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: 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.Article Citation - WoS: 21Citation - Scopus: 21Graphene-Supported Lafeo3 for Photocatalytic Hydrogen Energy Production(Wiley, 2021) Orak, Ceren; Yüksel, AslıHydrogen is a green, environmentally benign and sustainable energy source with no harmful combustion products to fulfil the increasing energy demand. Photocatalytic oxidation has various advantageous to produce hydrogen from different sources such as wastewater, alcohol solutions using different types of catalysts. Sucrose solution was chosen as a model solution to evolve hydrogen using LFO and GLFO catalysts under solar light irradiation, and graphene was used as a catalyst support to enhance the amount of produced hydrogen amount. A characterization study, which consists of SEM-EDX, BET, XRD, PL, TEM, XPS and FT-IR analyses, was carried out. A full factorial design was created via Minitab 18 to analyse the factors affecting the produced hydrogen amount, which are pH, catalyst loading, H2O2 concentration and graphene content statistically. Based on the results, graphene content is an important parameter and pH and H2O2 concentration have a synergetic effect over hydrogen production. Additionally, the effects of calcination temperature, pH, H2O2 concentration and catalyst loading over produced gases were investigated. The best promising result was obtained as 3388 mu mol/g(cat) at the following reaction conditions: 7.5 of pH, 0.1 g L-1 catalyst loading (GLFO, which is calcined at 700 degrees C) and using 15 mM H2O2 under solar light irradiation. Novelty Statement Hydrogen is produced from sucrose solution with low cost process requiring no special equipment, high pressure or temperature. First study that uses perovskite catalysts for the production of hydrogen from sucrose solution by photo-Fenton like oxidation GLFO is a promising photocatalyst for H-2 production by solar-Fenton like oxidation with the highest H-2 evaluation at 3388.34 mu mol/g(cat).Article Citation - WoS: 4Citation - Scopus: 4Adsorption Kinetics of Methane Reformer Off-Gases on Aluminum Based Metal-Organic Framework(Elsevier Ltd., 2020) Angı, Deniz; Çakıcıoğlu Özkan, Seher FehimeSolvothermal synthesis of aluminum based metal-organic frameworks (MIL-53(Al)s) were conducted by considering the effects of crystallization and activation temperatures, and the solvent at purification step. Adsorption kinetics of Steam Methane Reformer off gas components at 34, 70 and 100 °C temperatures was measured by using ZLC method. Henry constant decreases as diffusion coefficient of the gases increases with increasing temperature; It was determined that the CO gas has the highest activation energy. Adsorption kinetics of gases were controlled with electrostatic interaction. © 2020 Hydrogen Energy Publications LLC