Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA)

Multi-sensor characterization for an improved identification of polymers in WEEE recycling

Polymers represent around 25% of total waste from electronic and electric equipment. Any successful recycling process must ensure that polymer-specific functionalities are preserved, to avoid downcycling. This requires a precise characterization of particle compounds moving at high speeds on conveyor belts in processing plants. We present an investigation using imaging and point measurement spectral sensors on 23 polymers including ABS, PS, PC, PE-types, PP, PVC, PET-types, PMMA, and PTFE to assess their potential to perform under the operational conditions found in recycling facilities. The techniques applied include hyperspectral imaging sensors (HSI) to map reflectance in the visible to near infrared (VNIR), short-wave (SWIR) and mid-wave infrared (MWIR) as well as point Raman, FTIR and spectroradiometer instruments. We show that none of the sensors alone can identify all the compounds while meeting the industry operational requirements. HSI sensors successfully acquired simultaneous spatial and spectral information for certain polymer types. HSI, particularly the range between (1600-1900) nm, is suitable for specific identification of transparent and light-coloured (non-black) PC, PE-types, PP, PVC and PET-types plastics; HSI in the MWIR is able to resolve specific spectral features for certain PE-types, including black HDPE, and light-coloured ABS. Fast-acquisition Raman spectroscopy (down to 500 ms) enabled the identification of all polymers regardless their composition and presence of black pigments, however, it exhibited limited capacities in mapping applications. We therefore suggest a combination of both imaging and point measurements in a sequential design for enhanced robustness on industrial polymer identification.

Authentication of recycled and virgin polyethylene terephthalate based on UPLC-Q-TOF-MS using non-volatile organic compounds and chemometrics

Plastic packaging waste, such as polyethylene terephthalate (PET) has increased significantly in recent decades, arousing a considerable and serious public concern regarding the environment, economy, and policy. Plastic recycling is a useful tool to mitigate this issue. Here, a feasible study was performed to investigate the potential of a novel method for identifying virgin and recycled PET. Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was combined with various chemometrics, as a simple and reliable method that achieved a high discrimination rate for 105 batches of virgin PET (v-PET) and recycled PET (r-PET) based on 202 non-volatile organic compounds (NVOCs). Making use of orthogonal partial least-squares discrimination analysis (OPLS-DA) together with non-parametric tests, 26 marker compounds (i.e. 12 intentionally added substances (IAS) and 14 non-intentionally added substances (NIAS) as well as 31 marker compounds (i.e. 11 IAS and 20 NIAS) obtained from positive and combination of positive and negative ionization modes of UPLC-Q-TOF-MS, respectively, were successfully identified. Moreover, 100% accuracy was obtained using a decision tree (DT). Cross-discrimination based on misclassified samples using various chemometrics allowed the prediction accuracy to be improved and to identify a large sample set, thus greatly enhancing the application scope of this method. The possible origins of these detected compounds can be the plastic itself, as well as contamination from food, medicine, pesticides, industry-related substances, and degradation and polymerization products. As many of these compounds are toxic, especially those pesticide related, this indicates an urgent requirement for closed loop recycling. Overall, this analytical method provides a quick, accurate, and robust way to distinguish virgin from recycled PET and thus addresses the issue of potential virgin PET adulteration thereby detecting fraud in the area of PET recycling.

