Evaluation of most promising options for the C1 to C2-coupling: alternative formate coupling

This thesis work contains an overview of potential alternative options to couple formate produced from CO2 with other coupling partners than formate itself. Ultimately, the intent is to produce high value chemicals from CO2 at a high selectivity and conversion, whilst keeping the required utility of electrons in the electrochemical CO2 conversion at a minimum. To select and find new coupling partners, a framework was developed upon which a broad variety of candidates were assessed and ranked. A multi-stage process was used to select first potential classes of molecules. For each class, a variety of commercially available compounds was analysed in depth for its potential suitability in the reaction with the active carbonite intermediate. This analysis has shown that a wide variety of factors come into play and especially the reactivity of the hydride catalyst poses a mayor challenge. The three major potential classes of compounds suitable for the coupling are carbon oxides (CO2 & CO), and aldehydes. As a second step the remaining options were ranked to identify which compound to test first. In this ranking the reactants sustainability, ease of commercial operation and commercial attractiveness of the compound were considered. The highest-ranking compounds that proposed the highest potential are CO2, benzaldehyde and para-formaldehyde. In proof-of-principle experiments CO2 could successfully be incorporated in the form of carbonate, oxalate and potentially formate. The overall incorporation efficiency based on the hydride consumption was shown to be 50%. It is suggested to continue this work with mechanistic studies to understand the reaction in detail as, based on further gained knowledge, the reaction can then be optimized towards optimal CO2 incorporation in the form of oxalate.

A multi-objective genetic algorithm optimisation using variable speed pumps in water distribution systems

Due to its practical importance and inherent complexity, the optimisation of distribution networks for supplying drinking water has been the subject of extensive study for the past 30 years. The optimization is governed by sizing the pipes in the water distribution network (WDN) and / or optimises specific parts of the network such as pumps, tanks etc. or try to analyse and optimise the reliability of a WDN. In this thesis, the author has analysed two different WDNs (Anytown City and Cabrera city networks), trying to solve and optimise a multi-objective optimisation problem (MOOP). The main two objectives in both cases were the minimisation of Energy Cost (€) or Energy consumption (kWh), along with the total Number of pump switches (TNps) during a day. For this purpose, a decision support system generator for Multi-objective optimisation used. Its name is GANetXL and has been developed by the Center of Water System in the University of Exeter. GANetXL, works by calling the EPANET hydraulic solver, each time a hydraulic analysis has been fulfilled. The main algorithm used, was a second-generation algorithm for multi-objective optimisation called NSGA_II that gave us the Pareto fronts of each configuration. The first experiment that has been carried out was the network of Anytown city. It is a big network with a pump station of four fixed speed parallel pumps that are boosting the water dynamics. The main intervention was to change these pumps to new Variable speed driven pumps (VSDPs), by installing inverters capable to diverse their velocity during the day. Hence, it’s been achieved great Energy and cost savings along with minimisation in the number of pump switches. The results of the research are thoroughly illustrated in chapter 7, with comments and a variety of graphs and different configurations. The second experiment was about the network of Cabrera city. The smaller WDN had a unique FS pump in the system. The problem was the same as far as the optimisation process was concerned, thus, the minimisation of the energy consumption and in parallel the minimisation of TNps. The same optimisation tool has been used (GANetXL).The main scope was to carry out several and different experiments regarding a vast variety of configurations, using different pump (but this time keeping the FS mode), different tank levels, different pipe diameters and different emitters coefficient. All these different modes came up with a large number of results that were compared in the chapter 8. Concluding, it should be said that the optimisation of WDNs is a very interested field that has a vast space of options to deal with. This includes a large number of algorithms to choose from, different techniques and configurations to be made and different support system generators. The researcher has to be ready to “roam” between these choices, till a satisfactory result will convince him/her that has reached a good optimisation point.

Exploring the Higgs-muon coupling at a multi-TeV muon collider

We investigate the potential of a high-energy muon collider in measuring the muon Yukawa coupling (y_μ) in the production of two, three and four heavy bosons via muon-antimuon annihilations. We study the sensitivity of these processes to deviations of y_μ from the Standard Model prediction, parametrized by an effective dimension-6 operator in the Standard Model Effective Field Theory (SMEFT) framework. We also consider the κ framework, in which the deviation is simply parametrized by a strength modification of the μ+μ−h vertex alone. Both frameworks lead to an energy enhancement of the cross sections with one or more vector bosons, although the κ framework yields stronger effects, especially for the production of four bosons. On the contrary, for purely-Higgs final states the cross section is suppressed in the κ framework, while it is extremely sensitive to deviations in the SMEFT. We show that the triple-Higgs production is the most sensitive process to spot new physics effects on y_μ.

Graph-based Vision-Language Model for Multi-Modal Retrieval of Fashion Products

Artificial Intelligence is reshaping the field of fashion industry in different ways. E-commerce retailers exploit their data through AI to enhance their search engines, make outfit suggestions and forecast the success of a specific fashion product. However, it is a challenging endeavour as the data they possess is huge, complex and multi-modal. The most common way to search for fashion products online is by matching keywords with phrases in the product's description which are often cluttered, inadequate and differ across collections and sellers. A customer may also browse an online store's taxonomy, although this is time-consuming and doesn't guarantee relevant items. With the advent of Deep Learning architectures, particularly Vision-Language models, ad-hoc solutions have been proposed to model both the product image and description to solve this problems. However, the suggested solutions do not exploit effectively the semantic or syntactic information of these modalities, and the unique qualities and relations of clothing items. In this work of thesis, a novel approach is proposed to address this issues, which aims to model and process images and text descriptions as graphs in order to exploit the relations inside and between each modality and employs specific techniques to extract syntactic and semantic information. The results obtained show promising performances on different tasks when compared to the present state-of-the-art deep learning architectures.