Prediction of Plastic Fragments in Recycled Paper Using Near-Infrared Spectroscopy

Abstract: Sustainability has gained a lot of attention in the field of research. Researchers and consumers both prioritize sustainability and environmental issues over previously dominant materials, such as plastic. Packaging and disposable items that used to be made of plastic have largely been replaced with paper. Unfortunately, paper does not perform as well as plastic regarding barrier properties against grease, oxygen, or water vapor. Barrier properties are an important factor when choosing packaging material for food, among other things, as they help maintain the shelf life of the product. In order to improve the properties of the paper packaging and expand its use, the paper is coated with a polymer. However, the polymer contributes to challenges in the recycling of the products as some of the polymer attaches to the fibers, causing difficulties in the separation of each material. Small fragments of plastic may end up in the material streams and the recycled pulp due to the existing challenges in completely removing plastic from cellulosic substrates during recycling. This thesis analyzes the possibilities of identifying and classifying plastic fragments of polyethylene (PE) and polyvinyl alcohol (PVOH) in recycled paper sheets using near-infrared spectroscopy together with multivariate data analysis. The purpose of the work is to develop models that can identify possible residues that may appear in recycled products from various industries. Paper sheets of two different grammages and six different compositions of recycled fiber and virgin fiber were created and scanned by NIR, with and without plastic film under the sheets. The scans were used to develop classification models to identify and categorize scans not included in the calibration data set. The performance of the models was tested by applying them to images of sheets of paper with plastic fragments of different sizes and different type underneath. The results indicated potential in the method. The prediction of the paper sheets with a lower grammage was mostly correct, whereas the classification of polyethylene showed the best performance. There was some noise in the prediction of the plastic fragments, regardless of the grammage of the paper. The noise may be due to a wide variation in the calibration data set since it consisted of paper sheets of six different compositions. A large part of the noise was incorrectly classified as polyvinyl alcohol, which can be due to differences in the manufacturing process of the plastic films. The conclusion of the thesis is that it is feasible to identify and categorize plastic fragments of polyethylene and polyvinyl alcohol in recycled paper sheets with a certain margin of error. It can be stated that the method shows promise, but further research and development in the field is required to build models that can be applied to a wider range of samples.