Towards smart e-waste demanufacturing systems exploiting multisensor vision system capabilities

Waste electrical and electronic equipment (WEEE) is one of the fastest growing waste streams in the EU, expected to reach more than 12 million tons per year by 2020 [1]. End-of-Life (EoL) products and material mixtures found in E-waste are highly variable and in continuous evolutions. For example Cathod Ray Tube (CRT) televisions are rapidly being replaced by Liquid Crystal Display (LCD) and Light Emitting Diode (LED) TVs, cellular phones are being replaced by smart phones, traditional Hard Disk Drive (HDD) are being replaced by Solid State Disks (SSD). Moreover, new waste flows are entering the recycling streams, including photovoltaic panels and tablet PCs. Under the current business model and waste collection mechanism, the mechanical pre-treatment of these EoL products is usually performed by recyclers which apply (i) manual dismantling processes, and (ii) mechanical shredding and separation processes to reduce the material size and refine the input material into purified material streams which can be sold either to the market or to end-processing plants for further refinement. According to recent studies, the major causes for losses of recoverable metals, including precious and key metals, is due to mechanical pre-treatments (from 40% to 100% depending on the material), while only marginal losses are due to the downstream chemical end-processes. For recycling industries (typically SMEs), having dedicated treatment lines for each specific product type flow is impossible due to space and budget limitations. Therefore, batch production and high utilization of a single or few recycling lines is the typically adopted solution. The process parameters are then selected as a compromise between the different EoL product types. As a matter of fact, in spite of the high variability in the EoL products and material mixtures to be recovered, state of the art mechanical recycling systems are extremely rigid. The process parameters are traditionally set by the recycling machine designer based on a, typically small, material sample provided by the end-user, in the design phase. These parameters are normally kept constants throughout the systems life cycle. This rigidity of the system coupled with the high variability in the input material composition ultimately causes (i) poor recycling rates, (ii) abuse of landfill, also for materials that are potentially recyclable, (iii) lack of competitiveness and, ultimately, (iv) untapped market potentials. This scenario calls for a new generation of highly adaptable demanufacturing systems, endowed with the required level of adaptability to cope with evolving products and variable materials’ market conditions. In this perspective, the integration of specific technologies and on-line control strategies ensure the reconfigurability of process flow and machine parameters according to the specific mixture under treatment, allowing the improvement of process efficiency. In this framework, processes flexibility and reconfigurability capabilities are becoming increasingly important especially when they have to cope with ever-increasing EoL products complexity and variability. However, the lack of appropriate technologies, tools and methods to recover and re-use materials from industrial wastes and post-consumer products can negatively affect the sustainability potential of demanufacturing processes. This paper discusses the advantages related to the installation of a customized multisensor system to support the on-line identification of different material wastes in a new generation of smart demanufacturing systems. An example of integrated multisensor system implemented at the De- and Remanufacturing Pilot Plant at ITIA-CNR is presented.