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Real time probabilistic power system state estimation
, Article International Journal of Electrical Power and Energy Systems ; Vol. 62, issue , 2014 , p. 383-390 ; ISSN: 1420615 ; Rashidinejad, M ; Kouhi, S ; Fotuhi-Firuzabad, M ; Ravadanegh, S.N ; Sharif University of Technology
Abstract
Smartening of contemporaneous power delivery systems in conjunction with the increased penetration of renewable energies (REs), change the way to energize consumers who are willing to maximize their utility from energy consumption. However, there is a high degree of uncertainty in the electricity markets of such systems. Moreover, the unprecedented ascending penetration of distributed energy resources (DERs) mainly harvesting REs is a direct consequence of environmentally friendly concerns. This type of energy resources brings about more uncertainties into power system operation resulting in, necessitates probabilistic analysis of the system performance. In the smarter power markets,...
Lithological facies identification in Iranian largest gas field: A comparative study of neural network methods
, Article Journal of the Geological Society of India ; Vol. 84, issue. 3 , Sep , 2014 , p. 326-334 ; ISSN: 00167622 ; Masihi, M ; Sola, B. S ; Biniaz, E ; Sharif University of Technology
Abstract
Determination of different facies in an underground reservoir with the aid of various applicable neural network methods can improve the reservoir modeling. Accordingly facies identification from well logs and cores data information is considered as the most prominent recent tasks of geological engineering. The aim of this study is to analyze and compare the five artificial neural networks (ANN) approaches with identification of various structures in a rock facies and evaluate their capability in contrast to the labor intensive conventional method. The selected networks considered are Backpropagation Neural Networks (BPNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN),...
Evolving an accurate model based on machine learning approach for prediction of dew-point pressure in gas condensate reservoirs
, Article Chemical Engineering Research and Design ; Vol. 92, issue. 5 , May , 2014 , p. 891-902 ; ISSN: 02638762 ; Shokrollahi, A ; Arabloo, M ; Mahdikhani-Soleymanloo, R ; Masihi, M ; Sharif University of Technology
Abstract
Over the years, accurate prediction of dew-point pressure of gas condensate has been a vital importance in reservoir evaluation. Although various scientists and researchers have proposed correlations for this purpose since 1942, but most of these models fail to provide the desired accuracy in prediction of dew-point pressure. Therefore, further improvement is still needed. The objective of this study is to present an improved artificial neural network (ANN) method to predict dew-point pressures in gas condensate reservoirs. The model was developed and tested using a total set of 562 experimental data point from different gas condensate fluids covering a wide range of variables. After a...
Well Placement optimization using hybrid optimization technique combined with fuzzy inference system
, Article Petroleum Science and Technology ; Vol. 31, issue. 5 , Dec , 2009 , p. 481-491 ; ISSN: 10916466 ; Masihi, M ; Sharif University of Technology
Abstract
Decision on the location of new wells through infill drilling projects is a complex problem that depends on the reservoir rock and fluid properties, well and surface facilities specifications, and economic measures. Conventional approach to address this is a direct optimization that uses the numerical flow simulation. However, this is computationally very extensive. In this study the authors use a hybrid genetic algorithm (HGA) optimization technique based on the genetic algorithm (GA) with helper functions based on the polytope algorithm and the neural network. This hybridization introduces hill-climbing into the stochastic search and makes use of proxies created and calibrated iteratively...
The estimation of formation permeability in a carbonate reservoir using an artificial neural network
, Article Petroleum Science and Technology ; Vol. 30, issue. 10 , Apr , 2010 , p. 1021-1030 ; ISSN: 10916466 ; Masihi, M ; Fatholah,i S ; Sharif University of Technology
Abstract
Reservoir permeability is an important parameter that its reliable prediction is necessary for reservoir performance assessment and management. Although many empirical formulas are derived regarding permeability and porosity in sandstone reservoirs, these correlations cannot be accurately depicted in carbonate reservoir for the wells that are not cored and for which there are no welltest data. Therefore, having a framework for estimation of these parameters in reservoirs with neither coring samples nor welltest data is crucial. Rock properties are characterized by using different well logs. However, there is no specific petrophysical log for estimating rock permeability; thus, new methods...
