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)

Energy Efficiency for a farm company: NZEFC – Nearly Zero Energy Farm Company. Case of Study

In this report, the energy consumption of 25 agricultural companies, which produce milk for the Soresina consortium, was evaluated.The purpose was to determine a Standard Energy Diagnosis Procedure (SEDP), for the agricultural sector. In the case of study below, the farm company has decided to adhere to a process of technological renewal, able to reduce to zero, the energy consumption, related to electricity and agricultural naphtha. Three are the intervention that the company has decided to adopt: Integration the photovoltaic system with a Storage System (battery); castrating a new biogas plant, with 75 kW electrical power; replacing the unifeed wagon with a robot: Trioliet Triomatic T15, coupled with the kitchen Triomatic T30

Characterization of Kalimashi Chromite Ore (Albania) by Scanning Electron Microscopy

The textural and structural characteristics of chrome ores mined in Albania have been examined by means of scanning electron microscopy (SEM) analysis. Elemental mapping and microanalysis of Cr, Fe and Al (to identify the chromite phase) and of Mg and Si (to identify the olivine) have been carried out. A study has been made of the kind of micro-inclusions found in the ore, in order to evaluate the products of the beneficiation processes. Textural complexity of the ore explains the difficulties involved in obtaining good recoveries and high-quality concentrates

Characterization of the Complex Sulfide Ore of Sotiel (Spain) by Scanning Electron Microscopy and Electron Microprobe Analysis

This study complements a more exhaustive examination on the complex sulfide mineralization of Sotiel, (Spain), by optical microscopy and multispectral digital analysis of images. Electron microscopy analyses were carried out to document specific associations which are difficult to detect with conventional optical microscopy. This study utilizes the application of digital techniques to image processing, in an attempt to define microstructural and microtextural characteristics of the ore, since they play an important role in the subsequent beneficiation operations (i.e., flotation). Scientific and economic information bound to complex metallogenic phenomenology and referred to particular paragenetic scenes was acquired by the study in detail of inclusions of main mineralogical species such as pyrite, sphalerite, galena, chalcopyrite combined with quartzose-carbonate-micaceous gangues and a particular silico-marly slate . Scanning electron microscopy and optical microscopy observations and image analysis were used to characterize species of metallic and non-metallic submicroscopic inclusions

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

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

Computer-aided optical characterization and sensing applications: from minerals to waste

Optical based characterization techniques and related analytical methodologies, originally utilized in the mineral sector, can be profitably applied to solid waste streams products as resulting from different recycling processes. This approach, when supported by digital tools allows to perform a full characterization of compositional and textural attributes of the different particulate solids constituting the waste flow streams. To reach this goal specific physical-chemical attributes must be collected, analyzed and processed in order to define, according to market requirements, specific classes of quality to assume as reference to define optimal processing strategies. Computer-assisted optical characterization, coupled with hyperspectral sensing devices and embedding recognition/classification logics, can contribute to reach these goals, dramatically reducing analytical time and costs. In this work an example of this “transfer approach”, from minerals to waste, is presented, analyzed and discussed, with reference to a porphyry copper ore sample and a WEEE product

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 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