

● Model performance evaluation:

● Sensitivity (true positive rate)

● 1- specificity (false positive rate)

● Parameters are based on whether prediction is a true positive (TP), true

negative (TN), false positive (FP), or false negative (FN).

● Calculated for each class separately. Overall classification accuracy

of the model is then expressed as a mean value of sensitivities for all classes.

Predicted class

A B

Tru

e c

las

s A

TP

(true

positive)

FN

(false

negative)

B

FP

(false

positive)

TN

(true

negative)

10

Results and discussion

11


● FT-IR analyses reveals that samples in each group contained various amounts of plasticizers and

other additives making the identification challenging.

● Data pre-processing has significant impact on resulting overall accuracy of classification models.

Pre-processing method PLS-DA SVM RBNN

No pre-processing 64% - 90.6%

SGS 64.3% - 90.4%

SGS + mean centering 74.7% 92.4% 89.9%

SGS + SNV 88% 92.6% 89.6%

SGS + 1st derivative 85.1% 93.8% 90.7%

SGS + 1st derivative + mean centering 85.8% 94% 90.3%

SGS + 1st derivative + SNV 83.2% 91.5% 88.3%

SGS + 2nd derivative 84.4% 91.1% 88.8%

SGS + 2nd derivative + mean centering 84.9% 91.5% 88.6%

SGS + 2nd derivative + SNV 82.9% 87.2% 81.8%

4 best pre-treatments

used in prediction on

external validation set

12


Class

PLS-DA SVM RBNN

Sensitivity 1 - SpecificityMost confused

withSensitivity 1 - Specificity

Most confused

withSensitivity 1 - Specificity

Most confused

with

Backgr. 88.3% 4.0% incombustibles 97.8% 2.2% - 98.9% 4.0% -

Paper 74.3% 0.3% textile 97.5% 0.5% - 95.0% 1.3% -

Incomb. 60.9% 2.5% background 78.7% 3.0% background 64.4% 1.8% background

Food 98.4% 1.6% - 98.7% 0.0% - 96.4% 0.0% -

PE 89.5% 0.2% - 86.0% 0.2% incombustibles 85.3% 0.1% -

PET 97.3% 0.1% - 94.5% 0.0% - 91.6% 0.0% background

PP 90.4% 0.5% incombustibles 95.1% 0.1% - 92.2% 0.2% -

PS 73.7% 0.4% incombustibles 85.0% 1.0% incombustibles 86.0% 2.6% background

PVC 99.2% 0.1% - 97.9% 0.0% - 95.2% 0.0% -

Textile 97.8% 2.1% - 97.8% 0.1% - 94.7% 0.3% -

Wood 98.2% 1.3% - 98.2% 0.1% - 97.9% 0.0% -

● Detailed classification results for best pre-processing for each of the classification methods -

internal validation set

External validation

Wood

13


● Prediction of classes by PLS-DA model (external validation set)

Class

PLS-DA


with

Backgr. 86.0% 7.1% -

Paper 78.7% 4.2% textile

Incomb. 34.8% 1.6% paper

Food 98.0% 0.2% -

PE 75.6% 0.3% background

PET 66.0% 0.1% background

PP 88.5% 0.3% background

PS 91.9% 3.5% -

PVC 74.9% 0.1% background

Textile 97.7% 5.3% -

Wood 98.2% 1.5% -

Pre-processing method AccuracyClassification

time

SGS + SNV 77.3% 1.9 s

SGS + 1st derivative 81% 2.1 s

SGS + 1st derivative + MC 80.9% 1.9 s

SGS + 1st derivative + SNV 75.7% 1.9 s

Background

Paper

Incombustibles

Food

PE

PET

PP

PS

PVC

Textile

PET

HDPE

Textile

PS

PP PVC

Paper LDPE


CeramicsCardboard

Wood Print recycled

Print white Food

Metal

14


● Prediction of classes by SVM model (external validation set)