The neuro-fuzzy computing system with the capacity of implementation on a memristor crossbar and optimization-free hardware training
, Article IEEE Transactions on Fuzzy Systems ; Vol. 22, Issue. 5 , 2014 , Pages 1272-1287 ; ISSN: 10636706 ; Merrikh-Bayat, F ; Shouraki, S. B ; Sharif University of Technology
Abstract
In this paper, first we present a new explanation for the relationship between logical circuits and artificial neural networks, logical circuits and fuzzy logic, and artificial neural networks and fuzzy inference systems. This shows us that neural networks are working in the same way as logical circuits when the connection between them is through the fuzzy logic. However, themain difference between them is that logical circuits can be constructed without using any kind of optimization-based learning methods. Based on these results, we propose a new neuro-fuzzy computing system. As verified by simulation results, it can effectively be implemented on the memristor crossbar structure and...
The use of graphene in the self-organized differentiation of human neural stem cells into neurons under pulsed laser stimulation
, Article Journal of Materials Chemistry B ; Vol. 2, Issue. 34 , 2014 , Pages 5602-5611 ; ISSN: 20507518 ; Ghaderi, E ; Sharif University of Technology
Abstract
An effective and self-organized differentiation of human neural stem cells (hNSCs) into neurons was developed by the pulsed laser stimulation of the cells on graphene films (prepared by drop-casting a GO suspension onto quartz substrates). The effects of graphene oxide (GO) and hydrazine-reduced graphene oxide (rGO) sheets on the proliferation of hNSCs were examined. The higher proliferation of the cells on the GO was assigned to its better hydrophilicity. On the other hand, the rGO sheets, which have significantly better electrical conductivity than GO, exhibited more differentiation of the cells into neurons. The pulsed laser stimulation not only resulted in an accelerated differentiation...
Predictions of toughness and hardness by using chemical composition and tensile properties in microalloyed line pipe steels
, Article Neural Computing and Applications ; 2014 ; Khalaj, G ; Pouraliakbar, H ; Jandaghi, M. R ; Sharif University of Technology
Abstract
Artificial neural networks with multilayer feed forward topology and back propagation algorithm containing two hidden layers are implemented to predict the effect of chemical composition and tensile properties on the both impact toughness and hardness of microalloyed API X70 line pipe steels. The chemical compositions in the forms of "carbon equivalent based on the International Institute of Welding equation (CEIIW)", "carbon equivalent based on the Ito-Bessyo equation (CEPcm)", "the sum of niobium, vanadium and titanium concentrations (VTiNb)", "the sum of niobium and vanadium concentrations (NbV)" and "the sum of chromium, molybdenum, nickel and copper concentrations (CrMoNiCu)", as well...
An investigation of the oxidative dehydrogenation of propane kinetics over a vanadium-graphene catalyst aiming at minimizing of the COx species
, Article Chemical Engineering Journal ; Vol. 250 , 2014 , Pages 14-24 ; ISSN: 13858947 ; Kazemeini, M ; Khorasheh, F ; Rashidi, A ; Sharif University of Technology
Abstract
Application of the DOE with the ANN in kinetic study of the ODHP was investigated.•The catalyst of vanadium/graphene synthesized through the hydrothermal technique.•The ANN and RSM's simulations were utilized to generate the extra data points.•Power law models and corresponding parameters determined to describe the reactions.•The optimization conducted in order to minimize the COx production. In the current investigation, an application of the design of experiments (DOE) along with the artificial neural networks (ANN) in a kinetic study of oxidative dehydrogenation of propane (ODHP) reaction over a synthesized vanadium-graphene catalyst at 400-500. °C presented aiming at minimizing the CO. x...
