• Energy Research

The European politics is addressed, in their last years, more and more towards the abolition of landfills and oriented to verify the possibility to improve their recovery through actions of recycling, composting and energy production. Currently the European Directive 2008/98/EC, through the principle of End of Waste (EoW), is driving the utilization of new methods of waste processing in order to transform them it into a new "renewable" product. The post-consumer plastics resulting from packaging account for about 60% of total plastics waste (i.e. 23 million tons) produced in Europe. The high quality Solid Recovered Fuel (SRF) is a partial solution to the problem of waste and landfill management, becoming integral and essential in the closure of the waste cycle, representing one of virtuous processes for the treatment and recovery of waste. The application of recycling strategies, finalised to polymers recovery, can thus represent an important opportunity to reduce: i) not renewable raw materials utilization (i.e. oil), ii) carbon dioxide emissions and iii) the amount of plastic waste disposed-off. Aim of this work is to study the possibility offered by the integrated utilization of Chemical Imaging (CI) based techniques in order to obtain a good quality SRF characterized by a low content of PVC derived packaging to be used for the production of thermal energy. Packaging "final waste", in fact, is usually characterized by the presence of PolyVinyl Chloride (PVC). Therefore the association of a specific gravity separation architecture with the CI based sensing units, could strongly improve the quality of "final waste" fraction as resulting from classical recycling processes based on milling, classification and density separation. The presence of PVC, in fact, has a negative influence on the combustion and heat recovery of these products due to the production of dioxins and furans. The utilization of the proposed combined approach (i.e. gravity and CI based) could contribute to obtain a SRF characterized by a very low PVC content, thus allowing to certify SRF according to UNI EN 15359:2011 and to correctly utilise it for thermal energy production.

Polyvinylchloride (PVC) is one of the most produced polymers in Europe, with a share of 11% in terms of mass (8 milliontons) of total polymer consumption, but in 2010 only 5% of the total PVC production came from recycled materials, where other polymer recycling achieves a level of 15% on average. In order to find an innovative process to extract PVC from window frames waste, a combination of two innovative technologies was tested: magnetic density separation (MDS) and hyperspectral imaging (HSI). By its nature, MDS is a flexible high precision density separation technology that is applicable to any mixture of polymers and contaminants with non-overlapping densities. As PVC has a very distinctive high density, this technology was tested to obtain high-grade PVC pre-concentrates from window frame waste. HSI was used to perform a quality control of the products obtained by MDS showing that PVC was clearly discriminated from unwanted rubber particles of different colors. The results showed that the combined application of MDS and HSI techniques allowed to separate and to check the purity of PVC from window frame waste.

Nowadays, recycling of construction and demolition waste (C&DW) is a challenging opportunity for the management of such end-of-life (EOL) materials through alternative methods to environmentally unsustainable methods (i.e., landfilling). In order to make recycling processes more effective, quality control systems are needed. In this work, the possibility of developing a sensor-based procedure to recognize different demolition waste materials from a recycling perspective was explored. An automatic recognition of different predefined constituent classes of recyclables (i.e., concrete, mortar, natural stones, unbound aggregates, clay masonry units, bituminous materials) and contaminants (i.e., glass, metals, wood, cardboard, and gypsum plaster), as established by an European standard, was carried out using hyperspectral imaging (HSI) working in the short-wave infrared (SWIR) range (1000–2500 nm). The implemented classification strategies, starting from the collected hyperspectral images of the analyzed constituents, allowed for the identification of the different material categories. Two main models were built for identifying contaminants in recyclable materials and categorizing material groups based on technical specifications. The results showed accurate category identification with Sensitivity and Specificity values over 0.9 in all models. The possibility of performing a full detection of C&DW recycling products can dramatically contribute to increasing the quality of the final marketable products and their commercial value, at the same time reducing the amount of waste and the consumption of primary raw materials.

The life cycle of textiles (i.e., fabrics and apparel products) generates many environmental impacts, such as resource consumption, water, soil, and air pollution through the dispersion of chemical substances and greenhouse gases. For these reasons, in 2019, textiles were identified as a “priority product category for the circular economy” by the European Commission that proposed a new circular economy action plan focusing on recycling. An in-depth characterization of textile fabrics could lead to an ad hoc recycling procedure, reducing resource consumption and chemicals utilization. In this work, NIR (1000–1650 nm) spectroscopy was applied to extract information regarding fabric composition, with reference to cotton, silk, viscose, and some of their blends, using two different devices: a hyperspectral imaging (HSI) platform and a portable spectroradiometer. The different fabrics were correctly classified based on their spectral features by both detection instruments. The proposed methodological approach can be applied for quality control in the textile recycling sector at industrial and/or laboratory scale thanks to the easiness of use and the speed of detection.

