Thales Alenia Space Italia S.p.A.
Among circular manufacturing related procedures, those addressed to perform a preliminary analysis of materials to recycle and the products resulting from the recycling/recovery process play a preeminent role, thus fulfilling the 6Rs rules: reducing, reusing, remanufacturing, redesigning, recovering, and recycling. The use of hyperspectral images overcomes the conventional limitations of digital cameras, opening new opportunities for waste material identification and classification. This can strongly affect the efficiency and capacity of several phases of the circularity processes, e.g. material selection from waste collection, components identification in the disassembly, quality control of different separate fraction as resulting from mechanical-physical separation of wastes, etc. . HyperSpectral Single Shot (HSSS) and/or Imaging (HSI) based detection architectures are systematically utilized to reach the previously mentioned goals. HSSS and HSI approaches also fulfil an important goal: the optimization of energy consumption in manufacturing and End-of-Life processes according to the logic of Industrial Symbiosis not only for the recovery and the reuse of hi-quality recovered materials/products, but also for the utilization of sensing architecture characterized by a low environmental impact, i.e. full optical units performing chemical-physical analysis “off-line” and “at-line”, without any use of chemical, and/or “in-line” (i.e. laboratory scale) and “on-line” (i.e. processing line scale). This project is based on the application of both HSSS and HSI based techniques, coupled with chemometric logics, to characterize different solid waste typologies and resulting recovered products, through the development, implementation and setup of fast, robust, effective, low cost, non-invasive and non-destructive analytical approaches. Consequently, the team will work on HSSS and/HSI based hyperspectral sample acquisition of the different materials/products in the SWIR range (1000-2500 nm), on the creation of a hyperspectral library of collected materials signatures in the SWIR range (1000-2500 nm), on the source scrap materials/products classification by HSI according to their characteristics by PLS and MIA toolboxes (Eigenvector Research Inc., Wenatchee, WA, USA) running inside MATLAB environment. The detection and classification by HSI of other materials (i.e. pollutants) in respect of source scrap family and corresponding recovered products will bring to important comparison in terms of classification of materials and quality assessment of recovered concentrates.
The aim of this project is to identify the efficiency and effectiveness of circular processes. In fact, circular economy models are based on the optimization of different material flows in order to maximize the recovery/recycling/reuse of materials that can be reused in the production cycle by fostering a closed loop supply chain model. In this direction, it becomes strategic to identify which materials actually have potential, that is, which wastes can become valuable. At the same time, the other circular goal of minimizing waste from circular processes is also pursued.
Accordingly, we aim to support the realization of a circular factory in space by addressing the strengths and weaknesses of architectures applied to the preliminary analysis of materials to be recycled and/or products resulting from recycling/recovery processes.
Fast, robust, effective, low cost, non-invasive and non-destructive detection/recognition/analytical architectures and logics to characterize different solid waste typologies (i.e. waste plastics, e-waste, etc.) and resulting recovered products (i.e. quality certified mono-composition recovered materials flow streams, according to specific physical-chemical properties), pollutants separation and recovery.
These solutions will permit to reach two of the several aims of the project related to Circular and Sustainable Factory, that is: (i) produced scraps characterization (i.e. identification/classification) and their reuse/recycled as primary inputs in order to extend their resource value, and (ii) higher efficiency and effectiveness in respect of raw materials utilization (e.g. reducing primary raw material consumption, reducing production scraps and wastes.
