Papers by alireza baghban
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2017
ABSTRACT The present study was purposed to model the hydrate formation temperature of the pure ga... more ABSTRACT The present study was purposed to model the hydrate formation temperature of the pure gas containing CH4 and C2H6 in a certain pressure range from 1 to 50 MPa by Peng–Robinson (PR). Hydrate forming temperature depends on pressure and composition of gases. Accuracy of this model was evaluated by data collected from other previously published research. Results from the model showed the hydrate formation temperatures have great agreement with reported values. High value of R2 (0.99) confirms its satisfactory performance.
In fractured reservoirs with primary gas cap, the gas, having high mobility, moves faster than oi... more In fractured reservoirs with primary gas cap, the gas, having high mobility, moves faster than oil and consequently forms a new zone above the oil zone. In this zone, which is named gas invaded zone, gravity drainage is the major mechanism contributing to oil recovery. In a system comprising matrix blocks, a portion of oil drained from the upper matrix block can re-infiltrate the lower block through the top or lateral faces. In this two-part study, first, a single porosity simulation was used with a fine grid in the space occupied by the stack of matrix blocks and fractures. The particular characteristics and properties of each medium were allocated to different portions occupied by these systems in the grid. The impact of fracture and matrix capillary pressure values, as well as the matrix permeability on the oil recovery from each block, was investigated in this part. In the second part, a dual porosity simulation was employed, and a coarse grid was built having the same dimension...
Engineering Applications of Computational Fluid Mechanics, 2020
One of the important parameters illustrating the mass transfer process is the diffusion coefficie... more One of the important parameters illustrating the mass transfer process is the diffusion coefficient of carbon dioxide which has a great impact on carbon dioxide storage in marine ecosystems, saline aquifers, and depleted reservoirs. Due to the complex interpretation approaches and special laboratory equipment for measurement of carbon dioxide-brine system diffusivity, the computational and mathematical methods are preferred. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is coupled with five different evolutionary algorithms for predicting the diffusivity coefficient of carbon dioxide. The R 2 values forthe testing phase are 0.9978, 0.9932, 0.9854, 0.9738 and 0.9514 for ANFIS optimized by particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), backpropagation (BP), and differential evolution (DE), respectively. The hybrid machine learning model of ANFIS-PSO outperforms the other models.
Petroleum Science and Technology, 2018
In the recent years, requirement of suitable enhanced oil recovery (EOR) technique as a more prof... more In the recent years, requirement of suitable enhanced oil recovery (EOR) technique as a more proficient technology becomes significant because of increasing demand for energy. Nanofluids have great potential in order to improve oil recovery. In our study, the effect of SiO 2 , Al 2 O 3 , and MgO nanoparticles on oil recovery was investigated by using core flooding apparatus. Zeta potential and particle size distribution measurements were carried out to investigate the stability of nano particles and results showed SiO 2 has more stability than other ones. Interfacial tension and contact angle measurements between nanofluids and crude oil used to demonstrate that how nanoparticles enhance oil recovery. Experimental data reveals that SiO 2 nanoparticles introduce as the greatest agent among these nanoparticles for enhanced oil recovery. Lowest damage for SiO 2 nanofluids was observed and also it was observed that the concentration and injection rate have straight effects on permeability reductions.
Petroleum Science and Technology, 2018
Process optimization of CO 2 removal from natural gas by a polyvinylidene fluoride hollow-fiber m... more Process optimization of CO 2 removal from natural gas by a polyvinylidene fluoride hollow-fiber membrane contactor is a major goal of many computational fluid dynamics (CFD) simulations in this area. In this study, a 2D CFD model based on mass transfer equation inside the tube, the membrane, and the shell section of a HMFC at steady state and laminar conditions is developed and solved by COMSOL Multiphysics with finite element approach. Simulation results show an excellent agreement with experimental data. The model predicts that higher liquid velocity and membrane porosity results in higher CO 2 removal, because of enhancement of effective diffusion coefficient. Also, taller fiber length results in higher contact area and higher mass transfer of CO 2 from natural gas into distilled water. Although higher temperature will decrease the CO 2 removal.
Solar energy is a renewable resource of energy that is broadly utilized and has the least emissio... more Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
Processes, 2020
Calculating hydrocarbon components solubility of natural gases is known as one of the important i... more Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are ke...
