Afshin Tatar

Afshin Tatar

Higher Degree by Research Candidate

School of Chemical Engineering

Faculty of Sciences, Engineering and Technology

  • Language Competencies
    Language Competency
    English Can read, write, speak, understand spoken and peer review
    Persian Can read, write, speak, understand spoken and peer review

  • Journals
    Year Citation
    2024 Esmaeili-Jaghdan, Z., Tatar, A., Shokrollahi, A., Bon, J., & Zeinijahromi, A. (2024). Machine learning modelling of dew point pressure in gas condensate reservoirs: application of decision tree-based models. Neural Computing and Applications, 36(4), 1973-1995.
    DOI
    2024 Tatar, A., Haghighi, M., & Zeinijahromi, A. (2024). Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks. Journal of Rock Mechanics and Geotechnical Engineering.
    DOI
    2024 Foroughizadeh, P., Shokrollahi, A., Tatar, A., & Zeinijahromi, A. (2024). Hydrogen solubility in different chemicals: A modelling approach and review of literature data. Engineering Applications of Artificial Intelligence, 136(Part B), 108978-1-108978-24.
    DOI
    2023 Lashkenari, M. S., Bagheri, M., Tatar, A., Rezazadeh, H., & Inc, M. (2023). A further study in the prediction of viscosity for Iranian crude oil reservoirs by utilizing a robust radial basis function (RBF) neural network model. Neural Computing and Applications, 35(14), 10663-10676.
    DOI Scopus7 WoS4
    2023 Ghasemi, M., Tatar, A., Shafiei, A., & Ivakhnenko, O. P. (2023). Prediction of asphaltene adsorption capacity of clay minerals using machine learning. Canadian Journal of Chemical Engineering, 101(5), 2579-2597.
    DOI Scopus4 WoS1
    2023 Behvandi, R., Tatar, A., Shokrollahi, A., & Zeinijahromi, A. (2023). Evaluation of phase equilibrium conditions of clathrate hydrates in natural gas binary mixtures: Machine learning approach. Geoenergy Science and Engineering, 224, 26 pages.
    DOI Scopus1
    2023 Rayhani, M., Tatar, A., Shokrollahi, A., & Zeinijahromi, A. (2023). Exploring the power of machine learning in analyzing the gas minimum miscibility pressure in hydrocarbons. Geoenergy Science and Engineering, 226, 211778.
    DOI Scopus7
    2023 Tatar, A., & Shafiei, A. (2023). Connectionist Models for Asphaltene Precipitation Prediction by <i>n</i>-Alkane Titration─Pressure and Crude Oil Properties Considered. Industrial and Engineering Chemistry Research, 62(34), 13281-13302.
    DOI Scopus1
    2022 Shafiei, A., Tatar, A., Rayhani, M., Kairat, M., & Askarova, I. (2022). Artificial neural network, support vector machine, decision tree, random forest, and committee machine intelligent system help to improve performance prediction of low salinity water injection in carbonate oil reservoirs. Journal of Petroleum Science and Engineering, 219, 19 pages.
    DOI Scopus16 WoS5
    2022 Mirzaie, M., Esfandyari, H., & Tatar, A. (2022). Dew point pressure of gas condensates, modeling and a comprehensive review on literature data. Journal of Petroleum Science and Engineering, 211, 17 pages.
    DOI Scopus10 WoS5
    2022 Tatar, A., Esmaeili-Jaghdan, Z., Shokrollahi, A., & Zeinijahromi, A. (2022). Hydrogen solubility in n-alkanes: Data mining and modelling with machine learning approach. International Journal of Hydrogen Energy, 47(85), 35999-36021.
    DOI Scopus11 WoS1
    2022 Yerkenov, T., Tazikeh, S., Tatar, A., & Shafiei, A. (2022). Asphaltene Precipitation Prediction during Bitumen Recovery: Experimental Approach versus Population Balance and Connectionist Models. ACS Omega, 7(37), 33123-33137.
    DOI Scopus3 WoS2 Europe PMC2
    2021 Tatar, A., Askarova, I., Shafiei, A., & Rayhani, M. (2021). Data-Driven Connectionist Models for Performance Prediction of Low Salinity Waterflooding in Sandstone Reservoirs. ACS Omega, 6(47), 32304-32326.
