A Robust Method to Predict Equilibrium and Kinetics of Sulfur and Nitrogen Compounds Adsorption from Liquid Fuel on Mesoporous Material
This study presents a robust and rigorous method based on intelligent models, namely radial basis function networks optimized by particle swarm optimization (PSO-RBF), multilayer perceptron neural networks (MLP-NNs), and adaptive neuro-fuzzy inference system optimized by particle swarm optimization...
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| Format: | Article |
| Language: | English |
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Petroleum University of Technology
2020-04-01
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| Series: | Iranian Journal of Oil & Gas Science and Technology |
| Subjects: | |
| Online Access: | http://ijogst.put.ac.ir/article_109826_12c8184631716adaef9d16b763ba3d5f.pdf |
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| author | Mohammadreza Khosravi-Nikou Ahmad Shariati Mohammad Mohammadian Ali Barati Adel Najafi-Marghmaleki |
| author_facet | Mohammadreza Khosravi-Nikou Ahmad Shariati Mohammad Mohammadian Ali Barati Adel Najafi-Marghmaleki |
| author_sort | Mohammadreza Khosravi-Nikou |
| collection | DOAJ |
| description | This study presents a robust and rigorous method based on intelligent models, namely radial basis function networks optimized by particle swarm optimization (PSO-RBF), multilayer perceptron neural networks (MLP-NNs), and adaptive neuro-fuzzy inference system optimized by particle swarm optimization methods (PSO-ANFIS), for predicting the equilibrium and kinetics of the adsorption of sulfur and nitrogen containing compounds from a liquid hydrocarbon model fuel on mesoporous materials. All the models were evaluated by the statistical and graphical methods. The predictions of the models were also compared with different kinetics and equilibrium models. The results showed that although all the models lead to accurate results, the PSO-ANFIS model represented the most reliable and dependable predictions with the correlation coefficient (R2) of 0.99992 and average absolute relative deviation (AARD) of 0.039%. The developed models are also able to predict the experimental data with better precision and reliability compared to literature models. |
| first_indexed | 2024-12-22T16:31:16Z |
| format | Article |
| id | doaj.art-0f6cf63d5c884920825320aaf8bb25ea |
| institution | Directory Open Access Journal |
| issn | 2345-2412 2345-2420 |
| language | English |
| last_indexed | 2024-12-22T16:31:16Z |
| publishDate | 2020-04-01 |
| publisher | Petroleum University of Technology |
| record_format | Article |
| series | Iranian Journal of Oil & Gas Science and Technology |
| spelling | doaj.art-0f6cf63d5c884920825320aaf8bb25ea2022-12-21T18:20:02ZengPetroleum University of TechnologyIranian Journal of Oil & Gas Science and Technology2345-24122345-24202020-04-01929311810.22050/ijogst.2019.147638.1477109826A Robust Method to Predict Equilibrium and Kinetics of Sulfur and Nitrogen Compounds Adsorption from Liquid Fuel on Mesoporous MaterialMohammadreza Khosravi-Nikou0Ahmad Shariati1Mohammad Mohammadian2Ali Barati3Adel Najafi-Marghmaleki4Associate Professor, Department of Gas Engineering, Petroleum University of Technology, Ahwaz, IranAssociate Professor, Department of Gas Engineering, Petroleum University of Technology, Ahwaz, IranM.S. Student, Department of Gas Engineering, Petroleum University of Technology, Ahwaz, IranM.S. Student, Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, IranM.S. Student, Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, IranThis study presents a robust and rigorous method based on intelligent models, namely radial basis function networks optimized by particle swarm optimization (PSO-RBF), multilayer perceptron neural networks (MLP-NNs), and adaptive neuro-fuzzy inference system optimized by particle swarm optimization methods (PSO-ANFIS), for predicting the equilibrium and kinetics of the adsorption of sulfur and nitrogen containing compounds from a liquid hydrocarbon model fuel on mesoporous materials. All the models were evaluated by the statistical and graphical methods. The predictions of the models were also compared with different kinetics and equilibrium models. The results showed that although all the models lead to accurate results, the PSO-ANFIS model represented the most reliable and dependable predictions with the correlation coefficient (R2) of 0.99992 and average absolute relative deviation (AARD) of 0.039%. The developed models are also able to predict the experimental data with better precision and reliability compared to literature models.http://ijogst.put.ac.ir/article_109826_12c8184631716adaef9d16b763ba3d5f.pdfadsorptiondenitrogenationdesulfurizationequilibrium and kinetics modelpso-anfis |
| spellingShingle | Mohammadreza Khosravi-Nikou Ahmad Shariati Mohammad Mohammadian Ali Barati Adel Najafi-Marghmaleki A Robust Method to Predict Equilibrium and Kinetics of Sulfur and Nitrogen Compounds Adsorption from Liquid Fuel on Mesoporous Material Iranian Journal of Oil & Gas Science and Technology adsorption denitrogenation desulfurization equilibrium and kinetics model pso-anfis |
| title | A Robust Method to Predict Equilibrium and Kinetics of Sulfur and Nitrogen Compounds Adsorption from Liquid Fuel on Mesoporous Material |
| title_full | A Robust Method to Predict Equilibrium and Kinetics of Sulfur and Nitrogen Compounds Adsorption from Liquid Fuel on Mesoporous Material |
| title_fullStr | A Robust Method to Predict Equilibrium and Kinetics of Sulfur and Nitrogen Compounds Adsorption from Liquid Fuel on Mesoporous Material |
| title_full_unstemmed | A Robust Method to Predict Equilibrium and Kinetics of Sulfur and Nitrogen Compounds Adsorption from Liquid Fuel on Mesoporous Material |
| title_short | A Robust Method to Predict Equilibrium and Kinetics of Sulfur and Nitrogen Compounds Adsorption from Liquid Fuel on Mesoporous Material |
| title_sort | robust method to predict equilibrium and kinetics of sulfur and nitrogen compounds adsorption from liquid fuel on mesoporous material |
| topic | adsorption denitrogenation desulfurization equilibrium and kinetics model pso-anfis |
| url | http://ijogst.put.ac.ir/article_109826_12c8184631716adaef9d16b763ba3d5f.pdf |
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