A chemical-loop approach for the generation of hydrogen by means of ethanol reforming

The work investigates the feasibility of a new process aimed at the production of hydrogen with inherent separation of carbon oxides. The process consists in a cycle in which, in the first step, a mixed metal oxide is reduced by ethanol (obtained from biomasses). The reduced metal is then contacted with steam in order to split the water and sequestrating the oxygen into the looping material’s structure. The oxides used to run this thermochemical cycle, also called “steam-iron process” are mixed ferrites in the spinel structure MeFe2O4 (Me = Fe, Co, Ni or Cu). To understand the reactions involved in the anaerobic reforming of ethanol, diffuse reflectance spectroscopy (DRIFTS) was used, coupled with the mass analysis of the effluent, to study the surface composition of the ferrites during the adsorption of ethanol and its transformations during the temperature program. This study was paired with the tests on a laboratory scale plant and the characterization through various techniques such as XRD, Mössbauer spectroscopy, elemental analysis... on the materials as synthesized and at different reduction degrees In the first step it was found that besides the generation of the expected CO, CO2 and H2O, the products of ethanol anaerobic oxidation, also a large amount of H2 and coke were produced. The latter is highly undesired, since it affects the second step, during which water is fed over the pre-reduced spinel at high temperature. The behavior of the different spinels was affected by the nature of the divalent metal cation; magnetite was the oxide showing the slower rate of reduction by ethanol, but on the other hand it was that one which could perform the entire cycle of the process more efficiently. Still the problem of coke formation remains the greater challenge to solve.

Efficient wireless coverage of in-building environments with low electromagnetic impact

The city of tomorrow is a major integrating stake, which crosses a set of major broad spectrum domains. One of these areas is the instrumentation of this city and the ubiquity of the exchange of data, which will give the pulse of this city (sensors) and its breathing in a hyper-connected world within indoor and outdoor dense areas (data exchange, 5G and 6G). Within this context, the proposed doctorate project has the objective to realize cost- and energy- effective, short-range communication systems for the capillary wireless coverage of in-door environments with low electromagnetic impact and for highly dense outdoor networks. The result will be reached through the combined use of: 1) Radio over Fiber (RoF) Technology, to bring the Radio Frequency (RF) signal to the different areas to be covered. 2) Beamforming antennas to send in real time the RF power just in the direction(s) where it is really necessary.

Adjustable robust optimization with nonlinear recourses

Over the last century, mathematical optimization has become a prominent tool for decision making. Its systematic application in practical fields such as economics, logistics or defense led to the development of algorithmic methods with ever increasing efficiency. Indeed, for a variety of real-world problems, finding an optimal decision among a set of (implicitly or explicitly) predefined alternatives has become conceivable in reasonable time. In the last decades, however, the research community raised more and more attention to the role of uncertainty in the optimization process. In particular, one may question the notion of optimality, and even feasibility, when studying decision problems with unknown or imprecise input parameters. This concern is even more critical in a world becoming more and more complex —by which we intend, interconnected —where each individual variation inside a system inevitably causes other variations in the system itself. In this dissertation, we study a class of optimization problems which suffer from imprecise input data and feature a two-stage decision process, i.e., where decisions are made in a sequential order —called stages —and where unknown parameters are revealed throughout the stages. The applications of such problems are plethora in practical fields such as, e.g., facility location problems with uncertain demands, transportation problems with uncertain costs or scheduling under uncertain processing times. The uncertainty is dealt with a robust optimization (RO) viewpoint (also known as "worst-case perspective") and we present original contributions to the RO literature on both the theoretical and practical side.