Integrated micro X-ray fluorescence and chemometric analysis for printed circuit boards recycling

A novel approach, based on micro X-ray fluorescence (μXRF), was developed to define an efficient and fast automatic recognition procedure finalized to detect and topologically assess the presence of the different elements in waste electrical and electronic equipment (WEEE). More specifically, selected end-of-life (EOL) iPhone printed circuit boards (PCB) were investigated, whose technological improvement during time, can dramatically influence the recycling strategies (i.e. presence of different electronic components, in terms of size, shape, disposition and related elemental content). The implemented μXRF-based techniques allow to preliminary set up simple and fast quality control strategies based on the full recognition and characterization of precious and rare earth elements as detected inside the electronic boards. Furthermore, the proposed approach allows to identify the presence and the physical-chemical attributes of the other materials (i.e. mainly polymers), influencing the further physical-mechanical processing steps addressed to realize a pre-concentration of the valuable elements inside the PCB milled fractions, before the final chemical recovery

Hyperspectral imaging applied to end-of-life (EOL) concrete recycling

The recovery of materials from DW is an important target of the recycling industry and it is important to know which materials are presents in order to set up efficient sorting and/or quality control actions. The implementation of an automatic recognition system of recovered products from End-Of-Life (EOL) concrete materials can be an useful way to maximize DW conversion into secondary raw materials. In this paper a new approach, based on HyperSpectral Imaging (HSI) sensors, is investigated in order to develop suitable and low cost strategies finalized to the preliminary detection and characterization of materials constituting Demolition Waste (DW) flow stream. The described HSI quality control approach is based on the utilization of a device working in the near infrared range (1000-1700 nm). Acquired hyperspectral images were analyzed. Different chemometric methods were applied. Results showed that it is possible to recognize DW materials and to distinguish the recycled aggregates from the investigated contaminants (brick, gypsum, plastic, wood and foam)

HyperSpectral Imaging based approach for monitoring of micro-plastics from marine environment

The possibility to develop a sensor based procedure in order to monitor plastic presence in the marine environment was explored in this work. More in detail, this study was addressed to detect and to recognize different types of microplastics coming from sampling in different sea areas adopting a new approach, based on HyperSpectral Imaging (HSI) sensors. Moreover, a morphological and morphometrical particle characterization was carried by digital image processing. Morphological and morphometrical parameters, combined with hyperspectral imaging information, give a full characterization of each investigated particle, concurring to explain all the transportation, alteration and degradation phenomena suffered by each different polymer particle. Obtained results can represent an important starting point to develop, implement and set up monitor strategies to characterize marine microplastics. Moreover, the procedure developed in this work is fast, not expensive and reliable, making its utilization very profitable

hyperspectral imaging applied to demolition waste recycling innovative approach for product quality control

Hyperspectral imaging (HSI) based sensing devices were utilized to develop nondestructive, rapid, and low cost analytical strategies finalized to detect and characterize materials constituting demolition waste. In detail, HSI was applied for quality control of high-grade recycled aggregates obtained from end-of-life concrete. The described HSI quality control approach is based on the utilization of a platform working in the near-infrared range (1000–1700 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: principal component analysis for data exploration and partial least-square-discriminant analysis to build classification models. Results showed that it is possible to recognize the recycled aggregates from different contaminants (e.g., brick, gypsum, plastic, wood, foam, and so on), allowing the quality control of the recycled flow stream

Near InfraRed-based hyperspectral imaging approach for secondary raw materials processing in solid waste sector

In secondary raw materials and industrial recycling sectors there is the need of solving quality control issues. The development and deployment of an effective, fast and robust sensing architecture able to detect, characterize and sort solid waste products is of primary importance. Near InfraRed (NIR) based HyperSpectral Imaging (HSI) techniques to detect materials to recycle and/or solid waste products to process represents an interesting solution to address quality control issues in these sectors. In this paper, are presented two different case studies on the utilization of NIR-HSI to detect contaminants in household plastic packaging waste and recognize materials occurring in processed monitors and flat screen waste. The proposed approach consists of a cascade detection based on Partial Least Squares – Discriminant Analysis (PLS-DA) classifiers applied on hyprspectral images acquired in NIR range (1000-1700 nm)

Hyperspectral imaging logics: efficient strategies for agri-food products quality control

The increasingly normative severity and market competitiveness have led the agriculture sector and the food industry to constantly look for logic improvements that can be applied in processes monitoring systems. In a context where fast, non-destructive and reliable techniques are required, image analysis-based methods have gained interest, thanks to their ability to spatially characterize heterogeneous samples. In such a scenario, HyperSpectral Imaging (HSI) is an emerging technique that provides not only spatial information of imaging systems, but even spectral information of spectroscopy. The utilization of the HSI approach opens new interesting scenario to quality control logics in agricultural and food processing/manufacturing sectors. Three different case studies are presented in this paper. In particular, the utilization of an HSI system, working in SWIR range, was applied for: i) detecting contaminants in dried fruits to be packaged, ii) identifying olive fruits attacked by olive fruit flies and iii) recognizing flour type. In particular, the proposed approach is based on the application of Partial Least Squares – Discriminant Analysis (PLS-DA) classification method to HyperSpectral images in Short Wave InfraRed (SWIR) range (1000-2500 nm). The proposed case studies demonstrate that this logic can be successfully utilized as a quality control system on agri-food products coming from different manufacturing stages, but it can even be seen as an analytical core for sorting engines

