77 resultados para Innovative configuration
Resumo:
As the word population continues to grow and global resources are limited, the WHO definition of health is difficult to achieve for a large part of the population. Humanity is facing the need to improve both environmental and human wellbeing. This can be done through careful planning and management of natural resources, ensuring food safety and reducing and converting wastes. This work aims to contribute to the improvement of population and environmental health exploring different research fields: urban park ecosystem services, food chemical risk assessment and agri-food by-product valorization. To highlight the importance of urban parks and their ecosystem services, an ethnobotanical study was carried out in the Ausa urban park in Rimini, using a citizen science approach. The results showed that Ausa Park is an important focal point for plant gatherers in Rimini, as it allows for plant foraging and contributes to preserve the knowledge of the use of plants. Two food safety studies were carried out, looking at the exposure of Poles to bisphenol A through the consumption of soft drinks and to cadmium through the consumption of chocolate bars. The results, compared with EFSA’s scientific opinion, show that the exposure of the Polish population to BPA is of health concern, while cadmium is not. In the agri-food by-product valorization, a green extraction method was optimized to recover valuable phenolic compounds from red-fleshed apple pomace; moreover, the possibility of recovering pectin from the residue was evaluated. Furthermore, valuable compounds in four different types of wheat milling by-products, considered as an alternative source of bioactive compounds with potential human health benefits, were investigated. In conclusion, this work produced usable data in urban green area management and planning, in food chemical risk assessment and in business production decisions, thus contributing to improving environmental and people wellbeing.
Resumo:
In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.