    DOI Scopus13 WoS10 Europe PMC2
    2021 Sayahi, T., Tatar, A., Rostami, A., Anbaz, M. A., & Shahbazi, K. (2021). Determining solubility of CO<inf>2</inf> in aqueous brine systems via hybrid smart strategies. International Journal of Computer Applications in Technology, 65(1), 1-13.
    DOI Scopus11 WoS8
    2021 Ghorbani, H., Wood, D. A., Choubineh, A., Tatar, A., Abarghoyi, P. G., Madani, M., & Mohamadian, N. (2021). Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared (vol 6, pg 404, 2019). PETROLEUM, 7(2), 2 pages.
    2020 Mirzaie, M., & Tatar, A. (2020). Modeling of interfacial tension in binary mixtures of CH<inf>4</inf>, CO<inf>2</inf>, and N<inf>2</inf> - alkanes using gene expression programming and equation of state. Journal of Molecular Liquids, 320, 15 pages.
    DOI Scopus29 WoS20
    2020 Ghorbani, H., Wood, D. A., Choubineh, A., Mohamadian, N., Tatar, A., Farhangian, H., & Nikooey, A. (2020). Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared. Experimental and Computational Multiphase Flow, 2(4), 225-246.
    DOI Scopus38 WoS31
    2020 Tatar, A., Moghtadaei, G. M., Manafi, A., Cachadiña, I., & Mulero, Á. (2020). Determination of pure alcohols surface tension using Artificial Intelligence methods. Chemometrics and Intelligent Laboratory Systems, 201, 14 pages.
    DOI Scopus11 WoS7
    2020 Ghorbani, H., Wood, D. A., Choubineh, A., Tatar, A., Abarghoyi, P. G., Madani, M., & Mohamadian, N. (2020). Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared. Petroleum, 6(4), 404-414.
    DOI Scopus55
    2019 Barati-Harooni, A., Najafi-Marghmaleki, A., Hoseinpour, S. A., Tatar, A., Karkevandi-Talkhooncheh, A., Hemmati-Sarapardeh, A., & Mohammadi, A. H. (2019). Estimation of minimum miscibility pressure (MMP) in enhanced oil recovery (EOR) process by N<inf>2</inf> flooding using different computational schemes. Fuel, 235, 1455-1474.
    DOI Scopus35 WoS25
    2019 Rashid, S., Ghamartale, A., Abbasi, J., Darvish, H., & Tatar, A. (2019). Prediction of Critical Multiphase Flow Through Chokes by Using A Rigorous Artificial Neural Network Method. Flow Measurement and Instrumentation, 69, 9 pages.
    DOI Scopus26 WoS19
    2019 Ameli, F., Hemmati-Sarapardeh, A., Tatar, A., Zanganeh, A., & Ayatollahi, S. (2019). Modeling interfacial tension of normal alkane-supercritical CO<inf>2</inf> systems: Application to gas injection processes. Fuel, 253, 1436-1445.
    DOI Scopus14 WoS10
    2019 Tatar, A., Barati, A., Najafi, A., & Mohammadi, A. H. (2019). Radial basis function (RBF) network for modeling gasoline properties. Petroleum Science and Technology, 37(11), 1306-1313.
    DOI Scopus8 WoS3
    2019 Moghadasi, R., Rostami, A., Tatar, A., & Hemmati-Sarapardeh, A. (2019). An experimental study of Nanosilica application in reducing calcium sulfate scale at high temperatures during high and low salinity water injection. Journal of Petroleum Science and Engineering, 179, 7-18.
    DOI Scopus23 WoS18
    2019 Rahmati, A. S., & Tatar, A. (2019). Application of Radial Basis Function (RBF) neural networks to estimate oil field drilling fluid density at elevated pressures and temperatures. Oil and Gas Science and Technology, 74, 6 pages.
    DOI Scopus17 WoS12
    2018 Najafi-Marghmaleki, A., Tatar, A., Barati-Harooni, A., Arabloo, M., Rafiee-Taghanaki, S., & Mohammadi, A. H. (2018). Reliable modeling of constant volume depletion (CVD) behaviors in gas condensate reservoirs. Fuel, 231, 146-156.
