Aydin Larestani

514 total citations
17 papers, 377 citations indexed

About

Aydin Larestani is a scholar working on Mechanics of Materials, Ocean Engineering and Mechanical Engineering. According to data from OpenAlex, Aydin Larestani has authored 17 papers receiving a total of 377 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Mechanics of Materials, 8 papers in Ocean Engineering and 7 papers in Mechanical Engineering. Recurrent topics in Aydin Larestani's work include Hydrocarbon exploration and reservoir analysis (8 papers), Petroleum Processing and Analysis (5 papers) and Enhanced Oil Recovery Techniques (5 papers). Aydin Larestani is often cited by papers focused on Hydrocarbon exploration and reservoir analysis (8 papers), Petroleum Processing and Analysis (5 papers) and Enhanced Oil Recovery Techniques (5 papers). Aydin Larestani collaborates with scholars based in Iran, China and Russia. Aydin Larestani's co-authors include Abdolhossein Hemmati‐Sarapardeh, Menad Nait Amar, Fahimeh Hadavimoghaddam, Qichao Lv, Tongke Zhou, Majid Safaei-Farouji, Mehdi Mahdaviara, Masoud Riazi, Ali Naseri and Junjian Li and has published in prestigious journals such as Scientific Reports, Separation and Purification Technology and Journal of Petroleum Science and Engineering.

In The Last Decade

Aydin Larestani

16 papers receiving 374 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Aydin Larestani Iran 11 173 151 131 89 70 17 377
Noureddine Zeraibi Algeria 10 236 1.4× 87 0.6× 180 1.4× 50 0.6× 74 1.1× 15 416
Nait Amar Menad Algeria 8 176 1.0× 86 0.6× 156 1.2× 49 0.6× 92 1.3× 8 402
Majid Safaei-Farouji China 11 219 1.3× 230 1.5× 175 1.3× 48 0.5× 46 0.7× 29 478
Mahdi Abdi-Khanghah Iran 10 76 0.4× 83 0.5× 108 0.8× 88 1.0× 112 1.6× 18 340
Mehdi Mahdaviara Iran 10 200 1.2× 146 1.0× 153 1.2× 46 0.5× 48 0.7× 17 331
Hamidreza Yarveicy Iran 10 221 1.3× 170 1.1× 242 1.8× 56 0.6× 86 1.2× 11 499
Ashkan Jahanbani Ghahfarokhi Norway 14 363 2.1× 128 0.8× 321 2.5× 47 0.5× 65 0.9× 41 600
Hamid Reza Nasriani United Kingdom 13 344 2.0× 163 1.1× 259 2.0× 66 0.7× 66 0.9× 47 466
Amir Fayazi Canada 10 155 0.9× 109 0.7× 114 0.9× 88 1.0× 115 1.6× 17 340
Hossein Safari Iran 13 191 1.1× 117 0.8× 159 1.2× 76 0.9× 96 1.4× 18 448

Countries citing papers authored by Aydin Larestani

Since Specialization
Citations

This map shows the geographic impact of Aydin Larestani's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aydin Larestani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aydin Larestani more than expected).

Fields of papers citing papers by Aydin Larestani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aydin Larestani. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aydin Larestani. The network helps show where Aydin Larestani may publish in the future.

Co-authorship network of co-authors of Aydin Larestani

This figure shows the co-authorship network connecting the top 25 collaborators of Aydin Larestani. A scholar is included among the top collaborators of Aydin Larestani based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Aydin Larestani. Aydin Larestani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Larestani, Aydin, et al.. (2025). Leveraging advanced ensemble learning techniques for methane uptake prediction in metal organic frameworks. Scientific Reports. 15(1). 31832–31832.
2.
Larestani, Aydin, Saeid Atashrouz, Ali Abedi, et al.. (2025). Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state. Results in Engineering. 25. 104035–104035. 6 indexed citations
3.
Larestani, Aydin, et al.. (2025). Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state. Scientific Reports. 15(1). 8428–8428. 2 indexed citations
4.
Lv, Qichao, Xiaochen Li, Farzaneh Rezaei, et al.. (2024). White-box machine-learning models for accurate interfacial tension prediction in hydrogen–brine mixtures. Clean Energy. 8(5). 252–264. 12 indexed citations
5.
Larestani, Aydin, et al.. (2024). Toward reliable prediction of CO2 uptake capacity of metal–organic frameworks (MOFs): implementation of white-box machine learning. Adsorption. 30(8). 1985–2003. 7 indexed citations
6.
Larestani, Aydin, et al.. (2024). Predictive modeling of CO2 solubility in piperazine aqueous solutions using boosting algorithms for carbon capture goals. Scientific Reports. 14(1). 22112–22112. 12 indexed citations
7.
Liu, Bo, Aydin Larestani, Kouqi Liu, et al.. (2024). Micromechanical variation of organic matter (kerogen type I) under controlled thermal maturity progression. Journal of Rock Mechanics and Geotechnical Engineering. 17(3). 1387–1398. 4 indexed citations
8.
Lv, Qichao, Aydin Larestani, Fahimeh Hadavimoghaddam, et al.. (2023). 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. Separation and Purification Technology. 310. 123086–123086. 42 indexed citations
9.
10.
Larestani, Aydin, Abdolhossein Hemmati‐Sarapardeh, & Ali Naseri. (2022). Experimental measurement and compositional modeling of bubble point pressure in crude oil systems: Soft computing approaches, correlations, and equations of state. Journal of Petroleum Science and Engineering. 212. 110271–110271. 14 indexed citations
11.
Larestani, Aydin, et al.. (2022). Predicting the surfactant-polymer flooding performance in chemical enhanced oil recovery: Cascade neural network and gradient boosting decision tree. Alexandria Engineering Journal. 61(10). 7715–7731. 26 indexed citations
12.
Safaei-Farouji, Majid, et al.. (2021). Modeling of wax disappearance temperature (WDT) using soft computing approaches: Tree-based models and hybrid models. Journal of Petroleum Science and Engineering. 208. 109774–109774. 34 indexed citations
13.
Mahdaviara, Mehdi, Aydin Larestani, Menad Nait Amar, & Abdolhossein Hemmati‐Sarapardeh. (2021). On the evaluation of permeability of heterogeneous carbonate reservoirs using rigorous data-driven techniques. Journal of Petroleum Science and Engineering. 208. 109685–109685. 28 indexed citations
14.
15.
Larestani, Aydin, et al.. (2021). Predicting viscosity of CO2–N2 gaseous mixtures using advanced intelligent schemes. Journal of Petroleum Science and Engineering. 208. 109359–109359. 49 indexed citations
16.
Amar, Menad Nait, Aydin Larestani, Qichao Lv, Tongke Zhou, & Abdolhossein Hemmati‐Sarapardeh. (2021). Modeling of methane adsorption capacity in shale gas formations using white-box supervised machine learning techniques. Journal of Petroleum Science and Engineering. 208. 109226–109226. 52 indexed citations
17.
Larestani, Aydin, et al.. (2021). Predicting formation damage of oil fields due to mineral scaling during water-flooding operations: Gradient boosting decision tree and cascade-forward back-propagation network. Journal of Petroleum Science and Engineering. 208. 109315–109315. 47 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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