Optical sensors and machine learning algorithms in sensor-based material flow characterization for mechanical recycling processes: A systematic literature review

Digital technologies hold enormous potential for improving the performance of future-generation sorting and processing plants; however, this potential remains largely untapped. Improved sensor-based material flow characterization (SBMC) methods could enable new sensor applications such as adaptive plant control, improved sensor-based sorting (SBS), and more far-reaching data utilizations along the value chain. This review aims to expedite research on SBMC by (i) providing a comprehensive overview of existing SBMC publications, (ii) summarizing existing SBMC methods, and (iii) identifying future research potentials in SBMC. By conducting a systematic literature search covering the period 2000 - 2021, we identified 198 peer-reviewed journal articles on SBMC applications based on optical sensors and machine learning algorithms for dry-mechanical recycling of non-hazardous waste. The review shows that SBMC has received increasing attention in recent years, with more than half of the reviewed publications published between 2019 and 2021. While applications were initially focused solely on SBS, the last decade has seen a trend toward new applications, including sensor-based material flow monitoring, quality control, and process monitoring/control. However, SBMC at the material flow and process level remains largely unexplored, and significant potential exists in upscaling investigations from laboratory to plant scale. Future research will benefit from a broader application of deep learning methods, increased use of low-cost sensors and new sensor technologies, and the use of data streams from existing SBS equipment. These advancements could significantly improve the performance of future-generation sorting and processing plants, keep more materials in closed loops, and help paving the way towards circular economy.

Deep Learning for Plastic Waste Classification System

Plastic waste management is a challenge for the whole world. Manual sorting of garbage is a difficult and expensive process, which is why scientists create and study automated sorting methods that increase the efficiency of the recycling process. The plastic waste may be automatically chosen on a transmission belt for waste removal by using methods of image processing and artificial intelligence, especially deep learning, to improve the recycling process. Waste segregation techniques and procedures are applied to major groups of materials such as paper, plastic, metal, and glass. Though, the biggest challenge is separating different materials types in a group, for example, sorting different colours of glass or plastics types. The issue of plastic garbage is important due to the possibility of recycling only certain types of plastic (PET can be converted into polyester material). Therefore, we should look for ways to separate this waste. One of the opportunities is the use of deep learning and convolutional neural network. In household waste, the most problematic are plastic components, and the main types are polyethylene, polypropylene, and polystyrene. The main problem considered in this article is creating an automatic plastic waste segregation method, which can separate garbage into four mentioned categories, PS, PP, PE-HD, and PET, and could be applicable on a sorting plant or home by citizens. We proposed a technique that can apply in portable devices for waste recognizing which would be helpful in solving urban waste problems.

Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling

In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectroscopy) generally focus on identifying mono-materials of a few selected types which currently have a market-interest as secondary materials. Current progress in photonic sciences together with advanced data analysis, such as artificial intelligence, enable bridging practical challenges previously not feasible, for example in terms of classifying more complex materials. In the present paper, the different techniques are initially reviewed based on their main characteristics. Then, based on academic literature, their suitability for monitoring the composition of multi-materials, such as different types of multi-layered packaging and fibre-reinforced polymer composites as well as black plastics used in the motor vehicle industry, is discussed. Finally, some commercial systems with applications in those sectors are also presented. This review mainly focuses on the materials identification step (taking place after waste collection and before sorting and reprocessing) but in outlook, further insights on sorting are given as well as future prospects which can contribute to increasing the circularity of the plastic composites' value chains.

Degradation of Styrenic Plastics during Recycling: Accommodation of PP within ABS after WEEE Plastics Imperfect Sorting

With the development of dark polymers for industrial sorting technologies, economically profitable recycling of plastics from Waste Electrical and Electronical Equipment (WEEE) can be envisaged even in the presence of residual impurities. In ABS extracted from WEEE, PP is expected to be the more detrimental because of its important lack of compatibility. Hence, PP was incorporated to ABS at different rates (2 to 8 wt%) with a twin-screw extruder. PP was shown to exhibit a nodular morphology with an average diameter around 1-2 µm. Tensile properties were importantly diminished beyond 4 wt% but impact resistance was decreased even at 2 wt%. Both properties were strongly reduced as function of the contamination rate. Various potential compatibilizers for the ABS + 4 wt% PP system were evaluated: PPH-g-MA, PPC-g-MA, ABS-g-MA, TPE-g-MA, SEBS and PP-g-SAN. SEBS was found the most promising, leading to diminution of nodule sizes and also acting as an impact modifier. Finally, a Design Of Experiments using the Response Surface Methodology (DOE-RSM) was applied to visualize the impacts and interactions of extrusion temperature and screw speed on impact resistance of compatibilized and uncompatibilized ABS + 4 wt% PP systems. Resilience improvements were obtained for the uncompatibilized system and interactions between extrusion parameters and compatibilizers were noticed.

