An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach
Abstract This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87274-3 |
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| author | Sheetal Kumari Smriti Agarwal Manish Kumar Pinki Sharma Ajay Kumar Abeer Hashem Nouf H. Alotaibi Elsayed Fathi Abd-Allah Manoj Chandra Garg |
| author_facet | Sheetal Kumari Smriti Agarwal Manish Kumar Pinki Sharma Ajay Kumar Abeer Hashem Nouf H. Alotaibi Elsayed Fathi Abd-Allah Manoj Chandra Garg |
| author_sort | Sheetal Kumari |
| collection | DOAJ |
| description | Abstract This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface methodology (RSM). To evaluate the adsorbent’s potential, assessments were conducted using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The evaluation of RSM, ANN, and ANFIS included the quantification of R2, mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE) metrics. The regression coefficients from the process modelling demonstrated that RSM (R2 = 0.9216), ANN (R2 = 0.8864), and ANFIS (R2 = 0.9589) all accurately predicted MB adsorptive removal. However, comparative statistical analysis revealed that the ANFIS model exhibited superior accuracy in data-based predictions compared to ANN and RSM models. The ideal pH for MB adsorption utilizing OSSB was established as 7. Additionally, favourable outcomes were obtained with 60-minute contact durations, 20 mg adsorbent quantities, and temperatures of 30 °C. The pseudo 2nd -order kinetic model for MB adsorption by OSSB was confirmed. The equilibrium data exhibited a superior fit with the Langmuir isotherm model in comparison to the Freundlich model. The thermodynamic adsorption parameters, including (∆G = -9.1489 kJ/mol), enthalpy change (∆H = -1457.2 kJ/mol), and entropy change (∆S = -19.03 J mol−1 K−1) indicated that the adsorption of MB onto the OSSB surface is exothermic and spontaneous under the experimental conditions. This research effectively showcased the potential of RSM, ANN, and ANFIS in simulating dye removal using OSSB. The generated parameter data proved valuable for the design and control of the adsorption process. |
| format | Article |
| id | doaj-art-f91bd458e4a84132ab418a12910cbd07 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-f91bd458e4a84132ab418a12910cbd072025-01-26T12:26:15ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-025-87274-3An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approachSheetal Kumari0Smriti Agarwal1Manish Kumar2Pinki Sharma3Ajay Kumar4Abeer Hashem5Nouf H. Alotaibi6Elsayed Fathi Abd-Allah7Manoj Chandra Garg8Amity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh (AUUP)Motilal Nehru National Institute of Technology AllahabadAmity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh (AUUP)Indian Institute of Technology RoorkeeAmity Institute of Biotechnology, Amity UniversityBotany and Microbiology Department, College of Science, King Saud UniversityChemistry Department, College of Science, King Saud UniversityPlant Production Department, College of Food and Agricultural Sciences, King Saud UniversityAmity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh (AUUP)Abstract This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface methodology (RSM). To evaluate the adsorbent’s potential, assessments were conducted using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The evaluation of RSM, ANN, and ANFIS included the quantification of R2, mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE) metrics. The regression coefficients from the process modelling demonstrated that RSM (R2 = 0.9216), ANN (R2 = 0.8864), and ANFIS (R2 = 0.9589) all accurately predicted MB adsorptive removal. However, comparative statistical analysis revealed that the ANFIS model exhibited superior accuracy in data-based predictions compared to ANN and RSM models. The ideal pH for MB adsorption utilizing OSSB was established as 7. Additionally, favourable outcomes were obtained with 60-minute contact durations, 20 mg adsorbent quantities, and temperatures of 30 °C. The pseudo 2nd -order kinetic model for MB adsorption by OSSB was confirmed. The equilibrium data exhibited a superior fit with the Langmuir isotherm model in comparison to the Freundlich model. The thermodynamic adsorption parameters, including (∆G = -9.1489 kJ/mol), enthalpy change (∆H = -1457.2 kJ/mol), and entropy change (∆S = -19.03 J mol−1 K−1) indicated that the adsorption of MB onto the OSSB surface is exothermic and spontaneous under the experimental conditions. This research effectively showcased the potential of RSM, ANN, and ANFIS in simulating dye removal using OSSB. The generated parameter data proved valuable for the design and control of the adsorption process.https://doi.org/10.1038/s41598-025-87274-3Artificial neural networkANFISAdsorptionMethylene blueOryza sativa straw biomass |
| spellingShingle | Sheetal Kumari Smriti Agarwal Manish Kumar Pinki Sharma Ajay Kumar Abeer Hashem Nouf H. Alotaibi Elsayed Fathi Abd-Allah Manoj Chandra Garg An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach Scientific Reports Artificial neural network ANFIS Adsorption Methylene blue Oryza sativa straw biomass |
| title | An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach |
| title_full | An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach |
| title_fullStr | An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach |
| title_full_unstemmed | An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach |
| title_short | An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach |
| title_sort | exploration of rsm ann and anfis models for methylene blue dye adsorption using oryza sativa straw biomass a comparative approach |
| topic | Artificial neural network ANFIS Adsorption Methylene blue Oryza sativa straw biomass |
| url | https://doi.org/10.1038/s41598-025-87274-3 |
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