    DOI Scopus30 WoS20
    2018 Hemmat Esfe, M., Tatar, A., Ahangar, M. R. H., & Rostamian, H. (2018). A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant. Physica E: Low-Dimensional Systems and Nanostructures, 96, 85-93.
    DOI Scopus80 WoS65
    2018 Hoseinpour, S. A., Barati-Harooni, A., Nadali, P., Mohebbi, A., Najafi-Marghmaleki, A., Tatar, A., & Bahadori, A. (2018). Accurate model based on artificial intelligence for prediction of carbon dioxide solubility in aqueous tetra-n-butylammonium bromide solutions. Journal of Chemometrics, 32(2), 20 pages.
    DOI Scopus6 WoS6
    2018 Ahmadi, M. H., Tatar, A., Seifaddini, P., Ghazvini, M., Ghasempour, R., & Sheremet, M. A. (2018). Thermal conductivity and dynamic viscosity modeling of Fe<inf>2</inf>O<inf>3</inf>/water nanofluid by applying various connectionist approaches. Numerical Heat Transfer; Part A: Applications, 74(6), 1301-1322.
    DOI Scopus44 WoS40
    2018 Kahani, M., Ahmadi, M. H., Tatar, A., & Sadeghzadeh, M. (2018). Development of multilayer perceptron artificial neural network (MLP-ANN) and least square support vector machine (LSSVM) models to predict Nusselt number and pressure drop of TiO<inf>2</inf>/water nanofluid flows through non-straight pathways. Numerical Heat Transfer; Part A: Applications, 74(4), 1190-1206.
    DOI Scopus61 WoS56
    2018 Rostami, A., Kalantari-Meybodi, M., Karimi, M., Tatar, A., & Mohammadi, A. H. (2018). Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding. Oil and Gas Science and Technology, 73, 17 pages.
    DOI Scopus46 WoS38
    2018 Ahmadi, M. H., Tatar, A., Alhuyi Nazari, M., Ghasempour, R., Chamkha, A. J., & Yan, W. M. (2018). Applicability of connectionist methods to predict thermal resistance of pulsating heat pipes with ethanol by using neural networks. International Journal of Heat and Mass Transfer, 126, 1079-1086.
    DOI Scopus43 WoS38
    2017 Zendehboudi, A., & Tatar, A. (2017). Oil flooded scroll compressors: Predicting the energy performance and evaluating the experimental data. Measurement: Journal of the International Measurement Confederation, 112, 38-46.
    DOI Scopus12 WoS11
    2017 Zendehboudi, A., Tatar, A., & Li, X. (2017). A comparative study and prediction of the liquid desiccant dehumidifiers using intelligent models. Renewable Energy, 114, 1023-1035.
    DOI Scopus28 WoS25
    2017 Najafi-Marghmaleki, A., Barati-Harooni, A., Tatar, A., Mohebbi, A., & Mohammadi, A. H. (2017). On the prediction of Watson characterization factor of hydrocarbons. Journal of Molecular Liquids, 231, 419-429.
    DOI Scopus25 WoS19
    2017 Tatar, A., Barati-Harooni, A., Najafi-Marghmaleki, A., & Bahadori, A. (2017). Accurate prediction of CO<inf>2</inf> solubility in eutectic mixture of levulinic acid (or furfuryl alcohol) and choline chloride. International Journal of Greenhouse Gas Control, 58, 212-222.
    DOI Scopus20 WoS16
    2017 Najafi-Marghmaleki, A., Tatar, A., Barati-Harooni, A., & Mohammadi, A. H. (2017). A GEP based model for prediction of densities of ionic liquids. Journal of Molecular Liquids, 227, 373-385.
    DOI Scopus13 WoS11
    2017 Barati-Harooni, A., Najafi-Marghmaleki, A., Tatar, A., Mohebbi, A., Khaksar-Manshad, A., & Mohammadi, A. H. (2017). An accurate model for prediction of wax deposition in oil systems. Petroleum and Coal, 59(6), 797-807.