Kinetic modeling of oxidative dehydrogenation of propane (ODHP) over a vanadium-graphene catalyst: Application of the DOE and ANN methodologies
, Article Journal of Industrial and Engineering Chemistry ; Vol. 20, issue. 4 , July , 2014 , p. 2236-2247 ; ISSN: 1226086X ; Kazemeini, M ; Khorasheh, F ; Rashidi, A ; Sharif University of Technology
Abstract
In this research the application of design of experiment (DOE) coupled with the artificial neural networks (ANN) in kinetic study of oxidative dehydrogenation of propane (ODHP) over a vanadium-graphene catalyst at 400-500 °C and a method of data collection/fitting for the experiments were presented. The proposed reaction network composed of consecutive and simultaneous reactions with kinetics expressed by simple power law equations involving a total of 20 unknown parameters (10 reaction orders and 5 rate constants each expressed in terms of a pre-exponential factors and activation energies) determined through non-linear regression analysis. Because of the complex nature of the system, neural...
Optimization of anaerobic baffled reactor (abr) using artificial neural network in municipal wastewater treatment
, Article Environmental Engineering and Management Journal ; Vol. 13, Issue. 1 , 2014 , Pages 95-104 ; ISSN: 15829596 ; Jamshidi, S ; Hazrati, H ; Sharif University of Technology
Abstract
This study is focused on simulating and optimizing design and configuration of anaerobic baffled reactor (ABR) by means of artificial neural network (ANN). This approach is aimed to assess an efficient ABR performance in various operational conditions treating municipal wastewater. For this purpose, to analyze comprehensively on a base of experimental data, the system is operated in two pilots of 48 liters net volume made of 8 compartments. In 7 months, more than 130 sets of data are obtained to be introduced to MATLAB neural network. These include removal efficiency of chemical oxidation demand (COD) and volatile fatty acids (VFAs) parameters. The finest correlative architecture obtained...
Noise source reconstruction using ANN and hybrid methods in VVER-1000 reactor core
, Article Progress in Nuclear Energy ; Vol. 71, issue , 2014 , pp. 232-247 ; ISSN: 01491970 ; Vosoughi, N ; Sharif University of Technology
Abstract
The present paper consists of two separate sections. In the first section, the neutron noise source is reconstructed using Artificial Neural Network (ANN) in a typical VVER-1000 reactor core. In the first stage of this section, the neutron noise calculations are performed based on Galerkin Finite Element Method (GFEM). To this end, two types of noise sources including absorber of variable strength and vibrating absorber are considered. As the results of noise calculations, the neutron noise is obtained in the location of detectors. In the second stage, the multilayer perception neural network is developed for reconstruction of the noise source. Complex neutron noises (real and imaginary...
Introducing neural networks as a computational intelligent technique
, Article Applied Mechanics and Materials ; Vol. 464 , 2014 , pp. 369-374 ; ISSN: 16609336 ; Entessari, F ; Osgouie, K. G ; Rashnoodi, A. R ; Sharif University of Technology
Abstract
Neural networks have been applied very successfully in the identification and control of dynamic systems. The universal approximation capabilities of the multilayer perceptron have made it a popular choice for modeling nonlinear systems and for implementing general-purpose nonlinear controllers. In this paper we try to model and control the mass-spring-damper mechanism as a 1 DOF system using neural networks. The control architecture used in this paper is Model reference controller (MRC) as one of the popular neural network control architectures
Integration of adaptive neuro-fuzzy inference system, neural networks and geostatistical methods for fracture density modeling
, Article Oil and Gas Science and Technology ; Vol. 69, issue. 7 , 2014 , pp. 1143-1154 ; ISSN: 12944475 ; Kadkhodaie-Ilkhchi, A ; Sharghi, Y ; Ghaedi, M ; Sharif University of Technology
Abstract
Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural...
Lithological facies identification in Iranian largest gas field: A comparative study of neural network methods
, Article Journal of the Geological Society of India ; Vol. 84, issue. 3 , September , 2014 , PP. 326-334 ; ISSN: 00167622 ; Masihi, M ; Sola, B. S ; Biniaz, E ; Sharif University of Technology
Abstract
Determination of different facies in an underground reservoir with the aid of various applicable neural network methods can improve the reservoir modeling. Accordingly facies identification from well logs and cores data information is considered as the most prominent recent tasks of geological engineering. The aim of this study is to analyze and compare the five artificial neural networks (ANN) approaches with identification of various structures in a rock facies and evaluate their capability in contrast to the labor intensive conventional method. The selected networks considered are Backpropagation Neural Networks (BPNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN),...
