Abstract: Carboneous catalytical methane decomposition is an
attractive process because it produces two valuable products:
hydrogen and carbon. Furthermore, this reaction does not emit any
green house or hazardous gases. In the present study, experiments
were conducted in a thermo gravimetric analyzer using Fluka 05120
as carboneous catalyst to analyze its effectiveness in methane
decomposition. Various temperatures and methane partial pressures
were chosen and carbon mass gain was observed as a function of
time. Results are presented in terms of carbon formation rate,
hydrogen production and catalytical activity. It is observed that there
is linearity in carbon deposition amount by time at lower reaction
temperature (780 °C). On the other hand, it is observed that carbon
and hydrogen formation rates are increased with increasing
temperature. Finally, we observed that the carbon formation rate is
highest at 950 °C within the range of temperatures studied.
Abstract: In this paper, a new technique for fast painting with
different colors is presented. The idea of painting relies on applying
masks with different colors to the background. Fast painting is
achieved by applying these masks in the frequency domain instead of
spatial (time) domain. New colors can be generated automatically as a
result from the cross correlation operation. This idea was applied
successfully for faster specific data (face, object, pattern, and code)
detection using neural algorithms. Here, instead of performing cross
correlation between the input input data (e.g., image, or a stream of
sequential data) and the weights of neural networks, the cross
correlation is performed between the colored masks and the
background. Furthermore, this approach is developed to reduce the
computation steps required by the painting operation. The principle of
divide and conquer strategy is applied through background
decomposition. Each background is divided into small in size subbackgrounds
and then each sub-background is processed separately by
using a single faster painting algorithm. Moreover, the fastest painting
is achieved by using parallel processing techniques to paint the
resulting sub-backgrounds using the same number of faster painting
algorithms. In contrast to using only faster painting algorithm, the
speed up ratio is increased with the size of the background when using
faster painting algorithm and background decomposition. Simulation
results show that painting in the frequency domain is faster than that in
the spatial domain.