Machine Learning-Guided Prediction and Interpretation of Rhodamine B Removal by Biochar Derived from Marine Biomass

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dc.contributor.authorGumus, Dilek
dc.contributor.authorGumus, Fatih
dc.contributor.authorKadioglu, Elif Nihan
dc.contributor.authorEroglu, Handan Atalay
dc.date.accessioned2026-04-25T14:19:49Z
dc.date.available2026-04-25T14:19:49Z
dc.date.issued2026
dc.departmentSinop Üniversitesi
dc.description.abstractThis study aims to provide a sustainable solution for dye removal by developing environmentally friendly and renewable resource-based adsorbents. In this context, the removal of Rhodamin B (RhB) dye from aqueous solutions was investigated using composite biochar obtained from the seaweed species Ulva lactuca -Cystoseira sensu lato. The characterization of the adsorbent, which draws attention with its high surface area (797.91 m2/g), porous structure and rich functional groups, was carried out by SEM, FTIR and BET analyses. The data obtained as a result of adsorption experiments were evaluated with kinetic and isotherm models; the pseudo-second-order kinetic model (R2 = 0.993) and Freundlich isotherm (R2 = 0.994) successfully reflected the chemical and multi-layered nature of the process. In this context, the adsorption data were modelled with four different machine learning algorithms (Random Forest, XGBoost, CatBoost, Gradient Boosting). Among the models, Random Forest algorithm showed the highest accuracy on the test data (R2 = 0.93, RMSE = 4.15, MAE = 1.95) and successfully reflected the behaviour of the system. The decision mechanisms of the model were explained with SHAP analysis; the dominant effects of contact time and adsorption capacity on the prediction were determined. While PDP analyses visualized the individual effects of variables on the target variable, learning curves and residual analyses confirmed the generalization ability and stability of the Random Forest model. This multi-faceted approach shows that both biomass wastes can be converted into environmentally friendly adsorbents and machine learning-based methods can be used effectively in water treatment applications.
dc.identifier.doi10.1007/s11270-025-09054-z
dc.identifier.issn0049-6979
dc.identifier.issn1573-2932
dc.identifier.issue6
dc.identifier.orcid0000-0001-5707-9336
dc.identifier.orcid0000-0001-7665-3057
dc.identifier.orcid0000-0002-0550-1803
dc.identifier.scopus2-s2.0-105027033957
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s11270-025-09054-z
dc.identifier.urihttps://hdl.handle.net/11486/8211
dc.identifier.volume237
dc.identifier.wosWOS:001658181400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Int Publ Ag
dc.relation.ispartofWater Air and Soil Pollution
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260420
dc.subjectAdsorption
dc.subjectRhodamine B
dc.subjectComposite biochar
dc.subjectMachine Learning
dc.subjectSHAP analysis
dc.titleMachine Learning-Guided Prediction and Interpretation of Rhodamine B Removal by Biochar Derived from Marine Biomass
dc.typeArticle

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