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.
The aim of this study is to investigate the kinetics of copper removal from aqueous solutions using an electromembrane extraction (EME) system. To achieve this, a unique electrochemical cell design was adopted comprising two glass chambers, a supported liquid membrane (SLM), a graphite anode, and a stainless-steel cathode. The SLM consisted of a polypropylene flat membrane infused with 1-octanol as a solvent and bis(2-ethylhexyl) phosphate (DEHP) as a carrier. The impact of various factors on the kinetics constant rate was outlined, including the applied voltage, initial pH of the donor phase solution, and initial copper concentration. The results demonstrated a significant influence of the applied voltage on enhancing the rate of c
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The enhancement of ZnSe/Si Heterojunction by adding some elements (V, In and Cu) as impurities is the main goal because they contribute to the manufacturing of renewable energy equipment, such as solar cells. This paper describes the preparation of thin films ZnSe with V, In and Cu doped using thermal evaporation method with a vacuum of 10–5 Torr. The thin film was obtained from this work could be applied in heterojunction solar cell because of several advantages including high absorption coefficient value and direct band gap. The samples prepared on a glass and n-type Si wafer substrate. These films have been annealed for 1 h in 450 K. X-ray diffraction XRD results indicated that ZnSe thin film possesses poly-crystalline structure after
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ABSTRACT:In this paper, Cd10–xZnxS (x = 0.1, 0.3, 0.5) films were deposited by using chemical spray pyrolysis technique, the molar concentration precursor solution was 0.15 M/L. Depositions were done at 350°C on cleaned glass substrates. X-ray dif- fraction technique (XRD) studies for all the prepared film; all the films are crystalline with hexagonal structure .The optical properties of the prepared films were studied using measurements from VIS-UV-IR spectrophotometer at wave- length with the range 300 - 900 nm; the average transmission of the minimum doping ratio (Zn at 0.1%) was about 55% in the VIS region, it was decrease at the increasing of Zn concentration in the CdS films, The band gap of the doped CdS films was varied as 3.7, 3
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Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre
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