TY - JOUR

T1 - Qualitative analysis of post-consumer and post-industrial waste via near-infrared, visual and induction identification with experimental sensor-based sorting setup

AU - Friedrich, Karl

AU - Koinig, Gerald

AU - Pomberger, Roland

AU - Vollprecht, Daniel

N1 - Publisher Copyright: © 2022 The Authors

PY - 2022/4/2

Y1 - 2022/4/2

N2 - Sensor-based sorting in waste management is a method to separate valuable material or contaminants from a waste stream. Depending on the separation property different types of sensors are used. Separation properties and their corresponding sensors are e.g. molecular composition with near-infrared sensors, colour with visual spectroscopy or colour line scan cameras, or electric conductivity with electromagnetic sensors. The methods described in this paper deal with the development of sorting models for a specific near-infrared, a visual spectroscopy and an induction sensor. For near-infrared and visual spectroscopy software is required to create sorting models, while for induction only machine settings have to be adjusted and optimized for a specific sorting task. These sensors are installed in the experimental sensor-based sorting setup at the Chair of Waste Processing Technology and Waste Management located at the Montanuniversitaet Leoben. This sorting stand is a special designed machine for the university to make experiments on sensor-based sorting in lab scale. It can be used for a variety of waste streams depending on the grain size and the pre-conditioning for the sensor-based sorting machine. In detail the methods to create these sorting models are described and validated with plastic, glass and metal waste. • Near-infrared spectroscopy measures the molecular composition of near-infrared-active particles. • Visual spectroscopy measures the absorption of visible light by chemical compounds. • Induction sensors use induced currents to detect nearby metal objects.

AB - Sensor-based sorting in waste management is a method to separate valuable material or contaminants from a waste stream. Depending on the separation property different types of sensors are used. Separation properties and their corresponding sensors are e.g. molecular composition with near-infrared sensors, colour with visual spectroscopy or colour line scan cameras, or electric conductivity with electromagnetic sensors. The methods described in this paper deal with the development of sorting models for a specific near-infrared, a visual spectroscopy and an induction sensor. For near-infrared and visual spectroscopy software is required to create sorting models, while for induction only machine settings have to be adjusted and optimized for a specific sorting task. These sensors are installed in the experimental sensor-based sorting setup at the Chair of Waste Processing Technology and Waste Management located at the Montanuniversitaet Leoben. This sorting stand is a special designed machine for the university to make experiments on sensor-based sorting in lab scale. It can be used for a variety of waste streams depending on the grain size and the pre-conditioning for the sensor-based sorting machine. In detail the methods to create these sorting models are described and validated with plastic, glass and metal waste. • Near-infrared spectroscopy measures the molecular composition of near-infrared-active particles. • Visual spectroscopy measures the absorption of visible light by chemical compounds. • Induction sensors use induced currents to detect nearby metal objects.

KW - Identification model

KW - Induction sorting

KW - Near-infrared sorting (NIR Sorting)

KW - Qualitative analysis of post-consumer and post-industrial waste via near-infrared, visual and induction identification with experimental sensor-based sorting setup

KW - Sensor-based sorting

KW - Visual-spectroscopy sorting (VIS Sorting)

UR - http://www.scopus.com/inward/record.url?scp=85128310918&partnerID=8YFLogxK

U2 - 10.1016/j.mex.2022.101686

DO - 10.1016/j.mex.2022.101686

M3 - Article

AN - SCOPUS:85128310918

VL - 9.2022

JO - MethodsX

JF - MethodsX

SN - 2215-0161

M1 - 101686

ER -