Recycled material traceability: data to retain during processing

Recycled plastic can introduce variations in composition, thermal history, moisture, contamination and rheological properties. The extent and significance of these variations depend on the source, treatment, formulation and product specification. Retaining batch information helps processors investigate the effects on stability, quality and productivity.

Useful traceability links incoming material to the finished product. In a plant using secondary raw materials, incoming records need to connect with formulation, machine, recipe, process conditions, inspection and product destination.

What to record when material arrives

A batch record can include supplier, identification, declared origin, grade or polymer family, recycled fraction where documented, relevant properties, certificate of analysis, date, quantity and incoming inspection. Depending on the application, melt flow rate, moisture, density, colour, contamination or mechanical properties may be required.

Distinguish results measured by the processor from data declared by the supplier. Both sources are valuable, but involve different levels of control and metrological responsibility.

Link the batch to process conditions

During production, identify the batches feeding an order, the blend proportions used and the equipment processing them. Record setpoints and, where justified by the application, measured variables that describe the actual thermal and mechanical conditions.

In extrusion, the temperature setpoint, wall temperature and melt temperature represent different phenomena. In injection moulding, hydraulic pressure and cavity pressure also have different meanings. Choosing the right signals helps interpret changes in material behaviour.

Quality data must retain their link to production conditions. An increase in rejects can then be investigated in relation to the batch, blend, machine, tooling and process window, alongside other product-related factors.

Indicators for understanding variability

Results that can be analysed include the percentage of rejected material, defects by category, cycle stability, dimensional variation, process incidents and specific energy consumption. Comparisons between batches require control of geometry, tooling, blend proportions and relevant operating conditions.

When sufficient representative observations are available, statistical tools or machine learning models can explore relationships between incoming properties and outcomes. Validation through physical trials and independent measurements remains essential for decisions affecting conformity and performance.

Exchange information between recycler and processor

A circular value chain works better when participants agree on identifiers, terminology and material attributes. The recycler can provide information on origin, sorting, washing, formulation and characterisation results; the processor supplies evidence on processability, rejects and product conformity. This feedback helps define acceptance windows and reduce technical uncertainty.

ISO 59004, ISO 59010 and ISO 59020 provide frameworks for terminology, value networks and circularity assessment. Life cycle assessment methodologies, structured by ISO 14040 and ISO 14044, address a complementary question: the environmental impact associated with a product or process option. Material circulation and environmental impact require distinct metrics.

What role can a digital product passport play?

A digital product passport organises information for different participants across the product life cycle. In the European Union, the Digital Product Passport Registry became operational in July 2026 under Regulation (EU) 2024/1781. Specific requirements depend on product categories and their implementing provisions; a processing company needs to assess applicability to its products and customers.

Technical preparation can begin with data that are already useful to the plant: identification, documented composition, origin, batch, product version, properties and evidence of conformity. These records support traceability and quality and can facilitate future information exchange.

Explore production traceability with KERN IoP, quality management and the sector study on digitalisation.

References

• Andraju, N., Curtzwiler, G. W., Ji, Y., Kozliak, E., & Ranganathan, P. (2022). Machine-learning-based predictions of polymer and postconsumer recycled polymer properties: A comprehensive review. ACS Applied Materials & Interfaces, 14(38), 42771–42790. https://doi.org/10.1021/acsami.2c08301
• European Commission. (2026, 20 July). The Digital Product Passport Registry is now live. https://single-market-economy.ec.europa.eu/news/digital-product-passport-registry-now-live-2026-07-20_en
• Jensen, S. F., Kristensen, J. H., Adamsen, S., Christensen, A., & Wæhrens, B. V. (2023). Digital product passports for a circular economy: Data needs for product life cycle decision-making. Sustainable Production and Consumption, 37, 242–255. https://doi.org/10.1016/j.spc.2023.02.021
• PM Tec Servicios de Ingeniería SAS, KERN IoP. (2026). Estado del arte mundial de la digitalización en PYMEs de transformación de plásticos.