Alireza Baghban M.Sc (Petroleum University of Technology); B.S (University of Tehran); PhD (Amirkabir University of Technology)
University of Tehran, Tehran, Tehran · Iran
Editorial leadership for Journal of Polymer Science Research
Research interests
- Chemical Engineering
- Chemistry
- Computational Methods
- Artificial Intelligence
- Optimization
- Petroleum Engineering
- Statistical Analysis
Biography
Dr. Alireza Baghban, MSc, PhD, is affiliated with the University of Tehran, Tehran, Iran, where his research focuses on computational modeling and machine learning applications in petroleum engineering and energy systems. He earned his degrees from the Petroleum University of Technology, the University of Tehran, and Amirkabir University of Technology. With over 37 indexed publications, his work encompasses predictive modeling of thermophysical properties, phase behavior, and process optimization in oil and gas operations, including viscosity estimation for bitumen mixtures, asphaltene precipitation, gas hydrate formation, wax deposition, CO2 capture and separation, and heat transfer in nanofluids. His most-cited publication, "Modeling of cetane number of biodiesel from fatty acid methyl ester (FAME) information using GA-, PSO-, and HGAPSO- LSSVM models" (2020), has received 121 citations, reflecting his contributions to sustainable fuel characterization and advanced algorithmic approaches in energy research.
Selected publications
- Modeling of cetane number of biodiesel from fatty acid methyl ester (FAME) information using GA-, PSO-, and HGAPSO- LSSVM models 2020 cited 121×
- Estimation of adsorption capacity of CO2, CH4, and their binary mixtures in Quidam shale using LSSVM: Application in CO2 enhanced shale gas recovery and CO2 storage 2020 cited 90×
- Experimental, kinetic, and thermodynamic studies of adsorptive desulfurization and denitrogenation of model fuels using novel mesoporous materials 2019 cited 90×
- An insight into the estimation of fatty acid methyl ester based biodiesel properties using a LSSVM model 2019 cited 84×
- Rigorous prognostication of permeability of heterogeneous carbonate oil reservoirs: Smart modeling and correlation development 2019 cited 62×
- Insights into the estimation of capacitance for carbon-based supercapacitors 2021 cited 58×
Ranked by citation impact (Crossref) where available, newest otherwise · verified via ORCID.
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This journal is guided by Alireza Baghban (University of Tehran, Tehran, Tehran) and a peer-review board of practising researchers. Open access, author-retained copyright (CC BY), and a clear editorial process.