Kermanshah University of Technology

Kermānshāh, Iran

Kermanshah University of Technology

Kermānshāh, Iran
Time filter
Source Type

Ghaemi N.,Kermanshah University of Technology
Journal of Colloid and Interface Science | Year: 2017

A novel thin-film composite (TFC) nanofiltration membrane was prepared using polymerization of pyrrole monomers on the PES ultrafiltration membrane. To improve the characteristics of hydrophobic polypyrrole (PPy) thin-film layer, cross-linkable acrylate-functionalized alumoxane nanoparticles with different concentrations were embedded into the thin-film during polymerization process, and thin-film nanocomposite (TFNC) membranes were prepared. The characteristics and performance of TFC and TFNC membranes were assessed through the morphological analyses (SEM, AFM), measurement of hydrophilicity and solid–liquid interfacial free energy, water permeability and Mg2+ removal tests. Addition of proper amount of nanoparticles into the polymerization mixture led to the preparation of membranes with more hydrophilic, thinner and smoother active layer as well as higher water permeability compared to TFC control membrane. TFNC membrane prepared with 0.025 g of nanoparticles was the most efficient membrane since it exhibited the highest rejection of MgCl2 and MgSO4 salts. Antifouling capability of membranes, in terms of flux recovery and fouling parameters, demonstrated the high tolerance of TFNC against fouling. © 2016 Elsevier Inc.

Hemmati R.,Kermanshah University of Technology
Journal of Cleaner Production | Year: 2017

Home energy management system (HEMS) is an important problem that has been attracting significant attentions in the recent years. However, the conventional HEMS includes several shortcomings. The conventional HEMSs mainly utilize battery energy storage system (BESS) to deal with energy uncertainties. But they only ascertain optimal charging-discharging pattern for BESS and the power and capacity of BESS are not optimally determined. Furthermore, most of the HEMSs are modeled as a mixed integer linear programming (MILP) including linearization and relaxations. Additionally, considering all possible operating conditions for home has not been adequately addressed in the existing HEMSs. The possible operating conditions are (i) receiving energy from the main grid (i.e., purchasing energy), (ii) sending energy to the utility grid (i.e., selling energy), (iii) working on standalone mode as disconnected from the network (i.e., net-zero energy building). As a result, current paper deals with these existing challenges at the same time. This paper presents HEMS including small-scale wind turbine, BESS, load curtailment option, and fuel cell vehicle. The introduced HEMS not only determines optimal charging-discharging pattern for BESS, but also specifies optimal capacity and optimal rated power of the BESS at the same time. The proposed HEMS is expressed as a mixed integer nonlinear programming (MINLP) and solved by cultural algorithm as an effective Meta-heuristic optimization algorithm. All three operating conditions are considered for home. Output power of wind unit is modeled by Gaussian probability distribution function (PDF) and Monte-Carlo simulation (MCS) is applied to deal with uncertainties. Results emphasize on the feasibility and usefulness of the introduced HEMS. © 2017 Elsevier Ltd

In a recent article [C. Li et al., Phys. Plasmas 21, 072114 (2014)], Li et al. studied the propagation of surface waves on a magnetized quantum plasma half-space in the Voigt configuration (in this case, the magnetic field is parallel to the surface but is perpendicular to the direction of propagation). Here, we present a fresh look at the problem and obtain a new form of dispersion relation of surface waves of the system. We find that our new dispersion relation does not agree with the result obtained by Li et al. © 2016 Author(s).

Moradi A.,Kermanshah University of Technology
Surface Science | Year: 2015

We develop a new method for calculating the electrostatic surface and bulk plasmon modes of a spherical metal nanoparticle, by taking into account the quantum nonlocal effects. To describe these phenomena, we develop analytical theory based on the quantum hydrodynamical model of plasmon excitation. We derive new dispersion relation for the system and investigate its differences with previous treatments based on the standard nonlocal model. © 2015 Elsevier B.V.

Tipi A.D.,Kermanshah University of Technology
International Journal of Advanced Manufacturing Technology | Year: 2011

Two forces play the main role on the drop detachment in gas metal arc welding process: the electromagnetic and gravitational forces. Since in an automatic pipeline system, the welding angle varies from 0° to 180° in each pass, the total force will vary, so the drop detaching will not remain invariable during the welding time. In the previous works Doodman Tipi (Int J Adv Manu Technol 50:137-147, 2010 [1]), Doodman Tipi (Int J Adv Manu Technol 50:149-161, 2010 [2]), the angle variation effects on the metal transfer was studied on both transfer modes (free flight and short circuit). In this paper, the effect of the input parameters on the drop detaching is studied. Also, a welding current pattern is presented in order to neutralize the effect of the angle variation around the pipe. Furthermore, suitable patterns for other parameters (arc voltage, travel speed, and electrode speed) are introduced to keep constant the molten electrode volume, heat input, voltage to current relation, and energy density. This would guarantee stabilization of the other welding specifications. Finally, several experimental and simulation examples illustrate to achieve the regular detachment using the presented method, and results are compared with the un-neutralized case. © Springer-Verlag London Limited 2010.

