Modelling useful energy demand system as derived from basic needs in the household sector

Inter-fuel substitution in the household sector depends on whether their target energy use is similar or not. To account for the effect of end-use application on energy demand, the concept of useful energy is utilized in which energy carriers are grouped according to their end-use applications. Useful energy is assumed as a commodity demanded to satisfy needs. Therefore, it should possess certain characteristics in accordance with the nature of basic needs. These characteristics were investigated through a two-level budgeting system with demographic variables indicating rural and urban households of Iran. The model has been applied to estimate the behavioural parameters such as income and price elasticities of useful energy demand. The estimated values of income and own-price elasticity show that all categories of useful energy are necessities with a relatively inelastic demand. Among them, cooling and non-substitutable electricity has the highest income and own-price elasticity, while lighting and water heating are ranked as the most necessary types of useful energy due to their low-income elasticity. In addition, small values of cross price elasticities support the idea that different types of useful energy are almost independent. Therefore, the results confirm that useful energy demands could be considered as basic needs.     

Impact of innovation programs on development of energy system: Case of Iranian electricity-supply system

The paper presents further experiments with an extended version of a comprehensive model for assessment of energy technologies and research and development (R&D) planning to evaluate the impact of innovation programs on development of Iranian electricity-supply system. This analytical instrument is a model of energy R&D resource allocation with an explicit perspective of developing countries which has been linked to a bottom-up energy-systems model. Three emerging electricity generation technologies of solar PV, wind turbine and gas fuel cell are considered in the model and the impact of innovation programs on cost-reducing innovation for them is examined. The main results provided by the modeling approach include optimal allocation of R&D resources, induced capacity expansion policies to guarantee the effectiveness of R&D activities, competitive cost of emerging technologies, impact of innovation programs on optimal structure of electricity-supply system and benefits of innovation programs in the long-run.     

Comparison of essential oils compositions of Nepeta persica obtained by supercritical carbon dioxide extraction and steam distillation methods

Essential oil of Nepeta persica cultivated in Iran was obtained by steam distillation and supercritical (carbon dioxide) extraction methods. The oils were analysed by capillary gas chromatography using flame ionization and mass spectrometric detections. The compounds were identified according to their retention indices and mass spectra (EI, 70 eV). The effects of different parameters such as pressure, temperature, modifier volume and extraction times (dynamic and static) on the supercritical fluid extraction (SFE) of N. persica oil were investigated. The results showed that under the pressure of 20.3 MPa, temperature of 45 °C, methanol of 1.5% v/v), dynamic extraction time of 50 min and static extraction time of 25 min extraction was more selective for the 4αβ,7α,7aα-nepetalactone. Twelve compounds were identified in the steam-distilled oil. The major components of N. persica were 4αβ,7α,7aα-nepetalactone (26.5%), cis-β-farnesene (4.4%) and 3,4α-dihydro-4aα,7α,7aα-nepetalactone (3.5%). However, by using supercritical carbon dioxide under optimum conditions, only two components have more than 90.0% of the oil. The extraction yield based on steam distillation was 0.08% (v/w). On the other hand, using SFE extraction yield in the range of 0.22–8.90% (w/w) were obtained at different conditions. The results show that, in Iranian N. persica oil, 4αβ,7α,7aα-nepetalactone is a major component.     

Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR _poly and SVR _rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.     

A Real-Time Power Controller for Grid-Connected Inverters in LV Smart Microgrids

This paper presents a real-time power flow controller for VSIs （voltage source inverters） interfaced to low voltage microgrids. The proposed controller is modular, flexible, intelligent, inexpensive, portable, adaptive and designed to positively contribute in low voltage microgrids in which the lines R/X ratio is greater than the transmission lines. Therefore, the proposed control strategy is developed for operation in distribution lines. The controller strategy is different from the conventional grid-connected inverters which are designed based on transmission line characteristics. This controller, using a Texas Instrument general purpose DSP （digital signal processor）, is programmed and tuned using MATLAB/SIMULINK in order to enhance self-healing, reliability and stability of the grid. This general purpose controller makes proper decisions using its local measurements as the primary source of data. The controller has the capability of communicating with the adjacent controllers and sharing the information if/when needed. The power flow output of the inverter is tested for both islanded and grid-connected modes of operation. The inverter positively contributes to active and reactive power supply while operating in grid-connected mode. The proposed control method has been implemented on a Texas Instrument DSC （digital signal controller） chip and tested on a hardware test bench at the Alternative Energy Laboratory at WVU1T （West Virginia University Institute of Technology）. The system＇s experimental results veri~ the validity and efficiency of the proposed controller.     

