Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026Scopus SJRQ2/r/bayburt/10958
  Assessment of municipal water quality criteria in consideration of microbiological and physicochemical parameters using a fuzzy decision-making framework

  Suiçmez, Özlem; Teke, Çağatay; Özdemir, Akın

  Water is a significant element for the sustainability of life and is crucial for many aspects, such as agriculture, cities, ecosystems, industries, and people. This study focuses on municipal water quality assessments for urbanization activities and human health concerns when dealing with the sustainable city concept. For the first time, an integrated fuzzy decision-making framework is proposed to evaluate municipal water quality criteria with microbiological parameters, heavy metals, and other chemicals. For this aim, the eight main criteria and forty-four sub-criteria were specified to evaluate municipal water quality for a sustainable city. A case study was performed in Bayburt, Türkiye, to evaluate 130 water depots when dealing with a number of scenarios. Moreover, the Sustainable Development Goals (SDGs) 6.1 and 6.3 can be achieved through the study. Finally, this study presents how to develop safe and clean water management for the concept of a sustainable city.

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  İncele
• MakaleYalnız künye2025Scopus SJRQ1/r/bayburt/11030
  Optimization of surface modification parameters of fly ash with high calcium oxide (CaO) content to use as a filling material

  Uçurum, Metin; Özdemir, Akın; Teke, Çağatay

  Fly ash (FA) is the fine-grained waste product obtained by burning coal after being ground to specific sizes in thermal power plants, carried with flue gases, and kept in cyclones or electro-filters. Like every industrial waste, the possibilities of utilizing FA have been investigated, and it can be utilized as an additive in cement and concrete. Despite this, industrial waste, which increases daily in the world, brings many problems, especially environmental problems. For this reason, alternative usage areas of the waste in question are constantly being investigated. In this paper, FA containing approximately 50 % CaO from Afşin-Elbistan, Turkiye was processed with stearic acid in a planetary mill to be used as a filling material in industrial products, and it has a grain size of 68.10 μm on a d50 basis, a specific surface area (SSA) of 176.40 m2/g and a contact angle of 13.89. An optimization and characterization study was conducted to make the hydrophilic surface structure hydrophobic by mechanochemical surface modification. Also, surface modification parameters, such as operational speed (rpm), ball filling ratio (%), FA filling ratio (%), pulp density, stearic acid dosage (% of FA), and modification time (min.) were optimized with the D-optimal experimental design. Based on the optimum surface modification parameters, a coated fly ash (CFA) product was obtained with an active ratio of 99.70 %, a contact angle of 95.06o, a medium size (d50) size of 10.60 μm, and an SSA of 926.90 m2/g.

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  AtıfScopus8·WoS8·OpenAlex9

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• MakaleAçık Erişim2024/r/bayburt/16459
  Green Supplier Assessment with Fuzzy Expert System Approach

  Teke, Çağatay

  Today, reasons such as increasing pollution on a global extent and limited raw material resources are increasing the interest in green supply chain management (GSCM). GSCM includes the processes starting from the very beginning of the production process of a product, completing the production, delivering the product to the customer, and recycling the product at the end of its useful life. Its main purpose is to eliminate or minimize the damages caused to the environment in all of these processes. In order to achieve this goal, it has great importance to assess the suppliers, which are one of the most important components of the production process, in terms of becoming a green supplier. In this study, a fuzzy expert system model has been developed to assess the green suppliers based on green production technology, environmental management system, pollution control, product cost, quality, and lead time criteria. Mean Square Error (MSE) and coefficient of determination (R2) have been calculated to measure the performance of the developed model. These calculated values show that the green supplier assessment performance of the developed model is quite high.

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  AtıfOpenAlex1·TR Dizin1

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• MakaleAçık Erişim2024Scopus SJRQ4/r/bayburt/12832
  PREDICTION OF TURKEY'S COTTON SOCK EXPORTS TO GERMANY USING DEEP LEARNING APPROACH; [TÜRKİYE'NİN ALMANYA'YA PAMUKLU ÇORAP İHRACATININ DERİN ÖĞRENME YAKLAŞIMI İLE TAHMİNİ]

  Özbek, Ahmet; Teke, Çağatay

  Cotton socks are a strategic export product for Turkey. Therefore, the aim of this study is to forecast Turkey's exports to Germany, the world's largest cotton socks market. In order to achieve this objective, the determinants of exports were identified by analysing the literature. Then, expert opinion was sought to determine the importance of these factors for Turkey's cotton socks exports to Germany. Using the deep learning model created from the factors determined as a result of the expert opinion, the prediction of the export of Turkish socks to Germany was realised. A success rate of 96% was achieved with the prediction.

