Spent catalysts for sulfuric acid production have large amount of vanadium and due to environmental authority it is required to reduce the vanadium contain of the spent catalyst. Experimental investigation was conducted to study the vanadium recovery from spent catalyst via leaching process using sodium hydroxide to study the effect of process variables (temperatures, sodium hydroxide molarities, leaching time and particle size) on vanadium recovery. The effect of process variables (temperature, particle size,molarities of sodium hydroxide and leaching time) on the percentages of vanadium recovery were investigated and discussed .It was found that the percentage of vanadium recovery increased with increasing temperature up to 100 , increasing sodium hydroxide molarity from 2 to 4M, increasing leaching time, decreasing particle size from mesh 150, 100 and 65. A complete vanadium recovery was achieved at the following conditions: temperature (100˚c), particle size (150 mesh ) molarity of Na OH(4 molar) and leaching time(5 h).
In this study, several ionanofluids (INFs) were prepared in order to study their efficiency as a cooling medium at 25 °C. The two-step technique is used to prepare ionanofluid (INF) by dispersing multi-walled carbon nanotubes (MWCNTs) in two concentrations 0.5 and 1 wt% in ionic liquid (IL). Two types of ionic liquids (ILs) were used: hydrophilic represented by 1-ethyl-3-methylimidazolium tetrafluoroborate [EMIM][BF4] and hydrophobic represented by 1-hexyl-3-methylimidazolium hexafluorophosphate [HMIM][PF6]. The thermophysical properties of the prepared INFs including thermal conductivity (TC), density and viscosity were measured experimental
Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
... Show MoreWhile conservative access preparations could increase fracture resistance of endodontically treated teeth, it may influence the shape of the prepared root canal. The aim of this study was to compare the prepared canal transportation and centering ability after continuous rotation or reciprocation instrumentation in teeth accessed through traditional or conservative endodontic cavities by using cone-beam computed tomography (CBCT).
Forty extracted intact, matured, and 2-rooted human maxillary first premolars were selected for this
This study presents, for the first time, an innovative Jet Plasma-assisted technique for the green synthesis of TiO₂@Ag core–shell nanoparticles using chard leaf extract as a natural reducing and stabilizing agent. The Jet Plasma provides a highly energetic environment that accelerates nucleation and core–shell formation at low temperatures without toxic precursors. The synthesized nanoparticles exhibited uniform and stable structures, as confirmed by comprehensive characterization techniques including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), ultraviolet–visible (UV–Vis) spectroscopy, transmission electron microscopy (TEM), and zeta potential analysis. XRD patterns confirmed the crystalline anatase
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