New standard for recycled plastic

Assessing the quality of recycled plastic remains a challenge. What works for one application does not automatically meet the requirements for another. TNO and Maastricht University are therefore introducing the RecyQMeter. This tool is designed to enable an objective and transparent assessment of recyclates.

From subjective to a uniform score

The lack of standardized quality benchmarks is holding back the circular plastics economy. Quality assessments are often subjective and application-dependent. A recyclate suitable for the automotive industry may be unsuitable for packaging.

The RecyQMeter offers a solution. The tool translates laboratory measurements into a single, usable indicator. Recyclers input properties such as mechanical and optical values. These are then compared against a database with requirements for more than 200 applications. The tool subsequently calculates a score between 0 and 1. A score closer to 1 indicates higher quality, relative to the virgin material required for that specific application.

Insight and alignment across the chain

According to TNO scientist Milad Golkaram, the tool functions as a transparent marketplace. “Recyclers share what material they have, and end users specify their requirements.” This helps match recyclates more effectively with suitable applications.

In addition, the RecyQMeter calculates a so-called substitution ratio. This indicates how much virgin material can be replaced. This value can be used directly by LCA specialists in their environmental impact models. The tool therefore supports better substantiation of decisions within the plastics value chain. It can also contribute to a more efficient use of recyclates.

Foundation for further development

The RecyQMeter combines two models. TNO’s QMRP model focuses on a wide range of quality aspects. The RQ model from Maastricht University is highly accurate for mechanical properties. Together, they provide a combined assessment.

A next version of the tool is already in development. RecyQMeter 2.0 is expected to use machine learning. This will allow quality to be predicted based on material composition. It is intended to reduce the number of required laboratory measurements. The tool is already being applied in various supply chains. According to TNO, further development strongly depends on industry participation and data sharing.

Tno.nl