2 resultados para Numerous applications
em AMS Tesi di Laurea - Alm@DL - Università di Bologna
Resumo:
Due to the high price of natural oil and harmful effects of its usage, as the increase in emission of greenhouse gases, the industry focused in searching of sustainable types of the raw materials for production of chemicals. Ethanol, produced by fermentation of sugars, is one of the more interesting renewable materials for chemical manufacturing. There are numerous applications for the conversion of ethanol into commodity chemicals. In particular, the production of 1,3-butadiene whose primary source is ethanol using multifunctional catalysts is attractive. With the 25% of world rubber manufacturers utilizing 1,3-butadiene, there is an exigent need for its sustainable production. In this research, the conversion of ethanol in one-step process to 1,3-butadiene was studied. According to the literature, the mechanisms which were proposed to explain the way ethanol transforms into butadiene require to have both acid and basic sites. But still, there are a lot of debate on this topic. Thus, the aim of this research work is a better understanding of the reaction pathways with all the possible intermediates and products which lead to the formation of butadiene from ethanol. The particular interests represent the catalysts, based on different ratio Mg/Si in comparison to bare magnesia and silica oxides, in order to identify a good combination of acid/basic sites for the adsorption and conversion of ethanol. Usage of spectroscopictechniques are important to extract information that could be helpful for understanding the processes on the molecular level. The diffuse reflectance infrared spectroscopy coupled to mass spectrometry (DRIFT-MS) was used to study the surface composition of the catalysts during the adsorption of ethanol and its transformation during the temperature program. Whereas, mass spectrometry was used to monitor the desorbed products. The set of studied materials include MgO, Mg/Si=0.1, Mg/Si=2, Mg/Si=3, Mg/Si=9 and SiO2 which were also characterized by means of surface area measurements.
Resumo:
Combinatorial decision and optimization problems belong to numerous applications, such as logistics and scheduling, and can be solved with various approaches. Boolean Satisfiability and Constraint Programming solvers are some of the most used ones and their performance is significantly influenced by the model chosen to represent a given problem. This has led to the study of model reformulation methods, one of which is tabulation, that consists in rewriting the expression of a constraint in terms of a table constraint. To apply it, one should identify which constraints can help and which can hinder the solving process. So far this has been performed by hand, for example in MiniZinc, or automatically with manually designed heuristics, in Savile Row. Though, it has been shown that the performances of these heuristics differ across problems and solvers, in some cases helping and in others hindering the solving procedure. However, recent works in the field of combinatorial optimization have shown that Machine Learning (ML) can be increasingly useful in the model reformulation steps. This thesis aims to design a ML approach to identify the instances for which Savile Row’s heuristics should be activated. Additionally, it is possible that the heuristics miss some good tabulation opportunities, so we perform an exploratory analysis for the creation of a ML classifier able to predict whether or not a constraint should be tabulated. The results reached towards the first goal show that a random forest classifier leads to an increase in the performances of 4 different solvers. The experimental results in the second task show that a ML approach could improve the performance of a solver for some problem classes.