Revue des composites et des matériaux avancés, 2019
One of the auspicious nanomaterials which has exceptionally enticed researchers is carbon nanotub... more One of the auspicious nanomaterials which has exceptionally enticed researchers is carbon nanotubes (CNTs) as the result of their excellent thermal properties. In this investigation, an experiment was carried out on three kinds of CNTs-nanofluids with various CNTs added to de-ionized water to compared and analyze their thermal conductivity properties. The main purpose of this study was to introduce a combination of experimental and modelling approaches to forecast the amount of thermal conductivity using four different neural networks. Between MLP-ANN, ANFIS, LSSVM, and RBF-ANN Methods, it was found that the LSSVM produced better results with the lowest deviation factor and reflected the most accurate responses between the proposed models. the regression diagram of experimental and estimated values shows an R2 coefficient of 0.9806 and 0.9579 for training and testing sections of the ANFIS method in part a, and in the b, c and d parts of the diagram, coefficients of determination were 0.9893& 0.9967 and 0.9974 & 0.9992 and 0.9996& 0.9989 for training and testing part of MLP-ANN, RBF-ANN and LSSVM models. Also, the effect of different parameters was investigated using a sensitivity analysis method which demonstrates that the temperature is the most affecting parameter on the thermal conductivity with a relevancy factor of 0.66866.
Petroleum Science and Technology, 2018
The present contribution was performed in order to predict CO 2 loading capacity in aqueous sodiu... more The present contribution was performed in order to predict CO 2 loading capacity in aqueous sodium glycinate as a novel class of green solution under wide operating range using radial basis function artificial neural network (RBFANN). The predicted CO 2 loading capacity values were in brilliant agreement with those corresponding experimental values. The estimated values of MSE and R-squared were 0.00045 and 0.997, respectively. Accordingly, statistical and graphical analyses confirm satisfactory prediction of our proposed model.
Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost imp... more Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost important for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed ANFIS-PSO model the statistical meter of MARE% was implemented, which resulted 0.003266 and 0.013272 for training and testing respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into t...
Petroleum Science and Technology, 2017
ABSTRACT Association of water with natural gas streams can highly affect processing and transmiss... more ABSTRACT Association of water with natural gas streams can highly affect processing and transmission of natural gas. Therefore, the water content of the natural gas must be known in order to determine best possible processing and transmission conditions. This study aims to develop a simple predictive approach to predict water content of sweet gas in wide pressure and temperature ranges, using a radial basis function artificial neural network. The proposed model shows lower deviation from experimental data compared to existing empirical correlation. R-squared and mean relative error values are 0.998% and 4.07%, respectively.
Mercury content in the output gas from boilers was predicted using an Adaptive Neuro-Fuzzy Infere... more Mercury content in the output gas from boilers was predicted using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Input parameters were selected from coal characteristics and the operational configuration of boilers. The ANFIS approach is capable of developing a nonlinear model to represent the dependency of flue gas mercury content into the specifications of coal and also the boiler type. In this study, operational information from 82 power plants has been gathered and employed to educate and examine the proposed ANFIS model. Resulted values from the model were compared to the collected data and it indicates that the model possesses an extraordinary level of precision with a correlation coefficient of unity. The MARE% for training and testing parts were 0.003266 and 0.013272, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1% which confirm the accuracy of PSO-ANFIS model.
Chemical Engineering Research and Design, 2019
Highlights A new group contribution model was developed for predicting density of ionic liquids... more Highlights A new group contribution model was developed for predicting density of ionic liquids The model inputs are temperature, pressure, and 47 substructures A data bank containing 918 data points for 747 different ILs was used for the model Results indicate satisfactory predictions of suggested model than other existing models An outlier analysis was utilized to detect suspected data points
Engineering Applications of Computational Fluid Mechanics, 2019
wing Chau (2019) Towards experimental and modeling study of heat transfer performance of water-Si... more wing Chau (2019) Towards experimental and modeling study of heat transfer performance of water-SiO 2 nanofluid in quadrangular cross-section channels, Engineering
Petroleum Science and Technology, 2018
In the gas engineering the accurate calculation for pipeline and gas reservoirs requires great ac... more In the gas engineering the accurate calculation for pipeline and gas reservoirs requires great accuracy in estimation of gas properties. The gas density is one of major properties which are dependent to pressure, temperature and composition of gas. In this work, the Least squares support vector machine (LSSVM) algorithm was utilized as novel predictive tool to predict natural gas density as function of temperature, pressure and molecular weight of gas. A total number of 1240 experimental densities were gathered from the literature for training and validation of LSSVM algorithm. The statistical indexes, Root mean square error (RMSE), coefficient of determination (R 2) and average absolute relative deviation (AARD) were determined for total dataset as 0.033466, 1 and 0.0025686 respectively. The graphical comparisons and calculated indexes showed that LSSVM can be considered as a powerful and accurate tool for prediction of gas density.
