Semnan, Iran
Semnan, Iran

Semnan University is a prestigious university in Iran located in the city of Semnan, Iran, about 240 km east of Tehran.The university has over 15,000 students, 60 undergraduate programs, 95 graduate , and 55 PhD programs. It has 25 faculties, 2 colleges, 2 institutes, 9 research groups, one Science and Technology Park, one Advanced Technologies Incubator Centre. The initial nucleus of Semnan University was formed in 1975 with the establishment of Semnan Higher Education Center. It launched its activities with 580 students to study in seven programs with an area of 5000 square meters.After the victory of the Islamic Revolution, extensive and fundamental changes were implemented at the Centre. In 1989, Semnan Higher Education Centre started its work under new title of Semnan Higher Education Complex while it enhanced its Electronic & Civil programs to a Bachelor level. With opening of the faculty of engineering, faculty of teacher training and faculty of veterinary medicine, Semnan Higher Education Complex changed its status to Semnan University in 1994.Semnan University has so expanded to include four campuses: 1. Technical campus2. Basic science campus3. Human science campus4. New Science and Technology campusThe university has 608 full-time academic members. It is situated in the Northeast part of the Semnan city with an area of 800 hectares. Libraries, computer centres, sports halls, restaurants, coffee shop and several dormitories are other facilities of the university. Since Semnan University is relatively young and newly established it is still under expansion and construction. Wikipedia.

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Aghaei J.,Shiraz University of Technology | Amjady N.,Semnan University
International Journal of Electrical Power and Energy Systems | Year: 2012

Electricity market clearing is currently done using deterministic values of power system parameters considering a fixed network configuration. This paper presents a new day-ahead joint market clearing framework (including energy, spinning reserve and non-spinning reserve auctions), which considers dynamic security of power system in the market clearing. The proposed framework has a stochastic multiobjective model considering power system uncertainties. It consists of three stages. Firstly, the uncertainty sources, i.e. contingencies of generating units and branches, are modeled using the Monte Carlo simulation (MCS) method. Subsequently, in the second stage, the proposed multiobjective framework simultaneously optimizes competing objective functions of offer cost and dynamic security index, i.e. corrected transient energy margin (CTEM). This index is selected because of useful linearity properties which it posses based on the sensitivity of the CTEM with respect to power shift between generators. The optimization problem in the second stage takes DC power flow constraints and system reserve requirements into account. Finally, in the last stage, scenario aggregation based on the expected value of the decision variables produces the final results of the market clearing framework. The 10-machine New England test system is studied to demonstrate effectiveness of the proposed stochastic multiobjective market clearing scheme. © 2011 Elsevier Ltd. All rights reserved.


Amjady N.,Semnan University | Vahidinasab V.,University of Tehran
Energy Conversion and Management | Year: 2013

In this paper, a new security-constrained self-scheduling framework incorporating the transmission flow limits in both steady state conditions and post-contingent states is presented to produce efficient bidding strategy for generation companies (GENCOs) in day-ahead electricity markets. Moreover, the proposed framework takes into account the uncertainty of the predicted market prices and models the risk and profit tradeoff of a GENCO based on an efficient multi-objective model. Furthermore, unit commitment and inter-temporal constraints of generators are considered in the suggested model converting it to a mixed-integer programming (MIP) optimization problem. Sensitivity of the proposed framework with respect to both the level of the market prices and adopted risk level is also evaluated in the paper. Simulation results are presented on the IEEE 30-bus and IEEE 118-bus test systems illustrating the performance of the proposed self-scheduling model. © 2012 Elsevier Ltd. All rights reserved.


Nazemi A.,Shahrood University of Technology | Omidi F.,Semnan University
Transportation Research Part C: Emerging Technologies | Year: 2013

The shortest path problem is the classical combinatorial optimization problem arising in numerous planning and designing contexts. This paper presents a neural network model for solving the shortest path problems. The main idea is to replace the shortest path problem with a linear programming (LP) problem. According to the saddle point theorem, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle invariance principle, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the shortest path problem. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper. © 2012 Elsevier Ltd.


In present study, a refined nth-order shear deformation theory is proposed, formulated and validated for a variety of numerical examples of functionally graded (FG) plates resting on elastic foundation for the mechanical and thermal buckling responses. The present refined nth-order shear deformation theory is based on assumption that the in-plane and transverse displacements consist of bending and shear components, in which the bending components do not contribute toward shear forces and, likewise, the shear components do not contribute toward bending moments. The most interesting feature of this theory is that it accounts for a parabolic variation of the transverse shear strains across the thickness and satisfies the zero traction boundary conditions on the top and bottom surfaces of the plate without using shear correction factors. Governing equations are derived from the principle of minimum total potential energy. A Navier type closed form solution methodology is also proposed for simply supported FG plates resting on elastic foundation which provides accurate solution. The accuracy of the present theory is verified by comparing the obtained results with those predicted by classical plate theory (CPT), first-order shear deformation theory (FSDT), higher-order shear deformation theory (HSDT) and refined plate theory (RPT). Moreover, results show that the present theory can achieve the same accuracy of the existing higher-order shear deformation theories which have more number of unknowns. © 2014 Elsevier Ltd. All rights reserved.


