Improving the Physicochemical Properties ofSbVZrCe Catalyst Prepared By Reflux Method

Antimony-vanadium-oxide is known to be an active and selective catalyst for the ammoxidation of propane to Acrylonitrile(ACN) and doping with Zr-Ce can enhance the performance ofthis catalyst. Unfortunately the yield of21.3% is to low to be used commercially. Sb-V-Zr-Cecatalyst is prepared by using slurry method for the ammoxidation of propane to ACN. In an attemptto increasethe yield of propane to ACN, modifications methods solely focus on reflux method are introduced into the catalyst preparation method for this process. A combination of various physicochemical techniques such as FTIR, physical adsorption ofN2, BET surface area, X-ray Diffraction (XRD), scanning electron microscopy (SEM) and Temperature Programmed Desorption Reduction & Oxidation (TPDRO) are used to characterize the modified catalyst

Estimation of magnitudes of debris flows in selected torrential watersheds in Slovenia

In this paper the application of different methods for estimation of magnitudes of rainfall-induced debris flows in 18 torrents in the Upper Sava River valley, NW Slovenia, and in 2 torrents in Pohorje, N Slovenia is described. Additional verification of the methods was performed in the torrential watersheds with active debris flows in the recent past (Predelica and Brusnik in the Soca River basin, W Slovenia). For some of the methods, the knowledge of morphometric characteristics of a torrential watershed, torrential channel and torrential fan is enough. For other methods, a mathematical tool (HEC-HMS) had to be applied in order to develop a hydrologic run-off model of precipitation that can trigger debris flows. Computed debris-flow magnitudes were of the order between 6,500 m(3) and 340,000 m(3). Their values are a function of torrential watershed parameters, such as: watershed area, Melton number, fan gradient, and torrential channel gradient. The investigated fans were classified into 3 groups with regard to the debris-flow hazard: debris-flow fans (hazard exists), torrential fans (no hazard), and transitional fans (debris flows are possible, but with low possibility). A limit between debris-flow fans and torrential fans is proposed: Melton number 0.3 and torrential fan gradient 4 degrees, that is, 7%. Out of 24 investigated torrential fans, 13 fans were classified into the group of debris-flow fans, 5 fans were classified into the group of torrential fans, and the rest 6 fans were classified into the group of transitional fans

Price Risk For Coal Liquefaction in Moderate Scale Development

According to Talla et al,2017 the Linde Hampson Method can be used to process solid changes into liquid. In this study aims to calculate the Linde Hampson method in the temperature range of the Fischer Tropsch method (LTFT /Low temperature fischer tropsch). The temperatures used for comparison include low temperatures of 200 to 250 oC. Parameters compared from the four type of coal namely lignite, antrachitre, bituminous and subbituminous are tested with projects on a moderate scale of 100 – 1000 tons. Analysis of Price risk is carried out to see the trend of change (increase/decrease) in the price of selling syngas. Price can change because of supply and demand. The main factor that can change price is quantity and quality of heat (HHV) and composition. Development of subbituminous can have higher risk than the antrachite type. Based on the calculation of the standard deviation of the risk values obtained from the four types is 25,3$

Feature Augmentation via Nonparametrics and Selection (FANS) in High Dimensional Classification

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.Comment: 30 pages, 2 figure