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: 7Citation - Scopus: 6A Literature Review on Sustainable Buildings and Neighborhoods in Terms of Daylight, Solar Energy and Human Factors(Elsevier, 2025) Cogul, Ilgin cataroglu; Kazanasmaz, Zehra Tugce; Ekici, BerkSustainability has become the focus and interest of researchers with climate change's increasing impact and challenges. Considering various perspectives, published studies focus on sustainability in architecture and the built environment, such as using daylight more effectively, enhancing energy efficiency, and designing nearly zero-energy buildings. Given the attention to sustain- ability in this domain, this review assesses the abovementioned viewpoints in buildings regarding environmental factors in relation to the micro and macro scales of the buildings and neighborhoods. Human factor has increasingly been of interest in recent works of sustainable environments. This study identifies the gaps with respect to architectural design elements considering daylighting, energy efficiency and human factors on building and neighborhood scales. A comprehensive table of the reviewed studies summarizes the aim, methodology, optimization algorithm, objective function, machine learning algorithm, digital tools, location, independent and dependent variables, view, wellness, well-being, daylight/energy performance metrics, scale, and solar strategy. The results showed that the current state-of-the-art focus on energy efficiency mainly considers passive design strategies at the building scale. Studies in the daylight domain primarily consider window properties, shading devices, and orientation. Human-centric studies showed that daylighting improves the emotional well-being of building occupants but can have negative effects such as overheating and glare. Overall findings emphasize the necessity of a holistic approach in achieving sustainability goals in dwellings at the building and neighborhood scale.Review Citation - WoS: 4Citation - Scopus: 7Optimizing Lighting Design in Educational Settings for Enhanced Cognitive Performance: a Literature Review(Elsevier Science Sa, 2025) Celik, Meric; Didikoglu, Altug; Kazanasmaz, TugceLighting has more functions than simply illuminating spaces. For humans, light is the main signal that aligns our body's internal clock, regulating circadian rhythms. This process instructs our bodies to wake up in the morning, become alert during the day, and feel sleepy at night. Disruption of these rhythms can impact neurological and psychiatric health, including cognitive performance. We can utilize light for mood improvements and better cognitive performance to create a suitable learning environment for students in educational buildings. These non-visual effects of light need to be considered from the beginning of the design process, making an interdisciplinary effort necessary. Even with adequate light and dark, the human eye reacts differently under various conditions, influenced by light's photometric and colorimetric properties. While natural sunlight is ideal for aligning with our biological clock, it is not always sufficient, making artificial lighting essential indoors. LED technology offers promising solutions, catering to our non-visual needs in the absence of natural light and providing energy efficiency. This study reviews the literature that includes students' cognitive performance and well-being, energy efficiency, running costs, and environment-related issues such as light pollution. It aims to explore the impact of lighting design in learning environments.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: 52Citation - Scopus: 57Optimizing Hydrogen Evolution Prediction: a Unified Approach Using Random Forests, Lightgbm, and Bagging Regressor Ensemble Model(Elsevier Ltd, 2024) Bakır,R.; Orak,C.; Yüksel,A.Hydrogen, as a clean and versatile energy carrier, plays a pivotal role in addressing global energy challenges and transitioning towards sustainable energy systems. This study explores the convergence of machine learning (ML) for photocatalytic hydrogen evolution from sucrose solution using perovskite-type catalysts, namely LaFeO3 (LFO) and graphene-supported LaFeO3 (GLFO). This study pioneers the practical application of ML techniques, including Random Forests, LightGBM, and Bagging Regressor, to predict hydrogen yields in the presence of these photocatalysts. LFO and GLFO underwent a thorough characterization study to validate their successful preparation. Noteworthy, the highest hydrogen yield from the sucrose model solution was achieved using GLFO as 3.52 mmol/gcat. The optimum reaction conditions were experimentally found to be pH = 5.25, 0.15 g/L of catalyst amount, and 7.5 mM of HPC (hydrogen peroxide concentration). A pivotal contribution of this research lies in the practical application of ML models, culminating in the development of an ensemble model. This collaborative approach not only achieved an overall R2 of 0.92 but also demonstrated exceptional precision, as reflected in remarkably low error metrics. The mean squared logarithmic error (MSLE) was 0.0032, and the mean absolute error (MAE) was 0.049, underscoring the effectiveness of integrating diverse ML algorithms. This study advances both the understanding of photocatalytic hydrogen evolution and the practical implementation of ML in predicting intricate chemical reactions. © 2024 Hydrogen Energy Publications LLCBook Part Citation - Scopus: 1Biomass-Based Polygeneration Systems With Hydrogen Production: a Concise Review and Case Study(Springer Science and Business Media Deutschland GmbH, 2024) Hajimohammadi Tabriz,Z.; Mohammadpourfard,M.; Gökçen Akkurt,G.; Çağlar,B.This chapter discusses the importance of biomass-based polygeneration systems in producing hydrogen as a clean and safe energy carrier. The benefits of polygeneration systems, which can produce multiple products and minimize waste, are highlighted, and the need for clean and efficient hydrogen production is emphasized. This study gives a brief overview of hydrogen production from biomass-based polygeneration systems, which examines the systems in two main classifications: systems that use biomass as a potential and rich source of hydrogen and systems that exploit the energy content of biomass to run hydrogen production units. Furthermore, a new multigeneration system with hydrogen production has been introduced and thermodynamically evaluated. Also, its results have been obtained in a real situation. Overall, this chapter offers insights into the potential of biomass-based polygeneration systems in meeting energy demands while reducing environmental impact. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Article Citation - WoS: 4Citation - Scopus: 5Experimental Investigation of a Unique Electro-Biomembrane Based Integrated System for Wastewater Treatment and Simultaneous Clean Water, Hydrogen and Energy Production(Institution of Chemical Engineers, 2024) Goren,A.Y.; Dincer,I.; Khalvati,A.This paper concerns the design, development, and building of a unique electro-bio-membrane reactor for concurrent bioH2 production, desalination, and energy production by microorganisms in a single reactor. The effects of varying biomass amounts (5–50 g) and inoculum amounts (250–1500 mL) on the bioH2 production efficiency are also investigated. The lowest cumulative bioH2 yield of 24.2 mL/g is obtained using a biomass amount of 5 g, while it is 44.7 mL/g at a biomass amount of 50 g. The highest H2 production from water electrolysis is also found as 0.719 mL/min at improved conditions. Furthermore, the highest power and current density values are 2794.5 mW/m2 and 2786.1 mA/m2 at 1500 mL-inoculum, biomass amount of 30 g, initial pH of 5.5, and temperature of 37 °C in the dark fermentation (DF) cell. Moreover, the desalination efficiency increases from 41.6 to 65.8% with decreasing inoculum amounts from 1500 to 250 mL. © 2024 The Institution of Chemical EngineersArticle 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).