Influences of bioplastic polylactic acid on near-infrared-based sorting of conventional plastic.

Bioplastics are developed to replace oil-derived plastics due to the high consumption of oil and related environmental impacts of oil-derived plastics. It was predicted that bioplastics can potentially replace 94% of conventional plastic production. With their increasing market share, more bioplastics will end in conventional post-consumer plastic waste streams. Although part of bioplastics is biodegradable and could be biologically decomposed, mechanical recycling achieves higher ecological benefits mainly because of its low pollution risk and the reduction in requirement for virgin feedstock. In this study, the classification of lightweight packaging waste with inflow of bioplastics, more specifically polylactic acid (PLA), was analysed with near-infrared spectroscopy to evaluate the influence of bioplastics on sorting processes of conventional plastics. Besides which, the sortability of PLA was determined through investigating the physical and the spectroscopic characteristics of both non-degraded and degraded PLA. The results show that the classification of all the materials was possible with a pixel-based accuracy of higher than 97.4% and PLA does not influence the sorting process of conventional plastics regarding detection and classification. Furthermore, the sorting of PLA from post-consumer waste is possible, which makes further recycling theoretically achievable.

Improvement of the recycling of plastics in lightweight packaging treatment plants by a process control concept.

In Germany, only approximately 30% by mass of plastics from lightweight packaging waste is recycled; 65% by mass is transferred to inferior residual fractions (sorting residue and mixed plastics), which are currently only utilized thermally. An increase in the recycling of valuable resources in the sense of material recycling would both contribute to the saving of resources and improve the economic situation of plant operators. It is generally known from operating and planning experience that fluctuation in the amount of material loaded into the sorting process is one of the main reasons for suboptimal recycling quotas. In particular, overfilling in the input stream leads to a deterioration of the separation result of the entire process. A novel process control concept envisages equalizing the material flow in such a way that all separation steps are operated in the intended design range. For the example of a lightweight packaging treatment process, the requirements and technological solutions for a sensor-based process control concept will be presented.

A methodical approach for the assessment of waste sorting plants.

A techno-economical evaluation of the processing result of waste sorting plants should at least provide a realistic assessment of the recovery yields of valuable materials and of the qualities of the obtained products. This practical data is generated by weighing all the output products and sampling these products. Due to the technological complexity of sorting plants, for example, lightweight packaging waste treatments plants and the high expenditures concerning time and costs of sampling with subsequent manual sorting for quality determination, usually only final products undergo such an investigation. Thereby, the transferability of the results depends decisively on the boundary conditions (extent, throughput of the plant, process parameterization). Given that the process is too complex, not all relevant information of the process steps can be determined by sampling. By model calculations and/or adjustment of reasonable assumptions, information concerning weak points in the process can be identified, which can be used for further plant optimization. For the example of the recovery of beverage cartons from co-collected and mechanically recovered mixtures of lightweight packaging waste, a methodical approach for the assessment of processing results will be presented.

Separate collection of plastic waste, better than technical sorting from municipal solid waste?

The politically preferred solution to fulfil legal recycling demands is often implementing separate collection systems. However, experience shows their limitations, particularly in urban centres with a high population density. In response to the European Union landfill directive, mechanical biological waste treatment plants have been installed all over Europe. This technology makes it possible to retrieve plastic waste from municipal solid waste. Operators of mechanical biological waste treatment plants, both in Germany and the Netherlands, have started to change their mechanical separation processes to additionally produce plastic pre-concentrates. Results from mechanical biological waste treatment and separate collection of post-consumer packaging waste will be presented and compared. They prove that both the yield and the quality of plastic waste provided as feedstock for the production of secondary plastic raw material are largely comparable. An economic assessment shows which conditions for a technical sorting plant are economically attractive in comparison to separate collection systems. It is, however, unlikely that plastic recycling will ever reach cost neutrality.

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.

Recycling of polystyrene-based external thermal insulation composite systems - Application of combined mechanical and chemical recycling.

The material recycling of complex waste streams such as external thermal insulation composite systems (ETICS) is challenging, which is why their recycling in the sense of a circular economy is currently hardly established. Therefore, the combined mechanical and thermochemical recycling of ETICS based on expanded polystyrene (EPS) is investigated experimentally and by simulating full process chains in order to evaluate circular economy opportunities. Model ETICS as example for building and construction waste is pretreated mechanically, followed by either pyrolysis and / or gasification steps, and full mass and energy balances are derived. By the combined recycling, inorganic compounds can be separated to a large extent allowing a pre-concentrate generation. The plastic-rich pre-concentrate is converted into either pyrolysis oil with a high styrene monomer content of 51 wt% or to synthesis gas in the subsequent thermochemical conversions. The holistic approach enables a high carbon recycling rate between 53 and 68 wt%. In addition, the investigation reveals technology limitations and opportunities to be further developed and optimized.