Class

SVM


with

Backgr. 28.7% 0.8% PS

Paper 98.6% 2.5% -

Incomb. 78.9% 8.8% PS

Food 99.0% 0.2% -

PE 86.4% 1.6% incombustibles

PET 80.1% 0.2% PS

PP 93.7% 0.6% -

PS 94.5% 17.6% -

PVC 86.1% 0.2% incombustibles

Textile 92.9% 0.3% paper

Wood 97.5% 0.2% -

PET

HDPE

Textile

PS

PP PVC

Paper LDPE


CeramicsCardboard

Wood Print recycled

Print white Food

Metal

Background

Paper

Incombustibles

Food

Wood

PE

PET

PP

PS

PVC

Textile

Pre-processing method Accuracy Classification time

SGS + SNV 83.7% 228 s

SGS + 1st derivative 84.9% 219.8 s


SGS + 1st derivative + SNV 83% 223 s

15


PET

HDPE

Textile

PS

PP PVC

Paper LDPE


CeramicsCardboard

Wood Print recycled

Print white Food

Metal

● Prediction of classes by RBNN model (external validation set)

Class

RBNN


with

Backgr. 79.0% 4.5% PS

Paper 95.0% 3.0% -

Incomb. 58.3% 3.0% PS

Food 96.4% 0.1% -

PE 82.5% 0.3% incombustibles

PET 77.9% 0.3% background

PP 89.1% 0.2% background

PS 97.1% 7.8% -

PVC 77.3% 0.1% background

Textile 82.4% 0.4% paper

Wood 95.4% 0.1% -

Background

Paper

Incombustibles

Food

Wood

PE

PET

PP

PS

PVC

Textile

Pre-processing method Accuracy Classification time

SGS + SNV 75.4% 14.9 s

SGS + 1st derivative 83.5% 17.7 s


SGS + 1st derivative + SNV 84.5% 28.5 s

16

0,10

0,08

0,06

0,04

0,02

0

-0,02

-0,04

-0,06

-0,08

0,12

PE

PET

PP

PS

PVC

1000 1100 1200 1300 1400 1500 1600

Wavelengths (nm)

Reg

ress

ion

coef

ficie

nts

(-)

0,04

0,02

0

-0,02

-0,04

-0,06

Background

Paper

Incombustibles

Food

Textile

Wood

Most influential bands for classification

● Regression coefficients in PLS-DA model

● Pre-processing: SGS + SNV

● Most influential range for synthetic organic

polymers:

● 1100 -1250 nm

● 1350 -1450 nm.

● Most influential regions for classification of

food, textile, paper and wood:

● 1450 -1490 nm.

Reg

ress

ion

coef

ficie

nts

(-)

17

Conclusions

● NIR-HSI shows good potential for development of a classification model that can recognize most common

components of MSW→RDF.

● Models based machine learning methods can successfully classify NIR-HSI pixels belonging to textile,

wood, food, paper, and most common types of plastics with reasonable accuracy.

● Classification of materials such as metals, glass or ceramics i.e. not NIR-active materials is more challenging.

● SVM based model - accuracies of up to 94% for internal val., 85% for external val. and was the best in

identifying incombustible materials, but highly demanded on computational time.

● RBNN based model - accuracy 91% for internal val. and 85% for external val., 10 times faster than SVM

● PLS-DA based model - accuracy 88% for internal val. and 81% for external val., 100 times faster than SVM.

● Applicability of the developed models can be further enhanced by including new samples of varying composition

and new classes driven by a demand of which components need to be sorted.

Thank you

17th September 2019

19th International Council of Near

Spectroscopy Conference, Gold

Coast, Australia

Jan Skvaril

Email: [email protected]

Mobile: +46-73-6620977

Future Energy Center

Mälardalens University, Västerås

Sweden

Would you like to discover the inner

soul of the materials and processes

through exciting real-time analytical

techniques?

JOIN OUR NEW ONLINE

COURSE AT MÄLARDALEN

UNIVERSITY!

Applied Spectroscopy

for Future Energy and

Environmental SystemsLink:https://www.mdh.se/utbildning/livslangtlar

ande/futuree/applied-spectroscopy-for-

future-energy-and-environmental-systems-

1.118583?l=en_UK

Information

(www)

mailto:[email protected]




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