A low-cost LIBS detection system combined with chemometrics for rapid identification of plastic waste

We present, rapid and efficient identification of ten different types of post-consumer plastics obtained from a local recycling unit by deploying a low cost, compact CCD spectrometer in laser-induced breakdown spectroscopy (LIBS) technique. For this investigation, spectral emissions were collected by an Echelle spectrograph equipped with an intensified charge-coupled device (ES-ICCD) as well as a non-gated Czerny Turner CCD spectrometer (NCT-CCD). The performance is evaluated by interrogating the samples in a single-shot as well as accumulation mode (ten consecutive laser shots). The results from principal component analysis (PCA) have shown excellent discrimination. Further, the artificial neural network (ANN) analysis has demonstrated that individual identification accuracies/rates up to ~99 % can be achieved. The data acquired with ES-ICCD in the accumulation of ten shots have shown average identification accuracies ~97 %. Nevertheless, similar performance is achieved with the NCT-CCD spectrometer even in a single shot acquisition which reduces the overall analysis time by a factor of ~15 times compared to the ES-ICCD. Furthermore, the detector/collection system size, weight, and cost also can be reduced by ~10 times by employing a NCT-CCD spectrometer. The results have the potential in realizing a compact and low-cost LIBS system for the rapid identification of plastics with higher accuracies for the real-time application.

A novel bulk density-based recognition method for kitchen and dry waste: A case study in Beijing, China

Identification technology of household kitchen and dry solid waste has played a major part in improving the accuracy of residents' separation by intelligent outdoor trashcan, which is an effective integral solid waste management tool for growing household solid waste (HSW). Our study aims to present a novel and simple recognition method for kitchen and dry waste based on bulk density. In three communities in Beijing, 270 bagged waste samples were collected, and their moisture content, separation accuracy, and bulk density, characterized. Then a bulk density index was developed to straightforwardly express residents' waste source separation accuracy by linear regression analysis above physical properties. In the 3 Beijing communities, we demonstrated a clear distinction in the bulk density index, for dry, mixed, and kitchen waste of <115, 115-211, >211 kg/m³, respectively. Our results provide a theoretical basis for the establishment of an intelligent waste supervision system, which is of great significance for waste management in developing countries like China.

Quantum cascade laser-based reflectance spectroscopy: a robust approach for the classification of plastic type

The identification of plastic type is important for environmental applications ranging from recycling to understanding the fate of plastics in marine, atmospheric, and terrestrial environments. Infrared reflectance spectroscopy is a powerful approach for plastics identification, requiring only optical access to a sample. The use of visible and near-infrared wavelengths for plastics identification are limiting as dark colored plastics absorb at these wavelengths, producing no reflectance spectra. The use of mid-infrared wavelengths instead enables dark plastics to be identified. Here we demonstrate the capability to utilize a pulsed, widely-tunable (5.59 - 7.41 µm) mid-infrared quantum cascade laser, as the source for reflectance spectroscopy, for the rapid and robust identification of plastics. Through the application of linear discriminant analysis to the resulting spectral data set, we demonstrate that we can correctly classify five plastic types: polyethylene terephthalate (PET), high density polyethylene (HDPE), low density polyethylene (LDPE), polypropylene (PP), and polystyrene (PS), with a 97% accuracy rate.

