Yazar "Akbal, Feryal" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Öğe
    Arsenite removal by adsorption onto iron oxide-coated pumice and sepiolite
    (Springer, 2015) Dönmez Öztel, Merve; Akbal, Feryal; Altaş, Levent
    The present study describes the removal of arsenite by iron oxide-coated pumice and iron oxide-coated sepiolite. Pumice and sepiolite were coated with iron oxide and used as adsorbents for the removal of arsenite from aqueous solution in batch experiments. Arsenite concentration decreased exponentially with time, and equilibrium was attained in 84 h. The kinetics of the adsorption process was tested for the pseudo-first-order, pseudo-second-order, and intraparticle diffusion models. The adsorption of arsenite onto iron oxide-coated pumice and iron oxide-coated sepiolite followed the pseudo-second-order model. Freundlich, Dubinin-Radushkevitch, and Temkin isotherm models were applied to the experimental equilibrium data. The adsorption of arsenite fit these isotherm models. The results indicated that the iron oxide-coated pumice and iron oxide sepiolite could be alternative adsorbents for arsenite removal. Among the adsorbents, iron oxide-coated pumice was found to be more effective than iron oxide sepiolite in the removal of arsenite.
  • Küçük Resim
    Öğe
    Predictive insights into arsenic remediation: Advancing electro and chemical coagulation through machine learning models
    (Elsevier Ltd, 2025) Dönmez Öztel, Merve; Alver, Alper; Akbal, Feryal; Altaş, Levent; Kuleyin, Ayşe
    Arsenic contamination in water sources remains a critical environmental and public health challenge, mainly due to the toxicity of its trivalent (As(III)) and pentavalent (As(V)) forms. This study compares advanced predictive modeling to enhance arsenic remediation, comparing electrocoagulation (EC) and chemical coagulation (CC) processes for their efficiency and cost-effectiveness. Higher As(III) removal rates were achieved using iron and aluminum electrodes in EC (up to 99 % in 5 min using Fe electrodes) compared to CC (up to 90 % using Fe(II) coagulant). The study's results highlight the operational advantages of EC, including a 40 % cost reduction due to lower chemical usage and sludge production. Machine learning models, including Support Vector Machines (SVM), Regression Trees, Random Forest, and Gradient Boosting, were developed to predict removal efficiencies under diverse operational conditions. SVM exhibited the highest predictive accuracy for As(III) removal in EC with Fe electrodes (MSE = 0.340, R2 = 0.954). At the same time, Regression Trees outperformed other models for As(V) removal in CC with Fe(III) coagulants (MSE = 0.371, R2 = 0.997). These techniques are highly effective in optimizing arsenic removal processes, allowing for precise regulation of treatment parameters and reducing dependence on trial-and-error methods. The findings highlight electrocoagulation with iron electrodes as a sustainable and cost-effective approach to arsenic remediation, particularly for As(III), while underscoring the transformative role of predictive modeling in water treatment. This study successfully integrates experimental insights with machine learning, driving improvements in the efficiency and adaptability of arsenic removal technologies.