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通过基于实验传感器的分拣装置,利用近红外、视觉和感应识别对消费后和工业后废物进行定性分析。

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

作者信息

Friedrich K, Koinig G, Pomberger R, Vollprecht D

机构信息

Chair of Waste Processing Technology and Waste Management, Department of Environmental and Energy Process Engineering, Montanuniversitaet Leoben, Franz Josef-Strasse 18, 8700 Leoben, Austria.

出版信息

MethodsX. 2022 Apr 2;9:101686. doi: 10.1016/j.mex.2022.101686. eCollection 2022.

Abstract

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 for a specific . 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 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.

摘要

废物管理中的基于传感器的分类是一种从废物流中分离出有价值材料或污染物的方法。根据分离特性,使用不同类型的传感器。分离特性及其相应的传感器例如有:利用近红外传感器检测分子组成、利用可见光谱或彩色线扫描相机检测颜色、利用电磁传感器检测电导率。本文所述方法涉及针对特定[未明确内容]的开发。对于近红外和可见光谱,需要软件来创建分类模型,而对于感应方式,仅需针对特定分类任务调整和优化机器设置。这些传感器安装在位于莱奥本矿业大学的废物处理技术与废物管理系的[未明确内容]中。这个分类台是为该大学特别设计的一台机器,用于在实验室规模上进行基于传感器的分类实验。根据粒度和基于传感器的分类机的预处理情况,它可用于多种废物流。详细描述了创建这些分类模型的方法,并使用塑料、玻璃和金属废物进行了验证。

•近红外光谱法测量近红外活性颗粒的分子组成。

•可见光谱法测量化合物对可见光的吸收。

•感应传感器利用感应电流检测附近的金属物体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09cf/9036126/08fc4c5b31ba/ga1.jpg

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