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