Potential of sensor-based sorting in enhanced landfill mining
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In: Detritus, Vol. 8.2019, No. December, 12.2019, p. 24-30.
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TY - JOUR
T1 - Potential of sensor-based sorting in enhanced landfill mining
AU - Küppers, Bastian
AU - Parrodi, Juan Carlos Hernández
AU - Lopez, Cristina Garcia
AU - Pomberger, Roland
AU - Vollprecht, Daniel
PY - 2019/12
Y1 - 2019/12
N2 - In landfill mining, simple technologies and processing chains are frequently applied to excavated material in order to extract recyclable metals and high-calorific fractions used in energy recovery. Sensor-based sorting is one way to extract more and better material from a landfill. Two testing series have been performed using state-of-the-art technology to assess the technical feasibility of classifying and sorting landfill material with the aid of near-infrared spectroscopy. Fractions were classified as inert and combustible and sorted by particle sizes ranging from 90-30 mm, from 30-10 mm and from 10-4.5 mm for water content levels of 0 wt% and of 15 wt%, respectively. Additional tests applied different landfill mining materials. Polypropylene (PP), polyethylene (PE) and polyvinyl chloride (PVC) products were produced, using sensor-based sorting, from a mixed fraction of particle sizes ranging from 60-200 mm. Both test series applied air-classified heavy fractions gained from two distinct processing schemes of landfill mining projects in Belgium and in Austria. Results show that the separation and classification of inerts and combustibles is feasible, enriching inert fractions with purities of 97.7 wt% to 99.6 wt% derived from inputs whose inert contents achieved 85.6 to 98.8 wt%. Efficient sorting is a function of the level of pre-processing, water content, relative amounts of adhesive fines, input composition and particle size ranges of the input material. Results from the second test series show that PP, PE, PVC and other materials can be successfully distinguished, achieving correct classification and ejection into respective product fractions of 91.8-99.7 wt%.
AB - In landfill mining, simple technologies and processing chains are frequently applied to excavated material in order to extract recyclable metals and high-calorific fractions used in energy recovery. Sensor-based sorting is one way to extract more and better material from a landfill. Two testing series have been performed using state-of-the-art technology to assess the technical feasibility of classifying and sorting landfill material with the aid of near-infrared spectroscopy. Fractions were classified as inert and combustible and sorted by particle sizes ranging from 90-30 mm, from 30-10 mm and from 10-4.5 mm for water content levels of 0 wt% and of 15 wt%, respectively. Additional tests applied different landfill mining materials. Polypropylene (PP), polyethylene (PE) and polyvinyl chloride (PVC) products were produced, using sensor-based sorting, from a mixed fraction of particle sizes ranging from 60-200 mm. Both test series applied air-classified heavy fractions gained from two distinct processing schemes of landfill mining projects in Belgium and in Austria. Results show that the separation and classification of inerts and combustibles is feasible, enriching inert fractions with purities of 97.7 wt% to 99.6 wt% derived from inputs whose inert contents achieved 85.6 to 98.8 wt%. Efficient sorting is a function of the level of pre-processing, water content, relative amounts of adhesive fines, input composition and particle size ranges of the input material. Results from the second test series show that PP, PE, PVC and other materials can be successfully distinguished, achieving correct classification and ejection into respective product fractions of 91.8-99.7 wt%.
KW - Enhanced Landfill Mining
KW - NIR Spectroscopy
KW - Sensor-based Sorting
UR - http://www.scopus.com/inward/record.url?scp=85087771498&partnerID=8YFLogxK
U2 - 10.31025/2611-4135/2019.13875
DO - 10.31025/2611-4135/2019.13875
M3 - Article
AN - SCOPUS:85087771498
VL - 8.2019
SP - 24
EP - 30
JO - Detritus
JF - Detritus
SN - 2611-4135
IS - December
ER -