Il riciclo meccanico dei rifiuti di apparecchiature elettriche ed elettroniche. Una sfida tecnologica

Due to the technological progress, electronics became more and more part of our lifestyle. Alongside the continuous progress, the volume of electric and electronic waste (WEEE) steady arising. The WEEE, because of a large number of hazardous substances in various equipment, such as lead in printed circuit boards and cadmium in semiconductor chips, could cause serious environmental problems if not properly handled at the end of their life cycle (i.e. recycling and/or disposed-off). However, a significant amount of valuable materials is contained in WEEE, such as metals, high-quality plastic and other materials/elements that can be profitably recovered. WEEE recycling is considered a real opportunity for contrasting an inbound threat for the Industry and for the Environment. For these reasons it is thus essential to improve the WEEE recycling process, both from an economic and an environmental point of view. These two goals can be reached adopting new, and up-to-date, processing/recycling strategies based on innovative technologies allowing to implement more environmentally friendly and economically sustainable processing

Evaluation of elements distribution in printed circuit boards from mobile phones by micro x-ray fluorescence

A micro X-ray fluorescence-based approach for the chemical characterization of spent printed circuit boards (PCBSPCBSS) from mobile phones was applied. More in detail, twelve spent mobile phones were grouped into three clusters according to brands, models and year of release, and a study to evaluate the technological evolution of PCBSs over time was carried out. Precious metals and hazardous elements were investigated, revealing a few differences between samples from the different groups. For instance, the distribution of gold on PCBS layers was more widespread for the older analyzed samples, and smaller quantities of bromine and lead were detected in the more recent models in accordance with the Restriction of Hazardous Substances Directive 2002/95/EC. Analysis of PCBS composition should contribute towards correctly managing such a complex waste, maximizing the recovery of base, critical and precious metals and considering the possible presence of harmful elements requiring careful management. The experimental results showed how, using the proposed approach, distribution maps for chemical elements present in PCBSs could be obtained, thus allowing the definition of optimal strategies for further handling (i.e. classification) and processing (i.e. critical/precious metal recovery)

Hierarchical modelling for recycling-oriented classification of shredded spent flat monitor products based on hyperspectral imaging

The number of flat monitors from televisions, notebooks and tablets has increased dramatically in recent years, thus resulting in a corresponding rise in Waste from Electrical and Electronic Equipment (WEEE). This fact is linked to the production of new high-performance electronic devices. Taking into account a future volume growth trend of WEEE, the implementation of adequate recycling architectures embedding recognition/classification logics to handle the collected WEEE physical-chemical at-tributes, is thus necessary. These integrated hardware and software architectures should be efficient, reliable, low cost, and capable of performing detection/control actions to assess: i) WEEE composition and ii) physical-chemical attributes of the resulting recovered flow streams. This information is fundamental in setting up and implementing appropriate recycling actions. In this study, a hierarchical classification modelling approach, based on Near InfraRed (NIR)-Hyperspectral Imaging (HSI), was carried out. More in detail, a 3-step hierarchical modelling procedure was designed, implemented and set up in order to recognize different materials present in a specific WEEE stream: End-of-Life (EoL) shredded monitors and flat screens. By adopting the proposed approach, different categories are correctly recognized. The results obtained showed how the proposed approach not only allows the set up of a “one shot” quality control system, but also contributes towards improving the sorting process

Micro X-ray fluorescence imaging coupled with chemometrics to detect and classify asbestos fibers in demolition waste

Asbestos was largely used in the past by several countries all over the world. From 1900 to 1990 asbestos-containing materials (ACMs) were produced in large amounts and mainly utilized to produce insulation, flame retardant materials, as well as to improve the mechanical and the chemical characteristics of construction materials. Its extensive use has therefore led to the presence of fibers in existing buildings and within the construction and demolition waste. A fast, reliable and accurate recognition of ACMs represents an important target to be reached. In this paper the use of micro X-ray fluorescence (micro-XRF) technique coupled with a statistical multivariate approach was applied and discussed with reference to ACMs characterization. Different elemental maps of the ACMs were preliminary acquired in order to evaluate distribution and composition of asbestos fibers, then samples energy spectra where collected and processed using chemometric methods to perform an automatic classification of the different typologies of asbestos fibers. Spectral data were analyzed using PLS-Toolbox™ (Eigenvector Research, Inc.) running into Matlab® (The Mathworks, Inc.) environment. An automatic classification model was then built and applied. Results showed that asbestos fibers were correctly identified and classified according to their chemical composition. The proposed approach, based on micro-XRF analysis combined with an automatic classification of the elemental maps, is not only effective and non-destructive, it is fast, and it does not require the presence of a trained operator. The application of the developed methodology can help to correctly characterize and manage demolition waste where ACMs are present