Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Book Part Waste to Energy Management(Elsevier, 2025) Yagmur Goren, A.Y.; Kalinci, Y.; Dincer, I.Today, the world faces growing challenges with waste problems since people have moved the problems from past to future. The key question is: is waste a problem or a resource? The correct response to this question can be found by investigating, in more detail, the types of waste and implemented waste management methods. The chapter consists of six main sections. The first section is focused on classification, which explains what waste is and categorizes it according to the producer (e.g., municipal, industrial, and hazardous) and chemical composition (for instance, organic, inorganic, and microbiological). The second section presents legislative trends. It is seen that the waste management legislations are changing from country to country. Also, it can change over time because every technological development emerges its waste. The third section covers waste management methods such as recycling, refuse-derived fuel, landfill, and thermal methods. The landfill method is the oldest and the cheapest one. It is seen that the method will continue in the near future, too, though a lot of legal regulations have been made to reduce its usage. Thermal methods are commonly used in the industrial sector. Hence, thermal methods such as incineration, pyrolysis, and gasification are examined in detail. Considering environmental issues, thermal technology moves toward gasification systems to reduce greenhouse gas emissions and the formation of by-products. The fourth section presents illustrative examples related to using waste management methods or their combinations. Further, a case study, which consists of a circulated fluidized bed gasification system, is investigated from the exergy and exergoeconomic points of view. The chapter presents exergy and exergoeconomic analyses in detail. The analyses show that it can produce 1.17 MWe power and 0.521kg/s hydrogen with 3.33 $/kg cost from 8.5kg/s biomass waste. Finally, future scenarios for waste management are investigated. Also, to achieve zero waste targets in the future, circular economy and industrial symbiosis concepts are examined, and some successful examples from around the world are presented. © 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.Article Machine Learning Integrated Solvothermal Liquefaction of Lignocellulosic Biomass to Maximize Bio-Oil Yield(Elsevier Sci Ltd, 2025) Ocal, Bulutcem; Sildir, Hasan; Yuksel, AsliAccelerating consumption of limited fossil-based for economic growth and simultaneously mitigating greenhouse gas emissions create a dilemma that is waiting to be solved by researchers. In this context, solvothermal liquefaction of lignocellulosic biomass to produce bio-oil is a promising way to obtain green energy. However, maximizing bio-oil is challenging to optimize the operating parameters employing conventional techniques due to the complexity and non-linearity of the process. Lately, machine learning approaches have become powerful tools for addressing complex nonlinear problems by predicting process behavior and regulating operating parameters for optimization by learning from datasets. The current research demonstrates integrating experimental and a developed artificial neural network model to optimize solvothermal liquefaction of pinus brutia, based on temperature, water fraction, and biomass amount in maximizing bio-oil generation for the first time. The highest bio-oil yields were obtained at 31.40 %, 18.68 %, and 39.69 %, respectively, with 4 and 8 g biomass in the presence of water, ethanol, and water/ethanol mixture at 240 degrees C. Under the model conditions, the maximum biooil yield was experimentally verified at 46.20%, which was predicted at 48.8 %. Beyond providing accurate yield predictions, the approach highlights the potential of date-driven modeling to reduce experimental workload and cost while aiding parameter selection to improve efficiency. These outcomes emphasize the importance of machine learning integration into liquefaction process, providing remarkable results for future process design, optimization, and scalability. On the other hand, the study also includes characterization results (ultimate, proximate, FTIR, and GC-MS) of selected products and pinus brutia.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.Article Citation - WoS: 5Citation - Scopus: 5Nanoarchitectonics Approach To Graphite/Starch-supported Bioelectrode for Enhanced Supercapacitor Performance(Elsevier, 2025) Goren, Aysegul Yagmur; Dincer, IbrahimThere has been an increasing interest in finding suitable materials for supercapacitor applications in response to the growing need for energy, to use alternative energy sources to fossil fuels in addition to energy storage. In this regard, bio-based carbon-loaded materials can be a promising option for high-performance supercapacitors because of their abundance, diversity, and reproducibility with waste management strategies. In this study, a new graphite-loaded bioelectrode is synthesized for supercapacitor application. The electrochemical performance of the synthesized electrode is tested at room temperature using the cyclic voltammetry method, and the capacity and energy density of the electrodes are evaluated. The electrochemical performance of 1 g of graphiteloaded bioelectrode was 3.5 mA/cm2, while the specific capacitance value was 355.6 F/g at a current density of 0.5 A/g. Furthermore, the bioelectrode provided significant cyclic stability with 93.5% in specific capacitance value after 5000 charge/discharge