    2017 Zendehboudi, A., & Tatar, A. (2017). Utilization of the RBF network to model the nucleate pool boiling heat transfer properties of refrigerant-oil mixtures with nanoparticles. Journal of Molecular Liquids, 247, 304-312.
    DOI Scopus43 WoS36
    2017 Barati-Harooni, A., Nasery, S., Tatar, A., Najafi-Marghmaleki, A., Isafiade, A. J., & Bahadori, A. (2017). Prediction of H<inf>2</inf>S Solubility in Liquid Electrolytes by Multilayer Perceptron and Radial Basis Function Neural Networks. Chemical Engineering and Technology, 40(2), 367-375.
    DOI Scopus10 WoS9
    2017 Dadkhah, M. R., Tatar, A., Mohebbi, A., Barati-Harooni, A., Najafi-Marghmaleki, A., Ghiasi, M. M., . . . Pourfayaz, F. (2017). Prediction of solubility of solid compounds in supercritical CO<inf>2</inf> using a connectionist smart technique. Journal of Supercritical Fluids, 120, 181-190.
    DOI Scopus25 WoS20
    2016 Nasery, S., Barati-Harooni, A., Tatar, A., Najafi-Marghmaleki, A., & Mohammadi, A. H. (2016). Accurate prediction of solubility of hydrogen in heavy oil fractions. Journal of Molecular Liquids, 222, 933-943.
    DOI Scopus39 WoS31
    2016 Tatar, A., Barati-Harooni, A., Najafi-Marghmaleki, A., Mohebbi, A., Ghiasi, M. M., Mohammadi, A. H., & Hajinezhad, A. (2016). Comparison of two soft computing approaches for predicting CO<inf>2</inf> solubility in aqueous solution of piperazine. International Journal of Greenhouse Gas Control, 53, 85-97.
    DOI Scopus38 WoS31
    2016 Barati-Harooni, A., Najafi-Marghmaleki, A., Tatar, A., & Mohammadi, A. H. (2016). Experimental and modeling studies on adsorption of a nonionic surfactant on sandstone minerals in enhanced oil recovery process with surfactant flooding. Journal of Molecular Liquids, 220, 1022-1032.
    DOI Scopus90 WoS70
    2016 Barati-Harooni, A., Soleymanzadeh, A., Tatar, A., Najafi-Marghmaleki, A., Samadi, S. J., Yari, A., . . . Mohammadi, A. H. (2016). Experimental and modeling studies on the effects of temperature, pressure and brine salinity on interfacial tension in live oil-brine systems. Journal of Molecular Liquids, 219, 985-993.
    DOI Scopus57 WoS46
    2016 Tatar, A., Barati-Harooni, A., Partovi, M., Najafi-Marghmaleki, A., & Mohammadi, A. H. (2016). An accurate model for predictions of vaporization enthalpies of hydrocarbons and petroleum fractions. Journal of Molecular Liquids, 220, 192-199.
    DOI Scopus16 WoS16
    2016 Sayahi, T., Tatar, A., & Bahrami, M. (2016). A RBF model for predicting the pool boiling behavior of nanofluids over a horizontal rod heater. International Journal of Thermal Sciences, 99, 180-194.
    DOI Scopus55 WoS52
    2016 Barati-Harooni, A., Najafi-Marghmaleki, A., Tatar, A., Arabloo, M., Phung, L. T. K., Lee, M., & Bahadori, A. (2016). Prediction of frictional pressure loss for multiphase flow in inclined annuli during Underbalanced Drilling operations. Natural Gas Industry B, 3(4), 275-282.
    DOI Scopus10
    2016 Tatar, A., Barati-Harooni, A., Moradi, S., Nasery, S., Najafi-Marghmaleki, A., Lee, M., . . . Bahadori, A. (2016). Prediction of heavy oil viscosity using a radial basis function neural network. Petroleum Science and Technology, 34(21), 1742-1748.
    DOI Scopus10 WoS5
    2016 Najafi-Marghmaleki, A., Tatar, A., Barati-Harooni, A., Mohebbi, A., Kalantari-Meybodi, M., & Mohammadi, A. H. (2016). On the prediction of interfacial tension (IFT) for water-hydrocarbon gas system. Journal of Molecular Liquids, 224, 976-990.
