General theme: In a more circular economy the objective is to keep products, components and materials at their highest level of utility for the longest time possible. To achieve this the deployment of Industry 4.0 technologies (see also Research Line 6 of SIM² KU Leuven), such as artificial intelligence and robotics dismantling and sorting, are investigated for the splitting of products into their components or composing materials in support of reuse, repair, repurposing, remanufacturing and/or high-end recycling. While investigating smarter end-of-(first)-live treatment processes for products ranging from the smallest consumer electronics to electric vehicles and airplane jet engines, also opportunities for product (eco-)designs are always searched for.
In a circular economy the availability of detailed and reliable data on the characteristics of waste streams is crucial for any type of process innovation and business model evaluation. Therefore, methods to characterise and forecast the characteristics of future waste streams both at product and material level are development. In addition, automated systems adopting computer vision and spectroscopy for more cost-efficient and detailed characterisation are developed to be used for improved process control and quality management in the recycling sector.
The transition towards a circular economy will go hand in hand with the transition to an industry 4.0. The technologies that characterise the industry 4.0 also offers great opportunities for the development of more flexible and intelligent re- and demanufacturing systems. Both for the purpose of reuse and material recycling robotic systems are developed that can non-destructively or destructively disassemble or dismantle products in a man-machine cooperation leaving only some of the complex tasks for operators. To demonstrate the technical feasibility and economic viability of such innovative processes, lab and industrial pilots are developed, as well as the related software adopting various state-of-the art (deep learning) computer vision technologies.
The first step in an enhanced re- and demanufacturing process is the inspection and recognition of the product type or model, as well as the product condition. To increasing the overall efficiency of organisation performing reuse, repair, repurposing, refurbishing and remanufacturing operation applications adopting computer vision technologies are developed. These (web based) applications are developed to assist in product recognition, disassembly, testing, reassembly and reselling by provide information and guidance.
In a more circular economy the main objective is to keep products, components and materials at their highest level of utility for the longest possible time. For this it is often essential to sort products, components and materials with a high yield and purity both after collection and size reduction. To increase the sorting efficiency intelligent decision making algorithms, (deep learning) computer vision techniques and state-of-the-art spectroscopy technologies, such as laser induces breakdown spectrometry, are combined with robotic sorting and advanced pneumatic ejection systems.
To increase the efficiency and economic viability of flexible and intelligent re- and demanufacturing processes it is crucial to also design products and select materials while taking into account all the different phases of the product life cycle. In addition, in a more circular economy products will have to be designed differently to facilitate the application of the from end-of-life products recovered components and materials. To support organisations in better taking into account these aspects, as well as the total life cycle cost and the overall environmental impact, design evaluation and optimisation techniques are developed that verify the fitness of innovative product designs.