Synthesis of calcium carbonate-polyethylene oxide hybrid nanofibers through in-situ electrospinning

In this work, calcium carbonate nanoparticles-polyethylene oxide nanofibers as organic-inorganic hybrid were prepared via in-situ electrospinning. Thus, electrospinning of polyethylene oxide solution saturated with calcium hydroxide was carried out in gaseous carbon dioxide atmosphere. Transmission electron microscopy (TEM) showed that calcium carbonate (CaCO3) nanoparticles were formed on the produced nanofibers of 200-300 nm in diameter. The existence of the formed CaCO3 was also proved by thermogravimetric analysis (TGA) via loss of gaseous CO2 related to the decomposition of CaCO3 at about 500-840 degrees C. X-ray diffraction (XRD) analysis of the nanofibers showed that the formed CaCO3 nanoparticles have vaterite morphology. DSC analysis was used to determine melting point and to calculate the crystallinity of the produced hybrid nanofibers. The TEM, TGA, XRD and DSC analyses results of the obtained nanofibers were compared with those of the nanofibers produced in electrospinning of pure polyethylene oxide solution and polyethylene oxide solution having calcium hydroxide, both in air.     

Boost-wise pre-loaded mixture of experts for classification tasks

A modified version of Boosted Mixture of Experts (BME) is presented in this paper. While previous related works, namely BME, attempt to improve the performance by incorporating complementary features of a hybrid combining framework, they have some drawback. Analyzing the problems of previous approaches has suggested several modifications that have led us to propose a new method called Boost-wise Pre-loaded Mixture of Experts (BPME). We present a modification in pre-loading (initialization) procedure of ME, which addresses previous problems and overcomes them by employing a two-stage pre-loading procedure. In this approach, both the error and confidence measures are used as the difficulty criteria in boost-wise partitioning of problem space.

     

Computational Fluid Dynamic Modeling of Zinc Slag Fuming Process in Top-Submerged Lance Smelting Furnace

Slag fuming is a reductive treatment process for molten zinciferous slags for extracting zinc in the form of metal vapor by injecting or adding a reductant source such as pulverized coal or lump coal and natural gas. A computational fluid dynamic (CFD) model was developed to study the zinc slag fuming process from imperial smelting furnace (ISF) slag in a top-submerged lance furnace and to investigate the details of fluid flow, reaction kinetics, and heat transfer in the furnace. The model integrates combustion phenomena and chemical reactions with the heat, mass, and momentum interfacial interaction between the phases present in the system. A commercial CFD package AVL Fire 2009.2 (AVL, Graz, Austria) coupled with a number of user-defined subroutines in FORTRAN programming language were used to develop the model. The model is based on three-dimensional (3-D) Eulerian multiphase flow approach, and it predicts the velocity and temperature field of the molten slag bath, generated turbulence, and vortex and plume shape at the lance tip. The model also predicts the mass fractions of slag and gaseous components inside the furnace. The model predicted that the percent of ZnO in the slag bath decreases linearly with time and is consistent broadly with the experimental data. The zinc fuming rate from the slag bath predicted by the model was validated through macrostep validation process against the experimental study of Waladan et al. The model results predicted that the rate of ZnO reduction is controlled by the mass transfer of ZnO from the bulk slag to slag–gas interface and rate of gas-carbon reaction for the specified simulation time studied. Although the model is based on zinc slag fuming, the basic approach could be expanded or applied for the CFD analysis of analogous systems.     

An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

Neural Computing and Applications - The artificial bee colony (ABC) algorithm is a relatively new algorithm inspired by nature and has been shown to be efficient in contrast to other optimization...     

Implementing the homotopy continuation method in a hybrid approach to solve the kinematics problem of spatial parallel robots

In this paper, first the application of homotopy continuation method (HCM) in numerically solving kinematics problem of spatial parallel manipulators is investigated. Using the HCM the forward kinematics problem (F-Kin) of a six degrees of freedom (DOFs) 6–3 Stewart platform and the inverse kinematics problem (I-Kin) of a 3-DOF 3-PSP robot are solved. The governing equations of the kinematics problems of the robots are developed and embedded in the homotopy continuation function. The HCM is utilized in order to solve the nonlinear system of equations derived from the kinematics analysis of the robots. Then, to represent the real case application an initial guess far from the correct answer is selected. It is shown that, comparing with the Newton–Raphson method (NRM), the F-Kin calculation time for the Stewart robot is decreased by 43%. Therefore, using the HCM a hybrid method is suggested to solve the F-Kin of the Stewart robot. Furthermore, the HCM, as an innovative method, relieves other downsides of the conventional numerical methods, including a proper initial guess requirement as well as the problems of convergence.     

