Near infrared hyperspectral imagery for waste recycling...
Transcript of Near infrared hyperspectral imagery for waste recycling...
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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
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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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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
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Applications
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Metals from slag Household waste
C&D Recovered Paper Wood Sorting
Potato Sorting & Grading
E-Scrap
Nut Sorting
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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)
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HELIOS – Spectral Ranges
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Hyperspectral Imaging Technology
Camera internal classification and RGB visualization of chemical differences for real-time analysis
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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.
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Plastic Sorting
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Principal Component Analysis
Partial Least Squares
Chemical Color Imaging: MVDA-projection of HSI data into RGB space
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Plastic Sorting
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Principal Components
Principal Component Analysis
Partial Least Squares
wavelength wavelength
AU AU
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Recycling
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paper cardboard
PE-LD
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Mineral Sorting
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Calcite CaCO3 vs. Dolomite CaMg(CO3)2
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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
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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
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Pharmaceutical Engineering
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Pharmaceutical Engineering
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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.
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Pharmaceuticals
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Pharmaceuticals Quantitatively
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Pharmaceuticals Quantitatively
R^2=0.98 RMSEC = 1.2 mg/cm2
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Pharmaceuticals Quantitatively
R^2=0.98 RMSEC = 1.2 mg/cm2
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mg/cm2
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Food Food sorting – Blueberries and stems
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Food
Food sorting – Noodles and worms
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Inline Dry Matter Measurements
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• Dry matter content traced by spectral signatures
• Inferred FIR-filter kernel
allows for concentration prediction
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Inline Dry Matter Measurements
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HELIOS Camera
R = 0.9 RMS = 1 %
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Dry Matter Food Analyzer
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• Integrated in sorting machine (INSORT Observer Food Analyzer)
• Valdidated model (R^2=0.9, RMS=5 %)
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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
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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).
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Accuracy vs. Precision
Plots taken from http://en.wikipedia.org
higher precision
higher accuracy
Laboratory precision completed with inline inspection of entire
production stream
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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
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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.
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Award
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International FoodTec Award
DLG (Deutsche Landwirtschafts-Gesellschaft
/ German Agricultural Society)
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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.
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Acknowledgements
M. Jeindl
W. Märzinger
E. Leitner
S. Sacher
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Some References
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