Innovative sorting facility for the circularity of aluminium scraps

Project Information

SortCAS

Grant agreement ID: 101066062

DOI

10.3030/101066062

Project closed

EC signature date 7 June 2022

Start date 1 July 2022

End date 31 December 2024

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost

No data

EU contribution

€ 203 464,32

Coordinated by

TECHNISCHE UNIVERSITEIT DELFT
Netherlands

Periodic Reporting for period 1 - SortCAS (Innovative sorting facility for the circularity of aluminium scraps)

Reporting period: 2022-07-01 to 2024-12-31

Summary of the context and overall objectives of the project

Aluminium is the most widely used non-ferrous metal worldwide, and its global demand is expected to continually grow over the 21st century. Recycling of aluminium is important to meet the growing demand while minimizing the corresponding impacts of resource consumption and pollutant emissions. However, the EU loses about 1 million tonnes of recycled aluminium scrap annually due to impurities that make it unsuitable for the high-end EU smelting industry. This leads to the increased dependency on the imports of critical raw materials (e.g. bauxite), the loss of EU economy (about €960 million per year), and the increase of greenhouse gas emissions (about 9 million tonnes of CO2 equivalent per year). As such, it is of critical importance to develop innovative sorting technologies for obtaining high-quality aluminium scraps, thus mitigating the negative impacts of scrap exporting and facilitating the circularity of aluminium. The current trend of developing sorting technologies relies increasingly on sensors, AI, and automation/robotics. Nevertheless, developing efficient yet cost-effective sorting technologies is challenging in the recycling field. Also, the complex shapes of scraps and their random dynamics in industrial environments raise challenges for the effective integration and implementation of sensors, AI, and automation/robotics.

The SortCAS project aimed to develop and integrate multiple efficient yet cost-effective innovations for the automated sorting of high-quality aluminium scraps, and apply the developed/integrated innovations to industrial applications through collaboration with an industry partner, thus facilitating the circularity of aluminium.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Multiple innovations were developed for the automated sorting of aluminium scraps. First, to aid the design and optimization of the sorting system, we developed an advanced particle scale model, based on the discrete element method (DEM). The model enabled the particle-scale understanding of the scrap-scrap and scrap-equipment interactions in the system, as a foundation for the process/equipment design and optimization in a cost-effective way. Secondly, to identify small ferrous contaminants inside aluminium scraps, a novel magnetic image sensor was developed, based on both theoretical computation and experimental measurements. The sensor was proved to be effective in determining both the size and position of a small ferrous contaminant within a large aluminium scrap piece. Thirdly, an automated, efficient, and cost-effective scrap ejection system was developed, based on computational modelling and lab-scale/pilot-scale experiments.

To achieve effective industrial applications, we collaborated closely with an industry partner Myne (formerly Reukema) throughout this project. First, some material samples including different types of aluminium scraps were collected from Myne and analyzed in the lab. Secondly, the developed results in modelling and/or in the lab were tested and validated in a pilot-scale setup at Myne. Finally, the validated results were further used for the building and operation of a full-scale AI-powered scrap sorting plant, which has integrated the optimized scrap infeed flow, the advanced sensing of scrap properties, and the high-speed automated ejection of scraps into multiple products.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The SortCAS project created the following new knowledge and technological advancements:

1) A particle-scale model based on DEM was developed for simulating and understanding the flow behaviours of complex-shaped aluminium scraps. The model described realistic shapes of aluminium scraps by 3D scanning of real shapes, incorporated automated operations like sensing and ejection in the system, and selected parameters based on the calibration by comparing key performance indicators against the pilot-scale experiments. The design and the results generated by the model have been used in the full-scale sorting plant in the industry. Considering DEM is often regarded as less applicable for modelling full-scale industrial applications, the model and results from this work can thus advance the understanding of the particle modelling community, especially in dealing with complex particle shapes and automated systems at a full scale.

2) A singulation technology was created to regularize the flow of aluminium scraps, enabling the efficient and cost-effective sensing and sorting of aluminium scraps into specific types/products. In general, most existing vision and robotic sorting techniques require a good flow of materials preferably with minimal overlapping. To obtain a nonoverlapping flow while maintaining a high throughput is challenging, especially for complex-shaped particles like aluminium scraps. The developed singulation technology allows the flow of scraps with desirable interparticle space in a cost-effective way. This can bring new advancements into the sorting technologies for processing potentially various materials, not only metal scraps, but also E-waste, mineral ores, and building materials etc.

3) A magnetic image sensor was created for detecting small ferrous contaminants in aluminium scraps. Detecting ferrous contaminants is key to realizing high-quality aluminium scraps. Detecting small ferrous contaminants that are attached to or underneath large aluminium scraps can be difficult for vision systems, while advanced sensing by LIBS and/or XRT can be expensive. The current magnetic image sensor has been proven to be effective in sensing the small ferrous contaminants in a cost-effective way; it can also estimate the size/mass of the ferrous contaminants, which is important for estimating the purity of the sorted scraps.

4) A robotic ejection technology was developed to sort multiple types of aluminium scraps in one go. Compared with existing sorting technologies like pneumatic blowing nozzles and robotic pickers, the current robotic ejection technology/equipment is robust and accurate in sorting scraps that may have diverse properties, e.g. various shapes, surfaces, and weight. The ejection system can also be flexibly customized to sort a variable number of products (even more than 10 types), according to user needs.

The developed innovations have resulted in several patents (e.g. NL2031877B1, NL2031878B1, NL2031879B1, and WO2023224478A1), and have been implemented into a full-scale digital recycling plant at the company Myne. Such innovations are promising to advance the high-quality sorting of aluminium scraps towards circularity.

SortCAS