Saboori H.,Kermanshah University of Technology | Hemmati R.,Kermanshah University of Technology
IEEE Transactions on Sustainable Energy | Year: 2016

Nowadays, CO2 is the primary greenhouse gas pollutant and fossil fuel-fired electrical power plants are the major producer of CO2. In this regard, it is required to equip the electrical power plants with carbon capture and storage (CCS) systems. This paper addresses a multistage generation expansion planning (GEP) including nuclear units, renewable energy units, and different fossil fuel-fired units equipped with CCS. The proposed GEP minimizes the planning costs and CO2 at the same time, while it considers CCS cost and revenue. The problem is mathematically expressed as a constrained, mixed-integer, and nonlinear optimization problem and solved using particle swarm optimization (PSO) algorithm. The problem considers all practical constraints including security constraints of the network, and the generating units constraints of operation. Simulation results demonstrate that utilizing CCS significantly impacts on the planning output. Eventually, a comprehensive sensitivity analysis is carried out based on the CCS cost and revenue. © 2010-2012 IEEE.

Moradi A.,Kermanshah University of Technology
Physics of Plasmas | Year: 2015

We develop the Maxwell-Garnett theory for the effective medium approximation of composite materials with metallic nanoparticles by taking into account the quantum spatial dispersion effects in dielectric response of nanoparticles. We derive a quantum nonlocal generalization of the standard Maxwell-Garnett formula, by means the linearized quantum hydrodynamic theory in conjunction with the Poisson equation as well as the appropriate additional quantum boundary conditions. © 2015 AIP Publishing LLC.

Bahiraei M.,Kermanshah University of Technology
Journal of Dispersion Science and Technology | Year: 2014

Computational fluid dynamics (CFD) has already proven to be an important tool to study fluids flow characteristics. Due to the rapid growth of powerful computers, CFD has become more applicable nowadays. In the field of nanofluids, this tool has also been applied by different approaches to understand and explain the effective phenomena. This article reviews and summarizes the numerical investigations implemented on nanofluids including conventional and novel methods. The studies conducted using methods such as Lattice Boltzmann, Eulerian-Lagrangian, thermal dispersion, Eulerian-Eulerian, and so forth are assessed. © 2014 Taylor & Francis Group, LLC.

Bahiraei M.,Kermanshah University of Technology | Hangi M.,Iran University of Science and Technology
Journal of Magnetism and Magnetic Materials | Year: 2015

Magnetic nanofluids (MNFs) are suspensions which are comprised of a non-magnetic base fluid and magnetic nanoparticles. In this modern set of suspensions which can be called smart or functional fluids, fluid flow, particles movement and heat transfer process can be controlled by applying magnetic fields. Regarding unique characteristics of MNFs, studies in this field have witnessed a phenomenal growth. This paper reviews and summarizes recent investigations implemented on MNFs including those conducted on thermophysical properties, natural convection, forced convection, boiling as well as their practical applications. Moreover, this review identifies the challenges and opportunities for future research. © 2014 Elsevier B.V.

Bahiraei M.,Kermanshah University of Technology | Hangi M.,Iran University of Science and Technology
Energy Conversion and Management | Year: 2013

The current study attempts to investigate the performance of water based Mn-Zn ferrite magnetic nanofluid in a counter-flow double-pipe heat exchanger under quadrupole magnetic field using the two-phase Euler-Lagrange method. The nanofluid flows in the tube side as coolant, while the hot water flows in the annulus side. The effects of different parameters including concentration, size of the particles, magnitude of the magnetic field and Reynolds number are examined. Distribution of the particles is non-uniform at the cross section of the tube such that the concentration is higher at central regions of the tube. Application of the magnetic field makes the distribution of particles more uniform and this uniformity increases by increasing the distance from the tube inlet. Increasing each of the parameters of concentration, particle size and magnitude of the magnetic field will lead to a greater pressure drop and also higher heat transfer improvement. At higher Reynolds numbers, the effect of magnetic force is diminished. Optimization was performed using genetic algorithm coupled with compromise programming technique in order to reach the maximum overall heat transfer coefficient along with the minimum pressure drop. For this purpose, the models of objective functions of overall heat transfer coefficient and pressure drop of the nanofluid were first extracted in terms of the effective parameters using neural network. The neural network model predicts the output variables with a very good accuracy. The optimal values were obtained considering different conditions for relative importance of the objective functions. © 2013 Elsevier Ltd. All rights reserved.

Loading Kermanshah University of Technology collaborators
Loading Kermanshah University of Technology collaborators