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: 14Citation - Scopus: 15Predictive Modeling of Photocatalytic Hydrogen Production: Integrating Experimental Insights With Machine Learning on Fe/G-c3n4 Catalysts(Amer Chemical Soc, 2025) Arabaci, Bahriyenur; Bakir, Rezan; Orak, Ceren; Yuksel, AsliHydrogen emerges as a promising alternative to fossil fuels with its pollutant-free emissions, high energy density, versatility, and efficiency in generating power. In this study, photocatalytic hydrogen production from using 1000 ppm of model solution prepared with sucrose was investigated in the presence of Fe/g-C3N4 photocatalysts over Box-Behnken experimental design developed using the Minitab statistical software. The amount of hydrogen produced was optimized at different pH environments (3, 5, and 7) for 2 h reaction time with different amounts of metal loaded (10, 20, and 30 wt %), Fe/g-C3N4 (0.1, 0.2, and 0.3 g/L), and oxidant (H2O2; 0, 10, and 20 mM) concentrations. SEM, BET, XRD, FTIR, and PL analyses were employed for the characterization of synthesized photocatalysts. According to the response optimization, using Fe/g-C3N4, the optimal conditions for hydrogen production were found as 0.3 g/L catalyst loading, 18.8 mM H2O2, and 26.6% Fe loading by mass when the pH was 3 for the reaction medium. Furthermore, machine learning algorithms were employed to predict hydrogen evolution based on experimental parameters. Notably, ensemble models such as Voting Regressor combining the Bagging Regressor, Random Forest Regressor, LGBM Regressor, Extra Trees Regressor, XGB Regressor, and Gradient Boosting Regressor achieved superior performance with a mean squared error of 0.0068 and R-squared (R 2) of 0.9895. This integrated approach demonstrates the efficacy of machine learning in optimizing photocatalytic hydrogen generation processes.Article Citation - WoS: 15Citation - Scopus: 16A Machine Learning Ensemble Approach for Predicting Solar-Sensitive Hybrid Photocatalysts on Hydrogen Evolution(IOP Publishing, 2024) Bakır, Rezan; Orak, Ceren; Yuksel, AsliHydrogen, as the lightest and most abundant element in the universe, has emerged as a pivotal player in the quest for sustainable energy solutions. Its remarkable properties, such as high energy density and zero emissions upon combustion, make it a promising candidate for addressing the pressing challenges of climate change and transitioning towards a clean and renewable energy future. In an effort to improve efficiency and reduce experimental costs, we adopted machine learning techniques in this study. Our focus turned to predictive analyses of hydrogen evolution values using three photocatalysts, namely, graphene-supported LaFeO3 (GLFO), graphene-supported LaRuO3 (GLRO), and graphene-supported BiFeO3 (GBFO), examining their correlation with varying levels of pH, catalyst amount, and H2O2 concentration. To achieve this, a diverse range of machine learning models are used, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), XGBoost, Gradient Boosting, and AdaBoost-each bringing its strengths to the predictive modeling arena. An important step involved combining the most effective models-Random Forests, Gradient Boosting, and XGBoost-into an ensemble model. This collaborative approach aimed to leverage their collective strengths and improve overall predictability. The ensemble model emerged as a powerful tool for understanding photocatalytic hydrogen evolution. Standard metrics were employed to assess the performance of our ensemble prediction model, encompassing R squared, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The yielded results showcase exceptional accuracy, with R squared values of 96.9%, 99.3%, and 98% for GLFO, GBFO, and GLRO, respectively. Moreover, our model demonstrates minimal error rates across all metrics, underscoring its robust predictive capabilities and highlighting its efficacy in accurately forecasting the intricate relationships between GLFO, GBFO, and GLRO values and their influencing factors.Article Citation - WoS: 10Citation - Scopus: 12Novel Hybrid Adsorption-Electrodialysis (aded) System for Removal of