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در حال نمایش موارد 1 - 9 از 9
Modeling and Optimization of Anethole Ultrasound-Assisted Extraction from Fennel Seeds using Artificial Neural Network
(University of Tehran, 2020-06-01)
Extraction of essential oils from medicinal plants has received researcher's attention as it has a wide variety of applications in different industries. In this study, ultrasonic method has been used to facilitate the ...
Modeling and Experimental Prediction of Wastewater Treatment Efficiency in Oil Refineries Using Activated Sludge Process
(University of Tehran, 2014-06-01)
In this study, activated sludge process for wastewater treatment in a refinery was investigated. For such purpose, a laboratory scale rig was built. The effect of several parameters such as temperature, residence time, ...
A Comparative Survey of Modeling Absorption Tower Using Mixed Amines
(University of Tehran, 2011-06-01)
In natural gas treatment, the removal of CO2 and H2S in acid gases is a critical concern. There are various purification technologies that can be used for the removal of acid gas impurities. Absorption of acid gas into ...
Estimation of Binary Infinite Dilute Diffusion Coefficient Using Artificial Neural Network
(University of Tehran, 2014-06-01)
In this study, the use of the three-layer feed forward neural network has been investigated for estimating of infinite dilute diffusion coefficient ( D<sub>12</sub> ) of supercritical fluid (SCF), liquid and gas binary ...
Using Artificial Neural Network for Estimation of Density and Viscosities of Biodiesel–Diesel Blends
(University of Tehran, 2015-12-01)
In recent years, biodiesel has been considered as a good alternative of diesel fuels. Density and viscosity are two important properties of these fuels. In this study, density and kinematic viscosity of biodiesel-diesel ...
Modeling of Gas Hydrate Formation in the Presence of Inhibitors by Intelligent Systems
(University of Tehran, 2015-12-01)
Gas hydrate formation in production and transmission pipelines and consequent plugging of these lines have been a major flow-assurance concern of the oil and gas industry for the last 75 years. Gas hydrate formation rate ...
Ammonia Based Pretreatment Optimization of Cornstover Biomass Using Response Surface Methodology and Artificial Neural Network
(University of Tehran, 2021-06-01)
Effective pretreatment of lignocellulosic biomass could be used to produce fermentable sugar for renewable energy production, which reduces problems related to nonrenewable fuel. Therefore, the purpose of this study was ...
An Artificial Neural Network Model for Predicting the Pressure Gradient in Horizontal Oil–Water Separated Flow
(University of Tehran, 2015-12-01)
In this study, a three–layer artificial neural network (ANN) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. A total of 455 data points were collected from 13 data sources to develop the ANN model. Superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of the network while corresponding pressure gradient was selected as its output. A tansig and a linear function were chosen as transfer functions for hidden and output layers, respectively and Levenberg–Marquardt back–propagation algorithm were applied to train the ANN. The optimal topology of the ANN was achieved with 16 neurons in hidden layer, which made it possible to estimate the pressure gradient with a good accuracy (R<sup>2</sup>=0.996 &AAPE=7.54%). In addition, the results of the developed ANN model were compared to Al–Wahaibi correlation results (with R<sup>2</sup>=0.884&AAPE=17.17%) and it is found that the proposed ANN model has higher accuracy. Finally, a sensitivity analysis was carried out to investigate the relative importance of each input parameter on the ANN output. The results revealed that the pipe diameter (D) has the most relative importance (24.43%) on the ANN output, while the importance of the other parameters is nearly the same....
Bubble Pressure Prediction of Reservoir Fluids using Artificial Neural Network and Support Vector Machine
(University of Tehran, 2019-12-01)
Bubble point pressure is an important parameter in equilibrium calculations of reservoir fluids and having other applications in reservoir engineering. In this work, an artificial neural network (ANN) and a least square ...