In this study, sulfur was removed from imitation oil using oxidative desulfurization process. Silicoaluminophosphate (SAPO-11) was prepared using the hydrothermal method with a concentration of carbon nanotubes (CNT) of 0% and 7.5% at 190 °C crystallization temperature. The final molar composition of the as-prepared SAPO-11 was Al2O3: 0.93P2O5: 0.414SiO2. 4% MO/SAPO-11 was prepared using impregnation methods. The produced SAPO-11 was described using X-ray diffraction (XRD) and Brunauer-Emmet-Teller (N2 adsorption–desorption isotherms). It was found that the addition of CNT increased the crystallinity of SAPO-11. The results showed that the surface area of SAPO-11 containing 7.5% CNT was 179.54 m2/g, and the pore volume was 0.317 cm3/g. However, the surface area of SAPO-11 containing 0% CNT was 125.311 m2/g, and pore volume was 0.275 cm3/g, while nanoparticles with an average particle diameter of 24.8 nm were obtained. Then, the prepared SAPO-11 was used in the oxidative desulfurization process. The oxidative desulfurization was studied using several factors affecting desulfurization efficiency, such as time (40, 60, 80, 100, and 120) min, amount of MO/SAPO-11 (0.3, 0.4, 0.5, 0.6, and 0.7) g/100 ml of simulated oil (100 ppm of dibenzothiophene), the amount of hydrogen peroxide (4ml) oxidizer/100 ml of simulated oil, and the temperature ranges from (40, 50, 60, 70, and 80 °C). The results showed that an increase in MO/SAPO-11 led to an increase in desulfurization. The best removal percentage for sulfur content was 92.79%, obtained at 70 °C and 0.6 g of MO/SAPO-11 containing 7.5% CNT, and the removal was 82.34% at 0% CNT and the same other conditions. While the equilibrium was achieved after 100 min. The results revealed that Freundlich's model described the adsorption of sulfur compounds better than Langmuir's, where the R2 of the Freundlich model was 0.9979 and the R2 of the Langmuir model was 0.9554.
Sorption is a key factor in removal of organic and inorganic contaminants from their aqueous solutions. In this study, we investigated the removal of Xylenol Orange tetrasodium salt (XOTS) from its aqueous solution by Bauxite (BXT) and cationic surfactant hexadecyltrimethyl ammonium bromide modified Bauxite (BXT-HDTMA) in batch experiments. The BXT and BXT-HDTMA were characterized using FTIR, and SEM techniques. Adsorption studies were performed at various parameters i.e. temperature, contact time, adsorbent weight, and pH. The modified BXT showed better maximum removal efficiency (98.6% at pH = 9.03) compared to natural Bauxite (75% at pH 2.27), suggesting that BXT-HDTMA is an excellent adsorbent for the removal of XOTS from water. The equ
... Show Moregenerator the metal conductor is replaced by conducting gas plasma.
The spray quality of two spraying agents with different physical properties was investigated under laboratory conditions to find whether the measurement of deposited drops could be affected by spraying those agents. The first spraying agent Moddus, which is a plant growth regulator, has a surface tension of 28 mN m-1 with almost half the value of the second spraying agent Kelpak (58 mN m-1). A mini boom sprayer containing three flat fan nozzles (XR 11003) was used in the test with three traveling speeds (4.74, 5.42 and 8.13 km. h-1). The test was performed to evaluate the quality of spray drops (spray coverage, spray density and stains diameter) after they were deposited on water sensitive papers (WSP). The results showed a higher ability o
... Show MoreKE Sharquie, AA Noaimi, HA Al-Mudaris, Journal of Drugs in Dermatology: JDD, 2013 - Cited by 22
Linear programming currently occupies a prominent position in various fields and has wide applications, as its importance lies in being a means of studying the behavior of a large number of systems as well. It is also the simplest and easiest type of models that can be created to address industrial, commercial, military and other dilemmas. Through which to obtain the optimal quantitative value. In this research, we dealt with the post optimality solution, or what is known as sensitivity analysis, using the principle of shadow prices. The scientific solution to any problem is not a complete solution once the optimal solution is reached. Any change in the values of the model constants or what is known as the inputs of the model that will chan
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