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The AHOY-Project: Waste Wood Sorting with X-ray Technology

Waste wood is a valuable resource, but is hardly recycled despite increasing demand, predicted supply gaps (Mantau et al. 2010), and galloping wood prices since 2020 (Trading Economics 2022). In Germany alone around 10 million tons of waste wood accumulated in 2016. Only a minor part (1.7 million tons) is substantially reused in the production of chipboards. The majority (7.7 million tons) is fed into energy recovery, i.e., burned in one of the 80 German waste wood power plants (BMUV 2021), and is thus lost, while the supply of fresh wood is limited by slow growth cycles and finite acreage. In view of current environmental regulations, climate change and massive tree mortality, waste wood should be kept permanently in the circular economy as a high-quality raw material in the future.

In the context of circular economy, waste wood is an important resource. However,according to the German waste wood ordinance, sorting into four categories is necessary beforerecycling. These categories describe the purity of waste wood based on possible contamination,like paint, foreign bodies, or wood preservatives. Only the purest two categories are allowedto be recycled. For categorization, manual sorting is currently used. This is not only timeconsumingand expensive, but also relatively inaccurate. An automated, sensor-based solutionwould lead to cost savings for recycling companies and also support a sustainable use of resources.For this purpose, a non-contact measuring system, which can examine waste woodchips, will be developed within the project AHOY. As a proof of concept, this paper presents dualenergy X-ray transmission (DE-XRT) for the discrimination between clean wood and the mostcommon contaminants polyvinyl chloride and white lead paint. The results show that even thinlayers of contaminants can be detected. The DE-XRT data are intended for a later fusion withnear-infrared spectroscopy, which is sensitive to organic contaminants hard to detect by DEXRT.Artificial neural networks will be applied to the fused data to achieve a sorting process that yields waste wood chips of the purest two categories.

Copyright:	© Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft der Montanuniversität Leoben
Quelle:	Recy & Depotech 2022 (November 2022)
Seiten:	6
Preis:	€ 3,00
Autor:	R. Wagner Christine Bauer Johannes Leisner
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