MIR spectral characterization of plastic to enable discrimination in an industrial recycling context: III. Anticipating impacts of ageing on identification

Ageing of polymers entails important structural changes and degrades their functional properties, particularly their aspect. Since sorting is a primordial step in recycling to achieve acceptable mechanical properties, the use of promising technologies such as MIR-HSI (Mid-Infrared Hyperspectral Imagery), which could overcome black plastics sorting issue, has to take into account the influence of ageing on identification. As ageing strongly impacts spectra, it can create confusion between materials, especially in an automatized scheme. Based on laboratory FTIR-ATR (Fourier-Transform Infrared Attenuated Total Reflection), this work investigates spectral evolutions of natural and accelerated photodegradation of Waste of Electric and Electrical Equipment plastics (WEEE) as PE, PP, HIPS, ABS and PC to help identifying a polymer despite its ageing degree. Oxidation marks were described and retrieved within a stock of about one hundred of real waste samples, then differentiated from other sources of spectral alteration as formulation. Laboratory ageing data were found to be consistent and often more extreme than real waste samples values. Generally, styrenics showed stronger spectral alteration than polyolefins despite their respective aspects. No significant spectral alteration of PC was obtained here or observed in the waste stock. As an important oxidation marker, the carbonyl peak was also found to often enable fast identification through its wavenumber. If well taken in account, ageing should not induce confusion with other polymers, even formulated, as characteristic signals are different. Finally, the different industrial sub-ranges within MIR are not affected at the same degree, possibly influencing a technological choice for industrial sorting.

Independent components analysis (ICA) at the "cocktail-party" in analytical chemistry

Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals. Since the data acquired in many applications in analytical chemistry are mixtures of component signals, such a method is of great interest. In this article recent ICA applications for quantitative and qualitative analysis in analytical chemistry are reviewed. The following experimental techniques are covered: fluorescence, UV-VIS, NMR, vibrational spectroscopies as well as chromatographic profiles. Furthermore, we reviewed ICA as a preprocessing tool as well as existing hybrid ICA-based multivariate approaches. Finally, further research directions are proposed. Our review shows that ICA is starting to play an important role in analytical chemistry, and this will definitely increase in the future.

MIR spectral characterization of plastic to enable discrimination in an industrial recycling context: II. Specific case of polyolefins

Sorting at industrial scale is required to perform mechanical recycling of plastics in order to obtain properties that could be competitive with virgin polymers. As a matter of fact, the most part of the various types of plastic waste are not miscible and even compatible. Mid-Infrared (MIR) HyperSpectral Imagery (HSI) is viewed as one of the solutions to the problem of black plastic sorting. Many Waste of Electrical and Electronic Equipment (WEEE) plastics are black. Nowadays, these materials are difficult to sort at an industrial scale because the main used pigment to produce this color, carbon black, masks the Near-Infrared (NIR) spectra of polymers, the currently most used technology for acute sorting in industrial conditions. In this study, laboratory Fourier-Transform Infrared (FTIR) in Attenuated Total Reflection mode (ATR) has been used as a theoretical toolbox based on physical chemistry to help building an automated HSI discrimination despite its limited conditions, especially shorter wavelengths ranges. Weaker resolution and very short acquisition times are other HSI limitations. Helping fast and exhaustive laboratory characterizations of polymeric waste stocks is the other goal of this study. This study focusses on polyolefins as they represent the second biggest fraction of WEEE plastics (WEEP) after styrenics and since little quantities mixed to styrenics during mechanical recycling can lead to important decrease in mechanical properties. Twelve references were thus evaluated and compared between each other and with real waste samples to highlight spectral elements, which can enable differentiation. Charts compiling the signals of discussed polymers were built aiming to the same objective.

