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An optimized neural network model of desalination by vacuum membrane distillation using genetic algorithm
, Article CHISA 2012 - 20th International Congress of Chemical and Process Engineering and PRES 2012 - 15th Conference PRES ; 2012 ; Safavi, M ; Sharif University of Technology
2012
Abstract
An experimental based ANN model is constructed to describe the performance of vacuum membrane distillation process for desalination in different operating conditions. The vacuum pressure, feed inlet temperature, concentration of the feed salt aqueous solution, and feed flow rate are the input variables of this process, while response is the permeate flux. The neural network approach is capable for modeling this membrane distillation configuration. The application of Genetic Algorithm to optimize the ANN model parameters was also examined. This is an abstract of a paper presented at the CHISA 2012 - 20th International Congress of Chemical and Process Engineering and PRES 2012 - 15th...
An optimized neural network model of desalination by vacuum membrane distillation using genetic algorithm
, Article Procedia Engineering ; Volume 42 , 2012 , Pages 106-112 ; 18777058 (ISSN) ; Safavi, M ; Sharif University of Technology
Abstract
An experimental based ANN model is constructed to describe the performance of vacuum membrane distillation process for desalination in different operating conditions. The vacuum pressure, the feed inlet temperature, the concentration of the feed salt aqueous solution and the feed flow rate are the input variables of this process, whereas the response is the permeate flux. The neural network approach was found to be capable for modeling this membrane distillation configuration. The application of Genetic Algorithm (GA) to optimize the ANN model parameters was also investigated
Cell deformation modeling under external force using artificial neural network
, Article Journal of Solid Mechanics ; Volume 2, Issue 2 , 2010 , Pages 190-198 ; 20083505 (ISSN) ; Vossoughi, G. R ; Abbasi, A. A ; Raeissi, P ; Sharif University of Technology
2010
Abstract
Embryogenesis, regeneration and cell differentiation in microbiological entities are influenced by mechanical forces. Therefore, development of mechanical properties of these materials is important. Neural network technique is a useful method which can be used to obtain cell deformation by the means of force-geometric deformation data or vice versa. Prior to insertion in the needle injection process, deformation and geometry of cell under external point-load is a key element to understand the interaction between cell and needle. In this paper, the goal is the prediction of cell membrane deformation under a certain force and to visually estimate the force of indentation on the membrane from...
Application of artificial neural networks and mathematical modeling for the prediction of water quality variables (Case study: Southwest of Iran)
, Article Desalination and Water Treatment ; Volume 57, Issue 56 , 2016 , Pages 27073-27084 ; 19443994 (ISSN) ; Salari, M ; Ehteshami, M ; Bidokhti, N. T ; Ghadimi, H ; Sharif University of Technology
Taylor and Francis Inc
Abstract
River water quality monitoring using traditional water sampling and laboratory analyses is expensive and time-consuming. The application of artificial neural network (ANN) models to simulate water quality parameters is cost-effective, quick, and reliable. This study provides two methods of mathematical and ANN modeling to simulate and forecast five important river water quality indicators (DO, TDS, SAR, BOD5, HCO3) correlated with variables such as EC, temperature, and pH which can be measured easily and almost with no cost. The mathematical method is based on polynomial fitting with least square method and the neural network model was developed using a feed-forward algorithm. The 35 years’...
Forecasting crude oil prices: a comparison between artificial neural networks and vector autoregressive models
, Article Computational Economics ; 2017 , Pages 1-19 ; 09277099 (ISSN) ; Kianfar, F ; Sharif University of Technology
Abstract
Given the importance of crude oil prices for businesses, governments and policy makers, this paper investigates predictability of oil prices using artificial neural networks taking into account the exhaustible nature of crude oil and impact of monetary policy along with other major drivers of crude oil prices. A multilayer perceptron neural network is developed and trained with historical data from 1980 to 2014 and using mean square error for testing data, optimal number of hidden layer neurons is determined and the designed MLP neural network is used for estimation of the forecasting model. Meanwhile, an economic model for crude oil prices is developed and estimated using a vector...
Forecasting crude oil prices: A comparison between artificial neural networks and vector autoregressive models
, Article Computational Economics ; Volume 53, Issue 2 , 2019 , Pages 743-761 ; 09277099 (ISSN) ; Kianfar, F ; Sharif University of Technology
Springer New York LLC
2019
Abstract
Given the importance of crude oil prices for businesses, governments and policy makers, this paper investigates predictability of oil prices using artificial neural networks taking into account the exhaustible nature of crude oil and impact of monetary policy along with other major drivers of crude oil prices. A multilayer perceptron neural network is developed and trained with historical data from 1980 to 2014 and using mean square error for testing data, optimal number of hidden layer neurons is determined and the designed MLP neural network is used for estimation of the forecasting model. Meanwhile, an economic model for crude oil prices is developed and estimated using a vector...
