| Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks MA Ariana, B Vaferi, G Karimi Powder Technology 278, 1-10, 2015 | 163 | 2015 |
| Artificial neural network approach for prediction of thermal behavior of nanofluids flowing through circular tubes B Vaferi, F Samimi, E Pakgohar, D Mowla Powder technology 267, 1-10, 2014 | 134 | 2014 |
| Automatic recognition of oil reservoir models from well testing data by using multi-layer perceptron networks B Vaferi, R Eslamloueyan, S Ayatollahi Journal of Petroleum Science and Engineering 77 (3-4), 254-262, 2011 | 133 | 2011 |
| Comparison the capability of artificial neural network (ANN) and EOS for prediction of solid solubilities in supercritical carbon dioxide M Lashkarbolooki, B Vaferi, MR Rahimpour Fluid Phase Equilibria 308 (1-2), 35-43, 2011 | 103 | 2011 |
| Modeling of CO2 capture ability of [Bmim][BF4] ionic liquid using connectionist smart paradigms B Daryayehsalameh, M Nabavi, B Vaferi Environmental Technology & Innovation 22, 101484, 2021 | 100 | 2021 |
| Applying artificial neural networks for systematic estimation of degree of fouling in heat exchangers E Davoudi, B Vaferi Chemical Engineering Research and Design 130, 138-153, 2018 | 98 | 2018 |
| Estimation of pool boiling heat transfer coefficient of alumina water-based nanofluids by various artificial intelligence (AI) approaches M Hassanpour, B Vaferi, ME Masoumi Applied Thermal Engineering 128, 1208-1222, 2018 | 95 | 2018 |
| Electrical efficiency of the photovoltaic/thermal collectors cooled by nanofluids: Machine learning simulation and optimization by evolutionary algorithm Y Cao, E Kamrani, S Mirzaei, A Khandakar, B Vaferi Energy Reports 8, 24-36, 2022 | 94 | 2022 |
| Comparison between the artificial neural network, SAFT and PRSV approach in obtaining the solubility of solid aromatic compounds in supercritical carbon dioxide B Vaferi, M Karimi, M Azizi, H Esmaeili The Journal of Supercritical Fluids 77, 44-51, 2013 | 91 | 2013 |
| Investigating vapor–liquid equilibria of binary mixtures containing supercritical or near-critical carbon dioxide and a cyclic compound using cascade neural network M Lashkarbolooki, B Vaferi, A Shariati, AZ Hezave Fluid Phase Equilibria 343, 24-29, 2013 | 71 | 2013 |
| Prediction of viscosity of several alumina-based nanofluids using various artificial intelligence paradigms-Comparison with experimental data and empirical correlations E Gholami, B Vaferi, MA Ariana Powder Technology 323, 495-506, 2018 | 70 | 2018 |
| Prediction of transient pressure response in the petroleum reservoirs using orthogonal collocation B Vaferi, V Salimi, DD Baniani, A Jahanmiri, S Khedri Journal of Petroleum Science and Engineering 98, 156-163, 2012 | 67 | 2012 |
| Hydrogen solubility in aromatic/cyclic compounds: prediction by different machine learning techniques Y Jiang, G Zhang, J Wang, B Vaferi International Journal of Hydrogen Energy 46 (46), 23591-23602, 2021 | 63 | 2021 |
| Model identification for gas condensate reservoirs by using ANN method based on well test data N Ghaffarian, R Eslamloueyan, B Vaferi Journal of Petroleum Science and Engineering 123, 20-29, 2014 | 57 | 2014 |
| The feasibility of Levenberg–Marquardt algorithm combined with imperialist competitive computational method predicting drag reduction in crude oil pipelines H Moayedi, B Aghel, B Vaferi, LK Foong, DT Bui Journal of Petroleum Science and Engineering 185, 106634, 2020 | 56 | 2020 |
| Using artificial neural network to predict the pressure drop in a rotating packed bed M Lashkarbolooki, B Vaferi, D Mowla Separation Science and Technology 47 (16), 2450-2459, 2012 | 56 | 2012 |
| Monitoring the effect of surface functionalization on the CO2 capture by graphene oxide/methyl diethanolamine nanofluids Z Zhou, E Davoudi, B Vaferi Journal of Environmental Chemical Engineering 9 (5), 106202, 2021 | 54 | 2021 |
| Smart computing approach for design and scale-up of conical spouted beds with open-sided draft tubes M Karimi, B Vaferi, SH Hosseini, M Olazar, S Rashidi Particuology 55, 179-190, 2021 | 54 | 2021 |
| Application of machine learning methods for estimating and comparing the sulfur dioxide absorption capacity of a variety of deep eutectic solvents X Zhu, M Khosravi, B Vaferi, MN Amar, MA Ghriga, AH Mohammed Journal of Cleaner Production 363, 132465, 2022 | 50 | 2022 |
| Intelligent assessment of effect of aggregation on thermal conductivity of nanofluids—Comparison by experimental data and empirical correlations A Khalifeh, B Vaferi Thermochimica Acta 681, 178377, 2019 | 50 | 2019 |
