Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Comparative Analysis of Material Footprints in Electricity Generation of Deep Learning-Based Prediction Model and Energy Development Scenarios(SAGE Publications Ltd, 2026-03-25) Celik, Yasin; Unlu, Ramazan; Algorabi, Omer; Kocakaya, Mustafa Nabi; Aktog, Mehmet Arif; Namli, ErsinEscalating global production and consumption are driving rapid growth in energy demand, increasing pressure on finite natural resources. In response, this study proposes a data-driven framework that integrates deep learning-based electricity demand forecasting with economy-wide input-output material footprint analysis to support long-term energy planning and policymaking. The innovative aspect of this framework is its ability to jointly assess future electricity generation and related material requirements within a single analytical structure. A comparative analysis is conducted for Türkiye, Germany, and Spain, evaluating the material footprint of electricity generation across renewable and fossil-based energy sources under business-as-usual (BAU) and alternative energy development scenarios. The forecasting models demonstrate strong predictive performance, achieving Mean Absolute Percentage Error (MAPE) values of 1.39% for Türkiye, 4.39% for Germany, and 3.90% for Spain, significantly outperforming conventional statistical methods. Scenario-based results indicate that sustainability-oriented pathways (ST and GCA) can reduce material requirements by approximately 20-30% compared to the BAU scenario, particularly for metal-intensive inputs such as iron and refined oil. The findings underscore the importance of integrating material footprint considerations into energy transition strategies and provide practical insights for policymakers seeking to balance energy security with resource sustainability. The study highlights the value of integrated analytical approaches in supporting more resilient and resource-efficient energy systems.Article Contributions Toward Net-Zero Carbon in the Water Sector: Application to a Case Study(IWA Publishing, 2025-09-01) Ramos, Helena M.; Perez-Sanchez, Modesto; Correia, Tiago; Bekci, E.; Besharat, M.; Kuriqi, Alban; Coronado-Hernandez, Oscar E.This study presents an integrated smart water-energy nexus framework combining IoT-based water monitoring, hybrid renewables (hydropower/solar/wind), and AI-driven optimization. Real-time sensor data enables automated grid management, while AI analytics optimize operations and predict maintenance needs through a closed-loop system. The solution achieves bidirectional energy exchange, with the full hybrid system (G + H + PV + W) reducing costs by 41.5% (<euro>831K) and LCOE by 57.2% (<euro>0.0475/kWh). Financial analysis confirms viability with 26.4% IRR and 3.8-year payback, while achieving negative CO2 emissions (-160,476 kg/year). Progressive renewable integration enhances all key performance indicators (KPIs), cutting OPEX by 89.9% (<euro>7,156/year) through optimized operations. Dual water-energy performance metrics (leakage, pressure, % renewable share) ensure balanced and sustainable grid management. Key innovations include IoT-energy synergy, AI-driven predictive maintenance, and circular resource efficiency. The framework demonstrates how smart water grids can achieve both economic and environmental benefits through renewable energy integration and advanced digital solutions.Conference Object Citation - Scopus: 1Incorporating Worker Heterogeneity in Flexible Flow Shop Environment(ISRES Publishing, 2025-08-15) Ozpacaci, Kubra; Bekli, Seyma; Kayisoglu, BetulWe study the flexible flow shop scheduling problem with the heterogeneous worker assignment. In many real-life manufacturing systems with flow shop environments, one of the fundamental scheduling challenges that needs to be addressed is job sequences across multiple workers. In addition, the manufacturing system may require workers to have different skills at various stages during their assignment. Therefore, worker availability at each stage may vary during the scheduling horizon. Unlike traditional flexible flow shop scheduling problem, where homogeneous workers are assumed, we consider workers with different skill levels, capabilities, and capacities. We present a mixed integer linear programming model to find the optimal sequence of job assignments, guaranteeing that jobs follow their predefined operation sequence while assigning workers with various skill sets in a flexible flow shop environment. The proposed model is tested at a battery manufacturing company. By analyzing the solution, we confirm its capability to represent the problem accurately. The proposed model offers a