MIR spectral characterization of plastic to enable discrimination in an industrial recycling context: I. Specific case of styrenic polymers

One of the major limitations in polymer recycling is their sorting as they are collected in mixes. The majority of polymers are highly incompatible without compatibilizers. For sorting of polymers, high-speed online Near-Infrared (NIR) spectroscopy is nowadays relatively widespread. It is however limited by the use of carbon black as a pigment and UV-stabilizer, which strongly absorbs near-infrared signals. Mid-Infrared (MIR) hyperspectral cameras were recently put on the market. However, their wavelength ranges are smaller and their resolutions are poorer, in comparison with laboratory equipment based on Fourier-Transform Infrared (FTIR). The identification of specific signals of end-of-life polymers for recycling purposes is becoming an important stake since they are very diverse, highly formulated, and more and more used in copolymers and blends, leading to complex waste stocks mainly as WEEE (Waste Electrical and Electronic Equipment). Dark colored plastics are the major part of WEEE, which also contains mainly styrenics (ABS, HIPS and their blends). In addition, styrenics are especially concerned by the need of identification. In this framework, spectral characterizations of ten types of polymers were scrutinized through about eighty pristine and real waste samples. Polymer characteristic signals were aggregated in charts to help rapid and automatized distinction through specific signals, even in limited resolution and frequency ranges.

Distribution and characterization of microplastics in beach sand from three different Indian coastal environments

The occurrence of microplastic particles were evaluated on beaches along the Indian coast from three different locations Girgaon Mumbai (Arabian sea coast), Tuticorin, and Dhanushkodi (Bay of Bengal coast). Density separation method was adopted for isolation of microplastics from sand. Isolated microplastics were characterized using three different analytical techniques e.g. fluorescence microscopy (after staining with Nile Red), FTIR and SEM-EDS techniques. Microplastic concentrations in beach sands were from 45 ± 12 # MP kg^-1 to 220 ± 50 # MP kg^-1 of dry sand. The order of abundance of plastic type was polyethylene (43%) > polyethylene terephthalate (17.3%) ≈ polystyrene (17%) > polypropylene (12.3%) > Others (11%) > polyvinylchloride (1.33%), and very similar profile was observed for all monitored locations. SEM images show microplastics surfaces with characteristic cracks, suggests their polymer aging, mechanical and oxidative weathering, which was found highest for the microplastics collected from Mumbai.

Visual-Acoustic Sensor-Aided Sorting Efficiency Optimization of Automotive Shredder Polymer Residues Using Circularity Determination

To reduce the emissions and weight of vehicles, manufacturers are incorporating polymer materials into vehicles, and this has increased the difficulty in recycling End-of-Life vehicles (ELVs). About 25–30% (mass) of an ELV crushed mixture is the unrecyclable material known as automotive shredder residues (ASRs), and most of the vehicle polymers are concentrated in this fraction. Thus, these vehicle polymers are conventionally disposed of in landfills at a high risk to the environment. The only way to solve this problem is through the development of a novel separation and recycling mechanism for ASRs. Our previous research reported a novel sensor-aided single-scrap-oriented sorting method that uses laser-triangulation imaging combined with impact acoustic frequency recognition for sorting crushed ASR plastics, and we proved its feasibility. However, the sorting efficiencies were still limited, since, in previous studies, the method used for scrap size determination was mechanical sieving, resulting in many deviations. In this paper, a new method based on three-dimensional (3D) imaging and circularity analysis is proposed to determine the equivalent particle size with much greater accuracy by avoiding the issues that are presented by the irregularity of crushed scraps. In this research, two kinds of commonly used vehicle plastics, acrylonitrile-butadiene-styrene (ABS) and polypropylene (PP), and their corresponding composite materials, acrylonitrile-butadiene-styrene/polycarbonate (ABS/PC) and polypropylene/ethylene-propylene-diene-monomer (PP/EPDM), were studied. When compared with our previous study, with this new method, the sorting efficiency increased, with PP and PP/EPDM and ABS and ABS/PC achieving about 15% and 20% and 70% and 90%, respectively. The sorting efficiency of ASR polymer scraps can be optimized significantly by using sensor-aided 3D image measurement and circularity analysis.

