Browsing by Author "Kul, A. R."

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Comprehensive Investigation of Basic Red 46 Removal by Pinecone Adsorbent: Experimental, Isotherm, Kinetic and Thermodynamic Studies
(Springer, 2023) Aldemir, A.; Turan, A.; Kul, A. R.; Koyuncu, H.
In this study, pinecone sawdust (PCS) performance was investigated for Basic Red 46 (BR 46) dye adsorption from aqueous solutions. The physicochemical and morphological characterization of PCS before and after BR 46 adsorption were evaluated with the help of Fourier transform infrared spectra, X-ray diffraction and scanning electron microscopy/energy-dispersive X-ray spectroscopy analysis. Effects of initial dye concentration, temperature and contact time were determined for BR 46 adsorption. Seventeen equilibrium isotherm models and eight kinetic models were compared at 298 K, 308 K and 318 K temperatures for five different concentrations which varied from 20 to 60 mg/L. The obtained adsorption data best-fit the Freundlich model among all the applied isotherm models, and the maximum adsorption capacity (q(m)) was calculated as 129.87 mg/g at 298 K. The pseudo-second-order model was the best choice to describe the adsorption behavior among all the applied kinetic models. The removal percentage of BR 46 dye with PCS was 74.52% at 318 K for 60 mg/L concentration. The negative free energy (Delta G degrees), enthalpy (Delta H degrees) and entropy (Delta S degrees) values of adsorption were calculated as - 2837 kJ/mol, 18,898 kJ/mol and 68.51 J/mol K, respectively. This adsorption process was spontaneous and favorable, coinciding with the negative free energy. The activation energy (E-A) value of this process was determined with the Arrhenius equation as 19.92 kJ/mol. The reliability of all results was analyzed statistically and evaluated with correlation coefficient (R-2), sum of squares, sum of the square of error and mean square of error values. As a result of this study, PCS can be used effectively for BR 46 dye removal. [GRAPHICS] .
Removal of Heavy Metals Using Lichen-Derived Activated Carbons: Adsorption Studies, Machine Learning, and Response Surface Methodology Approaches
(Springer, 2025) Koyuncu, H.; Kul, A. R.; Akyavasoglu, O.
Biomass-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.