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Sepehr Sadighi
Research Institute of Petroleum Industry (RIPI), Catalysis and Nanotechnology Research Division, West Blvd., Azadi Sport complex, P.O. Box 14665137, Tehran
Chemical Engineering, Chemistry & Bioengineering Chemistry

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Effect of Lanthanum as a Promoter on Fe-Co/SiO2 Catalyst for Fischer-Tropsch Synthesis Ali Abbasi; Mohamadreza Ghasemi; Sepehr Sadighi
Bulletin of Chemical Reaction Engineering & Catalysis 2014: BCREC Volume 9 Issue 1 Year 2014 (April 2014)
Publisher : Masyarakat Katalis Indonesia - Indonesian Catalyst Society (MKICS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9767/bcrec.9.1.5142.23-27

Abstract

Iron-Cobalt catalyst is well known from both operational and economical aspects for Fischer-Tropsch synthesis. Effort to increase the efficiency of this kind of catalyst is an important research topic. In this work, the effect of lanthanum on characteristic behavior, conversion and selectivity of a Fe-Co/SiO2 Fischer-Tropsch catalyst was studied. The Fe-Co-La/SiO2 Catalysts were prepared using an incipient wetness impregnation method. These catalysts were then characterized by XRF-EDAX, BET and TPR techniques, and their performance were evaluated in a lab-scale reactor at 250ºC, H2/CO = 1.8 of molar ratio, 16 barg pressure and GHSV=600 h-1. TPR analysis showed that the addition of La lowered the reduction temperature of Fe-Co catalyst, and due to a lower temperature, the sintering of the catalyst can be mitigated. Furthermore, from the micro reactor tests (about 4 days), it was found that lanthanum promoted catalyst had higher selectivity toward hydrocarbons, and lower selectivity toward CO2. © 2014 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0)
Comparison of Kinetic-based and Artificial Neural Network Modeling Methods for a Pilot Scale Vacuum Gas Oil Hydrocracking Reactor Sepehr Sadighi; Gholam Reza Zahedi
Bulletin of Chemical Reaction Engineering & Catalysis 2013: BCREC Volume 8 Issue 2 Year 2013 (December 2013)
Publisher : Masyarakat Katalis Indonesia - Indonesian Catalyst Society (MKICS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9767/bcrec.8.2.4722.125-136

Abstract

An artificial neural network (ANN) and kinetic-based models for a pilot scale vacuum gas oil (VGO) hydrocracking plant are presented in this paper. Reported experimental data in the literature were used to develop, train, and check these models. The proposed models are capable of predicting the yield of all main hydrocracking products including dry gas, light naphtha, heavy naphtha, kerosene, diesel, and unconverted VGO (residue). Results showed that kinetic-based and artificial neural models have specific capabilities to predict yield of hydrocracking products. The former is able to accurately predict the yield of lighter products, i.e. light naphtha, heavy naphtha and kerosene. However, ANN model is capable of predicting yields of diesel and residue with higher precision. The comparison shows that the ANN model is superior to the kinetic-base models. © 2013 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0)
Co-Authors Ali Abbasi Gholam Reza Zahedi Mohamadreza Ghasemi