Yousefpour M.,Semnan University | Rahimi A.,Semnan University
Materials and Design | Year: 2014

In this study, Nano particles were co-deposited with chromium from a hexavalent chromium bath by the conventional electrodeposition onto steel substrate as a cathode. The main goal of this work is to improve the wear and corrosion resistance, microhardness, coefficient of friction and select the best coating condition to satisfy these parameters using combined Analytic Hierarchy Process (AHP) - Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The dependence of the mentioned parameters was investigated in relation to the Al2O3, TiO2, SiO2 concentration in bath and particle size and it was found that the best tribological behavior improves by decreasing the particle size and increasing the particles concentration in the bath up to 10g/l. AHP-TOPSIS method led to choose the Cr-Al2O3 nanocomposite coating achieved at 10g/l Al2O3 content with mean particle size of 10nm as the preferred alternative which is in good accordance with empirical findings. © 2013 Elsevier Ltd.


Oghbaei M.,Semnan University | Mirzaee O.,Semnan University
Journal of Alloys and Compounds | Year: 2010

Microwave sintering has emerged in recent years as a new method for sintering a variety of materials that has shown significant advantages against conventional sintering procedures. This review article first provides a summary of fundamental theoretical aspects of microwave and microwave hybrid sintering, and then advantages of microwave sintering against conventional methods are described. At the end, some applications of microwave sintering are mentioned which so far have manifested the advantages of this novel method. © 2010 Elsevier B.V. All rights reserved.


Moravej Z.,Semnan University | Akhlaghi A.,Semnan University
International Journal of Electrical Power and Energy Systems | Year: 2013

This paper presents a novel approach based on cuckoo search (CS) which is applied for optimal distributed generation (DG) allocation to improve voltage profile and reduce power loss of the distribution network. The voltage profile which is the main criterion for power quality improvement is indicated by two indices: voltage deviations from the target value which must be minimized and voltage variations from the initial network without DG which must be maximized. The CS was inspired by the obligate brood parasitism of some cuckoo species by putting their eggs in the nests of other species. Some host birds can engage direct contest with the infringing cuckoos. For example, if a host bird detects the eggs are not their own, it will either throw these alien eggs away. The CS has been compared with other evolutionary algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) and different cases have been investigated for indicating the applicability of the proposed algorithm. The results indicate the better performance of CS compared with other methods due to the fewer parameters which must be well-tuned in this method. In addition, in this method the convergence rate is not sensitive to the parameters used, so the fine adjustment is not needed for any given problems. © 2012 Elsevier Ltd. All rights reserved.


Amjady N.,Semnan University | Reza Ansari M.,Semnan University
Energy Conversion and Management | Year: 2013

This paper presents a new approach based on Benders decomposition (BD) to solve hydrothermal unit commitment problem with AC power flow and security constraints. The proposed method decomposes the problem into a master problem and two sets of sub-problems. The master problem applies integer programming method to solve unit commitment (UC) while the sub-problems apply nonlinear programming solution method to determine economic dispatch for each time period. If one sub-problem of the first set becomes infeasible, the corresponding sub-problem of the second set is called. Moreover, strong Benders cuts are proposed that reduce the number of iterations and CPU time of the Benders decomposition method. All constraints of the hydrothermal unit commitment problem can be completely satisfied with zero penalty terms by the proposed solution method. The methodology is tested on the 9-bus and IEEE 118-bus test systems. The obtained results confirm the validity of the developed approach. © 2012 Elsevier Ltd. All rights reserved.


Aluminum matrix composites (AMCs) reinforced with particles are of the most widely applied commercial materials. Among the numerous methods for AMCs production, the powder metallurgy is the most attractive technique since it gives good mechanical properties and is an inexpensive process. In the last decade, the Al-SiC composites have introduced most wide spread applications and hold the greatest promise for future growth. In present study, a comparison was carried out between technique for order preference by similarity to ideal solution (TOPSIS) and preference selection index (PSI) materials selection methods for determining a desirable combination of strength and workability in Al-SiC powder metallurgy composite. Selection of an Al-SiC composite with highest strength and workability is a MADM problem where some criteria must be considered in decision making among a set of available alternatives. Weights of each criterion were determined by using an analytic hierarchy process (AHP) method in TOPSIS method. The obtained results represented that the PSI method could be successfully applied to select best alternative without assigning the relative importance between attributes and it could be appropriately replaced to general materials selection methods such as TOPSIS method. Empirical findings in this study showed that both TOPSIS and PSI methods led to the choice of Al-5%SiC composite with SiC particle size of 16. μm and relative density of 90% milled for 12. h as the preferred alternative. © 2013 Elsevier Ltd.


In this paper, a radial basis function (RBF) neural network model was developed for estimating temperature elevation (TE) in multi-stage flash (MSF) desalination processes. The constructed artificial neural network (ANN) model use as input variables the boiling point temperature (BPT) and salinity. The developed RBF neural network was found to be precise in predicting TE from the input variables. The performance of the ANN model was analyzed by mean squared error (MSE). The developed RBF neural network was found to be highly precise in predicting TE for the new input data, which are kept unaware of the trained network showing its applicability to estimate the TE for seawater in MSF desalination plants better than the empirical correlations, thermodynamic models and MLP neural network. © 2010 Elsevier B.V.

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