ABSTRACT
The control of catalytic performance using synthesis conditions is one of the main goals of catalytic research. Two series of Pt-Ti/SBA-15 catalysts with different TiO2 percentages (n = 1, 5, 10, 30 wt.%) were obtained from tetrabutylorthotitanate (TBOT) and peroxotitanate (PT), as titania precursors and Pt impregnation. The obtained catalysts were characterized using X-ray diffraction, scanning electron microscopy (SEM) and transmission electron microscopy (TEM), N2 sorption, Raman, X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XAS), hydrogen temperature-programmed reduction (H2-TPR) and H2-chemisorption measurements. Raman spectroscopy showed framework titanium species in low TiO2 loading samples. The anatase phase was evidenced for samples with higher titania loading, obtained from TBOT, and a mixture of rutile and anatase for those synthesized by PT. The rutile phase prevails in rich TiO2 catalysts obtained from PT. Variable concentrations of Pt0 as a result of the stronger interaction of PtO with anatase and the weaker interaction with rutile were depicted using XPS. TiO2 loading and precursors influenced the concentration of Pt species, while the effect on Pt nanoparticles' size and uniform distribution on support was insignificant. The Pt/PtO ratio and their concentration on the surface were the result of strong metal-support interaction, and this influenced catalytic performance in the complete oxidation of methane at a low temperature. The highest conversion was obtained for sample prepared from PT with 30% TiO2.
ABSTRACT
Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the availability of large data measurements, prediction algorithms based on neural networks are widely used in accurate power prediction. Firstly, the particularity of our work is represented by the size of the dataset consisting of 4 years of continuous real-time data measurements collected from the CETATEA photovoltaic power plant, a research site for renewable energies located in Cluj-Napoca, Romania. Secondly, the high granularity of the dataset with more than 4.2 million unified production and consumption power values recorded every 30 s guarantees the overall prediction accuracy of the system. Performance metrics used to evaluate the prediction accuracy are the mean bias error, the mean square error, the convergence time of the prediction system, the test performance, and the train mean performance. Test results indicate that the predicted unified electric power production and consumption closely resembles the unified electric power measured values.
