Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network

In this paper we have investigated the differences between the prices of different commercial oils of the Persian Gulf region. The prices of 7 different crude oils from Iran, Kuwait, Saudi Arabia, Oman, Abu Dhabi and Dubai were compared with the benchmark light oil of Saudi Arabia over the period January 2000-April 2010. A neural network is introduced to forecast the price of any commercial oil in these crude oils, provided that the price of the benchmark light oil of Saudi Arabia is already known or is predicted by another forecasting method. The designed neural network is able to predict the differences in the oil prices with an average error of 8.82% for testing and 7.24% for training data. It is claimed that the present method can promote the forecasting power of existing models to predict the price of any commercial oil instead of an average or benchmark value.     

Dynamic model for market-based capacity investment decision considering stochastic characteristic of wind power

This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market with significant wind power generation. In such an environment, investment and construction decisions are based on price signal feedbacks and imperfect foresight of future conditions in electricity market. System dynamics concepts are used to model structural characteristics of power market such as, long-term firms’ behavior and relationships between variables, feedbacks and time delays. For conventional generation units, short-term price feedback for generation dispatching of forward market is implemented as well as long-term price expectation for profitability assessment in capacity investment. For wind power generation, a special framework is proposed in which generation firms are committed depending on the statistical nature of wind power. The method is based on the time series stochastic simulation process for prediction of wind speed using historical and probabilistic data. The auto-correlation nature of wind speed and the correlation with demand fluctuations are modeled appropriately. The Monte Carlo simulation technique is employed to assess the effect of demand growth rate and wind power uncertainties. Such a decision model enables the companies to find out the possible consequences of their different investment decisions. Different regulatory policies and market conditions can also be assessed by ISOs and regulators to check the performance of market rules. A case study is presented exhibiting the effectiveness of the proposed model for capacity expansion of electricity markets in which the market prices and the generation capacities are fluctuating due to uncertainty of wind power generation.     

Preparation and functional properties of fish gelatin–chitosan blend edible films

With the goal of improving the physico-chemical performance of fish gelatin-based films, composite films were prepared with increasing concentrations of chitosan (Ch) (100G:0Ch, 80G:20Ch, 70G:30Ch, 60G:40Ch and 0G:100Ch, gelatin:Ch), and some of their main physical and functional properties were characterised. The results indicated that the addition of Ch caused significant increase (p < 0.05) in the tensile strength (TS) and elastic modulus, leading to stronger films as compared with gelatin film, but significantly (p < 0.05) decreased the elongation at break. Ch drastically reduced the water vapour permeability (WVP) and solubility of gelatin films, as this decline for the blend film with a 60:40 ratio has been of about 50% (p < 0.05). The light barrier measurements present low values of transparency at 600 nm of the gelatin–chitosan films, indicating that films are very transparent while they have excellent barrier properties against UV light. The structural properties investigated by FTIR and DSC showed a clear interaction between fish gelatin and Ch, forming a new material with enhanced mechanical properties.     

Extending a low-order upwind-biased scheme to solve turbulent flames using detailed chemistry model

Abstract

Achieving more accurate reacting flow numerical solutions apparently demand employing higher-order schemes, utilizing finer grids, and benefiting from more advanced chemistry models. One major objective of this work is to extend an inclusive low-order upwind-biased scheme in the context of finite-volume-element method to predict turbulent reacting flows on coarse grid resolutions very reliably. In this regard, a low-order upwind-biased scheme is suitably extended to approximate the mixture fraction variances at the cell-faces. This scheme implements the reacting flow physics explicitly in deriving the proposed mixture fraction variance expressions. These physical implementations enhance the derived expressions to result in superior turbulent reacting flow solutions even on coarse grid resolutions. To assess the accuracy of new expressions, we simulate a sample turbulent non-premixed flame with strong non-equilibrium effects of turbulence on chemistry. The comparisons show that the current low-order scheme is robust enough to predict the complex structure of non-premixed flames very reliably even on coarse grids.     

