Life cycle assessment (lca) of six separation methods of polyethylene and aluminum in tetra pak

In food and beverage industry, packaging plays a crucial role in protecting food and beverages and maintaining their organoleptic properties. Their disposal, unfortunately, is still difficult, mainly because there is a lack of economically viable systems for separating composite and multilayer materials. It is therefore necessary not only to increase research in this area, but also to set up pilot plants and implement these technologies on an industrial scale. LCA (Life Cycle Assessment) can fulfil these purposes. It allows an assessment of the potential environmental impacts associated with a product, service or process. The objective of this thesis work is to analyze the environmental performance of six separation methods, designed for separating the polymeric from the aluminum fraction in multilayered packaging. The first four methods utilize the chemical dissolution technique using Biodiesel, Cyclohexane, 2-Methyltetrahydrofuran (2-MeTHF) and Cyclopentyl-methyl-ether (CPME) as solvents. The last two applied the mechanical delamination technique with surfactant-activated water, using Ammonium laurate and Triethanolamine laurate as surfactants, respectively. For all six methods, the LCA methodology was applied and the corresponding models were built with the GaBi software version 10.6.2.9, specifically for LCA analyses. Unfortunately, due to a lack of data, it was not possible to obtain the results of the dissolution methods with the solvents 2-MeTHF and CPME; for the other methods, however, the individual environmental performances were calculated. Results revealed that the methods with the best environmental performance are method 2, for dissolution methods, and method 5, for delamination methods. This result is confirmed both by the analysis of normalized and weighted results and by the analysis of 'original' results. An hotspots analysis was also conducted.

Model-Based validation of Driver Drowsiness Detection System for ADAS

The work described in this Master’s Degree thesis was born after the collaboration with the company Maserati S.p.a, an Italian luxury car maker with its headquarters located in Modena, in the heart of the Italian Motor Valley, where I worked as a stagiaire in the Virtual Engineering team between September 2021 and February 2022. This work proposes the validation using real-world ECUs of a Driver Drowsiness Detection (DDD) system prototype based on different detection methods with the goal to overcome input signal losses and system failures. Detection methods of different categories have been chosen from literature and merged with the goal of utilizing the benefits of each of them, overcoming their limitations and limiting as much as possible their degree of intrusiveness to prevent any kind of driving distraction: an image processing-based technique for human physical signals detection as well as methods based on driver-vehicle interaction are used. A Driver-In-the-Loop simulator is used to gather real data on which a Machine Learning-based algorithm will be trained and validated. These data come from the tests that the company conducts in its daily activities so confidential information about the simulator and the drivers will be omitted. Although the impact of the proposed system is not remarkable and there is still work to do in all its elements, the results indicate the main advantages of the system in terms of robustness against subsystem failures and signal losses.