An artificial neural network meta-model for constrained simulation optimization
, Article Journal of the Operational Research Society ; Vol. 65, issue. 8 , August , 2014 , pp. 1232-1244 ; ISSN: 01605682 ; Mahlooji, H ; Sharif University of Technology
Abstract
This paper presents artificial neural network (ANN) meta-models for expensive continuous simulation optimization (SO) with stochastic constraints. These meta-models are used within a sequential experimental design to approximate the objective function and the stochastic constraints. To capture the non-linear nature of the ANN, the SO problem is iteratively approximated via non-linear programming problems whose (near) optimal solutions obtain estimates of the global optima. Following the optimization step, a cutting plane-relaxation scheme is invoked to drop uninformative estimates of the global optima from the experimental design. This approximation is iterated until a terminating condition...
Valve fault diagnosis in internal combustion engines using acoustic emission and artificial neural network
, Article Shock and Vibration ; Vol. 2014 , 2014 ; ISSN: 10709622 ; Mehdigholi, H ; Behzad, M ; Sharif University of Technology
Abstract
This paper presents the potential of acoustic emission (AE) technique to detect valve damage in internal combustion engines. The cylinder head of a spark-ignited engine was used as the experimental setup. The effect of three types of valve damage (clearance, semicrack, and notch) on valve leakage was investigated. The experimental results showed that AE is an effective method to detect damage and the type of damagein valves in both of the time and frequency domains. An artificial neural network was trained based on time domain analysis using AE parametric features (AErms, count, absolute AE energy, maximum signal amplitude, and average signal level). The network consisted of five, six, and...
A new approach to solve multi-response statistical optimization problems using neural network, genetic algorithm, and goal attainment methods
, Article International Journal of Advanced Manufacturing Technology ; Vol. 75, issue. 5-8 , November , 2014 , pp. 1149-1162 ; Niaki, S. T. A ; Atyabi, S. M ; Sharif University of Technology
Abstract
Adjusting control factors (independent variables) to achieve an optimal level of output (response variable) is usually required in many real-world manufacturing problems. Common optimization methods often begin with estimating the relationship between a response and independent variables. Among these techniques, response surface methodology (RSM), due to its simplicity, has recently attracted extensive attention. However, on the one hand, in some cases, the relationship between a response and independent variables is too complex to be estimated using polynomial regression models. On the other hand, solving the obtained optimization model is not easy by exact methods. This paper introduces a...
Enhancing glass ionomer cement features by using the HA/YSZ nanocomposite: A feed forward neural network modelling
, Article Journal of the Mechanical Behavior of Biomedical Materials ; Vol. 29 , January , 2014 , pp. 317-327 ; ISSN: 17516161 ; Salehi, S ; Nemati, A ; Tavakoli, R ; Solati Hashjin, M ; Sharif University of Technology
Abstract
Despite brilliant properties of glass ionomer cement (GIC), its weak mechanical property poses an obstacle for its use in medical applications. The present research aims to formulate hydroxyapatite/yttria-stabilized zirconia (HA/YSZ) in the composition of GIC to enhance mechanical properties and to improve fluoride release of GIC. HA/YSZ was synthesized via a sol-gel method and characterized by applying X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray photo-emission spectroscopy (XPS) and simultaneous thermal analysis (STA) along with transmission electron microscopy (TEM) methods. The synthesized nanocomposite was mixed with GIC at a fixed composition of 5....
A probabilistic artificial neural network-based procedure for variance change point estimation
, Article Soft Computing ; Vol. 19, issue. 3 , May , 2014 , pp. 691-700 ; ISSN: 14327643 ; Niaki, S. T. A ; Moghadam, A. T ; Sharif University of Technology
Abstract
Control charts are useful tools of monitoring quality characteristics. One of the problems of employing a control chart is that the time it alarms is not synchronic with the time when assignable cause manifests itself in the process. This makes difficult to search and find assignable causes. Knowing the real time of manifestation of assignable cause (change point) helps to find assignable cause(s) sooner and eases corrective actions to be taken. In this paper, a probabilistic neural network (PNN)-based procedure was developed to estimate the variance change point of a normally distributed quality characteristic. The PNN was selected based on trial and error among different types of...