Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 16Citation - Scopus: 17Integrating Experimental and Machine Learning Approaches for Predictive Analysis of Photocatalytic Hydrogen Evolution Using Cu/G-c3n4(Pergamon-elsevier Science Ltd, 2024) Arabaci, Bahriyenur; Bakir, Rezan; Orak, Ceren; Yuksel, AsliThis study addresses environmental issues like global warming and wastewater generation by exploring waste-toenergy strategies that produce renewable hydrogen and treat wastewater simultaneously. Cu/g-C3N4 is used to evolve hydrogen from sucrose solution and the impact of reaction parameters such as pH (3, 5, and 7), Cu loading (5, 10, and 15 wt%), catalyst amount (0.1, 0.2, and 0.3 g/L), and oxidant (H2O2) concentration (0, 10, and 20 mM) on the evolved hydrogen amount is examined. Characterization study confirmed successful incorporation of Cu without significantly altering g-C3N4 properties. The highest hydrogen production (1979.25 mu mol g- 1 & sdot;h- 1) is achieved with 0.3 g/L catalyst, 20 mM H2O2, 5 % Cu loading, and pH 3. The experimental study concludes that Cu/g-C3N4 is an effective photocatalyst for renewable hydrogen production. In addition to the experimental investigations, various machine learning (ML) models, including Random Forest, Decision Tree, XGBoost, among others, are employed to analyze the impact of reaction parameters and forecast the quantities of produced hydrogen. Alongside these individual models, an ensemble approach is proposed and utilized. The R2 values of these ML models ranged from 0.9454 to 0.9955, indicating strong predictive performance across the board. Additionally, these models exhibited low error rates, further confirming their reliability in predicting hydrogen evolution.Article Citation - WoS: 4Citation - Scopus: 5Breakthrough Curve Analysis of Phosphorylated Hazelnut Shell Waste in Column Operation for Continuous Harvesting of Lithium From Water(Elsevier, 2024) Recepoğlu, Yaşar Kemal; Arar, Ozguer; Yuksel, AsliIn batch-scale operations, biosorption employing phosphorylated hazelnut shell waste (FHS) revealed excellent lithium removal and recovery efficiency. Scaling up and implementing packed bed column systems necessitates further design and performance optimization. Lithium biosorption via FHS was investigated utilizing a continuous-flow packed-bed column operated under various flow rates and bed heights to remove Li to ultra-low levels and recover it. The Li biosorption capacity of the FHS column was unaffected by the bed height, however, when the flow rate was increased, the capacity of the FHS column decreased. The breakthrough time, exhaustion time, and uptake capacity of the column bed increased with increasing column bed height, whereas they decreased with increasing influent flow rate. At flow rates of 0.25, 0.5, and 1.0 mL/min, bed volumes (BVs, mL solution/mL biosorbent) at the breakthrough point were found to be 477, 369, and 347, respectively, with the required BVs for total saturation point of 941, 911, and 829, while the total capacity was calculated as 22.29, 20.07, and 17.69 mg Li/g sorbent. In the 1.0, 1.5, and 2.0 cm height columns filled with FHS, the breakthrough times were 282, 366, and 433 min, respectively, whereas the periods required for saturation were 781, 897, and 1033 min. The three conventional breakthrough models of the Thomas, Yoon-Nelson, and Modified Dose-Response (MDR) were used to properly estimate the whole breakthrough behavior of the FHS column and the characteristic model parameters. Li's extremely favorable separation utilizing FHS was evidenced by the steep S-shape of the breakthrough curves for both parameters flow rate and bed height. The reusability of FHS was demonstrated by operating the packed bed column in multi-cycle mode, with no appreciable loss in column performance.