New Options for Conversion of Vegetable Oils to Alternative Fuels

Biodiesel from transesterification of vegetable oils is an excellent alternative fuel. There is, however, a need to develop a direct process for conversion of vegetable oils into gasoline-competitive biodiesel and other petroleum products. Methyl esters of vegetable oils have several outstanding advantages among other new-renewable and clean engine fuel alternatives. The purpose of the transesterification process is to lower the viscosity of vegetable oil. Compared to No. 2 diesel fuel, all of the vegetable oils are much more viscous, whereas methyl esters of vegetable oils are slightly more viscous. The methyl esters are more volatile than those of the vegetable oils. Conversion of vegetable oils to useful fuels involves the pyrolysis and catalytic cracking of the oils into lower molecular products. Pyrolysis produces more biogasoline than biodiesel fuel. Soap pyrolysis products of vegetable oils can be used as alternative diesel engine fuel. The soaps obtained from the vegetable oils can be pyrolyzed into hydrocarbon-rich products. Zinc chloride catalyst contributed greatly to high amounts of hydrocarbons in the liquid product. The yield of ZnCl ₂ catalytic conversion of the soybean oil reached the maximum 79.9% at 660 K.     

Use of Fenton oxidation to improve the biodegradability of a pharmaceutical wastewater

The applicability of Fenton's oxidation to improve the biodegradability of a pharmaceutical wastewater to be treated biologically was investigated. The wastewater was originated from a factory producing a variety of pharmaceutical chemicals. Treatability studies were conducted under laboratory conditions with all chemicals (having COD varying from 900 to 7000 mg/L) produced in the factory in order to determine the operational conditions to utilize in the full-scale treatment plant. Optimum pH was determined as 3.5 and 7.0 for the first (oxidation) and second stage (coagulation) of the Fenton process, respectively. For all chemicals, COD removal efficiency was highest when the molar ratio of H(2)O(2)/Fe(2+) was 150-250. At H(2)O(2)/Fe(2+) ratio of 155, 0.3M H(2)O(2) and 0.002 M Fe(2+), provided 45-65% COD removal. The wastewater treatment plant that employs Fenton oxidation followed by aerobic degradation in sequencing batch reactors (SBR), built after these treatability studies provided an overall COD removal efficiency of 98%, and compliance with the discharge limits. The efficiency of the Fenton's oxidation was around 45-50% and the efficiency in the SBR system which has two reactors each having a volume of 8m(3) and operated with a total cycle time of 1 day, was around 98%, regarding the COD removal.     

Evolution of the mosaic structure in InGaN layer grown on a thick GaN template and sapphire substrate

The In_xGa_1?xN epitaxial layers, with indium (x) concentration changes between 0.16 and 1.00 (InN), were grown on GaN template/(0001) Al₂O₃ substrate by metal organic chemical vapour deposition. The indium content (x), lattice parameters and strain values in the InGaN layers were calculated from the reciprocal lattice mapping around symmetric (0002) and asymmetric (10–15) reflection of the GaN and InGaN layers. The characteristics of mosaic structures, such as lateral and vertical coherence lengths, tilt and twist angle and heterogeneous strain and dislocation densities (edge and screw dislocations) of the InGaN epilayers and GaN template layers were investigated by using high-resolution X-ray diffraction (HR-XRD) measurements. With a combination of Williamson–Hall (W-H) measurements and the fitting of twist angles, it was found that the indium content in the InGaN epilayers did not strongly effect the mosaic structures’ parameters, lateral and vertical coherence lengths, tilt and twist angle, or heterogeneous strain of the InGaN epilayers.     

Performance study of a wireless mobile ad hoc network with orientation‐dependent internode communication scheme

A Petri‐net‐based simulation model of a wireless mobile ad hoc network is developed and studied. The model covers all the fundamental aspects of behaviour of such a network and uses a novel scheme of orientation‐dependent (or sector‐dependent) internode communication, with random states of links. The proposed scheme enables representation of reliability aspects of wireless communication, such as fading effects, interferences, presence of obstacles and weather conditions in a general and rather easy way. The simulation model was implemented in terms of a class of extended Petri nets to explicitly represent parallelism of events and processes in the WLAN as a distributed system. In the simulation, the behaviour of four fundamental performance metrics — packet delivery ratio, average number of hops, relative network traffic and end‐to‐end delay — were investigated with varying distance of transmission and different combinations of model parameters. Copyright © 2012 John Wiley & Sons, Ltd.     

Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks

In wheat and flour processing, the quality control needs quick analytical tools for predicting physical, rheological, and chemical properties. In this study, near infrared reflectance (NIR) spectroscopy combined with artificial neural network (ANN) was used to predict the flour quality parameters that are protein content, moisture content, Zeleny sedimentation, water absorption, dough development time, dough stability time, degree of dough softening, tenacity (P), extensibility (L), P/G, strength, and baking test (loaf volume and loaf weight). A total of 79 flour samples of different wheat varieties grown in different regions of Turkey were chemically analyzed, and the results of both NIR spectrum (400–2,498 nm) and chemical analysis were used to train/test the network by applying various ANN architectures. Prediction of protein, P, P/G, moisture content, Zeleny sedimentation, and water absorption in particular gave a very good accuracy with coefficient of determination (R ²) of 0.952, 0.948, 0.933, 0.920, 0.917, and 0.832, respectively. The results indicate that NIR combined with the ANN can successfully be used to predict the quality parameters of wheat flour.     

Big to Small: Ultrafine Mo2C Particles Derived from Giant Polyoxomolybdate Clusters for Hydrogen Evolution Reaction

Due to its electronic structure, similar to platinum, molybdenum carbides (Mo₂C) hold great promise as a cost‐effective catalyst platform. However, the realization of high‐performance Mo₂C catalysts is still limited because controlling their particle size and catalytic activity is challenging with current synthesis methods. Here, the synthesis of ultrafine β‐Mo₂C nanoparticles with narrow size distribution (2.5 ± 0.7 nm) and high mass loading (up to 27.5 wt%) on graphene substrate using a giant Mo‐based polyoxomolybdate cluster, Mo₁₃₂ ((NH₄)₄₂[Mo₁₃₂O₃₇₂(CH₃COO)₃₀(H₂O)₇₂]·10CH₃COONH₄·300H₂O) is demonstrated. Moreover, a nitrogen‐containing polymeric binder (polyethyleneimine) is used to create Mo? N bonds between Mo₂C nanoparticles and nitrogen‐doped graphene layers, which significantly enhance the catalytic activity of Mo₂C for the hydrogen evolution reaction, as is revealed by X‐ray photoelectron spectroscopy and density functional theory calculations. The optimal Mo₂C catalyst shows a large exchange current density of 1.19 mA cm^?2, a high turnover frequency of 0.70 s^?1 as well as excellent durability. The demonstrated new strategy opens up the possibility of developing practical platinum substitutes based on Mo₂C for various catalytic applications.     

Short term forecasting with support vector machines and application to stock price prediction

Forecasting a stock price movement is one of the most difficult problems in finance. The reason is that financial time series are complex, non stationary. Furthermore, it is also very difficult to predict this movement with parametric models. Instead of parametric models, we propose two techniques, which are data driven and non parametric. Based on the idea that excess returns would be possible with publicly available information, we developed two models in order to forecast the short term price movements by using technical indicators. Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship. This relationship comes from the technical analysis. Comparison shows that support vector regression (SVR) out performs the multi layer perceptron (MLP) networks for a short term prediction in terms of the mean square error. If the risk premium is used as a comparison criterion, then the SVR technique is as good as the MLP method or better.