Determining the composition of post-consumer flexible multilayer plastic packaging with near-infrared spectroscopy.

Flexible multilayer plastic packaging (MPP) has grown in popularity in the last years especially in food and medical sectors, and its share in the packaging industry is expected to increase further. Compared to traditional packaging with same functionalities, MPP is characterized by lower energy consumption in production and a reduced packaging weight. So far, the recycling of post-industrial MPP with specific material composition has been achieved by several companies. To our knowledge, all existing MPP recycling processes require a known material combination. In contrast to post-industrial MPP, post-consumer MPP still ends up in incinerators or as low-quality products, mainly because of the lacking ability to sort. This study investigates the detectability of post-consumer MPP with near-infrared spectroscopy, the state-of-the-art technology for sensor-based waste sorting. Firstly, MPP classification with near-infrared spectroscopy was analyzed with clean samples. Subsequently, the effect of waste collection and preprocessing in sorting plants on MPP classification was investigated. For this purpose, clean samples were covered with water and oil and mixed with lightweight packaging waste in a drum sieve. The results show it is possible to classify post-consumer MPP based on near-infrared spectra according to different sorting strategies. For the existing recycling processes which are suitable for post-consumer MPP, the corresponding object-based classification accuracy was found to exceed 96%.

Influence of long-term natural degradation processes on near-infrared spectra and sorting of post-consumer plastics.

The large-amount production and application of plastics since the 1950s has led to different environmental problems, and the production amount is still increasing. In 2015, 79 wt% of all plastic waste was accumulated in landfills or the natural environment. Due to their negative influence to the environment, the problems of landfilling and marine litter need urgent treatments. Accordingly, measures like excavation of landfill sites and ocean clean-ups were conducted to reduce their environmental influences and move further towards a closed loop of material cycles. For a possible recycling, the valuable material fractions need to be separated from other materials. Besides, to ensure a high-quality recycling and enable the different recycling processes of plastics in different degradation levels, it is necessary to separate degraded and non-degraded plastics. In this study, the possibility to classify and sort landfill and marine litter plastics is investigated. For this purpose, waste plastics from different origins (lightweight packaging (LWP) waste, landfill, and marine litter) were collected and analyzed with the state-of-the-art technology in sorting plants: near-infrared spectroscopy. With self-developed programs, the classification possibility and performance was determined. The classification accuracy of degraded plastics (from landfill and marine litter) is improved from > 75% to > 97% through adjusting the sorting recipe. Besides, the long-term degraded plastics under natural environment were able to be separated from LWP waste: the same kind of materials can be classified according to their origin (LWP or after long-term degradation), which makes a quality control possible and enables an extra treatment for degraded plastics.

Sensor-based particle mass prediction of lightweight packaging waste using machine learning algorithms.

Sensor-based material flow characterization (SBMC) promises to improve the performance of future-generation sorting plants by enabling new applications like automatic quality monitoring or process control. Prerequisite for this is the derivation of mass-based material flow characteristics from pixel-based sensor data, which requires known individual particle masses. Since particle masses cannot be measured inline, the prediction of particle masses of lightweight packaging (LWP) waste using machine learning (ML) algorithms is investigated. Five LWP material classes were sampled, preprocessed, and scanned on a custom-made test rig, resulting in a dataset containing 3D laser triangulation (3DLT) images, RGB images, and corresponding masses of n = 3,830 particles. Based on 66 extracted shape measurements, six ML models were trained for particle mass prediction (PMP). Their performance was compared with two state-of-the-art reference models using (i) material-specific mean particle masses and (ii) grammages. Obtained particle masses showed a high variation and significant differences between material classes and particle size classes. After feature selection, both reference models achieving R2-scores of (i) 0.422 ± 0.121 and (ii) 0.533 ± 0.224 were outperformed by all investigated ML models. A random forest regressor with an R2-score of 0.763 ± 0.091 and a normalized mean absolute error of 0.243 ± 0.050 achieved the most accurate PMP. In contrast to studies on primary raw materials, PMP of LWP waste is challenging due to influences of packaging design and post-consumer disposal behavior. ML algorithms are a promising approach for PMP that outperform state-of-the-art methods by 43% higher R2-scores.