Boron From Geothermal Brine(American Chemical Society, 2022) Altınbaş, Bekir Fırat; Orak, Ceren; Ökten, Hatice Eser; Yüksel, AslıA novel hybrid adsorption-electrodialysis (AdED) system to remove environmentally harmful boron from geothermal brine was designed and effective operating parameters such as pH, voltage, and flow rate were studied. A cellulose-based adsorbent was synthesized from glycidyl methacrylate (GMA) grafted cellulose and modified with a boron selective n-methyl-d-glucamine (NMDG) group and characterized with SEM-EDX, FT-IR, and TGA analyses. Batch adsorption studies revealed that cellulose-based adsorbent showed a remarkable boron removal capacity (19.29 mg/g), a wide stable operating pH range (2-10), and an adsorption process that followed the Freundlich isotherm (R2= 0.95) and pseudo-second-order kinetics (R2= 0.99). In the hybrid AdED system, the optimum operating parameters for boron removal were found to be a pH of 10, a voltage of 10 V, a flow rate of 100 mL/min, and an adsorbent dosage of 4 g/L. The presence of the adsorbent in the hybrid system increased boron removal from real geothermal brine (containing 199 ppm boron) from 7.2% to 73.3%. The results indicate that the designed AdED system performs better than bare electrodialysis for boron removal from ion-rich real geothermal brine while utilizing environmentally friendly cellulose-based adsorbent.Article Citation - WoS: 11Citation - Scopus: 12Box-Behnken Design for Hydrogen Evolution From Sugar Industry Wastewater Using Solar-Driven Hybrid Catalysts(American Chemical Society, 2022) Orak, Ceren; Yüksel, AslıHydrogen is a clean and green fuel and can be produced from renewable sources via photocatalysis. Solar-driven hybrid catalysts were synthesized and characterized (scanning electron microscopy (SEM), transmission electron microscopy (TEM), Brunauer-Emmett-Teller (BET), X-ray diffraction (XRD), photoluminescence (PL) spectroscopy, and UV-vis diffuse reflectance spectroscopy (DSR)), and the results implied that graphene-supported LaRuO3is a more promising photocatalyst to produce hydrogen and was used to produce hydrogen from sugar industry wastewater. To investigate the main and interaction effects of reaction parameters (pH, catalyst amount, and [H2O2]0) on the evolved hydrogen amount, the Box-Behnken experimental design model was used. The highest hydrogen evolution obtained was 6773 μmol/gcatfrom sugar industry wastewater at pH 3, 0.15 g/L GLRO, and 15 mM H2O2. Based on the Pareto chart for the evolved hydrogen amount using GLRO, among the main effects, the only effective parameter was the catalyst amount for the photocatalytic hydrogen evolution from sugar industry wastewater. In addition, the squares of pH and two-way interaction of pH and [H2O2]0were also statistically efficient over the evolved hydrogen amount.Article Citation - WoS: 2Citation - Scopus: 2Electrolytic Oxidation of 1,8-Diazabicyclo[5.4.0]undec in Hot-Compressed Water on a Titanium Electrode(American Chemical Society, 2020) Orak, Ceren; Yüksel Özşen, AslıThe nitrogen-containing heterocyclic organic compound, 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU), was chosen to prepare a model solution to represent nitrogen-containing industrial waste streams. A hybrid reactor system was designed to combine electrolysis with wet oxidation in hot compressed water using a titanium electrode. The effects of current density, NaOH concentration, and reaction time on DBU and total organic carbon (TOC) removal were investigated via Minitab 18 software to clarify the main and interaction effects. Statistical analysis shows that the NaOH concentration and current density had significant effects on DBU removal. The highest DBU (91.2%) and TOC (45%) removal was observed at the lowest DBU concentration (3 mM) for 90 min of reaction time. Last, the effect of temperature on DBU and TOC removal was investigated. TOC removal was described with the first-order reaction kinetic model. Rate constants were determined as 0.0025, 0.041, and 0.050 min(-1) at 200, 240, and 280 degrees C, respectively. The activation energy was calculated as 79.86 kJ/mol.