Assessing the resource potential of paper and board in lightweight packaging waste sorting plants through manual analysis and sensor-based material flow monitoring.

The recycling of paper and board (PB) yields economic and environmental advantages compared to primary paper production. However, PB from lightweight packaging (LWP) waste is currently not comprehensively reintegrated into the paper value stream. To develop an adapted recycling process for PB from LWP, PB quantities, qualities, and fluctuations ranges in LWP are required.

Developing a prediction model in a lightweight packaging waste sorting plant using sensor-based sorting data combined with data of external near-infrared and LiDAR sensors.

Sensor-based material flow monitoring allows for continuously high output qualities, through quality management and process control. The implementation in the waste management sector, however, is inhibited by the heterogeneity of waste and throughput fluctuations. In this study, challenges and possibilities of using different types of sensors in a lightweight packaging waste sorting plant are investigated.

NIR-MFCO dataset: Near-infrared-based false-color images of post-consumer plastics at different material flow compositions and material flow presentations.

Determining mass-based material flow compositions (MFCOs) is crucial for assessing and optimizing the recycling of post-consumer plastics. Currently, MFCOs in plastic recycling are primarily determined through manual sorting analysis, but the use of inline near-infrared (NIR) sensors holds potential to automate the characterization process, paving the way for novel sensor-based material flow characterization (SBMC) applications. This data article aims to expedite SBMC research by providing NIR-based false-color images of plastic material flows with their corresponding MFCOs.

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.

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.

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.

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.

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.