Near infrared hyperspectral imagery for waste recycling ...isfr.unileoben.ac.at/fileadmin/shares/isfr/docs/Kerschhaggl.pdf · Near infrared hyperspectral imagery for waste recycling

www.evk.biz © EVK DI Kerschhaggl GmbH 2014

Matthias Kerschhaggl ISFR 2015, 08/09/15

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Near infrared hyperspectral imagery for waste recycling and real-time quantitative

inline analysis of raw materials


EVK Introduction

We at EVK believe that we can contribute to increasing reuse & recovery of earth resources and to improve working conditions in

waste recovery

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www.evk.biz © EVK DI Kerschhaggl GmbH 2014 [email protected]

Who we are

♦ Smart sensing solutions since 1987

♦ 2006: First HSI camera & classification system introduced to market

♦ Real-time classification and sorting

♦ Industry proven in recycling, mining, food processing and pharmaceutical applications

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What We Do

♦Simple start From pixel to sorting

♦Imaging & Sorting system with high speed, high resolution and control of ejection unit

♦Software package adapts the system to specific application

5 ©EVK DI Kerschhaggl GmbH 2015


Applications

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Metals from slag Household waste

C&D Recovered Paper Wood Sorting

Potato Sorting & Grading

E-Scrap

Nut Sorting


Technical Background Material Identification with HSI

stretching bending rocking

twisting wagging stretching

pict

ure:

Tia

go B

ecer

ra P

aolin

i

Absorption spectra according to the molecular composition

C-O-H-N Compounds (e.g. C10H8O4)


Vorführender

Präsentationsnotizen

C10H8O4 = PET Grundlage für Chemical Imaging ist die Interaktion von elektromagnetischen Wellen mit Materie. Moleküle werden hierbei durch einen thermischen Strahler zu unterschiedlichen Schwingungen angeregt und absorbieren an bestimmten Wellenlängen Energie. Aufgrund der reflektierten oder transmittierten Reststrahlung können Rückschlüsse auf die molekulare Zusammensetzung des betrachteten Objektes gezogen werden. Über geeignete Algorithmen können verschiedenste Materialien, wie Kunststoffe, Papier, Mineralien aber auch Lebensmittel hinsichtlich ihrer Inhaltstoffe (z.B. Zuckergehalt, Fettgehalt oder Proteingehalt bei Lebensmittel) klassifiziert werden.


HELIOS – Spectral Ranges


www.evk.biz © EVK DI Kerschhaggl GmbH 2014 [email protected]

Hyperspectral Imaging Technology

Camera internal classification and RGB visualization of chemical differences for real-time analysis

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Vorführender

Präsentationsnotizen

LINE SCAN technology


Plastic Sorting

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Plastic Sorting

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PE PET PS

Fingerprint: NIR absorption bands due to functional groups (CH,NH,OH) translate into different spectral signatures.


Plastic Sorting

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Principal Component Analysis

Partial Least Squares

Chemical Color Imaging: MVDA-projection of HSI data into RGB space


Plastic Sorting

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Principal Components

Principal Component Analysis

Partial Least Squares

wavelength wavelength

AU AU


Recycling

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paper cardboard

PE-LD


Mineral Sorting

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Calcite CaCO3 vs. Dolomite CaMg(CO3)2


HSI – Quantitative Analysis

wellness.byu.edu

Fig 2: PLS schematics (Wold et al. 2001)

Fernadez-Novales et al. (2009)

X Y Predictors (spectra)

Observations (reference analysis)

X Y

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Copper Ores Quantitatively

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~600 nm bandpass ~130 nm bandpass @ max correlation


Copper Ores Quantitatively

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~600 nm bandpass

~130 nm bandpass @ max correlation

Full Range ROI

R2 0.4 0.9

RMSECV [%] 0.2 0.1


Pharmaceutical Engineering



Pharmaceutical Engineering



Pharmaceuticals

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(left) First derivative normalized spectra of acetylsalicylic acid (red) and paracetamol (green) and (right) paracetamol blister (red) as well as spectra taken from the acetylsalicylic acid package as recorded with the HELIOS hyperspectral imaging camera.


Pharmaceuticals

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Pharmaceuticals Quantitatively

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R^2=0.98 RMSEC = 1.2 mg/cm2

36 [email protected]



R^2=0.98 RMSEC = 1.2 mg/cm2

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mg/cm2

[email protected]


Food Food sorting – Blueberries and stems


Food

Food sorting – Noodles and worms


Inline Dry Matter Measurements

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• Dry matter content traced by spectral signatures

• Inferred FIR-filter kernel

allows for concentration prediction


Inline Dry Matter Measurements

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HELIOS Camera

R = 0.9 RMS = 1 %


Dry Matter Food Analyzer

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• Integrated in sorting machine (INSORT Observer Food Analyzer)

• Valdidated model (R^2=0.9, RMS=5 %)


Dry Matter

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Dry Matter Statistics

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• DM „heat map“ • DM value histogramm • Mean DM • Standard Deviation of

product DM


Dry Matter Food Analyzer

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Validation run of the dry matter measurement using HELIOS with respect to gravimetric reference methods (CEM, HB43).


Accuracy vs. Precision

[email protected] 47

Plots taken from http://en.wikipedia.org

higher precision

higher accuracy

Laboratory precision completed with inline inspection of entire

production stream


Precision vs. Accuracy

• The intrinsic variance of the product is greater than the precision of the lab reference, so better have full sampling rather than super precision!

• „Better you measure all the fries all the time with reasonable precision rather than 1 out of 100.000 with ultra-high precision!“

• It is about large scale trends in the whole product stream not about laboratory measurements of unrepresentatively small quantities!

• A RMS of e.g. 1 % for constant monitoring of the product throughout the entire input stream is already way better than what you can do with a precision of 0.1 % every 30 min on a spatially confined sample!

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Courtesy W. Märzinger


Quantitative Analysis: Prospects for Recycling of Polymeric Materials

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• enable also quantitative measurements of scanned organic material flows (e.g. moisture content, woodchips vs contaminants, deinking vs non-deink vs paperless etc).

• in-line characterization of organic bulk material streams: - moisture and chlorine content - calorific value for waste to energy processing - sugar content in biodegradable MSW for subsequent bio-based chemical production.


Award

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International FoodTec Award

DLG (Deutsche Landwirtschafts-Gesellschaft

/ German Agricultural Society)


Conclusions

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♦Hyperspectral Imaging is an already well established technology for sorting solutions in the recycling, mining and food industries.

♦The inferred spectral information is suitable for doing quantitative analysis in various applications.

♦The potential to bring the laboratory into the line with unprecedented statistical power is enormous.


Acknowledgements

M. Jeindl

W. Märzinger

E. Leitner

S. Sacher

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Some References

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Near infrared hyperspectral imagery for waste recycling ...isfr.unileoben.ac.at/fileadmin/shares/isfr/docs/Kerschhaggl.pdf · Near infrared hyperspectral imagery for waste recycling