Accurate modeling of equilibrium adsorption of liquid phase sulfur compounds using two soft computing approaches
This report introduces the application of two advanced intelligent models, an adaptively trained neuro-fuzzy inference logic in a hybrid configuration (Hybrid-ANFIS) and multilayer perceptron neural network (MLP-NN) to accurately determine the equilibrium sulfur adsorption in the liquid phase of hyd...
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Elsevier
2025-05-01
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| Series: | Results in Chemistry |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211715625002292 |
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| author | Armin Mohebbi Maryam Ahmadi-Pour Milad Mohebbi |
| author_facet | Armin Mohebbi Maryam Ahmadi-Pour Milad Mohebbi |
| author_sort | Armin Mohebbi |
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| description | This report introduces the application of two advanced intelligent models, an adaptively trained neuro-fuzzy inference logic in a hybrid configuration (Hybrid-ANFIS) and multilayer perceptron neural network (MLP-NN) to accurately determine the equilibrium sulfur adsorption in the liquid phase of hydrocarbon/ sulfur compound solution. Models were meticulously developed using a dataset of 107 empirical observations of seven types of sulfur compounds. These models incorporate the influence of input parameters, including initial sulfur level, adsorbent weight, molecular weights of the solvent and solute, densities of the solvent and solute, adsorbent particle diameter, temperature, and the Si/Al ratio of the adsorbent. Notably, the equilibrium sulfur adsorption amount was considered as the sole output variable. To evaluate the performance and precision of the implemented models, graphical representations and quantitative analyses were employed. Moreover, an assessment between the results of implemented models of the existing study and outcomes of previous reports were conducted. The results indicate that both developed models provide precise predictions. However, the Hybrid-ANFIS model demonstrates a strong correlation in predicting the adsorption empirical data, with an average absolute relative deviation of 0.36 % and an overall R2 value and 0.9997. In addition, superiority of the Hybrid-ANFIS model in providing the most reliable and accurate predictions of adsorption experimental data among all types of implemented models was concluded. This study sets a new benchmark in adsorption modeling by providing the most accurate and generalizable predictive framework to date. |
| format | Article |
| id | doaj-art-006d3c92b3cc456a9cdcc4aca1b49690 |
| institution | DOAJ |
| issn | 2211-7156 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
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| series | Results in Chemistry |
| spelling | doaj-art-006d3c92b3cc456a9cdcc4aca1b496902025-08-20T03:10:42ZengElsevierResults in Chemistry2211-71562025-05-011510224610.1016/j.rechem.2025.102246Accurate modeling of equilibrium adsorption of liquid phase sulfur compounds using two soft computing approachesArmin Mohebbi0Maryam Ahmadi-Pour1Milad Mohebbi2Chemical Engineering Department, Amirkabir University of Technology, Tehran, Iran; Corresponding author.Department of Gas Engineering, Ahwaz Faculty of Petroleum, Petroleum University of Technology, Ahwaz, IranYoung Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, IranThis report introduces the application of two advanced intelligent models, an adaptively trained neuro-fuzzy inference logic in a hybrid configuration (Hybrid-ANFIS) and multilayer perceptron neural network (MLP-NN) to accurately determine the equilibrium sulfur adsorption in the liquid phase of hydrocarbon/ sulfur compound solution. Models were meticulously developed using a dataset of 107 empirical observations of seven types of sulfur compounds. These models incorporate the influence of input parameters, including initial sulfur level, adsorbent weight, molecular weights of the solvent and solute, densities of the solvent and solute, adsorbent particle diameter, temperature, and the Si/Al ratio of the adsorbent. Notably, the equilibrium sulfur adsorption amount was considered as the sole output variable. To evaluate the performance and precision of the implemented models, graphical representations and quantitative analyses were employed. Moreover, an assessment between the results of implemented models of the existing study and outcomes of previous reports were conducted. The results indicate that both developed models provide precise predictions. However, the Hybrid-ANFIS model demonstrates a strong correlation in predicting the adsorption empirical data, with an average absolute relative deviation of 0.36 % and an overall R2 value and 0.9997. In addition, superiority of the Hybrid-ANFIS model in providing the most reliable and accurate predictions of adsorption experimental data among all types of implemented models was concluded. This study sets a new benchmark in adsorption modeling by providing the most accurate and generalizable predictive framework to date.http://www.sciencedirect.com/science/article/pii/S2211715625002292Sulfur adsorptionPredictionMLP-NNHybrid-ANFISModel |
| spellingShingle | Armin Mohebbi Maryam Ahmadi-Pour Milad Mohebbi Accurate modeling of equilibrium adsorption of liquid phase sulfur compounds using two soft computing approaches Results in Chemistry Sulfur adsorption Prediction MLP-NN Hybrid-ANFIS Model |
| title | Accurate modeling of equilibrium adsorption of liquid phase sulfur compounds using two soft computing approaches |
| title_full | Accurate modeling of equilibrium adsorption of liquid phase sulfur compounds using two soft computing approaches |
| title_fullStr | Accurate modeling of equilibrium adsorption of liquid phase sulfur compounds using two soft computing approaches |
| title_full_unstemmed | Accurate modeling of equilibrium adsorption of liquid phase sulfur compounds using two soft computing approaches |
| title_short | Accurate modeling of equilibrium adsorption of liquid phase sulfur compounds using two soft computing approaches |
| title_sort | accurate modeling of equilibrium adsorption of liquid phase sulfur compounds using two soft computing approaches |
| topic | Sulfur adsorption Prediction MLP-NN Hybrid-ANFIS Model |
| url | http://www.sciencedirect.com/science/article/pii/S2211715625002292 |
| work_keys_str_mv | AT arminmohebbi accuratemodelingofequilibriumadsorptionofliquidphasesulfurcompoundsusingtwosoftcomputingapproaches AT maryamahmadipour accuratemodelingofequilibriumadsorptionofliquidphasesulfurcompoundsusingtwosoftcomputingapproaches AT miladmohebbi accuratemodelingofequilibriumadsorptionofliquidphasesulfurcompoundsusingtwosoftcomputingapproaches |