S. Mousavian
Publications
6 Prediction of Vapor Liquid Equilibrium for Dilute Solutions of Components in Ionic Liquid by Neural Networks
Authors: S. Mousavian, A. Abedianpour, A. Khanmohammadi, S. Hematian, Gh. Eidi Veisi
Abstract:
Ionic liquids are finding a wide range of applications from reaction media to separations and materials processing. In these applications, Vapor–Liquid equilibrium (VLE) is the most important one. VLE for six systems at 353 K and activity coefficients at infinite dilution [(γ)_i^∞] for various solutes (alkanes, alkenes, cycloalkanes, cycloalkenes, aromatics, alcohols, ketones, esters, ethers, and water) in the ionic liquids (1-ethyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide [EMIM][BTI], 1-hexyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide [HMIM][BTI], 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide [OMIM][BTI], and 1-butyl-1-methylpyrrolidinium bis (trifluoromethylsulfonyl) imide [BMPYR][BTI]) have been used to train neural networks in the temperature range from (303 to 333) K. Densities of the ionic liquids, Hildebrant constant of substances, and temperature were selected as input of neural networks. The networks with different hidden layers were examined. Networks with seven neurons in one hidden layer have minimum error and good agreement with experimental data.
Keywords: Neural Networks, ionic liquid, VLE, dilute solution
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11015 Investigation Bubble Growth and Nucleation Rates during the Pool Boiling Heat Transfer of Distilled Water Using Population Balance Model
Authors: V. Nikkhah Rashidabad, M. Manteghian, M. Masoumi, S. Mousavian
Abstract:
In this research, the changes in bubbles diameter and number that may occur due to the change in heat flux of pure water during pool boiling process. For this purpose, test equipment was designed and developed to collect test data. The bubbles were graded using Caliper Screen software. To calculate the growth and nucleation rates of bubbles under different fluxes, population balance model was employed. The results show that the increase in heat flux from q=20 kw/m2 to q= 102 kw/m2 raised the growth and nucleation rates of bubbles.
Keywords: Heat Flux, bubble growth, bubble nucleation, population balance model
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22074 Modeling and Prediction of Zinc Extraction Efficiency from Concentrate by Operating Condition and Using Artificial Neural Networks
Authors: S. Mousavian, D. Ashouri, F. Mousavian, V. Nikkhah Rashidabad, N. Ghazinia
Abstract:
PH, temperature and time of extraction of each stage, agitation speed and delay time between stages effect on efficiency of zinc extraction from concentrate. In this research, efficiency of zinc extraction was predicted as a function of mentioned variable by artificial neural networks (ANN). ANN with different layer was employed and the result show that the networks with 8 neurons in hidden layer has good agreement with experimental data.
Keywords: Neural Networks, Efficiency, zinc extraction, operating condition
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13093 Modification of Rk Equation of State for Liquid and Vapor of Ammonia by Genetic Algorithm
Authors: S. Mousavian, F. Mousavian, V. Nikkhah Rashidabad
Abstract:
Cubic equations of state like Redlich–Kwong (RK) EOS have been proved to be very reliable tools in the prediction of phase behavior. Despite their good performance in compositional calculations, they usually suffer from weaknesses in the predictions of saturated liquid density. In this research, RK equation was modified. The result of this study show that modified equation has good agreement with experimental data.
Keywords: Modification, Genetic Algorithm, Equation of state, Ammonia
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23942 Application of Neural Networks to Predict Changing the Diameters of Bubbles in Pool Boiling Distilled Water
Authors: V. Nikkhah Rashidabad, M. Manteghian, M. Masoumi, S. Mousavian, D. Ashouri
Abstract:
In this research, the capability of neural networks in modeling and learning complicated and nonlinear relations has been used to develop a model for the prediction of changes in the diameter of bubbles in pool boiling distilled water. The input parameters used in the development of this network include element temperature, heat flux, and retention time of bubbles. The test data obtained from the experiment of the pool boiling of distilled water, and the measurement of the bubbles form on the cylindrical element. The model was developed based on training algorithm, which is typologically of back-propagation type. Considering the correlation coefficient obtained from this model is 0.9633. This shows that this model can be trusted for the simulation and modeling of the size of bubble and thermal transfer of boiling.
Keywords: Neural Network, Heat Flux, bubble diameter, training algorithm
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12031 Study of a Crude Oil Desalting Plant of the National Iranian South Oil Company in Gachsaran by Using Artificial Neural Networks
Authors: H. Kiani, S. Moradi, B. Soltani Soulgani, S. Mousavian
Abstract:
Desalting/dehydration plants (DDP) are often installed in crude oil production units in order to remove water-soluble salts from an oil stream. In order to optimize this process, desalting unit should be modeled. In this research, artificial neural network is used to model efficiency of desalting unit as a function of input parameter. The result of this research shows that the mentioned model has good agreement with experimental data.