    DOI Scopus16 WoS13
    2016 Tatar, A., Barati-Harooni, A., Najafi-Marghmaleki, A., Ahmadi-Pour, M., Nadali, P., & Mohammadi, A. H. (2016). Prediction of moisture content of natural gases using a GA-RBF model. Journal of Molecular Liquids, 223, 994-999.
    DOI Scopus4 WoS4
    2016 Najafi-Marghmaleki, A., Tatar, A., Barati-Harooni, A., Choobineh, M. J., & Mohammadi, A. H. (2016). GA-RBF model for prediction of dew point pressure in gas condensate reservoirs. Journal of Molecular Liquids, 223, 979-986.
    DOI Scopus24 WoS14
    2016 Tatar, A., Nasery, S., Bahadori, A., Bahadori, M., Najafi-Marghmaleki, A., & Barati-Harooni, A. (2016). Implementing radial basis function neural network for prediction of surfactant retention in petroleum production and processing industries. Petroleum Science and Technology, 34(11-12), 992-999.
    DOI Scopus10 WoS10
    2016 Tatar, A., Nasery, S., Bahadori, M., Bahadori, A., Bahadori, M., Barati-Harooni, A., & Najafi-Marghmaleki, A. (2016). Prediction of water removal rate in a natural gas dehydration system using radial basis function neural network. Petroleum Science and Technology, 34(10), 951-960.
    DOI Scopus14 WoS12
    2016 Tatar, A., Najafi-Marghmaleki, A., Barati-Harooni, A., Gholami, A., Ansari, H. R., Bahadori, M., . . . Bahadori, A. (2016). Implementing radial basis function neural networks for prediction of saturation pressure of crude oils. Petroleum Science and Technology, 34(5), 454-463.
    DOI Scopus21 WoS19
    2016 Tatar, A., Naseri, S., Bahadori, M., Hezave, A. Z., Kashiwao, T., Bahadori, A., & Darvish, H. (2016). Prediction of carbon dioxide solubility in ionic liquids using MLP and radial basis function (RBF) neural networks. Journal of the Taiwan Institute of Chemical Engineers, 60, 151-164.
    DOI Scopus65 WoS50
    2016 Tatar, A., Barati, A., Yarahmadi, A., Najafi, A., Lee, M., & Bahadori, A. (2016). Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models. International Journal of Greenhouse Gas Control, 47, 122-136.
    DOI Scopus56 WoS50
    2016 Tatar, A., Barati-Harooni, A., Najafi-Marghmaleki, A., Norouzi-Farimani, B., & Mohammadi, A. H. (2016). Predictive model based on ANFIS for estimation of thermal conductivity of carbon dioxide. Journal of Molecular Liquids, 224, 1266-1274.
    DOI Scopus33 WoS26
    2016 Rezaei, A., Abdi-Khangah, M., Mohebbi, A., Tatar, A., & Mohammadi, A. H. (2016). Using surface modified clay nanoparticles to improve rheological behavior of Hydrolized Polyacrylamid (HPAM) solution for enhanced oil recovery with polymer flooding. Journal of Molecular Liquids, 222, 1148-1156.
    DOI Scopus99 WoS89
    2016 Naseri, S., Tatar, A., & Shokrollahi, A. (2016). Development of an accurate method to prognosticate choke flow coefficients for natural gas flow through nozzle and orifice type chokes. Flow Measurement and Instrumentation, 48, 1-7.
    DOI Scopus33 WoS21
    2016 Afshin, T., Ali, B. H., Hossein, M., Saeid, N., Meysam, B., Moonyong, L., . . . Adel, N. M. (2016). Prediction of water formation temperature in natural gas dehydrators using radial basis function (RBF) neural networks. Natural Gas Industry B, 3(2), 173-180.
    DOI Scopus7
    2015 Tatar, A., Shokrollahi, A., Lee, M., Kashiwao, T., & Bahadori, A. (2015). Prediction of supercritical CO<inf>2</inf>/brine relative permeability in sedimentary basins during carbon dioxide sequestration. Greenhouse Gases: Science and Technology, 5(6), 756-771.