Occurrence of microplastics in fishes from two landing sites in Tuticorin, South east coast of India

Microplastics pollution of the marine environment has been reported worldwide. Here, we investigate the occurrence of microplastics in two species of fishes namely Rastrilleger kanagurta and Epinephalus merra bought from Thirespuram and Punnakayal fish landing sites at Tuticorin. Out of the total 40 fish, 12 fish showed the presence of microplastic particulates in the intestine. The particulates included microfibers (80%) in red, black and translucent colors and irregularly shaped microplastic fragments (20%). The microplastics were identified as Polyethylene and Polypropylene by Fourier Transform Infrared Radiation analysis. Though microplastics were detected in the gut of the species, the risk of transfer due to consumption can be safely ruled out as the fish are degutted prior to consumption here. Presence of microplastics in the Tuticorin coast is a matter of concern due to its proximity to the Gulf of Mannar, a sensitive coral reef patch already threatened by marine pollution.

A discrimination model in waste plastics sorting using NIR hyperspectral imaging system

Classification of plastics is important in the recycling industry. A plastic identification model in the near infrared spectroscopy wavelength range 1000-2500 nm is proposed for the characterization and sorting of waste plastics using acrylonitrile butadiene styrene (ABS), polystyrene (PS), polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). The model is built by the feature wavelengths of standard samples applying the principle component analysis (PCA), and the accuracy, property and cross-validation of the model were analyzed. The model just contains a simple equation, center of mass coordinates, and radial distance, with which it is easy to develop classification and sorting software. A hyperspectral imaging system (HIS) with the identification model verified its practical application by using the unknown plastics. Results showed that the identification accuracy of unknown samples is 100%. All results suggested that the discrimination model was potential to an on-line characterization and sorting platform of waste plastics based on HIS.

Evaluation of olive oils from the Mediterranean region by UV-Vis spectroscopy and Independent Component Analysis

Extra-virgin olive oil (EVOO) from Mediterranean were analyzed by Ultraviolet-Visible (UV-Vis) spectroscopy and Independent Component Analysis (ICA). The use of ICA resolution provided information over dienes (primary oxidation compound), polyphenolic compounds, tocopherol, carotenoids and chlorophylls when EVOO was evaluated by UV-Vis spectroscopy. Based on these data, ICA could be used to determine the contribution of chemical compounds to the composition of EVOO produced in different regions from Mediterranean. The results suggest that the combination of UV-Vis measurements and ICA makes possible the EVOO evaluation, and can contribute to suggesting that a foodstuff comes from an alleged origin. The proposed methodology is a low cost, fast and sample preparation free methodology to highlights the EVOO characteristics produced in the Mediterranean region.

Multi-material classification of dry recyclables from municipal solid waste based on thermal imaging

There has been a significant rise in municipal solid waste (MSW) generation in the last few decades due to rapid urbanization and industrialization. Due to the lack of source segregation practice, a need for automated segregation of recyclables from MSW exists in the developing countries. This paper reports a thermal imaging based system for classifying useful recyclables from simulated MSW sample. Experimental results have demonstrated the possibility to use thermal imaging technique for classification and a robotic system for sorting of recyclables in a single process step. The reported classification system yields an accuracy in the range of 85-96% and is comparable with the existing single-material recyclable classification techniques. We believe that the reported thermal imaging based system can emerge as a viable and inexpensive large-scale classification-cum-sorting technology in recycling plants for processing MSW in developing countries.

