Environmental assessment of industrial chemical and primary sector processes from a life cycle perspective

During the PhD program in chemistry at the University of Bologna, the environmental sustainability of some industrial processes was studied through the application of the LCA methodology. The efforts were focused on the study of processes under development, in order to assess their environmental impacts to guide their transfer on an industrial scale. Processes that could meet the principles of Green Chemistry have been selected and their environmental benefits have been evaluated through a holistic approach. The use of renewable sources was assessed through the study of terephthalic acid production from biomass (which showed that only the use of waste can provide an environmental benefit) and a new process for biogas upgrading (whose potential is to act as a carbon capture technology). Furthermore, the basis for the development of a new methodology for the prediction of the environmental impact of ionic liquids has been laid. It has already shown good qualities in identifying impact trends, but further research on it is needed to obtain a more reliable and usable model. In the context of sustainable development that will not only be sector-specific, the environmental performance of some processes linked to the primary production sector has also been evaluated. The impacts of some organic farming practices in the wine production were analysed, the use of the Cereal Unit parameter was proposed as a functional unit for the comparison of different crop rotations, and the carbon footprint of school canteen meals was calculated. The results of the analyses confirm that sustainability in the industrial production sector should be assessed from a life cycle perspective, in order to consider all the flows involved during the different phases. In particular, it is necessary that environmental assessments adopt a cradle-to-gate approach, to avoid shifting the environmental burden from one phase to another.

Design, development and characterization of DHA-based emulsion for Nutrilipidomics strategies

The industrial PhD project presented here is part of the R&D strategies of the Lipinutragen company. The innovation brought by the company concerns nutrilipidomics, i.e. the correlation between the lipid composition (in fatty acids) of the cell membrane and lipid-based nutraceuticals, especially starting from the well-known dependence of the lipid composition on the intake of essential fats, omega- 6 and omega-3 polyunsaturated fatty acids. Among the results obtained from the membrane lipidomic profiles, the case of autistic subjects is here highlighted, showing the significant deficiency of docosahexaenoic acid (DHA). The activity during the PhD was devoted to the nutrilipidomic approach. Part of the activities were devoted to scientific research in lipidomics: a) the study of lipidomic profiles in the frame of two collaboration projects: one with the group of Dr. I. Tueros at AZTI, Bilbao, regading obese population, and the other one regarding seed germination with the changes of the fatty acid profiles with the group of prof. A. Balestrazzi of the University of Parma; b) the liposome preparation for protection and lifetime prolongation of the peptide somatostatin, which was an important premise to the formulation of the DHA-containing microemulsion. The activities was also focused on the development of DHA-containing nutraceutical formulations in the form of emulsion, overcoming the difficulty of the capsule ingestion, to be administered orally. The work pointed to study the combination of active ingredients, based on the previous know-how regarding the bioavailability for the cell membrane incorporation. The ingredients of the formulation were studied and tested in vitro for the bioavailability of DHA to be incorporated in the cell membranes of different types of cultured cells. Part of this study is covered by non-disclosure agreement since it belongs to the know-how of Lipinutragen.

Development of predictive energy management strategies for hybrid electric vehicles supported by connectivity

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of the global warming. In this context, the transportation sector plays a vital role, since it is responsible for a large part of carbon dioxide production. In order to address these issues, the present thesis deals with the development of advanced control strategies for the energy efficiency optimization of plug-in hybrid electric vehicles (PHEVs), supported by the prediction of future working conditions of the powertrain. In particular, a Dynamic Programming algorithm has been developed for the combined optimization of vehicle energy and battery thermal management. At this aim, the battery temperature and the battery cooling circuit control signal have been considered as an additional state and control variables, respectively. Moreover, an adaptive equivalent consumption minimization strategy (A-ECMS) has been modified to handle zero-emission zones, where engine propulsion is not allowed. Navigation data represent an essential element in the achievement of these tasks. With this aim, a novel simulation and testing environment has been developed during the PhD research activity, as an effective tool to retrieve routing information from map service providers via vehicle-to-everything connectivity. Comparisons between the developed and the reference strategies are made, as well, in order to assess their impact on the vehicle energy consumption. All the activities presented in this doctoral dissertation have been carried out at the Green Mobility Research Lab} (GMRL), a research center resulting from the partnership between the University of Bologna and FEV Italia s.r.l., which represents the industrial partner of the research project.

A distributed middleware for IT/OT convergence in modern industrial environments

The modern industrial environment is populated by a myriad of intelligent devices that collaborate for the accomplishment of the numerous business processes in place at the production sites. The close collaboration between humans and work machines poses new interesting challenges that industry must overcome in order to implement the new digital policies demanded by the industrial transition. The Industry 5.0 movement is a companion revolution of the previous Industry 4.0, and it relies on three characteristics that any industrial sector should have and pursue: human centrality, resilience, and sustainability. The application of the fifth industrial revolution cannot be completed without moving from the implementation of Industry 4.0-enabled platforms. The common feature found in the development of this kind of platform is the need to integrate the Information and Operational layers. Our thesis work focuses on the implementation of a platform addressing all the digitization features foreseen by the fourth industrial revolution, making the IT/OT convergence inside production plants an improvement and not a risk. Furthermore, we added modular features to our platform enabling the Industry 5.0 vision. We favored the human centrality using the mobile crowdsensing techniques and the reliability and sustainability using pluggable cloud computing services, combined with data coming from the crowd support. We achieved important and encouraging results in all the domains in which we conducted our experiments. Our IT/OT convergence-enabled platform exhibits the right performance needed to satisfy the strict requirements of production sites. The multi-layer capability of the framework enables the exploitation of data not strictly coming from work machines, allowing a more strict interaction between the company, its employees, and customers.

