AI Transforms Plastic Waste into Recyclates Suitable for Industrial Use

Recycling AI Transforms Plastic Waste into Recyclates Suitable for Industrial Use

2026-09-02 By Fraunhofer LBF | Translated by AI 2 min Reading Time

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As part of the “K3I-Cycling” project, researchers at Fraunhofer LBF, in collaboration with 16 partners, have developed AI-based methods for sorting post-consumer plastics—such as those collected in yellow bags. The focus is on the practical production and evaluation of recycled materials on a laboratory and pilot scale, as well as the development of additive packages.

In the K3I-Cycling project, the participants are developing new methods to produce high-quality plastic recyclates from mixed lightweight packaging waste.(Source: Kay Herschelmann) — In the K3I-Cycling project, the participants are developing new methods to produce high-quality plastic recyclates from mixed lightweight packaging waste.
(Source: Kay Herschelmann)

Mixed lightweight packaging waste (LVP) is a challenging source of raw materials because its composition varies. Contaminants and the degree of aging affect the quality of the recycled materials produced from it. This is precisely where the “K3I-Cycling” project comes in. In this project, funded by the BMFTR, the Fraunhofer Institute for Structural Durability and System Reliability LBF is contributing its expertise in materials evaluation, system reliability, digitalization, and circularity to the “Recycling and Recyclate Production” work package. The researchers are developing new methods to produce high-quality plastic recyclates from mixed lightweight packaging waste.
The focus is on practical recyclate production on a laboratory and pilot scale, the evaluation of material properties, and the development of additive packages. This also includes bio-based stabilizers that can be used to specifically improve the properties of the recycled materials.To this end, Fraunhofer LBF combines real-world materials analysis with machine learning. Polyolefin recyclates are classified according to their degree of aging and impurities and grouped into quality clusters. This results in reliable material quality levels that can be incorporated into new standards and digital product passports.

Specifications Ensure Reliability for Demanding Applications

The research provides comprehensive and reliable performance data for high-quality plastic recyclates used in demanding products. This is important for packaging manufacturers, recyclers, and brand owners. They can evaluate recycled materials more precisely and use them in applications that place high demands on material quality and reliability. This also includes packaging intended for food contact.To meet these requirements, the Artificial Neural Twin (ANT) was developed as part of the project. It maps the entire sorting chain from collection to the end user of the recycled material and enables the targeted optimization of individual parameters (e.g., purity, short logistics, low price) across the entire value chain.
AI is also used to protect sorting facilities: DangerSort can reliably detect and eject lithium batteries before they cause fires. Industry and municipalities benefit from more reliable decision-making and fail-safe systems. They can meet recycling targets cost-effectively, measurably reduce CO₂ emissions, and secure the supply of secondary raw materials. This strengthens a resilient European circular economy and combines virtual development with real-world validation.

Rymoplast has invested in a recycling plant in Markranstädt, Saxony, near Leipzig. There, high-quality granulate is produced from plastic films. However, the thermal management of the processing systems must be optimal for this. Technotrans had the right solution ... (Image: Rymoplast)

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