Keywords: Neural Networks, Simulation, separation, Recovery, desalting unit, crude oil
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3822Abstracts
7 Prediction of Vapor Liquid Equilibrium for Dilute Solutions of Components in Ionic Liquid by Neural Networks
Authors: S. Mousavian, A. Abedianpour, A. Khanmohammadi, S. Hematian, Gh. Eidi Veisi
Abstract:
Ionic liquids are finding a wide range of applications from reaction media to separations and materials processing. In these applications, Vapor–Liquid equilibrium (VLE) is the most important one. VLE for six systems at 353 K and activity coefficients at infinite dilution 〖(γ〗_i^∞) for various solutes (alkanes, alkenes, cycloalkanes, cycloalkenes, aromatics, alcohols, ketones, esters, ethers, and water) in the ionic liquids (1-ethyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide [EMIM][BTI], 1-hexyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide [HMIM][BTI], 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide [OMIM][BTI], and 1-butyl-1-methylpyrrolidinium bis (trifluoromethylsulfonyl) imide [BMPYR][BTI]) have been used to train neural networks in the temperature range from (303 to 333) K. Densities of the ionic liquids, Hildebrant constant of substances, and temperature were selected as input of neural networks. The networks with different hidden layers were examined. Networks with seven neurons in one hidden layer have minimum error and good agreement with experimental data.Keywords: Neural Networks, ionic liquid, VLE, dilute solution
Procedia PDF Downloads 1516 Modification of Underwood's Equation to Calculate Minimum Reflux Ratio for Column with One Side Stream Upper Than Feed
Authors: S. Mousavian, A. Abedianpour, A. Khanmohammadi, S. Hematian, Gh. Eidi Veisi
Abstract:
Distillation is one of the most important and utilized separation methods in the industrial practice. There are different ways to design of distillation column. One of these ways is short cut method. In short cut method, material balance and equilibrium are employed to calculate number of tray in distillation column. There are different methods that are classified in short cut method. One of these methods is Fenske-Underwood-Gilliland method. In this method, minimum reflux ratio should be calculated by underwood equation. Underwood proposed an equation that is useful for simple distillation column with one feed and one top and bottom product. In this study, underwood method is developed to predict minimum reflux ratio for column with one side stream upper than feed. The result of this model compared with McCabe-Thiele method. The result shows that proposed method able to calculate minimum reflux ratio with very small error.Keywords: Distillation, minimum reflux ratio, side stream, Underwood’s method
Procedia PDF Downloads 2135 Study of a Crude Oil Desalting Plant of the National Iranian South Oil Company in Gachsaran by Using Artificial Neural Networks
Authors: H. Kiani, S. Moradi, B. Soltani Soulgani, S. Mousavian
Abstract:
Desalting/dehydration plants (DDP) are often installed in crude oil production units in order to remove water-soluble salts from an oil stream. In order to optimize this process, desalting unit should be modeled. In this research, artificial neural network is used to model efficiency of desalting unit as a function of input parameter. The result of this research shows that the mentioned model has good agreement with experimental data.Keywords: Neural Networks, Simulation, separation, Recovery, desalting unit, crude oil
Procedia PDF Downloads 2244 Application of Neural Networks to Predict Changing the Diameters of Bubbles in Pool Boiling Distilled Water
Authors: V. Nikkhah Rashidabad, M. Manteghian, M. Masoumi, S. Mousavian, D. Ashouri
Abstract:
In this research, the capability of neural networks in modeling and learning complicated and nonlinear relations has been used to develop a model for the prediction of changes in the diameter of bubbles in pool boiling distilled water. The input parameters used in the development of this network include element temperature, heat flux, and retention time of bubbles. The test data obtained from the experiment of the pool boiling of distilled water, and the measurement of the bubbles form on the cylindrical element. The model was developed based on training algorithm, which is typologically of back-propagation type. Considering the correlation coefficient obtained from this model is 0.9633. This shows that this model can be trusted for the simulation and modeling of the size of bubble and thermal transfer of boiling.Keywords: Neural Network, Heat Flux, bubble diameter, training algorithm
Procedia PDF Downloads 3223 Investigation Bubble Growth and Nucleation Rates during the Pool Boiling Heat Transfer of Distilled Water Using Population Balance Model
Authors: V. Nikkhah Rashidabad, M. Manteghian, M. Masoumi, S. Mousavian
Abstract:
In this research, the changes in bubbles diameter and number that may occur due to the change in heat flux of pure water during pool boiling process. For this purpose, test equipment was designed and developed to collect test data. The bubbles were graded using Caliper Screen software. To calculate the growth and nucleation rates of bubbles under different fluxes, population balance model was employed. The results show that the increase in heat flux from q=20 kw/m2 to q=102 kw/m2 raised the growth and nucleation rates of bubbles.Keywords: Heat Flux, bubble growth, bubble nucleation, population balance model
Procedia PDF Downloads 2942 Modification of Rk Equation of State for Liquid and Vapor of Ammonia by Genetic Algorithm
Authors: S. Mousavian, F. Mousavian, V. Nikkhah Rashidabad
Abstract:
Cubic equations of state like Redlich–Kwong (RK) EOS have been proved to be very reliable tools in the prediction of phase behavior. Despite their good performance in compositional calculations, they usually suffer from weaknesses in the predictions of saturated liquid density. In this research, RK equation was modified. The result of this study shows that modified equation has good agreement with experimental data.Keywords: Modification, Genetic Algorithm, Equation of state, Ammonia
Procedia PDF Downloads 2261 Modeling and Prediction of Zinc Extraction Efficiency from Concentrate by Operating Condition and Using Artificial Neural Networks
Authors: S. Mousavian, D. Ashouri, F. Mousavian, V. Nikkhah Rashidabad, N. Ghazinia
Abstract:
PH, temperature, and time of extraction of each stage, agitation speed, and delay time between stages effect on efficiency of zinc extraction from concentrate. In this research, efficiency of zinc extraction was predicted as a function of mentioned variable by artificial neural networks (ANN). ANN with different layer was employed and the result show that the networks with 8 neurons in hidden layer has good agreement with experimental data.Keywords: Neural Networks, Efficiency, zinc extraction, operating condition
Procedia PDF Downloads 381