Search Results - "Larestani, Aydin"

Predictive modeling of CO2 solubility in piperazine aqueous solutions using boosting algorithms for carbon capture goals by Mohammadi, Mohammad-Reza, Larestani, Aydin, Schaffie, Mahin, Hemmati-Sarapardeh, Abdolhossein, Ranjbar, Mohammad

“…Carbon dioxide (CO 2 ) is the main greenhouse gas that drives global warming, climate change, and other environmental issues. CO 2 absorption using amine…”
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Predicting the surfactant-polymer flooding performance in chemical enhanced oil recovery: Cascade neural network and gradient boosting decision tree by Larestani, Aydin, Mousavi, Seyed Pezhman, Hadavimoghaddam, Fahimeh, Ostadhassan, Mehdi, Hemmati-Sarapardeh, Abdolhossein

“…Surfactant-polymer flooding is one of the most important enhanced oil recovery (EOR) techniques, which refers to the injection of surfactant slugs and polymer…”
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Toward reliable prediction of CO2 uptake capacity of metal–organic frameworks (MOFs): implementation of white-box machine learning by Larestani, Aydin, Jafari-Sirizi, Ahmadreza, Hadavimoghaddam, Fahimeh, Atashrouz, Saeid, Nedeljkovic, Dragutin, Mohaddespour, Ahmad, Hemmati-Sarapardeh, Abdolhossein

“…The burning of fossil fuels is the major cause of the surge in atmospheric CO 2 concentration. The unique properties of Metal–organic frameworks (MOFs) have…”
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Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State by Larestani, Aydin, Hemmati-Sarapardeh, Abdolhossein, Samari, Zahra, Ostadhassan, Mehdi

“…This communication primarily concentrates on developing reliable and accurate compositional oil formation volume factor (B o) models using several advanced and…”
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Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery by Lv, Qichao, Zheng, Rong, Guo, Xinshu, Larestani, Aydin, Hadavimoghaddam, Fahimeh, Riazi, Masoud, Hemmati-Sarapardeh, Abdolhossein, Wang, Kai, Li, Junjian

“…[Display omitted] •Minimum miscibility pressure of crude oil-CO2 is modeled by computational approaches.•Tree-based, deep learning, mixing cell, and empirical…”
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Predictive modeling of CO 2 solubility in piperazine aqueous solutions using boosting algorithms for carbon capture goals by Mohammadi, Mohammad-Reza, Larestani, Aydin, Schaffie, Mahin, Hemmati-Sarapardeh, Abdolhossein, Ranjbar, Mohammad

“…Carbon dioxide (CO ) is the main greenhouse gas that drives global warming, climate change, and other environmental issues. CO absorption using amine solvents…”
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White-box machine-learning models for accurate interfacial tension prediction in hydrogen–brine mixtures by Lv, Qichao, Xue, Jinglei, Li, Xiaochen, Rezaei, Farzaneh, Larestani, Aydin, Norouzi-Apourvari, Saeid, Abdollahi, Hadi, Hemmati-Sarapardeh, Abdolhossein

“…The severity of climate change and global warming necessitates the need for a transition from traditional hydrocarbon-based energy sources to renewable energy…”
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Predicting formation damage of oil fields due to mineral scaling during water-flooding operations: Gradient boosting decision tree and cascade-forward back-propagation network by Larestani, Aydin, Mousavi, Seyed Pezhman, Hadavimoghaddam, Fahimeh, Hemmati-Sarapardeh, Abdolhossein

“…Water-flooding is one of the main options employed by the oil industry to meet the world's ever-increasing demand for oil, as the primary source of energy…”
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Experimental measurement and compositional modeling of bubble point pressure in crude oil systems: Soft computing approaches, correlations, and equations of state by Larestani, Aydin, Hemmati-Sarapardeh, Abdolhossein, Naseri, Ali

“…No one can deny the ever-increasing importance of oil since it has influenced every aspect of humans' life. One of the most important…”
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Modeling of methane adsorption capacity in shale gas formations using white-box supervised machine learning techniques by Nait Amar, Menad, Larestani, Aydin, Lv, Qichao, Zhou, Tongke, Hemmati-Sarapardeh, Abdolhossein

“…Energy demand is increasing worldwide and shale gas formations have gained increasing attention and have become crucial energy sources. Therefore, accurate…”
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Modeling of wax disappearance temperature (WDT) using soft computing approaches: Tree-based models and hybrid models by Amiri-Ramsheh, Behnam, Safaei-Farouji, Majid, Larestani, Aydin, Zabihi, Reza, Hemmati-Sarapardeh, Abdolhossein

“…Solid scales can cause significant problems in oil production and transmission systems such as oil flow rate reduction. Wax is one of the most critical…”
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Experimental measurement and modeling of water-based drilling mud density using adaptive boosting decision tree, support vector machine, and K-nearest neighbors: A case study from the South Pars gas field by Hashemizadeh, Abbas, Maaref, Ahmad, Shateri, Mohammadhadi, Larestani, Aydin, Hemmati-Sarapardeh, Abdolhossein

“…Exact determination of drilling mud weight in order to prevent fracture pressure and at the same time overcoming pore pressure is a key parameter in the…”
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On the evaluation of permeability of heterogeneous carbonate reservoirs using rigorous data-driven techniques by Mahdaviara, Mehdi, Larestani, Aydin, Nait Amar, Menad, Hemmati-Sarapardeh, Abdolhossein

“…This study probes the application of Cascade Forward Neural Network (CFNN), Least Square Support Vector Machine (LSSVM), Multilayer Perceptron (MLP), and…”
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Predicting viscosity of CO2–N2 gaseous mixtures using advanced intelligent schemes by Naghizadeh, Arefeh, Larestani, Aydin, Nait Amar, Menad, Hemmati-Sarapardeh, Abdolhossein

“…Acquiring accurate knowledge about the viscosity of carbon dioxide, nitrogen, and their mixtures as an extremely fundamental thermo-physical property for a…”
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