Methylene Blue Removal Using Activated Carbon from Olive Pits: Response Surface Approach and Artificial Neural Network

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dc.contributor.authorOzcelik, Tijen Over
dc.contributor.authorAltintig, Esra
dc.contributor.authorCetinkaya, Mehmet
dc.contributor.authorAk, Dilay Bozdag
dc.contributor.authorSarici, Birsen
dc.contributor.authorAtes, Asude
dc.date.accessioned2025-10-11T20:47:42Z
dc.date.available2025-10-11T20:47:42Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study evaluated the efficiency of methylene blue (MB) removal by using activated carbon produced from olive pits. The activated carbon (OPAC) was characterized by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and Brunauer-Emmett-Teller (BET). The adsorption process was optimized in two stages using factorial design. Based on the existing literature, the first stage selected the most influential variables (reaction time, dosage, pH, and dye concentration). Response surface methodology (RSM) and artificial neural network (ANN) approaches have been combined to optimize and model the adsorption of MB. To assess the optimal conditions for MB adsorption, RSM was initially applied using four controllable operating parameters. Throughout the optimization process, various independent variables were employed, including initial dye concentrations ranging from 25 to 125 mg/L, adsorbent dosages ranging from 0.1 to 0.9 g/L, pH values spanning from 1 to 9, and contact times ranging from 15 to 75 min. Moreover, the R2 value (R2 = 0.9804) indicates that regression can effectively forecast the response of the adsorption process within the examined range. Thermodynamic studies were performed for three different temperatures between 293 and 303 K. Isothermal analysis parameters and negative Gibbs free energy indicate that the process is spontaneous and favorable. The data best fit the Langmuir model. This research showcases the effectiveness of optimizing and predicting the color removal process through the combined RSM-ANN approach. It highlights the effectiveness of adsorption using OPAC as a viable primary treatment method for the removal of color from wastewater-containing dyes.en_US
dc.identifier.doi10.3390/pr13020347
dc.identifier.issn2227-9717
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85219024235en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/pr13020347
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21533
dc.identifier.volume13en_US
dc.identifier.wosWOS:001429722300001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofProcessesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectadsorptionen_US
dc.subjectdye removalen_US
dc.subjectthermodynamicen_US
dc.subjectwasteen_US
dc.subjectmodeling artificial neural networken_US
dc.titleMethylene Blue Removal Using Activated Carbon from Olive Pits: Response Surface Approach and Artificial Neural Networken_US
dc.typeArticleen_US

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