Simulation of the effects of pozzolanic additives and corrosion inhibitor on the corrosion of reinforced concrete by artificial neural networks
, Article Revista Romana de Materiale/ Romanian Journal of Materials ; Volume 49, Issue 4 , 2019 , Pages 535-543 ; 15833186 (ISSN) ; Nobakhti, A ; Shokrgozar, A ; Afshar, A ; Sharif University of Technology
Fundatia Serban Solacolu
2019
Abstract
In this research, we simulate the corrosive behavior of steel reinforcements on 5 different mixtures to investigate the effect of two powerful protective methods, including pozzolanic additives and corrosion inhibitor on concrete, by artificial neural networks (ANNs). Related to this model, fly ash (FA), micro silica (MS), and slag were used as pozzolanic materials at an optimum 25%, 10%, and 25% of cement weight, respectively. Moreover, Ferrogard 901 as an inhibitor was also utilized. The producer recommends using12 kg/m3 to get the best possible results. The non-linear corrosion of concrete into a marine solution (3.5% NaCl) was simulated by the feed forward back propagation (FFBP)...
Simulation of the effects of pozzolanic additives and corrosion inhibitor on the corrosion of reinforced concrete by artificial neural networks
, Article Revista Romana de Materiale/ Romanian Journal of Materials ; Volume 49, Issue 4 , 2019 , Pages 535-543 ; 15833186 (ISSN) ; Nobakhti, A ; Shokrgozar, A ; Afshar, A ; Sharif University of Technology
Fundatia Serban Solacolu
2019
Abstract
In this research, we simulate the corrosive behavior of steel reinforcements on 5 different mixtures to investigate the effect of two powerful protective methods, including pozzolanic additives and corrosion inhibitor on concrete, by artificial neural networks (ANNs). Related to this model, fly ash (FA), micro silica (MS), and slag were used as pozzolanic materials at an optimum 25%, 10%, and 25% of cement weight, respectively. Moreover, Ferrogard 901 as an inhibitor was also utilized. The producer recommends using12 kg/m3 to get the best possible results. The non-linear corrosion of concrete into a marine solution (3.5% NaCl) was simulated by the feed forward back propagation (FFBP)...
A novel hybrid HMM/ANN structure for discriminative training in speech recognition
, Article Scientia Iranica ; Volume 7, Issue 3-4 , 2000 , Pages 186-196 ; 10263098 (ISSN) ; Nayebi, K ; Sharif University of Technology
Sharif University of Technology
2000
Abstract
In this paper, a new formulation for discriminative training of HMMs is introduced as a solution to several speech recognition problems. This formulation uses a properly trained MLP in a simple interconnection with HMMs called "Cascade HMM/ANN Hybrid". The training algorithm has simple realization in comparison with other discriminative training for HMMs such as MDI and MMI. Also a rigid mathematical proof of its convergence has been presented. No significant increase in computational requirements is needed in recognition phase and the recognition task can still be performed in real-time. This structure has been employed in some isolated and continuous speaker-independent speech recognition...
Scaling of counter-current imbibition recovery curves using artificial neural networks
, Article Journal of Geophysics and Engineering ; Volume 15, Issue 3 , 2018 , Pages 1062-1070 ; 17422132 (ISSN) ; Masihi, M ; Nasiri Zarandi, M ; Sharif University of Technology
Institute of Physics Publishing
2018
Abstract
Scaling imbibition curves are of great importance in the characterization and simulation of oil production from naturally fractured reservoirs. Different parameters such as matrix porosity and permeability, oil and water viscosities, matrix dimensions, and oil/water interfacial tensions have an effective on the imbibition process. Studies on the scaling imbibition curves along with the consideration of different assumptions have resulted in various scaling equations. In this work, using an artificial neural network (ANN) method, a novel technique is presented for scaling imbibition recovery curves, which can be used for scaling the experimental and field-scale imbibition cases. The...
Development of a Model for Prediction of Inhibitors of HIV1 Virus
, M.Sc. Thesis Sharif University of Technology ; Jalali Heravi, Mehdi (Supervisor)
Abstract
The main aim of this study is developing a robust QSAR model for describing and predicting the inhibitory activities of O-(2-phthalimidoethyl)-N-substituted thiocarbamates derivatives as novel HIV-1 non-nucleoside reverse transcriptase (HIV-1 NNRTIs) inhibitors. These drugs change the active site of the reverse transcriptase enzyme, and finally halter the HIV reproduction cycle. As the first step of this study, a multiple linear regression (MLR) model was built but it has no satisfied prediction ability. As a next step, the nonlinear correlation of the molecular descriptors and activities has been investigated by using artificial neural networks (ANN). In this section the effects of variable...
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...
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...
Deformation prediction by a feed forward artificial neural network during mouse embryo micromanipulation
, Article Animal Cells and Systems ; Volume 16, Issue 2 , Jan , 2012 , Pages 121-126 ; 19768354 (ISSN) ; Vossoughi, G. R ; Ahmadian, M. T ; Sharif University of Technology
2012
Abstract
In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence...
