Starting from the 1950s, plastic has found its way into many aspects of life; from packaging to transportation and construction. The annual global plastic production was estimated to be 330 million metric tons in 2017 and will be doubled within 20 years if the current growth rate persists. In Europe, packaging accounts for 40% of the annual plastic production. To decrease dependence on fossil fuels and the related emissions and reduce the negative impact of plastics that enter the environment, the EU targets to recycle 55% of plastic packaging material by 2025. A steady production of large quantities of high-grade plastic recyclate is essential to sell the recycled material on the market and substitute virgin plastics.
But, achieving this high grade is difficult due to the wide range of polymers, including additives and fillers. This research focuses on the sorting step of post-consumer packaging waste in a Material Recovery Facility (MRF) that takes place before recycling.

In MRF's, Near Infrared (NIR) sensor-based sorting systems are state of the art to create high-grade sorting products. NIR's use reflection spectra to identify and distinguish a range of materials including different types of polymers. However, while sorting, False Calls (FC) occur when particles are incorrectly classified leading to a loss of valuable material or contamination of the product. Thus, this research aims to increase the understanding of false calls of NIR sensor systems, contributing to higher quality sorting products.

To reach this goal, possible causes of FC's and their dependencies on the feedrate, feed composition and feeding mechanism were identified. These causes were arranged into a framework consisting of six types of FC's. A distinction was made between errors that are working range related and those that are not. Particles outside the working range may be classified incorrectly even when fed in a monolayer and under perfect circumstances due to the properties of the particle.

The created framework was applied to a NIR in an MRF in the Netherlands which was tasked to separate non-plastic particles and PVC from plastic particles. Experiments were performed to single out the probability of each type of error to occur for 13 material classes. Next, the test results were implemented in a statistical model and a Monte Carlo simulation was performed. In this research, the varying input values were used to imitate the changing feed composition. Three scenarios of adjusted feed characteristics were analysed using the statistical model.

It was concluded that to optimize the sorting performance of the analysed NIR, all types of False Calls should be tackled. The feed of the analysed NIR contained a large share of particles outside the working range. So, even if the particles were to be fed in a monolayer the average grade of the product is expected to be around 85%. The combination of the experiments and the statistical model allows for an effective evaluation of the sorting performance and a clear indication of the most problematic aspects of the feed characteristics.

In 2017, the Dutch government has set the goal to achieve a 100% circular economy by 2050. A transition from the current linear economy towards a circular economy is necessary to reach this goal. In the circular economy, products at end-of-life are no longer seen as waste but will be reused as a secondary product or resource.

The general aim of this research is to investigate the implementation of the circular economy concept in the public space. From this study, it became clear that Dutch municipalities are willing to implement circularity for assets released from the public space. However, the municipalities are not aware of the actions which are required to increase the circularity within its maintenance activities. A method is proposed which gives decision makers of a municipality guidelines to justify their decision for a maintenance plan of an urban area. This method includes PAVE (maintenance plan analysis tool to value enhancement of circularity in the public space): a tool which quantifies the environmental and economic impact of maintenance plans. To express the impacts four criteria were selected: the carbon footprint, financial footprint, material loss and monetary value loss. The tool follows a life cycle assessment (LCA) framework.

In this research, the use of PAVE was investigated during the maintenance of a neighbourhood in Almere. It was concluded that to be able to implement PAVE in an effective manner, the asset database of the municipality needs to be updated to fill in the information gap. Part of the case area database was renewed to conduct an inventory of the assets in the selected urban area for this research. In PAVE, the impacts of the selected assets were calculated for both the circular project plan of the contractor and the traditional linear plan. PAVE indicated that using the new project plan for the case area results in approximately 1455 ton less CO2 emissions and is €4304000 cheaper compared to the traditional linear maintenance plan. Furthermore, PAVE demonstrated that the use of the new plan leads to 238 m3 and €424.000 less material and monetary value loss, respectively.

In this research, it was evident that to select a maintenance plan among others, the tool should be included in the decision-making scheme of municipalities. By implementing PAVE, decision makers will be able to compare the impacts of different maintenance plans and select the most suitable plan based on environmental and economic considerations. The implementation of the tool can be ensured by making the provided budget dependent on implementation. Finally, the proposed plans of contractors should be evaluated on their circularity. Guidelines have been formulated to support the selection of the maintenance plan for urban areas in the public space.