Automatic identification and classification of compostable and biodegradable plastics using hyperspectral imaging

In the UK waste management systems biodegradable and compostable packaging are not automatically detected and separated. As a result, their fate is generally landfill or incineration, neither of which is an environmentally good outcome. Thus, effective sorting technologies for compostable plastics are needed to help improve composting rates of these materials and reduce the contamination of recycling waste streams. Hyperspectral imaging (HSI) was applied in this study to develop classification models for automatically identifying and classifying compostable plastics with the analysis focused on the spectral region 950–1,730 nm. The experimental design includes a hyperspectral imaging camera, allowing different chemometric techniques to be applied including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) to develop a classification model for the compostable materials plastics. Materials used in this experimental analysis included compostable materials (sugarcane-derived and palm leaf derived), compostable plastics (PLA, PBAT) and conventional plastics (PP, PET, and LDPE). Our strategy was to develop a classification model to identify and categorize various fragments over the size range of 50 x 50 mm to 5 x 5 mm. Results indicated that both PCA and PLS-DA achieved classification scores of 100% when the size of material was larger than 10 mm x 10 mm. However, the misclassification rate increased to 20% for sugarcane-derived and 40% for palm leaf-based materials at sizes of 10 x 10 mm or below. In addition, for sizes of 5 x 5 mm, the misclassification rate for LDPE and PBAT increased to 20%, and for sugarcane and palm-leaf based materials to 60 and 80% respectively while the misclassification rate for PLA, PP, and PET was still 0%. The system is capable of accurately sorting compostable plastics (compostable spoons, forks, coffee lids) and differentiating them from identical looking conventional plastic items with high accuracy

Study of semi-synthetic plastic objects of historic interest using non-invasive total reflectance FT-IR

A significant proportion of modern and contemporary artifacts and objects of historical interest, are composed of materials in the form of synthetic, semi-synthetic, and natural polymers. Each class of polymer and corresponding composite plastics are subject to different degradation processes. This means that conservators and curators of 20th century collections are faced with varied, nontrivial preservation issues. An unresolved problem is the identification of early plastics based on semi-synthetic polymers such as cellulose nitrate, cellulose acetate, and casein formaldehyde, which were often used to imitate the more valuable natural materials such as ivory, tortoiseshell, ebony, and bone. This exemplifies the need for non-invasive methods specifically tailored for identification of plastic materials in collections, so as to provide conservators with a means of materials classification to support preventive conservation strategies and interventive treatments. The present work is aimed at evaluating the effectiveness of non-invasive Total Reflectance (TR) FT-IR spectroscopy, coupled with a custom reference spectral TR FT-IR library, for the identification of materials comprising a series of unknown objects. A set of ten heritage objects made from early semi-synthetic materials was used as a training test set to validate the proposed methodological approach. The FT-IR data acquired on the test objects were pre-processed and finally classified using commercial software tools used for the automatic classification of spectra in FT-IR spectroscopy. The procedure was successfully applied to several cases, although residual uncertainties remained in a few examples. The results obtained are critically analyzed and discussed in the perspective of proposing a robust method for in-field prescreening of the chemical composition of plastic artistic and historical objects

Analysis of Biodegradable and Non-Biodegradable Materials Using Selected Deep Learning Algorithms

It is possible to divide the materials used in the world into recyclable and nonrecyclable. Biodegradable materials contain elements naturally degraded by microorganisms such as foods, plants, fruits, etc. Waste from this material can be processed into compost. non-biodegradable materials include materials that do not naturally decompose, such as plastics, metals, inorganic elements, etc. Waste from this material can only be reused by converting it into new materials. In this study, the classification of biodegradable and non-biodegradable materials was done using deep learning methods. Convolutional Neural Network (CNN) performs steps such as preprocessing and feature extraction in classification. 5430 images were used for the dataset. 70% of this dataset was used as training data, 15% as validation data, and 15% as test data. Of the Deep Learning methods, the pre-trained neural networks AlexNet, ShuffleNet, SqueezeNet, and GoogleNet were used. For each algorithm, the performances were evaluated by classifying them as biodegradable and non-biodegradable. With this study, we can identify, track, sort, and process waste materials by classifying materials

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

Carbon Free Boston: Waste Technical Report

Part of a series of reports that includes: Carbon Free Boston: Summary Report; Carbon Free Boston: Social Equity Report; Carbon Free Boston: Technical Summary; Carbon Free Boston: Buildings Technical Report; Carbon Free Boston: Transportation Technical Report; Carbon Free Boston: Energy Technical Report; Carbon Free Boston: Offsets Technical Report; Available at http://sites.bu.edu/cfb/OVERVIEW: For many people, their most perceptible interaction with their environmental footprint is through the waste that they generate. On a daily basis people have numerous opportunities to decide whether to recycle, compost or throwaway. In many cases, such options may not be present or apparent. Even when such options are available, many lack the knowledge of how to correctly dispose of their waste, leading to contamination of valuable recycling or compost streams. Once collected, people give little thought to how their waste is treated. For Boston’s waste, plastic in the disposal stream acts becomes a fossil fuel used to generate electricity. Organics in the waste stream have the potential to be used to generate valuable renewable energy, while metals and electronics can be recycled to offset virgin materials. However, challenges in global recycling markets are burdening municipalities, which are experiencing higher costs to maintain their recycling. The disposal of solid waste and wastewater both account for a large and visible anthropogenic impact on human health and the environment. In terms of climate change, landfilling of solid waste and wastewater treatment generated emissions of 131.5 Mt CO2e in 2016 or about two percent of total United States GHG emissions that year. The combustion of solid waste contributed an additional 11.0 Mt CO2e, over half of which (5.9 Mt CO2e) is attributable to the combustion of plastic [1]. In Massachusetts, the GHG emissions from landfills (0.4 Mt CO2e), waste combustion (1.2 Mt CO2e), and wastewater (0.5 Mt CO2e) accounted for about 2.7 percent of the state’s gross GHG emissions in 2014 [2]. The City of Boston has begun exploring pathways to Zero Waste, a goal that seeks to systematically redesign our waste management system that can simultaneously lead to a drastic reduction in emissions from waste. The easiest way to achieve zero waste is to not generate it in the first place. This can start at the source with the decision whether or not to consume a product. This is the intent behind banning disposable items such as plastic bags that have more sustainable substitutes. When consumption occurs, products must be designed in such a way that their lifecycle impacts and waste footprint are considered. This includes making durable products, limiting the use of packaging or using organic packaging materials, taking back goods at the end of their life, and designing products to ensure compatibility with recycling systems. When reducing waste is unavoidable, efforts to increase recycling and organics diversion becomes essential for achieving zero waste. [TRUNCATED]Published versio

WoodCircus, Underpinning the vital role of the forest-based sector in the Circular Bioeconomy. D2.2 Resource Efficiency, Side Streams and Value Chain Analysis – WP2 Final Report

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