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    Application of Optimization Response Surface for the Adsorption of Methylene Blue Dye onto Zinc-coated Activated Carbon
    (Springer, 2024) Altintig, Esra; Sarici, Birsen; Bozdag, Dilay; Ozcelik, Tijen Over; Karakas, Mehtap; Altundag, Huseyin
    The activated carbon was produced in the first phase of this investigation by chemically activating hazelnut shell waste with H3PO4. Composite materials were obtained by coating the activated carbon with zinc oxide, whose BET surface area was calculated as 1278 m2 g-1. ZnO-doped ZnO/AC composite was synthesized as an adsorbent for its possible application in the elimination of organic dyestuff MB, and its removal efficiency was investigated. Morphological properties of ZnO/AC were characterized using analytical methods such as XRD, SEM, and BET. The adsorption system and its parameters were investigated and modeled using the response surface method of batch adsorption experiments. The experimental design consisted of three levels of pH (3, 6.5, and 10), initial MB concentration (50, 100, and 150 mg L-1), dosage (0.1, 0.3, and 0.5 g 100 mL-1), and contact time (5, 50, and 95 min). The results from the RSM suggested that the MB removal efficiency was 98.7% under the optimum conditions of the experimental factors. The R2 value, which expresses the significance of the model, was determined as 99.05%. Adsorption studies showed that the equilibrium data fit well with the Langmuir isotherm model compared to Freundlich. The maximum adsorption capacity was calculated as 270.70 mg g-1.
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    Methylene Blue Removal Using Activated Carbon from Olive Pits: Response Surface Approach and Artificial Neural Network
    (Mdpi, 2025) Ozcelik, Tijen Over; Altintig, Esra; Cetinkaya, Mehmet; Ak, Dilay Bozdag; Sarici, Birsen; Ates, Asude
    This 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.
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    A novel methodological approach to SaaS churn prediction using whale optimization algorithm
    (Public Library Science, 2025) Kotan, Muhammed; Seymen, Omer Faruk; Calli, Levent; Kasim, Sena; Yavuz, Burcu Carkli; Ozcelik, Tijen Over
    Customer churn is a critical concern in the Software as a Service (SaaS) sector, potentially impacting long-term growth within the cloud computing industry. The scarcity of research on customer churn models in SaaS, particularly regarding diverse feature selection methods and predictive algorithms, highlights a significant gap. Addressing this would enhance academic discourse and provide essential insights for managerial decision-making. This study introduces a novel approach to SaaS churn prediction using the Whale Optimization Algorithm (WOA) for feature selection. Results show that WOA-reduced datasets improve processing efficiency and outperform full-variable datasets in predictive performance. The study encompasses a range of prediction techniques with three distinct datasets evaluated derived from over 1,000 users of a multinational SaaS company: the WOA-reduced dataset, the full-variable dataset, and the chi-squared-derived dataset. These three datasets were examined with the most used in literature, k-nearest neighbor, Decision Trees, Na & iuml;ve Bayes, Random Forests, and Neural Network techniques, and the performance metrics such as Area Under Curve, Accuracy, Precision, Recall, and F1 Score were used as classification success. The results demonstrate that the WOA-reduced dataset outperformed the full-variable and chi-squared-derived datasets regarding performance metrics.