Development of advanced methods for the simulation of the reacting mixture formation in internal combustion engines with the use of machine learning algorithms

Besides increasing the share of electric and hybrid vehicles, in order to comply with more stringent environmental protection limitations, in the mid-term the auto industry must improve the efficiency of the internal combustion engine and the well to wheel efficiency of the employed fuel. To achieve this target, a deeper knowledge of the phenomena that influence the mixture formation and the chemical reactions involving new synthetic fuel components is mandatory, but complex and time intensive to perform purely by experimentation. Therefore, numerical simulations play an important role in this development process, but their use can be effective only if they can be considered accurate enough to capture these variations. The most relevant models necessary for the simulation of the reacting mixture formation and successive chemical reactions have been investigated in the present work, with a critical approach, in order to provide instruments to define the most suitable approaches also in the industrial context, which is limited by time constraints and budget evaluations. To overcome these limitations, new methodologies have been developed to conjugate detailed and simplified modelling techniques for the phenomena involving chemical reactions and mixture formation in non-traditional conditions (e.g. water injection, biofuels etc.). Thanks to the large use of machine learning and deep learning algorithms, several applications have been revised or implemented, with the target of reducing the computing time of some traditional tasks by orders of magnitude. Finally, a complete workflow leveraging these new models has been defined and used for evaluating the effects of different surrogate formulations of the same experimental fuel on a proof-of-concept GDI engine model.

Development and Implementation of the Plasma Focus Technology for Radiation Therapy Applications

A Plasma Focus device can confine in a small region a plasma generated during the pinch phase. When the plasma is in the pinch condition it creates an environment that produces several kinds of radiations. When the filling gas is nitrogen, a self-collimated backwardly emitted electron beam, slightly spread by the coulomb repulsion, can be considered one of the most interesting outputs. That beam can be converted into X-ray pulses able to transfer energy at an Ultra-High Dose-Rate (UH-DR), up to 1 Gy pulse-1, for clinical applications, research, or industrial purposes. The radiation fields have been studied with the PFMA-3 hosted at the University of Bologna, finding the radiation behavior at different operating conditions and working parameters for a proper tuning of this class of devices in clinical applications. The experimental outcomes have been compared with available analytical formalisms as benchmark and the scaling laws have been proposed. A set of Monte Carlo models have been built with direct and adjoint techniques for an accurate X-ray source characterization and for setting fast and reliable irradiation planning for patients. By coupling deterministic and Monte Carlo codes, a focusing lens for the charged particles has been designed for obtaining a beam suitable for applications as external radiotherapy or intra-operative radiation therapy. The radiobiological effectiveness of the UH PF DR, a FLASH source, has been evaluated by coupling different Monte Carlo codes estimating the overall level of DNA damage at the multi-cellular and tissue levels by considering the spatial variation effects as well as the radiation field characteristics. The numerical results have been correlated to the experimental outcomes. Finally, ambient dose measurements have been performed for tuning the numerical models and obtaining doses for radiation protection purposes. The PFMA-3 technology has been fully characterized toward clinical implementation and installation in a medical facility.

Development of a Computer-Based methodology for tolerance selection and optimization applied to the automotive sector

The design optimization of industrial products has always been an essential activity to improve product quality while reducing time-to-market and production costs. Although cost management is very complex and comprises all phases of the product life cycle, the control of geometrical and dimensional variations, known as Dimensional Management (DM), allows compliance with product and process requirements. Hence, the tolerance-cost optimization becomes the main practice to provide an effective application of Design for Tolerancing (DfT) and Design to Cost (DtC) approaches by enabling a connection between product tolerances and associated manufacturing costs. However, despite the growing interest in this topic, a profitable application in the industry of these techniques is hampered by their complexity: the definition of a systematic framework is the key element to improving design optimization, enhancing the concurrent use of Computer-Aided tools and Model-Based Definition (MBD) practices. The present doctorate research aims to define and develop an integrated methodology for product/process design optimization, to better exploit the new capabilities of advanced simulations and tools. By implementing predictive models and multi-disciplinary optimization, a Computer-Aided Integrated framework for tolerance-cost optimization has been proposed to allow the integration of DfT and DtC approaches and their direct application for the design of automotive components. Several case studies have been considered, with the final application of the integrated framework on a high-performance V12 engine assembly, to achieve both functional targets and cost reduction. From a scientific point of view, the proposed methodology provides an improvement for the tolerance-cost optimization of industrial components. The integration of theoretical approaches and Computer-Aided tools allows to analyse the influence of tolerances on both product performance and manufacturing costs. The case studies proved the suitability of the methodology for its application in the industrial field, providing the identification of further areas for improvement and refinement.