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Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Access Right: Closed AccessAuthor: search.filters.author.Kızılkaya Aydoğan, Emel

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  • Article
    Citation - WoS: 21
    Citation - Scopus: 25
    The Impact of Organic Cotton Use and Consumer Habits in the Sustainability of Jean Production Using the LCA Approach
    (Springer Heidelberg, 2022-09-14) Fidan, Fatma Sener; Aydogan, Emel Kizilkaya; Uzal, Nigmet; Şener Fidan, Fatma; Kızılkaya Aydoğan, Emel
    Due to the rise in clothing consumption per person and growing consumer awareness of environmental issues with products, the textile industry must adopt new practices for improving sustainability. The current study thoroughly investigates the benefits of using organic cotton fiber instead of conventional cotton fiber. Because of the extensive use of natural resources in the production of cotton, the primary raw material for textiles, which accounts for the environmental effects of a pair of jeans, a life cycle assessment methodology was used to examine these effects in four different scenarios. The additional scenarios were chosen based on the user preferences for washing temperatures, drying methods, and the type of cotton fiber used in the product. The environmental impact categories of global warming potential, eutrophication potential terrestrial ecotoxicity potential, acidification potential, and freshwater ecotoxicity potential were analyzed by the CML-IA method. The life cycle assessment results revealed that the lowest environmental impacts were obtained for scenario 4 with 100% organic cotton fiber with an improvement of 87% in terrestrial ecotoxicity potential and 59% in freshwater ecotoxicity potential. All of the selected environmental impacts of a pair of jeans are reduced in all scenarios when organic cotton is used. Additionally, consumer habits had a significant impact on all impact categories. Using a drying machine instead of a line dryer during the use phase is just as important as the washing temperature. The environmental impact hotspots for a pair of jeans were revealed to be the eutrophication potential, acidification potential, and global warming potential categories during the use phase, and the terrestrial ecotoxicity potential and freshwater ecotoxicity potential categories during the fabric manufacturing including cotton cultivation. The use of organic cotton as a raw material in manufacturing processes, as well as consumer preferences for washing temperature and drying methods, appears to have significant environmental impacts on a pair of jeans' further sustainable life cycle.
  • Article
    Citation - WoS: 34
    Citation - Scopus: 38
    Green Building Envelope Designs in Different Climate and Seismic Zones: Multi-Objective ANN-Based Genetic Algorithm
    (Elsevier, 2022-10) Himmetoglu, Salih; Delice, Yilmaz; Aydogan, Emel Kizilkaya; Uzal, Burak; Kızılkaya Aydoğan, Emel
    In recent years, the major component of green building designs adopted by governments in order to reduce CO2 emissions as well as energy consumption is the green building envelope. The green envelope has the most important share in terms of thermal energy consumption, environment, and indoor comfort criteria. Determining the most suitable building envelope combination in the building life cycle is an important problem for designers. This study presents a new multi-objective approach that determines the most suitable green envelope designs for the buildings in different climate and earthquake zones, taking into account CO2 emissions, heating/cooling energy consumption, and material cost in terms of life cycle cost analysis. To this end, EnergyPlus building performance simulation program, artificial neural network (ANN), and genetic algorithm are used together. After the heating and cooling energy consumption, CO2 emissions, and material cost values are obtained for a certain number of the envelope alternatives with the EnergyPlus, ANN models that learn the working mechanism of EnergyPlus are trained according to these values. An ANN-based genetic algorithm procedure is developed to search the whole envelope alternative space by using the trained ANN models with EnergyPlus. The proposed approach allows searching in a very short time the whole alternative space, which is almost impossible to scan with EnergyPlus by reducing the time spent and the number of alternatives required for the design and simulation processes of the green building envelope. The proposed approach is performed for a design-stage city hospital structure in Turkey. Window type, the internal/external plaster, wall, and insulation materials along with the thicknesses of these materials, which consist of 46 different variables, are determined as envelope attributes for four different climate and seismic zones. The green building envelope designs obtained with the proposed approach are entered into EnergyPlus and the consistency of the results is compared. ANN models with an average accuracy of over 97% are developed. Without the CO2 emission cost in the life cycle cost, the mean absolute percent error (MAPE) values for each region are 0.67%, 0.6%, 0.58%, and 1.78%, respectively. With the CO2 emission cost in life cycle cost, the MAPE values for each region are 0.96%, 0.88%, 0.86%, and 0.43%, respectively. According to the obtained results, there is a consistency of over 99% between EnergyPlus and the proposed approach.