    DOI Scopus13 WoS11
    2015 Darvish, H., Nouri-Taleghani, M., Shokrollahi, A., & Tatar, A. (2015). Geo-mechanical modeling and selection of suitable layer for hydraulic fracturing operation in an oil reservoir (south west of Iran). Journal of African Earth Sciences, 111, 409-420.
    DOI Scopus27 WoS20
    2015 Shokrollahi, A., Tatar, A., & Safari, H. (2015). On accurate determination of PVT properties in crude oil systems: Committee machine intelligent system modeling approach. Journal of the Taiwan Institute of Chemical Engineers, 55, 17-26.
    DOI Scopus60 WoS52
    2015 Tatar, A., Shokrollahi, A., Halali, M. A., Azari, V., & Safari, H. (2015). A Hybrid Intelligent Computational Scheme for Determination of Refractive Index of Crude Oil Using SARA Fraction Analysis. Canadian Journal of Chemical Engineering, 93(9), 1547-1555.
    DOI Scopus26 WoS23
    2015 Nouri-Taleghani, M., Mahmoudifar, M., Shokrollahi, A., Tatar, A., & Karimi-Khaledi, M. (2015). Fracture density determination using a novel hybrid computational scheme: A case study on an Iranian Marun oil field reservoir. Journal of Geophysics and Engineering, 12(2), 188-198.
    DOI Scopus26 WoS17
    2015 Tatar, A., Naseri, S., Sirach, N., Lee, M., & Bahadori, A. (2015). Prediction of reservoir brine properties using radial basis function (RBF) neural network. Petroleum, 1(4), 349-357.
    DOI Scopus31
    2015 Naseri, S., Tatar, A., Bahadori, M., Rozyn, J., Kashiwao, T., & Bahadori, A. (2015). Application of Radial Basis Function Neural Network for Prediction of Titration-Based Asphaltene Precipitation. Petroleum Science and Technology, 33(23-24), 1875-1882.
    DOI Scopus10 WoS9
    2015 Tatar, A., Naseri, S., Bahadori, M., Rozyn, J., Lee, M., Kashiwao, T., & Bahadori, A. (2015). Evaluation of Different Artificial Intelligent Models to Predict Reservoir Formation Water Density. Petroleum Science and Technology, 33(20), 1749-1756.
    DOI Scopus7 WoS5
    2014 Tatar, A., Yassin, M. R., Rezaee, M., Aghajafari, A. H., & Shokrollahi, A. (2014). Applying a robust solution based on expert systems and GA evolutionary algorithm for prognosticating residual gas saturation in water drive gas reservoirs. Journal of Natural Gas Science and Engineering, 21, 79-94.
    DOI Scopus45 WoS40
    2014 Hemmati-Sarapardeh, A., Shokrollahi, A., Tatar, A., Gharagheizi, F., Mohammadi, A. H., & Naseri, A. (2014). Reservoir oil viscosity determination using a rigorous approach. Fuel, 116, 39-48.
    DOI Scopus120 WoS106
    2013 Tatar, A., Shokrollahi, A., Mesbah, M., Rashid, S., Arabloo, M., & Bahadori, A. (2013). Implementing Radial Basis Function Networks for modeling CO<inf>2</inf>-reservoir oil minimum miscibility pressure. Journal of Natural Gas Science and Engineering, 15, 82-92.
    DOI Scopus144 WoS119
    - Tatar, A., Shokrollahi, A., Zeinijahromi, A., & Haghighi, M. (n.d.). Deep Learning for Predicting Hydrogen Solubility in n-Alkanes: Enhancing Sustainable Energy Systems. Sustainability, 16(17), 7512.
    DOI

  • Book Chapters
    Year Citation
    2018 Tatar, A. (2018). Microbial Enhanced Oil Recovery: Microbiology and Fundamentals. In Fundamentals of Enhanced Oil and Gas Recovery from Conventional and Unconventional Reservoirs (pp. 291-508). Elsevier.
    DOI Scopus14
  • Lecturer "Python Programming", Islamic Azad University, Iran, 2023
  • Teaching Assistant “Formation Evaluation, Petrophysics & Rock Properties”, The University of Adelaide, Australia, 2023
  • Teaching Assistant “Unconventional Resources and Recovery”, The University of Adelaide, Australia, 2024
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