Image Credit: NETZSCH-Gerätebau GmbH
Recycling plastic presents many challenges across the manufacturing workflow. One of the biggest obstacles to overcome is controlling feedstock quality. Such variations in quality occur due to the mixtures of different polymer types in the recycling process. As a consequence, the issue arises that leads to many product manufacturers being reluctant to use recycled plastics because the quality variations across batches can have a serious impact on maintaining the quality of the end-products.
Companies Need a Fast and Cost-Effective Solution
Therefore, effective quality control of recycled plastics is a vital step in the production process for many companies. They need to be able to monitor, detect, and identify impurities of recycled materials in a rapid, dependable and cost-efficient way.
Step 1: Verify the Polymer Type of the Granule
A mixture of re-granulates varying in color was introduced into the polymer waste stream. Differential scanning calorimetry measurements were performed using a NETZSCH DSC 214 Polyma at a heating rate of 10 K/min in an N2 atmosphere.
Figure 1. Recycled PP granulate. Image Credit: NETZSCH-Gerätebau GmbH
NETZSCH Proteus® software comes equipped with the Identify feature, which can make an accurate comparison of the measurement results to entries in the database. The database contains literature data, individual measurement curves, and statistical classes of polymers and other materials.
Figure 2 demonstrates the measurement of the green polypropylene (PP) sample as well as the database entry in pink. The comparison of both curves reveals a correlation of 99.45% between the green re-granule and a standard polypropylene sample. Here, the Identify software compares important measurement points of the sample such as melting, glass transition temperature, and recrystallization effects.
Figure 2. Using Proteus® software to compare measurement curves. Image Credit: NETZSCH-Gerätebau GmbH
Step 2: Detect Impurities in Recycled Plastic Materials
Furthermore, granules of varying colors were also analyzed in the process. The measurement curves of the white and black granules (see figure 3) each illustrate an additional peak, which is melting effects, e.g., of another polymer type. Therefore, the delivered recycled PP re-granulate contains impurities therein.
Figure 3. Comparison of three different granules of recycled PP. Image Credit: NETZSCH-Gerätebau GmbH
Step 3: Identify Impurities in Recycled Plastic Materials
Figure 4 shows that the Identify database integrated into the NETZSCH Proteus® software detects the source of contamination in the re-granulate. By only assessing a user-defined area of the curve, in this case the first peak, Identify scrolls through the database to reveal a stored measurement curve that best matches the measured peak.
In figure 4, further analysis of the black sample was carried out to identify the impurity correctly. The software demonstrated contamination of the sample of the recycled material with linear low-density polyethylene (PE-LLD).
Figure 4. Identification of the second polymer type in the sample. Image Credit: NETZSCH-Gerätebau GmbH
Step 4: Use Thermogravimetric Analysis for an Additional Insight
A second method may be required for cases where a singular method is not adequate to distinguish between different polymer types. In the case of DSC, utilizing complementary thermogravimetric analysis (TGA) is an option. With this technique, the Identify software feature can also detect and identify the various polymer types using TGA curves or using a combined TGA and DSC database search.
Figure 5. TGA measurement and evaluation carried out using the unique AutoEvaluation software feature followed by the identification of the components using Identify. Image Credit: NETZSCH-Gerätebau GmbH
Differential Scanning Calorimetry (DSC) is an efficient, reliable and easy way to control the quality of recycled plastic materials in the manufacturing industry. The special features of the Proteus® software offer supplementary support and enable a cost-efficient quality control process by automatically detecting and identifying impurities.
This information has been sourced, reviewed and adapted from materials provided by NETZSCH-Gerätebau GmbH.
For more information on this source, please visit NETZSCH-Gerätebau GmbH.