Prediction of Fischer-Tropsch synthesis products distribution in the presence of Ni/HZSM-5 catalyst using neural/fuzzy networks based on hybrid of genetic algorithm and swarm of particles

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ESFANDYARI M.; MOSAYEBI A.; ABEDINI R.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

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Journal: Journal of Applied Research in Chemistry Year:2020 | Volume:13 | Issue:4 Page(s): 113-125

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Title

Prediction of Fischer-Tropsch synthesis products distribution in the presence of Ni/HZSM-5 catalyst using neural/fuzzy networks based on hybrid of genetic algorithm and swarm of particles

Author(s)

ESFANDYARI M. | MOSAYEBI A. | ABEDINI R. | Issue Writer Certificate

Keywords

Fischer-Tropsch synthesisQ1

Ni/HZSM-5 catalystQ1

Neuro-FuzzyQ1

Genetic AlgorithmQ2

Particle Swarm AlgorithmQ1

Abstract

In this study, Ni/HZSM-5 nano-structure catalysts were synthesized through reverse microemulsion method. The main advantages of this synthesis method compared to other typical methods are better control on particle size distribution, favorable dispersion, surface area, and reducibility. The experiments of Fischer-Tropsch synthesis in the presence of Ni/HZSM-5 catalyst were conducted under operating conditions (i. e., temperature 493-513 K, pressure 15-25 bar, and gas hourly space velocity 900-2300 1/h). The purpose of training the adaptive Neuro-Fuzzy network is to find the size of the weights and biases in such a way as to minimize the error of the training data. To optimize the Neuro-Fuzzy model, Genetic Algorithm and Particle Swarm optimization were used to predict the product distribution of Fischer-Tropsch synthesis products using ANFIS, GA-ANFIS and PSO-ANFIS networks. For modeling, 17 experimental data were used, of which 80% were for training and the rest for model validation. All presented models have a correlation coefficient (R2) higher than 0. 97 which indicates the accuracy of modeling. Regarding the correlation coefficient and ARE, AARE and SD errors are the best networks for modeling the desired process of PSO-ANFIS.

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APA: Copy

ESFANDYARI, M., MOSAYEBI, A., & ABEDINI, R.. (2020). Prediction of Fischer-Tropsch synthesis products distribution in the presence of Ni/HZSM-5 catalyst using neural/fuzzy networks based on hybrid of genetic algorithm and swarm of particles. JOURNAL OF APPLIED RESEARCHES IN CHEMISTRY (JARC), 13(4 ), 113-125. SID. https://sid.ir/paper/180160/en

Vancouver: Copy

ESFANDYARI M., MOSAYEBI A., ABEDINI R.. Prediction of Fischer-Tropsch synthesis products distribution in the presence of Ni/HZSM-5 catalyst using neural/fuzzy networks based on hybrid of genetic algorithm and swarm of particles. JOURNAL OF APPLIED RESEARCHES IN CHEMISTRY (JARC)[Internet]. 2020;13(4 ):113-125. Available from: https://sid.ir/paper/180160/en

IEEE: Copy

M. ESFANDYARI, A. MOSAYEBI, and R. ABEDINI, “Prediction of Fischer-Tropsch synthesis products distribution in the presence of Ni/HZSM-5 catalyst using neural/fuzzy networks based on hybrid of genetic algorithm and swarm of particles,” JOURNAL OF APPLIED RESEARCHES IN CHEMISTRY (JARC), vol. 13, no. 4 , pp. 113–125, 2020, [Online]. Available: https://sid.ir/paper/180160/en

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