Project duration: 1 April 2020 – 31 March 2024


The rapid identification of critical compounds is crucial for an adequate sorting and inherently the adapted recycling that will enable Circular Economy. RAMSES-4-CE innovates optical spectroscopy-based multi-sensor systems for the recycling industry. We focus on (1) developing a Raman sensor, (2) its integration in a LiF-HSI system (EIT inSPECtor), (3) advanced multi-source data fusion + machine learning for rapid data integration.

The solution (technology)

The steeply increasing demand for electronic devices and equipment, combined with the recent rise of high-technology greedy societal orientations like e-mobility and energy transition leads to vast amounts of e-wastes (WEEE). Most of the million tons of WEEE generated annually are only partially recycled up to now. This impedes dramatically EU goals towards Circular Economy. In order to improve recycling efficiency and thus minimize our environmental footprint, modern recycling plants need multi-component sensors that can identify rapidly complex materials accurately. We address this challenge by using a combination of imaging sensors to identify key chemical compounds in material streams. By means of hyperspectral (HSI) reflectance spectroscopy in the near- and mid-infrared range certain alloys, ceramics, and plastics can be identified and classified. Laser-induced fluorescence (LiF) spectroscopy enables the detection of rare earth elements (REEs) and low-reflective black plastics among others. In order to increase the range of waste classes characterized by our system, we propose to add a rapid, non-destructive and cost-efficient Raman sensor within the project RAMSES-4-CE. This module will be integrated into an existing sensor system, comprising laser-induced fluorescence (LiF) and hyperspectral imaging (HSI). For industrial applications, the requirements for a sensor-based sorting system implies high measurement speed (up to 1 m/s) for in-line high throughput processing, as well a high spatial resolution (about 2 mm) for the identification of the shredded recycling materials.Thus, the data generated by the individual sensors must be processed, integrated and analysed extremely rapidly. For this purpose, a rapid data processing based on machine learning will be developed. All the sensors are expected to allow integrated imaging of material streams on a conveyor belt of about 50 cm width, for comprehensive characterization and sorting. The same facility can be adapted for identification of minerals/rocks for exploration and mining purposes. The final product is a versatile and agile solution for the recycling industry, of which several industrial partners have confirmed the urgent need. The project consortium consists of a sensor technology company (FI), the geological survey of Finland (GTK), and two research institutes (IAP and HIF) who, by their specific expertises, experience and leadership will ensure the development of a prototype able to work under operational conditions.


  • Freiberg Instruments GmbH, Germany
  • Geological Survey of Finland, Finland
  • Technische Universit√§t Bergakademie Freiberg (TUBAF), Germany
  • Helmholtz-Zentrum Dresden-Rossendorf e.V. (HZDR) (Lead Partner), Germany