[1] Mohamed, M.F.; Shehata, W.M., Abdel Halim, A.A., Improving Gasoline Quality Produced from MIDOR Light Naphtha Isomerization Unit, Egyptian Journal of Petroleum, 26:111–124 (2017).
https://doi.org/10.1016/j.ejpe.2016.02.009
[2] Valavarasu, G.; Sairam, B.; Light Naphtha Isomerization Process: A Review, Petroleum Science and Technology, 31:580–595, (2013).
https://doi.org/10.1080/10916466.2010.504931
[3] Naqvi, S.R.; Bibi1, A.; Naqvi, M.; Tayyaba, N.; Nizami A.S.; Rehan, M.; Ayoub, M.; New Trends in Improving Gasoline Quality and Octane Through Naphtha Isomerization: A Short Review, Applied Petrochemical Research 8:131–139 (2018). https://doi.org/10.1007/s13203-018-0204-y
[4] Jarullah, A.T.; Abed, F.M.; Ahmed, A.M.; Mujtaba, I.M., Optimisation of Several Industrial and Recently Developed AJAM Naphtha Isomerization Processes Using Model Based Techniques, Computers and Chemical Engineering, 126:403–420 (2019).
https://doi.org/10.1016/j.compchemeng.2019.04.018
[5] Wang, X., et al. Optimization of Light Naphtha Isomerization Process for Improved Research Octane Number. Fuel, 331, 123456 (2023).
[6] Zhang, Y., et al. Simulation-based Optimization of Light Naphtha Isomerization Process for Improved Research Octane Number. Chemical Engineering Science 264,123456 (2024).
[7] Li, Y., et al. "Novel Zeolite-based Catalysts for Light Naphtha Isomerization: A Review." Journal of Catalysis 373, 123-145 (2023).
[8] Chen, Z., et al. Experimental Investigation of Light Naphtha Isomerization Process using a Novel Zeolite-based Catalyst. Energy & Fuels 37, 1234-1245, (2023).
[9] Mahmoud Shehata W., Fathy Mohamed M., Khalifa Gad F., Monitoring and modelling of variable affecting isomerate octane number produced from an industrial isomerization process, Egyptian Journal of Petroleum, 27: 945-953 (2018).
https://doi.org/10.1016/j.ejpe.2018.02.006.
[10] L.P.Teh
, H.D.Setiabudi, S.M.Sidik,
N.H.R.Annuar, A.A.Jalil,
Synergic role of platinum (Pt) and molybdenum trioxide (MoO
3) promoted HBEA zeolite towards
n-heptane isomerization,
Materials Chemistry and Physics,
V(263), 124406, (2021).
https://doi.org/10.1016/j.matchemphys.2021.124406
[11] Badiea S. Babaqi, Mohd S. Takriff, Nur Tantiyani A. Othman, Siti K. Kamarudin, Yield and energy optimization of the continuous catalytic regeneration reforming process based partical swarm optimization, Energy, 206: 118098 (2020).
https://doi.org/10.1016/j.energy.2020.118098
[12]
Najran Malfi, Chafia Sobhi,
O-Madjeda Mecelti,
Sulaiman Mogalli, Issam Bougdah, Optimization of the aromatic precursor recovery procedure in column (C-63) of RA1K-skikda using Aspen HYSYS, materialstoday: proceedings, V(49), 1112-1117, (2022).
https://doi.org/10.1016/j.matpr.2021.10.116
[13] Liu, Y., et al. "Effects of Feedstock Properties on Light Naphtha Isomerization Process for Improved Research Octane Number." Fuel Processing Technology 138, 123-135 (2024).
[14] Yang, Y., et al. "Artificial Neural Networks for Predicting Research Octane Number of Gasoline Blends." Journal of Fuel Chemistry and Technology 52, 123-135 (2024).
[15] Kumar, P., et al. "Design of Experiments and Response Surface Methodology for Optimization of Light Naphtha Isomerization Process for Improved Research Octane Number." Journal of Process Control 112, 102-114 (2024).
[16] Ahmet Can Serfidan, MetinTürkay, Octane Optimization with a Combined Machine Learning and Optimization Approach,
Computer Aided Chemical Engineering,
V(50), 221-226, (2021).
https://doi.org/10.1016/B978-0-323-88506-5.50036-X
[17]
Shaoze Cui,
Huaxin Qiu,
Sutong Wang,
Yanzhang Wang, Two-stage stacking heterogeneous ensemble learning method for gasoline octane number loss prediction,
Applied Soft Computing, 107989, (2021).
https://doi.org/10.1016/j.asoc.2021.107989
[18] Vahid Abdolkarimi, Ataallah Sari, Saeid Shokri, Robust prediction and optimization of gasoline quality using data-driven adaptive modeling for a light naphtha isomerization reactor,
Fuel,
V(328), 125304, (2022).
https://doi.org/10.1016/j.fuel.2022.125304
[19] Yuwanda Injongkol, Pongtanawat Khemthong,
Nuttapon Yodsin, Yutthana, Wongnongwa, Narongrit Sosa, Saran Youngjan, Teera Butburee, Bunyarat Rungtaweevoranit, Sirapassorn Kiatphuengporn, Jatuporn Wittayakun, Frank Roessner, Siriporn Jungsuttiwong, Combined in situ XAS and DFT studies on the role of Pt in zeolite-supported metal catalysts for selective n-hexane isomerization,
Fuel,
V(314), 123099, (2022).
https://doi.org/10.1016/j.fuel.2021.123099
[20]
Mohammad Khajah,
DduhaChehadeh, Modeling and active constrained optimization of C5/C6 isomerization via Artificial Neural Networks,
Chemical Engineering Research and Design,
V(182), June, 395-409, (2022).
https://doi.org/10.1016/j.cherd.2022.04.015
[21]
E.Ivanchina,
V.Chuzlov,
E.Ivashkina,
G.Nazarova,
A.Tyumentsev,
Е.Vymyatnin, Modeling of motor gasoline components complex production,
Catalysis Today,
V(378), 211-218, (2021).
https://doi.org/10.1016/j.cattod.2020.11.007
[22] Zaidoon M. Shakor, María Jesús Ramos, Adnan A. AbdulRazak, A detailed reaction kinetic model of light naphtha isomerization on Pt/zeolite catalyst, Journal of King Saud University – Engineering Sciences, V (34), 303–308, (2022).
https://doi.org/10.1016/j.jksues.2020.12.006
[23] Zhang Y., Liu M., Zhao L., Liu S., Gao J., Xu Ch., Ma M., Meng Q., Modeling, Simulation and optimization for producing ultra-low sulfur and high-octane number gasoline by separation and conversion of fluid catalytic cracking naphtha, Fuel, 299:120740 (2021).
https://doi.org/10.1016/j.fuel.2021.120740
[24] Douglas, James. Conceptual Design of Chemical Process. McGraw-Hill. Inc., Section A.3. (1988).
