Challenges of in-line, sensor-based characterisation of recycling streams

Optical sensors are a key enabler for an in-line, real-time characterisation, quality control and monitoring in industrial, conveyor-based raw material processing. Innovators are actively exploring non-invasive optical sensing to solve current problems in economic, socially acceptable and ecologic resource handling with high efficiency. Despite the evident advantages, integrating available optical technologies into sensor systems poses various challenges. A detailed understanding of physical parameters as well as smart solutions are required to mitigate or circumvent some of the limitations. Realistic solutions for the industry rely on understanding what is possible, where are the key limiting factors and which of the challenges can be overcome in the future.

In this contribution, we present four examples from our HELIOS lab research projects in the field of recycling of society-relevant material streams to discuss the major challenges. Our focus lies on the suitability of optical sensor systems for industrial applications. We emphasize the pathways from scientific setups to industrial demonstrators and highlight the relevant parameters, when operating sensors such as RGB, hyperspectral reflectance imaging (HSI), laser-induced fluorescence (LiF) together with Raman scattering, x-ray fluorescence (XRF) and laser-induced breakdown spectroscopy (LIBS).  

Extremely relevant to the industry is speed (or material throughput). Common conveyor belt speeds of several meters per second imply low signal integration times for the optical sensors (or high frame rates in the case of cameras). While industrial high-speed RGB cameras are well suitable, HSI cameras rely on longer integration times to collect signals with adequate intensities across hundreds of detection bands. Current HSI technology is successful in a variety of conveyor belt applications (2D dynamic setup) at a few meters per second, however, a transfer to applications for material detection in air flows (3D dynamic setup) outlines the trade-off between signal quality and acquisition speed. Similarly, signals of very low intensities as seen in laser-induced fluorescence hyperspectral scanning highlight the multi-parameter trade-off between integration times, acquisition speed and excitation power, where the latter is largely dependent on available optical components.

Most of our consumer products are not made of pristine, pure material but come with coating, as compounds and/or with additives to improve appearance and performance of the materials. For recycling, this poses significant challenges for material separation and processing. Using optical sensors in recycling operations then often implies extrapolating the surface properties as representative of the actual material. We demonstrate with examples from several projects, how coating and additives affect the spectral signatures in polymers (esp. black polymers) and metals (steel and aluminum), and how a combination with additional validation sensors (e.g. Raman, LIBS) can provide important information in materials. This information is essential for an adequate and high-quality recycling process.

The given examples and related research are based on collaborations with the industry and aim at developing and testing new concepts and evaluating corresponding tools for data acquisition and real-time processing in recycling facilities. We gratefully acknowledge project funding for RAMSES-4-CE (KIC RM 19262), Digisort (03XP0337B), Car2Car (19S22007B) and FINEST (KA2-HSC-10).