First-principles study of H2 adsorption on the pristine and oxidized (8,0) carbon nanotube

The effect of oxygen, hydrogen, and (oxygen + hydrogen) molecules adsorption on the structural and electrical properties of (8,0) carbon nanotube (CNT) are investigated through density functional theory. The obtained results indicate endothermical chemisorption of O₂ on the nanotube surface with a large binding energy of about 598 meV and a significant charge transfer of about 0.43 e per molecule. It is discussed that the O₂ chemisorption creates hole carries in the (8,0) carbon nanotube and thus increases the work function of the system. In the case of hydrogen molecule, a weak physisorption on the surface of CNT (∼−5 meV) is identified. The adsorption of H₂ on CNT is also accompanied by hole doping and increment of the work function of the CNT, while the charge transfer between CNT and H₂ is negligible. The band offsets in the H₂-CNT junction are calculated to examine and describe the observed hole doping in this system. The effect of oxygenation of CNT on hydrogen adsorption is also investigated and the most favorable adsorption configuration is found and the related adsorption energy is calculated. It is argued that the oxygenation of CNT enhances the physisorption of hydrogen molecules. It is shown that hydrogen molecule adsorption on the oxidized CNT cancels hole doping and hence decreases the work function of the system.     

A Modified LAPSUS Model to Enhance the Effective Rainfall Estimation by SCS-CN Method

In this study, LAPSUS model is modified to enhance the effective rainfall estimation by SCS curve number method. The LAPSUS model calculates discharge based on effective rainfall and routs it towards lower neighbouring grid cells following the multiple flow direction principle. Then, the sediment transport capacity and sediment transport rate are calculated in each grid cell. Finally, erosion or sedimentation is calculated by comparing the sediment transport rate with the sediment already in the transport of each grid cell. The amount of rainfall, curve number, convergence factor, discharge exponent, slope exponent, erodibility factor, and sedimentation ability factor are inputted to the application page of the modified model that was created in the C⁺⁺ programming. The outputs of the model are runoff and erosion maps in ASCII format. Evaluating performance of the modified model showed a high accuracy of its results. The value of the coefficient of determination (R²) calculated 0.99 for runoff and 0.97 for erosion. The Nash-Sutcliffe efficiency was 0.96 for runoff and 0.97 for erosion. The value of the precision index calculated 0.81 for both runoff and erosion. Also, the nRMSE calculated 3% for both runoff and erosion. The result showed that the modified model capable to estimate the runoff and erosion on a landscape in a micro sub-catchment scale.     

电池充电管理：新兴趋势

简介手机、便携式媒体播放器、便携式导航工具及蓝芽（Bluetooth）耳机等新一代手持装置不断缩小体积外型,同时加强效能与功能。就充电管理而言,这些强化的功能使得设计工程人员必须同时顾及用户的便利性与体验感受,以及整体充电时间与成本的均衡。     

CFD and artificial neural network modeling of two-phase flow pressure drop

A large number of experiments in a 2 cm diameter and 6 m length tube were carried out in order to study the two-phase flow regimes and pressure drops in it. The two-phase flow in the experimental tube was modeled using commercial CFD code, Fluent 6.2. An Artificial Neural Network (ANN) with three inputs including gas and liquid velocities and tube slope was designed and trained to predict average pressure drop across the tube. The comparison between CFD and ANN predictions of pressure drops with experimental measurements shows that the CFD results are more accurate than the ANN evaluations for new conditions.     

The synthesis, characterization, thermal and optical properties of copper, nickel, and vanadyl complexes derived from azo dyes

Two, novel, tetradentate Schiff-base ligands, namely bis-5-phenylazosalicylaldehyde diethylenetriimine and bis-5-[(4-methoxyphenyl)azo]salicylaldehyde diethylenetriimine, as well as their Cu²⁺, Ni²⁺, and VO²⁺ complexes, were synthesized and characterized using elemental analysis, infrared and also UV–Visible spectroscopy, ¹HNMR and mass spectra. The thermal stability of the free ligands and the related metal complexes, as determined using differential scanning calorimetry and thermal gravimetric analysis, were found to be thermally stable upto 240–275 °C depending on the type of ligand and the central metal atom. The λ_max of the ligands and their transition metal complexes in the region 300–800 nm are discussed. The novel metal complexes offer potential for application as recording media owing to both their absorption spectra in the blue-violet light region and high thermal stability.     

Optimization of the activity of CaO/Al2O3 catalyst for biodiesel production using response surface methodology

In this work the response surface methodology (RSM) in conjunction with the central composite design (CCD) were used to optimize the activity of CaO/Al₂O₃ solid catalysts for the production of biodiesel. In order to measure the catalyst activity, we used palm oil as a representative raw material for the conversion to biodiesel. The biodiesel production was carried out in a batch laboratory scale reactor. The results showed that both the calcination temperature and the amount of calcium oxide loaded on the support had significant positive effects on the biodiesel yield. The maximum basicity and biodiesel yield obtained were about 194 μmol/g and 94%, respectively. Overall, the catalyst showed high performance at moderate operating conditions and its activity was maintained after two cycles.