[Hmim][HSO4], a Green and Recyclable Acidic Ionic Liqiud Medium for the One‐Pot Nitration of TADB to HNIW

An efficient and mild procedure for the one‐pot preparation of CL‐20 from TADB in the presence of the Brønsted acidic ionic liquid 1‐methylimidazolium hydrogen sulfate [Hmim][HSO₄] as catalyst was developed. The ionic liquid was stable during the reaction process and could also be reused several times with steady activity. In order to optimize the process parameters to obtain higher yield and purity, a study was carried out with variation of the ionic liquids and some parameters like temperature, mole ratio of [Hmim][HSO₄] and nitric acid. This procedure may be a practical alternative to the existing procedures to meet the nees of academics as well as industry.     

On the dispersion of CNTs in polyamide 6 matrix via solution methods: assessment through electrical, rheological, thermal and morphological analyses

In this study, polyamide 6 (PA 6)/multi-walled carbon nanotube (MWCNT) nanocomposites were prepared by different solution methods based on phase inversion, drop-casting and simple evaporation processes. Optical microscopy and field emission scanning electron microscopy techniques were used to investigate the dispersion states of the nanotubes in PA 6 matrix. The results indicated that the dispersion state of MWCNTs in the nanocomposites prepared by the phase inversion-based method was better than those in the nanocomposites prepared by the other two methods. Electrical, rheological, differential scanning calorimetry and thermo-gravimetric analysis measurements showed that the PA 6/MWCNTs nanocomposites prepared by the phase inversion-based method had higher electrical conductivity, storage modulus, crystallization temperature and thermal stability in comparison with those prepared by the other two methods, attributed to the better dispersion state of MWCNTs. These results confirmed achievement of a good dispersion state of MWCNTs within PA 6 matrix by the phase inversion-based efficient approach.     

On Density-Based Data Streams Clustering Algorithms: A Survey简

Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the nmnber of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density- based methods in the clustering process and at the same time overcoming the constraints, which are put out by data streanFs nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms＇ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.     

Corrosion and time dependent passivation of Al 5052 in the presence of H<Subscript>2</Subscript>O<Subscript>2</Subscript>

Corrosion and time–dependent oxide film growth on AA5052 Aluminum alloy in 0.25M Na₂SO₄ solution containing H₂O₂ was studied using electrochemical impedance spectroscopy, potentiodynamic polarization, chronoamperometric and open circuit potential monitoring. It was found that sequential addition of H₂O₂ provokes passivation of AA5052 which ultimately thickens the oxide film and brings slower corrosion rates for AA5052. H2O2 facilitates kinetics of oxide film growth on AA 5052 at 25° and 60 °C which is indicative of formation of a thick barrier film that leads to an increment in the charge transfer resistance. Pitting incubation time increases by introduction of H₂O₂ accompanied by lower pitting and smoother surface morphologies. At short exposure (up to 8 h) to H₂O₂–containing solution, the inductive response at low frequencies predominantly determined the corrosion mechanism of AA5052. On the other hand, at prolonged exposure times (more than 24 h) to 0.25M Na₂SO₄+1vol% H₂O₂ solution, thicker oxide layers resulted in the mixed inductive–Warburg elements in the spectra.     

An intelligent based-model role to simulate the factor of safe slope by support vector regression

An infrastructure development in landscape and clearing of more vegetated areas have provided huge changes in Malaysia gradually leading to slope instabilities accompanied by enormous environmental effects such as properties and destructions. Thus, prudent practices through vegetation incorporating to use slope stability is an option to the general stabilized technique. Few researches have investigated the effectiveness of vegetative coverings related to slope and soil parameters. The main goal of this study is to provide an intelligent soft computing model to predict the safety factor (FOS) of a slope using support vector regression (SVR). In the other words, SVR has investigated the surface eco-protection techniques for cohesive soil slopes in Guthrie Corridor Expressway stretch through the probabilistic models analysis to highlight the main parameters. The aforementioned analysis has been performed to predict the FOS of a slope, also the estimator’s function has been confirmed by the simulative outcome compared to artificial neural network and genetic programing resulting in a drastic accurate estimation by SVR. Using new analyzing methods like SVR are more purposeful than achieving a starting point by trial and error embedding multiple factors into one in ordinary low-technique software.