Petroleum Science and Technology, 2017
ABSTRACT Wax deposition in petroleum industry is one of the major problems requiring accurate pre... more ABSTRACT Wax deposition in petroleum industry is one of the major problems requiring accurate predictive procedures to reduce the deficiencies and effective designing of the process. An adaptive neuro fuzzy inference system (ANFIS) model is proposed to predict the wax deposition in oily systems. Parameters of the ANFIS model are optimized using the particle swarm optimization (PSO) method. Results are then compared to those previously reported by Kamari et al., demonstrating better performance of the proposed ANFIS model. Statistical and graphical approaches are employed to investigate the reliability of the proposed model, illustrating the model's capability of precise estimation of the wax deposition. Coefficient of determination (R2) and mean squared error (MSE) values of 0.994 and 0.053 are obtained for the proposed ANFIS model, revealing the reliable prediction of wax deposition by the proposed ANFIS model.
Petroleum Science and Technology, 2018
The accurate estimations of processes in gas engineering need a high degree of accuracy in calcul... more The accurate estimations of processes in gas engineering need a high degree of accuracy in calculations of gas properties. One of these properties is gas density which is straightly affected by pressure and temperature. In the present work, the Adaptive neuro fuzzy inference system (ANFIS) algorithm joined with Particle Swarm Optimization (PSO) to estimate gas density in terms of pressure, temperature, molecular weight, critical pressure and critical temperature of gas. In order to training and testing of ANFIS-PSO algorithm a total number of 1240 experimental data were extracted from the literature. The statistical parameters, Root mean square error (RMSE), coefficient of determination (R 2) and average absolute relative deviation (AARD) were determined for overall process as 0.14, 1 and 0.039 respectively. The determined statistical parameters and graphical comparisons expressed that predicting mode is a robust and accurate model for prediction of gas density. Also the predicting model was compared with three correlations and obtained results showed the better performance of the proposed model respect to the others.
Fuel, 2019
Biodiesel as an environmental friendly and renewable fuel assigns a great potential to substituti... more Biodiesel as an environmental friendly and renewable fuel assigns a great potential to substitution of petroleum diesel. The biodiesel characteristics are highly dependent on the structure of its basis oil. The main aim of the present work was to develop an accurate model based on LSSVM-PSO algorithm to estimate biodiesel properties, i.e., pour point, cloud point, iodine value and kinematic viscosity as a function of fatty acids composition. The temperature, molecular weight, weight percent of saturated acids, poly unsaturated fatty acids and mono unsaturated fatty acids, number of double bonds, and carbon number are involved variables for development of the models. The performance of LSSVM-PSO model is evaluated using different statistical criteria which result in the coefficients of determination of 0.99995, 0.99981, 0.99848 and 0.99930 for pour point, cloud point, iodine value and kinematic viscosity, respectively. In addition, the outcomes of the proposed model are compared with those from an ANFIS model indicating the great potential of the LSSVM-PSO model to estimate the biodiesel properties.
Journal of Cleaner Production, 2018
The purpose of rotating packed bed is to intensify process conditions by using centrifugal forces... more The purpose of rotating packed bed is to intensify process conditions by using centrifugal forces. The effective interfacial area is a critical design factor and has a direct relationship with operational condition and mass transfer rate. Process intensification by the rotating packed bed is an emerging technology to improve the mass transfer rate in a high gravity system. Since there are limited modeling studies in order to control rotating packed bed parameters, in the present study, the multilayer perceptron artificial neural network (MLP) framework was successfully used to investigate the gas-liquid effective interfacial area in a rotating packed bed. In this regard, a number of 265 experimental data for the gas-liquid effective interfacial area was utilized by considering three groups including operational factors, physical dimension, and gasliquid properties as the network' inputs. The mean relative error and R-square as analogy factors for verification of the model accuracy obtained to be 8.2% and 0.97, respectively. Accordingly, the present model can be a huge value in the CO 2-liquid system and it is introduced as a novel strategy to determine the gas-liquid effective interfacial area in a rotating packed bed.
Fuel, 2018
Cetane number (CN) is one of the key factors of biodiesels and other diesel fuels. It is an indic... more Cetane number (CN) is one of the key factors of biodiesels and other diesel fuels. It is an indicator of ignition speed and required compression for ignition. CN estimation of biodiesel based on fatty acid methyl esters (FAME) composition was the main goal of this work. Application of artificial neural network (ANN) combined with particle swarm optimization (PSO) and teaching-learning based optimization (TLBO) is discussed in this communication. A number of 232 fuel samples was derived from the literature as the raw data for the models development. Different evaluative factors prove the satisfactory performance of the proposed ANN models. The obtained values of R-squared and mean square of errors are 0.973 & 3.538 and 0.951 & 6.324 for the proposed TLBO-ANN and PSO-ANN, respectively. Based on the outcome of this study, ANN coupled with PSO and TLBO algorithms can be suitable tools, especially TLBO algorithm to estimate CN of biodiesels.
Uploads
Papers by alireza baghban