[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.     

Successful Use of Geoelectrical Surveys in Area 3 of the Gol-e-Gohar Iron Ore Mine,Iran

Geoelectrical surveys were conducted in Area 3 of the Gol-e-Gohar iron ore mine to provide geological and hydrogeological information. Open pit mining is currently underway in the northern part of the Area, and underground mining operations are planned for the southern section. Groundwater has already been encountered in the open pit mine. Twenty five resistivity soundings were first performed in the mine area; then, induced polarization (IP) measurements were carried out to remove ambiguities between clay and water-bearing layers. To investigate fault zones as water conduits, combined resistivity profiling surveys were also carried out. These measurements provided a detailed structural map of the pit area. Resistivity and IP results have subsequently been confirmed by observations at three monitoring wells and the mine pit wall. Monitoring and piezometric wells will be drilled at locations suggested by the results of the geoelectrical surveys. Drainage galleries may be developed to dewater the open pit mine. However, another option being considered is to start the underground mining with the idea that it will initially simply serve as a dewatering mechanism.     

Optimal visual sensor placement for coverage based on target location profile

The proper placement of visual sensors across a sensor field for covering targets with arbitrary location and orientation is a mission-critical decision in surveillance applications. The specifics of sensor deployment in these applications not only determine the maximum achievable coverage, but it also affects the quality of the target’s appearance in cameras for subsequent use in vision tasks. However, the inaccuracies inherent in localization techniques and the lack of knowledge regarding the target orientation render existing proposals insufficient for real-life scenarios. In this paper, we address both challenges. First, we extend the conventional point representation of targets with a circular model to account for full-angle coverage of targets with known location yet with unknown orientation from all directions. We then assume, in the absence of precise location information, a trajectory profile of targets could instead be generated through the importance sampling of the environment, indicating spots where the target is most likely located. This profile-based abstraction enables us to capture the uncertainty in target’s location by encircling every agglomeration of proximal samples within one cluster. Each cluster can then be viewed as a virtual macroscopic circular target for which we formulate the coverage problem in terms of a Binary Integer Programming (BIP) model. We have also taken into account the presence of obstruction in between multiple targets by calculating the angles of view of the sensors in an occlusion-dependant manner, effectively determining optimal placement for maximal instead of full-angle coverage. Evaluation results derived from our simulation experiments reveal that the proposed mechanism can effectively achieve high coverage accuracy with minimum number of deployed sensors.     

A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity

This study presents a flexible neuro-fuzzy approach for location optimization of solar plants with possible complexity and uncertainty. The flexible approach is composed of artificial neural network (ANN) and fuzzy data envelopment analysis (FDEA). The intelligent approach of this study is applied for location optimization of solar plants in Iran. First, FDEA is validated by DEA, and then it is used for ranking of solar plant units (SPUs) and the best α-cut is selected based on the test of Normality. Also, several ANNs are developed through multi layer perceptron (MLP) for ranking of solar plants and the best one with minimum mean absolute percentage of error (MAPE) is selected for further considerations. Finally, the preferred model (FDEA or ANN) is selected based on test of Normality. The implementation of the flexible approach for solar plants in Iran identifies the preferred FDEA at α = 0.3, where is the level of data uncertainty. This indicates that the data are collected from the uncertain and fuzzy environment. This is the first study that presents a flexible approach for identification of optimum location of solar plants with possible noise, non-linearity, complexity and environmental uncertainty. This would help policy makers to identify the preferred Strategy for location optimization problems associated with solar plant units.     

Thermal Energy Storage Module Design Using Energy and Exergy Analysis

In this paper, irreversibility of a thermal energy storage system is numerically investigated. The system consists of two concentric cylinders. The outer cylinder is filled with phase change material (PCM), while working fluid flows inside the inner pipe. The system works periodically. The related governing equations are solved by a control volume-based finite difference method. The effects of different parameters such as PCM size and melting point temperature are examined on the irreversibility of the system. The results show that the irreversibility of thermal storage module is strongly affected by the size of PCM (diameter and length of the external cylinder) and melting temperature. Based on the obtained results, the irreversibility of the system can be reduced by proper selection of PCM size and melting temperature.     

