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Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

机译:鉴于其后回收率,适用于自动监测多材料废物组成的光子技术

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摘要

In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites' value chains.
机译:在提高回收的越来越迫切的上下文中,光学技术呈现了支持足够分类塑料的潜力。然而,市售的解决方案(例如,采用近红外光谱)通常集中在识别目前具有市场利益作为二级材料的少数选定类型的单体材料。光子科学的当前进展与高级数据分析,如人工智能,实现以前不可行的桥接实用挑战,例如在分类更复杂的材料方面。在本文中,最初根据其主要特征来审查不同的技术。然后,基于学术文献,讨论了它们对监测多材料组成的适用性,例如不同类型的多层包装和纤维增强的聚合物复合材料以及机动车行业中使用的黑色塑料。最后,还提出了一些具有这些部门应用的商业系统。本综述主要关注材料识别步骤(在废物收集后进行,分类和再处理发生),但在前景中,给出了对分类的进一步见解以及可能有助于增加塑料复合材料价值链的圆形度的未来前景。

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