Removal of heavy metals using lichen-derived activated carbons: adsorption studies, machine learning, and response surface methodology approaches

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dc.authorid0000-0002-6756-4973
dc.contributor.authorKoyuncu, H.
dc.contributor.authorKul, A. R.
dc.contributor.authorAkyavasoglu, O.
dc.date.accessioned2026-02-08T15:15:06Z
dc.date.available2026-02-08T15:15:06Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractBiomass-based activated carbons are promising as they are effective and low-cost for wastewater remediation. In this study, the removal of lead, copper, and zinc was investigated using activated carbons obtained from two different lichens. The performance of the 5th-order Response Surface methodology (RSM), Machine Learning (ML), and Artificial Neural Network (ANN) model based on Face-Centered Central Composite Design (FCCCD) was evaluated considering initial concentration, temperature, and time effects. The effectiveness of using ANN for accurate prediction in lead and copper removal and the superior performance of ML-based 5th-order RSM for zinc removal were demonstrated. Among the Langmuir, Freundlich, and Temkin isotherm models, the Freundlich model best described the adsorption processes, and the Langmuir maximum adsorption capacities were found to be 105.26 mg/g (Pb/AC-1), 59.52 mg/g (Cu/AC-1), and 53.19 mg/g (Cu/AC-2). Additionally, the pseudo-first-order, pseudo-second-order, and intra-particle diffusion models were examined, and it was found that the adsorption processes followed the pseudo-second-order kinetics and intra-particle diffusion played a significant role. The activation energies and Delta H0 values less than 40 kJ/mol and Delta G0 values below - 20 kJ/mol showed that the metals were adsorbed by physical mechanisms. The novelty of this study is that the 5th-order RSM model is applied to adsorption processes for the first time, and a multi-faceted approach is used to analyse adsorption processes, including machine learning and ANN, isotherm modeling, thermodynamic evaluation, kinetics analysis, and activation energy calculations.
dc.identifier.doi10.1007/s13762-024-06001-z
dc.identifier.endpage5968
dc.identifier.issn1735-1472
dc.identifier.issn1735-2630
dc.identifier.issue7
dc.identifier.scopus2-s2.0-105001066377
dc.identifier.scopusqualityQ1
dc.identifier.startpage5947
dc.identifier.urihttps://doi.org/10.1007/s13762-024-06001-z
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5587
dc.identifier.volume22
dc.identifier.wosWOS:001309355200014
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofInternational Journal of Environmental Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subject5th-order response surface methodology
dc.subjectFace-centered central composite design
dc.subjectLead
dc.subjectCopper
dc.subjectZinc
dc.subjectWater pollution
dc.titleRemoval of heavy metals using lichen-derived activated carbons: adsorption studies, machine learning, and response surface methodology approaches
dc.typeArticle

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