Development of a new approach based on midwave infrared spectroscopy for post-consumer black plastic waste sorting in the recycling industry

Waste sorting is key to the process of waste recycling. Exact identification of plastic resin and wood products using Near Infrared (NIR, 1-1.7µm) sensing is currently in use. Yet, dark targets characterized by low reflectance, such as black plastics, are hard to identify by this method. Following the recent success of Midwave Infrared (MWIR, 3-12µm) measurements to identify coloured plastic polymers, the aim of this study was to assess whether this technique is applicable to sorting black plastic polymers and wood products. We performed infrared reflectance contact measurements of 234 plastic samples and 29 samples of wood and paper products. Plastic samples included black, coloured and transparent Polyethylene Terephthalate (PET), Polyethylene (PE), Polyvinyl Chloride (PVC), Polypropylene (PP), Polylactic acid (PLA) and Polystyrene (PS). The spectral signatures of the black and coloured plastic samples were compared with clear plastic samples and signatures documented in the literature to identify the polymer spectral features in the presence of coloured material. This information was used to determine the spectral bands that best suit the sorting of black plastic polymers. The main NIR-MWIR absorption features of wood, cardboard and paper were identified as well according to the spectral measurements. Good agreement was found between our measurements and the absorption features documented in the literature. The new approach using MWIR spectral features appears to be useful for black plastics as it overcomes some of the limitations in the NIR region to identify them. The main limitation of this technique for industrial applications is the trade-off between the signal-to-noise ratio of the sensor operating in standoff mode and the speed at which waste is moved under the sensor. This limitation can be resolved by reducing the system's spectral resolution to 16cm^-1, which allows for faster spectra acquisition while maintaining a reasonable signal-to-noise ratio.

Critical review of real-time methods for solid waste characterisation: Informing material recovery and fuel production

Waste management processes generally represent a significant loss of material, energy and economic resources, so legislation and financial incentives are being implemented to improve the recovery of these valuable resources whilst reducing contamination levels. Material recovery and waste derived fuels are potentially valuable options being pursued by industry, using mechanical and biological processes incorporating sensor and sorting technologies developed and optimised for recycling plants. In its current state, waste management presents similarities to other industries that could improve their efficiencies using process analytical technology tools. Existing sensor technologies could be used to measure critical waste characteristics, providing data required by existing legislation, potentially aiding waste treatment processes and assisting stakeholders in decision making. Optical technologies offer the most flexible solution to gather real-time information applicable to each of the waste mechanical and biological treatment processes used by industry. In particular, combinations of optical sensors in the visible and the near-infrared range from 800nm to 2500nm of the spectrum, and different mathematical techniques, are able to provide material information and fuel properties with typical performance levels between 80% and 90%. These sensors not only could be used to aid waste processes, but to provide most waste quality indicators required by existing legislation, whilst offering better tools to the stakeholders.

A review on automated sorting of source-separated municipal solid waste for recycling

A crucial prerequisite for recycling forming an integral part of municipal solid waste (MSW) management is sorting of useful materials from source-separated MSW. Researchers have been exploring automated sorting techniques to improve the overall efficiency of recycling process. This paper reviews recent advances in physical processes, sensors, and actuators used as well as control and autonomy related issues in the area of automated sorting and recycling of source-separated MSW. We believe that this paper will provide a comprehensive overview of the state of the art and will help future system designers in the area. In this paper, we also present research challenges in the field of automated waste sorting and recycling.

Influence of 2015 flood on the distribution and occurrence of microplastic pellets along the Chennai coast, India

The sources, distribution, surface features, polymer composition and age of microplastic pellets (MPPs) in surface sediments along the Chennai coast during March 2015 (pre-Chennai flood) and November 2015 (post-Chennai flood) were characterised using a Stereoscopic microscope and FTIR-ATR spectroscopy. White MPPs were the most abundant, and specifically polyethylene (PE) and polypropylene (PP) were the dominant polymer types of MPPs found on the coast during both the times. The abundance of MPPs in November 2015 was three-fold higher than those found in March 2015, confirming that huge quantity of fresh MPPs washed through Cooum and Adyar rivers from land during the flood. The winds and surface currents during November were the driving forces for the transportation and deposition of MPPs from the sea to beaches. The results of this study will be useful to formulate beach MPPs litter management policies to effectively create long-term solutions.