systematic scheduling approach for a flexible flow shop environment with a heterogeneous workforce and can be implemented in other industries. © 2025 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Hybrid Renewable Energy to Greener and Smarter Cities: A Case Study of Kayseri Province(Springer Science and Business Media Deutschland GmbH, 2024) Bekçi, Eyüp; Koca, KemalIn this study, a hybrid energy system was implemented to fulfill the electricity requirements of the trams operating in Kayseri province. The tram's annual electricity consumption data was acquired on a monthly basis from the local electricity company in Kayseri. Utilizing the obtained data, energy and cost simulations were conducted employing the Homer-Pro program. The primary objective of this investigation is to enhance sustainability while satisfying electricity demands with minimal carbon emissions. Consequently, the established hybrid energy system incorporates renewable energy sources, specifically wind, solar, and biomass energy, with the inclusion of batteries for energy storage. Furthermore, generators and converters are integrated for energy conversion purposes. The study encompasses a detailed cost analysis to identify the most economically efficient hybrid energy system, determined through optimization studies. Through this research, it is anticipated that the implementation of such a system will significantly diminish carbon emissions in Kayseri, contributing to a substantial increase in sustainability. © 2024 Elsevier B.V., All rights reserved.Article Citation - WoS: 9Citation - Scopus: 11Evaluation of Diatomite Substitute With Thermal Power Plant Waste Fly Ash in Sustainable Geopolymer Through Life Cycle Assessment(Springer, 2025-02-28) Ilkentapar, Serhan; Orklemez, Ezgi; Durak, Ugur; Gulcimen, Sedat; Bayram, Savas; Uzal, Nigmet; Atis, Cengiz DuranThis research demonstrates the potential of diatomite as a fly ash replacement to improve mechanical properties and environmental sustainability and presents it as a viable alternative for sustainable construction. Additionally, a life cycle assessment (LCA) was conducted on the produced mortars to quantitatively compare their environmental impacts using a cradle-to-gate approach. In mixtures, it was used by replacing the diatomite in the ratios of 1%, 2%, 3%, 4%, and 5% by weight of the fly ash. Workability, unit weight, flexural and compressive strength, abrasion resistance, elevated temperature resistance and microstructure analysis were carried out. The results indicated that replacing 1%, 2%, and 3% diatomite increased the compressive and flexural strength of mortars due to their higher specific surface area. Two percent replacement of diatomite provided the best results. FESEM results of 3% diatomite inclusion showed more intense and compact microstructure of geopolymer. Diatomite inclusion increased the abrasion resistance of geopolymer. Since 2% diatomite replacement was found to be optimum, the LCA results showed that geopolymer mortar with 2% diatomite has 25% lower impacts in terms of global warming potential and 10% lower impacts in terms of terrestrial ecotoxicity than conventional Portland cement mortar.Article Citation - Scopus: 24Analyzing the Nexus Between Environmental Sustainability and Clean Energy for the USA(Springer, 2024-03-22) Dogan, Eyup; Si Mohammed, Kamel; Khan, Zeeshan Anis; BinSaeed, Rima Hassan; Mohammed, Kamel SiEnvironmental sustainability is a key target to achieve sustainable development goals (SDGs). However, achieving these targets needs tools to pave the way for achieving SDGs and COP28 targets. Therefore, the primary objective of the present study is to examine the significance of clean energy, research and development spending, technological innovation, income, and human capital in achieving environmental sustainability in the USA from 1990 to 2022. The study employed time series econometric methods to estimate the empirical results. The study confirmed the long-run cointegrating relationship among CO<inf>2</inf> emissions, human capital, income, R&D, technological innovation, and clean energy. The results are statistically significant in the short run except for R&D expenditures. In the long run, the study found that income and human capital contribute to further aggravating the environment via increasing CO<inf>2</inf> emissions. However, R&D expenditures, technological innovation, and clean energy help to promote environmental sustainability by limiting carbon emissions. The study recommends investment in technological innovation, clean energy, and increasing R&D expenditures to achieve environmental sustainability in the USA. © 2024 Elsevier B.V., All rights reserved.