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The early stages of coal oxy-combustion and pyrolysis were investigated in a drop tube furnace operated at 1300°C with different O2/N2/O2/CO2 atmosphere. The different types of carbonaceous solids produced at short residence times (50-100 ms) in the tube were separated to discriminate soot from incipient char for further analysis. In particular, a procedure involving dispersion in ethanol by ultrasonic mixing, followed by settling, and decanting to produce top and bottom products enriched in the coarse and fine particle fractions, respectively, was set up for separating soot from char. The procedure was repeated several times and the separation efficiency was checked by electron microscopy and size determination. Soot and char, separated and weighted after solvent removal, can be further characterized by a wide array of techniques in order to highlight the differences between them and their relation with the atmosphere employed. Beside electron microscopy and laser granulometry, thermogravimetry, elemental analysis and spectroscopic analysis (UV-visible and FT-IR absorption, RAMAN) were applied to soot and char for giving insights on the determination of the conditions under which the amount of soot and its chemical and physical characteristics are of practical significance for full-scale power plant particulate emissions and ash disposal.

The global electric car sales continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises. The final objective is estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. We test the proposed approach in a combination of datasets from 2 different campus facilities in Italy and Brazil. The overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.