Chemical Engineering / Kimya Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/14
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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: 13Citation - Scopus: 12Comparison of Photocatalytic Performances of Solar-Driven Hybrid Catalysts for Hydrogen Energy Evolution From 1,8–diazabicyclo[5.4.0]undec-7 (dbu) Solution(Elsevier, 2022) Orak, Ceren; Yüksel, AslıHydrogen is evolved from 1,8–Diazabicyclo [5.4.0]undec-7-ene (DBU) model solution which is a nitrogen-containing heterocyclic organic compound using different solar-driven hybrid photocatalysts. A characterization study is performed and the results of PL analysis show that the most promising solar-driven hybrid catalyst is graphene supported LaFeO3. Then, an experimental design matrix is built using the Box Behnken model to main and interaction effects of reaction parameters (pH, catalyst loading, and [H2O2]0). Based on the experimental results relatively higher hydrogen amounts are achieved using GLFO and this finding is supported by PL analysis. The highest hydrogen amount and DBU removal are determined as 3058.31 μmol/gcat and 90.3%, respectively. Statistical analysis shows that the square of catalyst loading is the only effective parameter over the produced hydrogen amount from the DBU model solution using GLFO and the R2 of model is 92.47%. Thus, hydrogen production and wastewater treatment could be achieved via photocatalytic oxidation as concomitant.