Influence of Preparation Method on Copper Loaded Titania Nanoparticles: Textural, Structural Properties and Its Photocatalytic Activity towards P-Nitrophenol

TiO2 nanopowder, loaded with copper, was prepared under both impregnation and co-precipitation methods. The morphology and structure of TiO2 were studied by XRD, TEM, FTIR and BET techniques. The photocatalytic activity of samples was studied by monitoring the degradation of nitro phenol, using a UV-visible spectrophotometer. Total organic compound measurements showed that p-nitrophenol photodegradation efficiency reached 46% after 150 min over Cu-TiO2 catalysts. Key words: TiO2 nanopowder, copper, impregnation, co-preceptation, photodegradation, nitrophenol

Numerical Evaluation and Analysis for Hydrogen Production Via Ethanol Steam Reforming

In the present study, two series of Ni/Ce/ZrO2 catalysts were prepared. The first one is n% Ni/Ce0.74Zr0.26O2 (n = 0, 2, 10 and 20 wt %). The second is 10%Ni / m (Ce/ZrO2) (m = 0, 4, 6 and 8). Catalysts have been investigated for ethanol steam reforming (ESR) to produce hydrogen. The reaction was studied in an atmospheric flow system, the temperature range was 200-600 ºC and water/ethanol (6, 8, 10 molar ratio). The effect of using H2O2 as an oxidant in auto-thermal reforming of ethanol has been also investigated (at 400 ºC, and H2O2/ethanol ratio = 8) to get highest hydrogen selectivity with lower CO ratio. Numerical evaluation and analysis have been performed for the above obtained results. It has been observed that the ethanol conversion, hydrogen production and some of the various investigated relations are functions of more than one independent variable. So, the response surface methodology (RSM) has been employed to evaluate these relations. Key Words: Numerical analysis, Response surface methodology, Ethanol steam reforming, Ni/Ce/ZrO2 catalysts

Intensification of toxic chlorophenolic compounds degradation over efficient microwave-dried silica-doped tetragonal zirconia nanocatalysts

The work aims to evaluate the efficient microwave (MW) drying method of silica-doped tetragonal zirconia nanocatalysts (SZN-M) for intensification of the degradation of toxic chlorophenolic compounds. The catalyst dried under a conventional oven (SZN-O) was also conducted for comparison. The MW drying time was reduced six times and three times less energy was used than the conventional oven drying. The catalysts were characterized by Fourier-transform infrared, X-ray diffraction, electron spin resonance, nitrogen adsorption-desorption analyses, zeta potential, ultraviolet–visible diffuse reflectance spectroscopy and photoluminescence analyses. Compared with SZN-O, the SZN-M possessed a higher number of Si-O-Zr bonds that led to a greater amount of oxygen vacancies, metal defect sites, larger pore size as well as surface area, and hence displayed excellent performance toward the degradation of toxic 2-chlorophenol, 2-CP (92%), while only 67% for the former. The SZN-M achieved to reduce the total organic carbon and biological oxygen demand up to 88% and 89%, respectively, while for SZN-O, the reduction was up to 82% and 84%. The catalysts still remained active after five cycles and are highly capable of degrading various chlorophenolic compounds that could be very beneficial for the wastewater treatment

A swarm intelligence approach in undersampling majority class

Over the years, machine learning has been facing the issue of imbalance dataset. It occurs when the number of instances in one class significantly outnumbers the instances in the other class. This study investigates a new approach for balancing the dataset using a swarm intelligence technique, Stochastic Diffusion Search (SDS), to undersample the majority class on a direct marketing dataset. The outcome of the novel application of this swarm intelligence algorithm demonstrates promising results which encourage the possibility of undersampling a majority class by removing redundant data whist protecting the useful data in the dataset. This paper details the behaviour of the proposed algorithm in dealing with this problem and investigates the results which are contrasted against other techniques

Connecting Fuzzifying Topologies and Generalized Ideals by Means of Fuzzy Preorders

The present paper investigates the relations between fuzzifying topologies and generalized ideals of fuzzy subsets, as well as constructing generalized ideals and fuzzifying topologies by means of fuzzy preorders. Furthermore, a construction of generalized ideals from preideals, and vice versa, is obtained. As a particular consequence of the results in this paper, a construction of fuzzifying topology generated by generalized ideals of fuzzy subsets via a given I-topology is given. The notion of σ-generalized ideal is introduced and hence every σ-generalized ideal is shown to be a fuzzifying topology induced by some fuzzy preorder

Fuzzyfing Ideals on T-Fuzzy Ordered Sets

Abstract Different approaches of fuzzyfing the supremum and the infimum of a subset of fuzzy preordered sets, are investigated. The associativity of such operations under various fuzzyfing forms of supremums and infimums, are studied. Also, such fuzzy ordered sets are defined by fuzzyfing the reflexivity, transitivity and antisymmetry axioms. Accordingly, a fuzzyfing ideal on fuzzy ordered sets via the t-norms and fuzzy orders are introduced. The characterizations of fuzzyfing ideal by its level sets and by its fuzzy points are deduced. Finally, we introduce the quasi fuzzy ideal, hence fuzzy ideals, in the sense of (Yuan Bo [11] ) are completely described by quasi fuzzy ideals