Synthesis gas production from various biomass feedstocks

The decomposition of five different biomass samples was studied in a horizontal laboratory reactor. The samples consisted of esparto grass, straw, Posidonea Oceanic seaweed, waste from urban and agricultural pruning and waste from forest pruning. Both pyrolysis in inert atmosphere and combustion in the presence of oxygen were studied. Different heating rates were used by varying the input speed. Major gas compounds were analyzed. The experimental results show that the amount of CO formed is lower in less dense species. It is also found that there is an increase of hydrocarbons formed at increasing feeding rates, in particular methane, while there is a decrease in the production of hydrogen.

Hydrogen Production by Steam Reforming of Ethanol on Rh-Pt Catalysts: Influence of CeO2, ZrO2, and La2O3 as Supports

CeO2-, ZrO2-, and La2O3-supported Rh-Pt catalysts were tested to assess their ability to catalyze the steam reforming of ethanol (SRE) for H2 production. SRE activity tests were performed using EtOH:H2O:N2 (molar ratio 1:3:51) at a gaseous space velocity of 70,600 h−1 between 400 and 700 °C at atmospheric pressure. The SRE stability of the catalysts was tested at 700 °C for 27 h time on stream under the same conditions. RhPt/CeO2, which showed the best performance in the stability test, also produced the highest H2 yield above 600 °C, followed by RhPt/La2O3 and RhPt/ZrO2. The fresh and aged catalysts were characterized by TEM, XPS, and TGA. The higher H2 selectivity of RhPt/CeO2 was ascribed to the formation of small (~5 nm) and stable particles probably consistent of Rh-Pt alloys with a Pt surface enrichment. Both metals were oxidized and acted as an almost constant active phase during the stability test owing to strong metal-support interactions, as well as the superior oxygen mobility of the support. The TGA results confirmed the absence of carbonaceous residues in all the aged catalysts.

On the suitability of Prototype Selection methods for kNN classification with distributed data

In the current Information Age, data production and processing demands are ever increasing. This has motivated the appearance of large-scale distributed information. This phenomenon also applies to Pattern Recognition so that classic and common algorithms, such as the k-Nearest Neighbour, are unable to be used. To improve the efficiency of this classifier, Prototype Selection (PS) strategies can be used. Nevertheless, current PS algorithms were not designed to deal with distributed data, and their performance is therefore unknown under these conditions. This work is devoted to carrying out an experimental study on a simulated framework in which PS strategies can be compared under classical conditions as well as those expected in distributed scenarios. Our results report a general behaviour that is degraded as conditions approach to more realistic scenarios. However, our experiments also show that some methods are able to achieve a fairly similar performance to that of the non-distributed scenario. Thus, although there is a clear need for developing specific PS methodologies and algorithms for tackling these situations, those that reported a higher robustness against such conditions may be good candidates from which to start.