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: 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: 14Citation - Scopus: 12Photocatalytic Hydrogen Energy Evolution From Sugar Beet Wastewater(Wiley-VCH Verlag, 2021) Orak, Ceren; Yüksel, AslıHydrogen is a clean, environmentally friendly, storable, and sustainable green energy source as well as a potential fuel. It could be produced from various biomass, wastewater, or other sources by different processes. In this study, hydrogen was evolved from sucrose model solution and real sugar beet wastewater by photocatalytic oxidation using a perovskite catalyst under solar light irradiation. In this context, firstly, the graphene supported LaFeO3 (GLFO) was synthesized and then, a characterization study shows that GLFO is successfully synthesized. To optimize the reaction parameters (pH, catalyst loading, and initial hydrogen peroxide concentration), an experimental matrix was created using the Box Behnken model. Whereas the highest hydrogen evolution from sucrose model solution was observed as 3520 μmol/gcat, the highest hydrogen evolution from sugar beet wastewater was obtained as 7035 μmol/gcat. The highest TOC removal (99.73 %) from sugar beet wastewater was also achieved at the same reaction conditions.