ReVise-UP: Improving the process efficiency of mechanical recycling of post-consumer plastic packaging waste through intelligent material flow management

Innovative sensor technologies

At 3.2 million tonnes per year, post-consumer packaging waste is the largest plastic waste stream in Germany. However, the recycling rate is low: In 2021, only around 27 wt% of post-consumer plastics were converted into recyclates, while only around 12 wt% were used to substitute virgin plastics. One of the main reasons is that the recycling process often lacks information on process-relevant material flow characteristics. This is due to the high cost of manual, sampling-based material flow characterization. The ReVise-UP researchers are using in-line sensor technology to automatically characterize post-consumer material flows. The aim is to improve collection and recyclate quality through more accurate material flow characterization; to efficiently adapt sorting, processing, and plastic conversion processes to fluctuating material flow characteristics and to create a solid database for a holistic assessment of plastic waste to better allocate technical investments, where they create the largest environmental and economic benefits.

Increasing transparency

ReVise-UP seeks to develop sensor-based monitoring for various process steps in plastic recycling. Using near and mid-infrared spectroscopy in combination with artificial intelligence methods, researchers in ReVise-UP aim to enable an automatic inline material flow characterization. In contrast to traditional, sampling-based characterization methods, the aim here is to achieve continuous monitoring of pre-concentrates from sorting plants. Based on this monitoring data, the aim is to promote optimized sorting and ultimately contribute to an improved overall quality of the plastic recyclates.

Higher yield through adaptive process parameterization

In addition, the researchers in ReVise-UP aim at developing the first prototypes of adaptive process parameterization in sorting, processing, and plastic conversion plants. Improved process feeding and adaptive parameterization of preconditioning units shall increase process efficiency in sorting plants. In addition, sensor-based material flow monitoring in plastic processing plants shall enable more precise addition of additives and optimized fine-tuning during compounding.

With view to the entire life cycle, it is being investigated which sorting and processing depths in sub-processes - for example, sensor-based sorting - are ecologically and economically advantageous. A particular focus will be on how the targeted coordination of processes can minimize the overall energy consumption and operating costs. The researchers are also interested in the influence of product design on the respective process. In addition, ReVise-UP will develop an incentive model to implement the optimization steps throughout the value chain. The ReVise-UP research project, coordinated by the Department of Anthropogenic Material Cycles at RWTH Aachen University, involves eight partners from academia and industry.

Progress and Results of the First Project Phase (Status April 2025)

Optimized recycling of post-consumer plastic packaging waste requires an improved data basis for material composition. To this end, the technical feasibility of sensor-based quality monitoring in lightweight packaging waste sorting plants was investigated in large-scale test series and an initial prototype was developed for long-term trials.

Furthermore, the effect of additional technical effort in sorting, preparation and plastics processing on the process efficiency of mechanical recycling of post-consumer plastic packaging waste is being investigated. For this purpose, a series of tests were implemented on an industrial scale with defined sorting and processing depths. The analysis of the results illustrates the relationship between the technical effort, the product quality and the achievable recyclate quantity.
In order to reduce the loss of recyclable materials to incineration due to so-called miss-throws, the focus is placed on the user's perspective regarding disposal behaviour. For this purpose, the LWP material from defined collection areas was stored separately to enable conclusions about the collection quality. Due to the bulk heights in the input of LWP sorting plants, sensor-optical detection is associated with technical difficulties. For an improved dataset generation, a concept for sensor fusion between RGB and NIR was developed and set up in an LVP sorting system. The database for the sensor fusion is currently being created for further evaluation.