Artificial neural network modeling for predict performance of pressure filters in a water treatment plant
, Article Desalination and Water Treatment ; Volume 39, Issue 1-3 , Feb , 2012 , Pages 192-198 ; 19443994 (ISSN) ; Gholikandi, G. B ; Hazrati, H ; Sharif University of Technology
Taylor and Francis Inc
2012
Abstract
Pressure filters are popular in small municipal water treatment plants. One of the principles for designing and using the various units of water treatment plants is the ability of assigning and predicting the performance of those units under different and various conditions that could be verified by making pilot scale tests and could be modeled by means of available programs and software such as artificial neural network. The goals of this study that was conducted to predict pressure filter efficiency are: (1) evaluations of pressure filter efficiency for turbidity removal under different conditions such as turbidity of raw water, filtration rate and filter pressure changes; (2) statistical...
An artificial neural network model for the prediction of pressure filters performance and determination of optimum turbidity for coli-form and total bacteria removal
, Article Journal of Environmental Studies ; Volume 37, Issue 60 , 2012 , Pages 129-136 ; 10258620 (ISSN) ; Hazrati, H ; Rostamian, H ; Sharif University of Technology
2012
Abstract
In water treatment processes, because of complicated and nonlinear relationships between a number of physical, chemical and operational parameters, using analytical models with the ability to capture underlying relationships using examples of the desired input-output mapping is quite suitable. Artificial Neural Networks (ANN) has been increasingly applied in the area of environmental and water resources engineering. The main advantage of Artificial Neural Networks over physical-based models is that they are data-driven. The purpose of this research is to study the performance of pressure filters on turbidity removal from water according to several parameters such as turbidity, filtration...
Biofiltration of hexane vapor: Experimental and neural model analysis
, Article Clean - Soil, Air, Water ; Volume 39, Issue 9 , 2011 , Pages 813-819 ; 18630650 (ISSN) ; Halladj, R ; Saber, M ; Ferdowsi, M ; Nasernejad, B ; Sharif University of Technology
Abstract
Biofiltration is a commonlypracticed biological technique to remove volatile compounds from waste gas streams. From an industrial view-point, biofilter (BF) operation should be flexible to handle temperatures and inlet load (IL) variations. A compost BF was operated at different temperatures (30-45°C) and at various inlet loading rates (ILR; 8-598gm -3h -1) under intermittent loading conditions. Complete removal of n-hexane was observed at 30 and 35°C at ILRs up to 330gm -3h -1. Besides, 20-75% of the pollutant was removed at 40°C, corresponding to the different ILs applied to the BF. Increasing the temperature to 45°C decreased the removal efficiency (RE) significantly. A feed forward...
Neural Network Meta-Modeling of Steam Assisted Gravity Drainage Oil recovery processes
, Article Iranian Journal of Chemistry and Chemical Engineering ; Volume 29, Issue 3 , Summer , 2010 , Pages 109-122 ; 10219986 (ISSN) ; Pishvaie, M. R ; Vahid, T ; Sharif University of Technology
2010
Abstract
Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-Error-Propagation (BEP) learning algorithm to provide a versatile SAGD forecasting and analysis framework. The constructed neural network architectures are capable of estimating the recovery factors of the SAGD production as an enhanced oil recovery method satisfactorily. Rigorous studies regarding the hybrid...
Application of artificial neural network to predict the effects of severe shot peening on properties of low carbon steel
, Article Advanced Structured Materials ; Volume 61 , 2016 , Pages 45-60 ; 18698433 (ISSN) ; Farrahi, G. H ; Sherafatnia, K ; Sharif University of Technology
Springer Verlag
2016
Abstract
Mechanical failures in most cases originate from the exterior layers of the components. It is considerably effective to apply methods and treatments capable to improve the mechanical properties on component’s surface. Surface nanocrystallization produced by severe plastic deformation (SPD) processes such as severe shot peening (SSP) is increasingly considered in the recent years. However, artificial intelligence systems such as artificial neural network (ANN) as an efficient approach instead of costly and time consuming experiments is widely employed to predict and optimize the science and engineering problems in the last decade. In the present study the application of ANN in predicting of...
Modeling of severe shot peening effects to obtain nanocrystalline surface on cast iron using artificial neural network
, Article Materials Today: Proceedings ; Volume 3, Issue 6 , 2016 , Pages 2197-2206 ; 22147853 (ISSN) ; Sharif University of Technology
Elsevier Ltd
2016
Abstract
Severe plastic deformation methods such as severe shot peening are used in order to improve mechanical properties of the components by surface microstructure nanocrystallization. Severe shot peening is one of the popular mechanical surface treatments generally aimed at generating nanograined layer and compressive residual stress close to the surface. Moreover, artificial neural network has been used as an efficient approach to predict and optimize the engineering problems. In present study effects of conventional and severe shot peening on cast iron were modelled by means of artificial neural networks and they were compared. The obtained results indicate that severe shot peening has superior...