The present work is devoted to investigate the performance of a homemade Y-shape catalytic microreactor for degradation of dibenzothiophene (DBT), as a model of sulphur compounds including in gas oil, utilizing solar incident energy. The microchannel was coated with TiO2 nanoparticles which were used as a photocatalyst. Performance of the microreactor was investigated using different conditions (e.g., DBT concentration, LHSV, operating temperature, and (H2O2/DBT) ratio). Our experiments show that, in the absence of UV light, no reaction takes place. The results revealed that outlet concentration of DBT decreases as the mean residence time in the microreactor increases. Also, it was noted that operating temperature showed a positive impact on the degradation rate of DBT while LHSV showed a different image. The results reported an optimum (H2O2/DBT) ratio which gave maximum conversion of DBT which vary with initial concentration. Kinetic study was carried out which confirmed that desulfurization of DBT followed a pseudo-first order reaction at 30 and 50oC, respectively. However deviation from linearity was observed at 60oC. Comparison between microreactor´s performance and performance of batch reactors from published literature were illustrated. The Comparison confirmed the unique characteristics of the microreactor.
Empirical equation has been presented to predict the optimum hydrodynamic
pressure gradient with optimum mud flow rate (one equation) of five Iraqi oil wells
to obtain the optimum carrying capacity of the drilling fluid ( optimum transport
cuttings from the hole to the surface through the annulus).
This equation is a function of mud flow rate, mud density and penetration
rate without using any charts or graphs.
The correlation coefficient accuracy is more than 0.9999.
The current study uses the flame fragment deposition (FFD) method to synthesize carbon nanotubes (CNTs) from Iraqi liquefied petroleum gas (LPG), which is used as a carbon source. To carry out the synthesis steps, a homemade reactor was used. To eliminate amorphous impurities, the CNTs were sonicated in a 30 percent hydrogen peroxide (H2O2) solution at ambient temperature. To remove the polycyclic aromatic hydrocarbons (PAHs) generated during LPG combustion, sonication in an acetone bath is used. The produced products were investigated and compared with standard Multi-walled carbon nanotube MWCNTs (95%), Sigma, Aldrich, using X-ray diffraction (XRD), thermo gravimetric analysis (TGA), Raman spectroscopy, scanning el
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This paper is concerned with introducing and studying the new approximation operators based on a finite family of d. g. 'swhich are the core concept in this paper. In addition, we study generalization of some Pawlak's concepts and we offer generalize the definition of accuracy measure of approximations by using a finite family of d. g. 's.
: Sound forecasts are essential elements of planning, especially for dealing with seasonality, sudden changes in demand levels, strikes, large fluctuations in the economy, and price-cutting manoeuvres for competition. Forecasting can help decision maker to manage these problems by identifying which technologies are appropriate for their needs. The proposal forecasting model is utilized to extract the trend and cyclical component individually through developing the Hodrick–Prescott filter technique. Then, the fit models of these two real components are estimated to predict the future behaviour of electricity peak load. Accordingly, the optimal model obtained to fit the periodic component is estimated using spectrum analysis and Fourier mod
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Image Fusion Using A Convolutional Neural Network
Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time. In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as D
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