A case study on energy and exergy analyses for an industrial-scale vertical roller mill assisted grinding in cement plant

In this study, the analyses of energy and exergy were implemented for an industrial-scale vertical roller mill (VRM) of Kerman Momtazan Cement Company (KMCC) of Iran. The energy and exergy analyses demonstrated the first law efficiency of the VRM is 62.1%, while the second law efficiency of the VRM is 34.6%. Comparing to the widely applied ball milling, the second law efficiency is 16.4% higher for the VRM than the ball mill. Results also showed when the classifier rotor speed increases from 53 to 65 rpm, the particle size of the product decreases from P_90µm = 18.2% to P_90µm = 10.8%, but the power consumption of the VRM unit increases from 19.7 to 22.3 kWh/t of raw materials. Finally, the power consumption of the VRM unit compared with 14 raw mill units around Iran and the international best available technology (IBAT). The results demonstrated that the VRM unit consumes around 81% (9.75 kWh/t of raw materials), and 36% (5.8 kWh/t of raw materials) more energy to grind raw material than the IBAT unit and domestic best raw mill (DBRM), respectively.     

Solving infinite horizon nonlinear optimal control problems using an extended modal series method

This paper presents a new approach for solving a class of infinite horizon nonlinear optimal control problems (OCPs).In this approach,a nonlinear two-point boundary value problem (TPBVP),derived from P...     

A joint-ONU interval-based dynamic scheduling algorithm for Ethernet passive optical networks

This paper proposes a new dynamic bandwidth allocation system for Ethernet Passive Optical Networks (EPONs), subject to requirements of fairness, efficiency, and cost. An Optical Line Terminal (OLT)-centric bandwidth allocation model is proposed which employs a credit pooling technique combined with a weighted-share policy to partition the upstream bandwidth among different classes of service, and to prevent Optical Network Units (ONUs) from monopolizing the bandwidth. The OLT-centric model allows global optimization of network resources, a characteristic which is not found in many earlier proposals. Supported by the new bandwidth allocation, the paper proposes a joint-ONU interval-based packet scheduling algorithm, referred to herein as COPS (Class-of-service Oriented Packet Scheduling), that meets the requirements set out above. We compare COPS with another well-known scheduling algorithm which employed a standard priority-based bandwidth sharing. We show that COPS is superior in terms of network utilization and maximum packet delay, with the consequence of an increase in average packet delay for the premium traffic. This drawback is overcome by combining COPS with a rate-based optimization scheme.     

Distributed faulty node detection and recovery scheme for wireless sensor networks using cellular learning automata

Wireless Networks - In a wireless sensor network (WSN), there is always the possibility of failure in sensor nodes. Quality of Service (QoS) of WSNs is highly degraded due to the faulty sensor...     

Improving Radio Resource Utilization and User Level Fairness in OFDMA Femtocell Networks

Poor indoor coverage and high cost of cellular network operators are among the main motivations for the employment of femtocell networks. Since femto access points (FAPs) and macrocells share same spectrum resources, radio resource allocation is an important challenge in OFDMA femtocell networks. Mitigating interference and improving fairness among FAPs are the main objectives in previous resource allocation methods. However, the main drawback is that user level fairness has not been adequately addressed in the previous methods, and moreover, most of them suffer from inefficient utilization of radio resources. In this paper, modeling the problem as a graph multi-coloring, a centralized algorithm is proposed to obtain both user level fairness and spectrum efficiency. This method employs a priority-based greedy coloring algorithm in order to increase the reuse factor and consequently the spectrum efficiency. Moreover, in situations where the number of available OFDM resources is not sufficient, the proposed method employs a novel fairness index to fairly share those remaining resources among users of FAPs. The performance comparison between the proposed and previous methods shows that the proposed method improves the balance between user-level fairness and resource utilization. In addition, the presented analyses show that the time complexity of the proposed method is less than that of conventional methods.