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: 9Citation - Scopus: 10Investigation of Reactive Extraction of Monocarboxylic Acids With Menthol-Based Hydrophobic Deep Eutectic Solvent by Response Surface Methodology(Taylor & Francis Inc, 2023) Yıldız, Esra; Lalikoğlu, Melisa; Aşçı, Yavuz Selim; Sırma Tarım, BurcuThe growing demand for producing organic acids by fermentative techniques has increased the significance of separating carboxylic acids from their fermentation broth with the reactive extraction process. Considering the environmental impacts, deep eutectic solvents can be considered as a potential green alternative for the replacement of volatile organic solvents commonly used in the extraction process. In this study, a new type of green solvent named hydrophobic deep eutectic solvent (HDES) based on decanoic acid as a hydrogen bond acceptor and menthol as a hydrogen bond donor was utilized for the reactive extraction of formic, acetic, and propionic acids from their aqueous solutions. The effect of initial acid concentration, HDES molar ratio, and tri-n-octyl amine (TOA) concentration on extraction efficiency was investigated. Modeling of the reactive extraction process was performed via a response surface methodology with a central composite design. Herein, the effect of the parameters of TOA concentration, HDES molar ratio, and initial acid concentration on the distribution coefficient was investigated. According to the results, it was reported that the most effective parameter on the extraction efficiency (%E) was the amount of extractant. The results of the experimental studies showed that the highest separation efficiency was obtained for 5% initial concentrations of formic, acetic, and propionic acids by using a mixture of 0.5 HDES molar ratio solvent and 1.9 mol/L TOA. The extraction efficiencies of these acids were found to be 88.71, 92.52, and 95.90 with +/- 0.1 standard deviation, respectively.Article Citation - WoS: 11Citation - Scopus: 12Treatment of Sugar Industry Wastewater by Using Subcritical Water as a Reaction Media(Wiley, 2023) Orak, Ceren; Öcal, Bulutcem; Yüksel, AslıThe sugar industry is one of the most wastewater-producing industries and it contains high content of organic and inorganic substances. Treating and reusing wastewater has significant importance because sugar industry needs to use a high volume of water. In this study, sugar industry wastewater was treated under subcritical conditions and the impacts of reaction temperature and duration over TOC removal percentage were investigated. Additionally, the impact of NaOH concentration over TOC removal percentage was examined. The highest TOC removal was obtained almost 95 % in the presence of 0.1 M of NaOH at 240 degrees C for 90 min of reaction duration. Treatment of sugar industry wastewater by subcritical water oxidation followed the second-order reaction kinetic model and the activation energy was found as 11.41 kJ/mol. Furthermore, the intermediate products were identified via GC-MS.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: 1Citation - Scopus: 1Mineralization of Olive Mill Wastewater Under Hydrothermal Conditions(Desalination Publications, 2019) Ersanlı, Çağlar; Yüksel Özşen, AslıOlive mill wastewater (OMW) is highly dangerous for land and aquatic environments because of its high phenolic content, acidity, and salinity. Hence, hydrothermal degradation of OMW in subcritical water medium with and without using external oxidizer (H2O2) was studied to decrease its total organic carbon (TOC), total phenolic content (TPC), and color. Effects of reaction temperature (150 degrees C-250 degrees C), reaction time (30-120 min), and H2O2 content (0-100 mM) on TOC and TPC of OMW were investigated. Box-Behnken-type experimental design and statistical analysis (analysis of variance) were practiced by Design Expert 11. According to statistical analysis, effect of reaction temperature significantly affected TOC removal efficiency. The best TOC reduction (31.65%) was achieved without using H2O2 at 250 degrees C for 120 min.