cycles at the current density of 0.5 A/g. Consequently, the synthesized bioelectrode can be a promising option for energy storage as a sustainable electrode due to its superior conductivity, stability, and low cost.Article Citation - WoS: 6Citation - Scopus: 6A New Electro-Biomembrane Integrated Renewable-Based System To Produce Power, Fresh Water and Hydrogen for Sustainable Communities(Elsevier, 2025) Goren, A. Yagmur; Dincer, Ibrahim; Khalvati, AliAs the consequences of global warming become more severe, it is more crucial than ever to capitalize on all locally accessible potential renewable energy sources and produce sufficient useable energy outputs to meet community demands while causing the least damage to the ecosystem. Therefore, this paper focuses on a unique parabolic trough collector solar system-powered electro-biomembrane unit that combines a heat and power system with fresh water, electricity and hydrogen production. The proposed integrated system contains the following subsystems: a combining parabolic trough collector solar system, an organic Rankine cycle, a steam Rankine cycle, a multi-stage flash desalination system, and an electro-biomembrane H2 and freshwater production system. A thorough analysis and parametric research are performed on the multigeneration system to determine how important characteristics affect system performance and evaluate the energy and exergy efficiencies, and exergy destruction levels for particular system elements. The study results show that solar irradiation is the most critical parameter for improving system performance. The highest freshwater production of 1,303,333.3 L/day is observed at the solar irradiation of 935,768 kWh/day. Furthermore, the combined output of three electricity production technologies exceeds 2,000,000 kWh/day, highlighting the ability of the system to harness solar thermal energy effectively. The study findings indicate that using solar power and biomass as renewable energy sources, the proposed integrated system provided 328.56 kg of biohydrogen per day. Overall, the energy and exergy efficiencies of the integrated system are obtained as 34.3 and 29.5 %, respectively.Article Citation - WoS: 10Citation - Scopus: 10The Role of Effective Catalysts for Hydrogen Production: a Performance Evaluation(Pergamon-elsevier Science Ltd, 2025) Goren, A. Yagmur; Temiz, Mert; Erdemir, Dogan; Dincer, IbrahimIn recent years, research on hydrogen (H2) production for alternative and environmentally-benign energy solution as fuel, storage medium and feedstock has been one of the most highly demanded subjects. It aims to reduce the pressures set by carbon dioxide emissions and the depletion of fossil fuel supplies. Nevertheless, largescale H2 production is limited by its high cost and low yield. The distinct photo-electrochemical characteristics of catalysts have shown them to have great promise for enhancing the production of H2. This article presents an updated and comprehensive review of enhanced H2 production using various catalysts in biological, thermochemical, and water-based processes. Various operational parameters (reactor configuration, catalyst dosage, catalyst type, catalyst modification methods, temperature, pH, and inoculum type) are summarized to improve the H2 production performance and reduce the environmental impacts and costs of these processes. For instance, in dark fermentation, biological H2 production is enhanced by 3.2-38 % with certain metal catalysts. Overall, results revealed that catalysts, specifically inorganic catalysts such as iron, nickel, titanium oxide, and silver, have improved the production rate of H2. This review has provided the application fields and working principles of catalysts in different H2 production processes. Finally, we suggested the main concerns that need to be prioritized in the long-term advancement of H2 production using catalysts.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: 7Citation - Scopus: 7Hydrogen Production From Energetic Poplar and Waste Sludge by Electrohydrogenesis Using Membraneless Microbial Electrolysis Cells(Pergamon-elsevier Science Ltd, 2024) Goren, A. Yagmur; Kilicaslan, A. Faruk; Dincer, Ibrahim; Khalvati, AliMembraneless microbial electrolysis cells (MECs) are potentially considered to produce biohydrogen (bioH2) in a green manner and simultaneously minimize agricultural and wastewater facility wastes. However, effective, sustainable, and cost-effective system configuration and improvement of operating variables, working at ambient conditions, are needed to make the MEC a sustainable process. Therefore, this study investigates the bioH2 production from poplar leaves and anaerobic sludge mixture by incorporating nanomaterials comprising Al2O3, MgO, and Fe2O3 metal oxides at various dosages. Moreover, the effects of applied cell voltage (0.5-1.5 V) and inoculum amount (20-40 mL) on bioH2 production and organic matter removal performance are evaluated. The maximum bioH2 production value is 417 mL at an applied voltage of 1.5 V with a chemical oxygen demand (COD) removal efficiency of 37.6 % under operating times of 5 min using 40 ml of inoculum. The bioH2 production of the MEC system is reduced with the decrease in inoculum amount. The highest bioH2 production of 828 mL is obtained at improved conditions in the presence of 1 g of Fe2O3 metal oxide. Overall, this study provides the potentiality of simultaneous waste minimization and bioH2 production under ambient conditions that highlight the waste-to-energy pathway for membraneless and green bioelectrochemical process.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 LLC