The separation of pure polyolefins, polyethylene (PE) and polypropylene (PP), from post-consumer plastic waste is of increasing interest in the last years according to market requirements for their further recycling. In this paper new analytical inspection strategies, based on HyperSpectral Imaging (HSI) in the VIS-NIR and NIR fields (400-1000 and 1000-1700 nm, respectively), have been investigated and set up in order to define quality control logics that could be applied at industrial plant level for polyolefins recycling. Such an approach was developed inside the European FP7 Project W2Plastics “Magnetic Sorting and Ultrasound Sensor Technologies for Production of High Purity Secondary Polyolefins from Waste”. The core of this project is the separation of pure PP and PE based on an innovative process, the Magnetic Density Separation (MDS). Spectra of virgin polyolefins and of plastic particles and contaminants from post-consumer complex wastes (Automotive Shredder Residue and Building & Construction Waste) have been acquired by HSI and by Raman spectroscopy. The classification results obtained applying the principal component analysis (PCA) on HSI data have been compared with those obtained by Raman spectroscopy, in order to validate the innovative sensing methodology. Results shown that it is possible to identify both polyolefins and contaminants based on HSI sensing technique, and that HSI has a great potentiality to be applied for quality control of process feed (presence of contaminants in the plastic waste) and of the two different pure PP and PE flow streams obtained from the MDS-based recycling process.

The implementation of Nature-based Solutions (NbS) in urban environments is gaining momentum as a means to address environmental challenges and promote sustainable development. However, effective monitoring and evaluation are essential to assess the performance of NbS interventions and to guide decision-making. This research paper introduces a combined approach of proximal and remote sensing, based on visible and near-infrared spectroscopy, to monitor and evaluate NbS implementation in urban areas. The study focuses on the case of the UPPER (Urban Productive Parks for Sustainable Urban Regeneration) project and aims to establish urban Productive Parks as a novel NbS approach in the town of Latina (Italy). Field-based proximal sensing techniques (i.e., near-infrared spectroscopy, NIR) and satellite-based remote sensing data from the Sentinel-2 mission are employed. By integrating these techniques, the study enables comprehensive and multi-scale monitoring of vegetation health and assessment of vegetated areas. Various band ratio indices are calculated to assess vegetation coverage, water content, and urbanization. Temporal variations in these indices are analyzed to evaluate the effectiveness of NbS interventions and their impact on the urban environment. The combined approach of proximal and remote sensing demonstrates the potential for comprehensive and multi-scale monitoring of NbS in urban environments. The research findings contribute to the existing knowledge on NbS monitoring and evaluation, providing valuable insights for sustainable urban development and evidence-based decision-making.

In this study, the possibility of applying the hyperspectral imaging (HSI) technique in the Short-Wave InfraRed (SWIR) spectral range to characterize polymeric parts coming from Waste from Electric and Electronic Equipment (WEEE) is explored. Different case studies are presented referring to the identification of (i) plastic flakes inside a mixed waste stream coming from a recycling plant of monitors and flat screens, (ii) different polymers inside a mixed plastic waste stream coming from End-Of-Life (EOL) electronic device housings and trims, (iii) contaminants (i.e., metals) in a mix of shredded plastic particles coming from a recycling line of electrical cables, and (iv) brominated plastics in mixed streams constituted by small appliances (i.e., cathode-ray tube televisions and monitors). The application of chemometric techniques to hyperspectral data demonstrated the potentiality of this approach for systematic utilization for material characterization, quality control and sorting purposes. The experimental findings highlight the feasibility of employing this method due to its user-friendly nature and quick detection response. To increase and optimize WEEE valorization avoiding disposal in landfills or incineration, recycling-oriented characterization and/or quality control of the processed products are fundamental to identify and quantify substances to be recovered.

Abstract Texture distribution in an Albanian chrome ore has been ascertained by image analysis and the results compared with the degree of liberation determined on the comminution products. A relationship has been found between liberation functions and texture parameters.