| 1 |
Operation parameters effect on yield and octane number for monometallic, bimetallic and trimetallic catalysts in naphtha reforming process
Talaghat MR, Karimi MS
Energy Sources Part A-recovery Utilization and Environmental Effects, 42(2), 176, 2020 |
| 2 |
An investigative study on replacing the conventional furnaces of naphtha reforming with chemical looping combustion for clean hydrogen production
Ebrahimian S, Iranshahi D
International Journal of Hydrogen Energy, 45(38), 19405, 2020 |
| 3 |
Effect of the indium precursor nature on Pt/Al2O3In-Cl reforming catalysts
Tregubenko VY, Veretelnikov KV, Vinichenko NV, Gulyaeva TI, Muromtsev IV, Belyi AS
Catalysis Today, 329, 102, 2019 |
| 4 |
Optimization of a novel multifunctional reactor containing m-xylene hydrodealkylation and naphtha reforming
Shakeri M, Iranshahi D, Naderifar A
International Journal of Hydrogen Energy, 44(39), 21882, 2019 |
| 5 |
Thermal Integration of Sulfuric Acid and Continuous Catalyst Regeneration of Naphtha Reforming Plants
Iranshahi D, Hamedi N, Nategh M, Saeedi R, Saeidi S
Chemical Engineering & Technology, 41(3), 637, 2018 |
| 6 |
Enhanced BTX Production in Refineries withSulfur Dioxide Oxidation by Thermal Integrated Model
Karimi M, Rahimpour MR, Iranshahi D
Chemical Engineering & Technology, 41(9), 1746, 2018 |
| 7 |
Dimensions and Analysis of Uncertainty in Industrial Modeling Process
Ahmad I, Kano M, Hasebe S
Journal of Chemical Engineering of Japan, 51(7), 533, 2018 |
| 8 |
Multi-objective optimization of thermally coupled reactor of CCR naphtha reforming in presence of SO2 oxidation to boost the gasoline octane number and hydrogen
Saeedi R, Iranshahi D
Fuel, 206, 580, 2017 |
| 9 |
Hydrogen and aromatic production by means of a novel membrane integrated cross flow CCR naphtha reforming process
Saeedi R, Iranshahi D
International Journal of Hydrogen Energy, 42(12), 7957, 2017 |
| 10 |
A hybrid statistical approach for modeling and optimization of RON: A comparative study and combined application of response surface methodology (RSM) and artificial neural network (ANN) based on design of experiment (DOE)
Elfghi FM
Chemical Engineering Research & Design, 113, 264, 2016 |
