Optimization and modeling of sulfur removal from liquid fuel using carbon-based adsorbents through synergistic application of RSM and machine learning
Abstract This research investigates the adsorption desulfurization of liquid fuels using artificial neural networks (ANN) and response surface methodology (RSM) approaches. The effectiveness of sulfur removal was predicted by analyzing five important factors: temperature, concentration, surface area...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-88434-1 |
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| Summary: | Abstract This research investigates the adsorption desulfurization of liquid fuels using artificial neural networks (ANN) and response surface methodology (RSM) approaches. The effectiveness of sulfur removal was predicted by analyzing five important factors: temperature, concentration, surface area, fuel/adsorbent, and time. We employed radial basis function (RBF) and multilayer perceptron (MLP) algorithms for ANN modeling. The optimal MLP configuration, utilizing the Levenberg–Marquardt (Trainlm) algorithm, consisted of three hidden layers with 20, 17, and 9 neurons, respectively, while the optimal RBF network contained 43 neurons. The MLP network’s determination coefficient (R2) was 0.98 over 30 epochs, and its mean squared error (MSE) was 0.0028. The RBF network also obtained an R2 of 0.98 and an MSE of 0.0026 over 40 epochs. A two-factor interaction design served as the basis for the RSM model, which produced an R2 of 0.91. A comparison of the RSM, MLP, and RBF models, using the average absolute relative deviation, indicated that the ANN models, particularly the RBF model, produced more accurate predictions than the RSM model. The findings show that temperature and concentration were the two most significant factors influencing sulfur removal efficiency. Overall, artificial neural networks outperformed the RSM approach in predicting desulfurization performance, providing a more reliable modeling tool for optimizing the sulfur removal process. |
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| ISSN: | 2045-2322 |