Optimization of Short Load Forecasting in Electricity Market of Iran Using Artificial Neural Networks

Accurate short-term load forecasting (STLF) is one of the essential requirements for power systems. In this paper, two different seasonal artificial neural networks (ANNs) are designed and compared in terms of model complexity, robustness, and forecasting accuracy. Furthermore, the performance of ANN partitioning is evaluated. The first model is a daily forecasting model which is used for forecasting hourly load of the next day. The second model is composed of 24 sub-networks which are used for forecasting hourly load of the next day. In fact, the second model is partitioning of the first model. Time, temperature, and historical loads are taken as inputs for ANN models. The neural network models are based on feed-forward back propagation which are trained and tested using data from electricity market of Iran during 2003 to 2005. Results show a good correlation between actual data and ANN outcomes. Moreover, it is shown that the first designed model consisting of single ANN is more appropriate than the second model consisting of 24 distinct ANNs. Finally ANN results are compared to conventional regression models. It is observed that in most cases ANN models are superior to regression models in terms of mean absolute percentage error (MAPE).     

The Electrochemical Behavior of Au/AuNPs/PNA/ZnSe-QD/ACA Electrode Towards CySH Oxidation

Nano-Micro Letters - This work describes the electrochemical behavior of azodicarboxamide (ACA) film immobilized on the surface of penicillamine (PNA)/ZnSe-quantum dot (ZnSe-QD) gold nanoparticle...     

An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors

This paper introduces an integrated approach based on data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT) for total energy efficiency assessment and optimization in energy intensive manufacturing sectors. Total energy efficiency assessment and optimization of the proposed approach considers structural indicators in addition conventional consumption and manufacturing sector output indicators. The validity of the DEA model is verified and validated by PCA and NT through Spearman correlation experiment. Moreover, the proposed approach uses the measure-specific super-efficiency DEA model for sensitivity analysis to determine the critical energy carriers. Four energy intensive manufacturing sectors are discussed in this paper: iron and steel, pulp and paper, petroleum refining and cement manufacturing sectors. To show superiority and applicability, the proposed approach has been applied to refinery sub-sectors of some OECD (Organization for Economic Cooperation and Development) countries. This study has several unique features which are: (1) a total approach which considers structural indicators in addition to conventional energy efficiency indicators; (2) a verification and validation mechanism for DEA by PCA and NT and (3) utilization of DEA for total energy efficiency assessment and consumption optimization of energy intensive manufacturing sectors.     

Preparation of polymer-coated, scintillating ion-exchange resins for monitoring of 99Tc in groundwater

The present study was oriented to prepare new scintillating anion-exchange resins for measurement of (99)TcO(4)(-) in natural waters. The organic fluor 2-(1-naphthyl)-5-phenyloxazole was diffused into (chloromethyl)polystyrene resin. Thereafter, a thin layer of poly[[2-(methacryloyloxy)ethyl]trimethylammonium chloride] was grafted from the resin surface by surface-initiated atom transfer radical polymerization as an attempt to overcome potential problems related to the leaching of fluor molecules during usage. The residual chloromethyl groups of the polymer-coated resin were aminated by reaction with two different tertiary amines, triethylamine (TEA) and methyldioctylamine (MDOA). Off- and on-line quantification of (99)Tc was achieved with high detection efficiencies of 60.72 ± 1.93% and 72.83 ± 0.81% for resin with TEA and MDOA functional groups, respectively. The detection limit was determined to be less than the maximum contaminant level (33 Bq L(-1)) established under the Safe Drinking Water Act. The two functionalized resins were demonstrated to be selective for pertechnetate from synthetic groundwater containing up to 1000 ppm Cl(-), SO(4)(2-), and HCO(3)(-) and up to 1200 ppb Cr(2)O(7)(2-) in an acidic medium.     

A robust decision-making methodology for evaluation and selection of simulation software package

Today, simulation has a broad range of applications from solving service problems to analyzing manufacturing problems such as warehousing and logistic systems. Simulation has become a popular methodology and selecting an appropriate simulation software package is one of the decisions that any industrial engineer may face at work. As a result, numerous types of simulation software packages have been developed for modeling simulation problems. The increasing variety of simulation software packages in the software market makes the selection of an appropriate simulation software package a critical decision. Selecting an inappropriate software package will be followed by many negative consequences. This paper will present a robust decision-making methodology based on Fuzzy Analytical Hierarchy Process (FAHP) for evaluating and selecting the appropriate simulation software package. The robust decision method aggregates the experts’ judgments for the criteria weights and the suitability of simulation software alternatives. The FAHP is used to prioritize and evaluate existing alternatives based on the proposed criteria for choosing the proper simulation software. The proposed methodology is applied to selecting the appropriate simulation software as an experiment and results are provided.