CHARACTERISE TO SORT - uestuest.ntua.gr/athens2017/proceedings/presentations/Geurts.pdf · ADVANCED...
Transcript of CHARACTERISE TO SORT - uestuest.ntua.gr/athens2017/proceedings/presentations/Geurts.pdf · ADVANCED...
CHARACTERISE‐TO‐SORTADVANCED SOLID WASTE CHARACTERISATION BY MULTI‐SENSOR DATA
R. Geurts, A. Maul, K. Broos, D. Van Loo, M. Boone, P. Segers, J. Vanhees and M. Quaghebeur
Waste Characterisation: a common task
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Waste inventory Feasibility & planning Quality control
Waste Characterisation: hand sorting
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Hand sorting is still the conventional way, but has many problems Slow Subjective Batch Unpleasant Representative samples = huge Limited to bulk data: mass balance, particle size distribution
There is a need for a new method, that is Fast Objective Continuous Accurate Data on particle‐level: material, mass, density, volume, shape, size
X‐Ray CT (computed tomography)
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metal
stoneplastic
wood
bottlecap
nailSpringlike object
wire
X‐Ray CT (computed tomography)
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metal
metal
stoneplastic
wood
Rock, glass aluminum
rock
glass
Dense sheetaluminum
X‐Ray CT (computed tomography)
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metal
stoneplastic
wood
plasticX‐Ray CT (computed tomography)
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metal
stoneplastic
wood
X‐Ray CT (computed tomography) woodCHARACTERISE‐TO‐SORT
metal
stoneplastic
wood
Partners
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Subcontractor Sounding board
Technology: Multi‐sensor combination
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X Laser
Density
Material
Shape
Size
Mass
Dual Energy X-Ray
3D Laser Triangulation
Online Data Processing Quality Management
Composition in wt%
How? Dual energy X‐ray Transmission
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Measure density & atom numberMaterial discrimination
How? 3D Laser Triangulation
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How? RGB Camera
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Use additional visual features: Color Texture Text
WHY Characterise‐to‐Sort?
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CtSCtS
QHigh‐Quality
OutputProcessoptimization
Input characterization
Re‐processing
Waste Processing Plant
The device installed @VITO, Belgium
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Sample materials
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PMD sorting residue
Flmuff from scrap sorting
C&D waste light fraction
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X‐ray results: HE vs LE
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SS
Brass
Cu
Al
Wood
Wood
X‐ray results: HE vs LE
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SS
Brass
Cu
Al
Wood
Wood
Color, 3D, and X‐Ray
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Color + 3D
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Preliminary results: Accurate mass measurement
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200 particles Procedure: Weigh each particle Scan
Preliminary results: material recognition
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Outlook
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Data Collection Campaign
Machine learning: Train algorithms Artifical neural network on particle‐level‐derived features Object / material recognition by convolutional neural networks on RGB data
Optimize a metal scrap sorting plant using the CtS technology
Conclusions
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Unique multi‐sensor combination Dual energy X‐ray 3D laser triangulation RGB camera More sensors to come…
Volume‐based technology (vs. surface technology) Accurate mass balance (Big) Data on particle‐level
Flexible research prototype: explore novel waste characterisation (sensor) technologies
Looking for new waste streams to characterise & sorting processes to optimize