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  AtıfScopus1·OpenAlex1·TR Dizin1

  İncele
• MakaleAçık Erişim2023/r/bayburt/16609
  Implementation of Fuzzy Linear Programming Approach for More Accurate Demand Forecasting in a Make-to-Stock Company

  Teke, Çağatay

  Demand forecasting is of crucial importance for make-to-stock companies because product demand is uncertain and it changes with time. Fuzzy linear programming (FLP) can be an optimum approach for such uncertain situations. In this study, the FLP model was used for more accurate demand forecasting in a make-to-stock company. Demand forecasting study was carried out according to the FLP method and linear programming (LP) method. Solutions of FLP and LP were compared in terms of imputed shortage cost, inventory carrying cost, and net profit. Results show that the applied FLP method is more advantageous than LP as it provides a 67% decrease in costs and a 15% increase in net profit.

  İncele
• MakaleYalnız künye2023Scopus SJRQ2/r/bayburt/11763
  Prediction of gamma ray spectrum for 22Na source by feed forward back propagation ANN model

  Teke, Cagatay; Akkurt, Iskender; Arslankaya, Seher; Ekmekci, Ismail; Gunoglu, Kadir

  The radiation has been used in a variety of different fields since its discovery and thus its measurement becomes vital in these industries. Different type detector may be used to measure gamma rays depends on the purposes of measurements. Gamma ray energy spectrum is an important to determine either elemental analysing of a sample or radiation shielding purposes. On the other hand, Artificial Neural Network (ANN) may be used to predict and analysing of gamma-ray spectrum. In this study, gamma ray spectrum from 22Na source detected in NaI (Tl) detector was estimated by ANN. There have been installed ten different ANN models to find the network structure that produces the best predictive value for the gamma ray spectrum NaI (Tl) Detector. Estimation study has been continued with the ANN model with be possessed of lowest error value. ANN model was created by using energy, distance and gamma-rays energy spectrum (called Io) values. In the ANN model developed using the feed forward back propagation algorithm, were used artificial neurons two in the input layer, ten in the hidden layer and one in the output layer. For the case of present work, the experimental data was used 70% for education, 20% for validation and 10% for testing. The estimated values obtained with the ANN model were compared with the experimental results and a good correlation has been found between them (R2 = 0.99).

  AtıfScopus18·WoS9·OpenAlex27

  İncele
• MakaleAçık Erişim2022/r/bayburt/15746
  Bireylerin Koroner Arter Hastalığı Risk Seviyesinin Bulanık Uzman Sistem Yaklaşımı İle Belirlenmesi

  Teke, Çağatay

  Koroner Arter Hastalığı (KAH) dünya genelinde insanların hayatını kaybetmesine sebep olan en önemli hastalıklardan biridir. Tıp alanında yaşanan gelişmeler bu hastalığın tedavisini kolaylaştırsa da risk faktörlerinin belirlenmesi ve değerlendirilmesinde hala birtakım yetersizlikler söz konusudur. Bu çalışmada, KAH ile ilgili yaygın belirti ve şikayetleri olan bireyler göz önüne alınarak tanıda kullanılan çeşitli risk faktörleri belirlenmiştir. Ayrıca bulanık uzman sistem yöntemi kullanılarak bireylerin KAH risk düzeylerini tespit etmek amacıyla bir yapay zeka sistemi geliştirilmiştir. Tasarlanan sistem kural tabanlı olup, bu kural tabanı yapısı tıp uzmanlarından edinilen bilgilerle oluşturulmuştur. Sistem, bireylerin hastalık riskini azaltmak için kendi kendine risk değerlendirmesi ve özelleştirilmiş öneriler sunmaktadır. Bu sayede koroner arter hastalığından muzdarip kişilerin sayısındaki artış önlenebilir veya geciktirilebilir.

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  AtıfOpenAlex1·TR Dizin1

  İncele
• MakaleAçık Erişim2022Scopus SJRQ3/r/bayburt/13424
  Investigation of the Effect of Solidification Time and Addition Amount of Inoculation on Microstructure and Hardness in Lamellar Graphite Cast Iron

  Çolak, M.; Uslu, E.; Teke, Ç.; Şafak, F.; Erol, Ö.; Erol, Y.; Çoban, Y.; Yavuz, M.

  Material suppliers typically recommend different additive amounts and applications for foundry practices. Therefore, even in the production of the same standard materials, different results may be obtained from various production processes on different foundry floors. In this study, the liquid metal prepared with the addition of different proportions of a FeSi-based inoculation, which is most commonly used in foundries in the production of a cast iron material with EN-GJL-250 lamellar graphite cast iron, was cast into sand molds prepared with a model designed to provide different solidification times. In this way, the optimization of the inoculation amounts on the casting structure for different solidification times was investigated. In addition, hardness values were determined depending on solidification time in varying amounts of inoculation additions. SolidCast casting simulation software was used to determine the casting model geometry and solidification time. In the scope of the study, sand casting, modeling, microstructure analysis, image analysis, microstructure analysis, and hardness tests techniques were used. When the results are examined, the required amount of inoculation for the optimal structure is optimized for the application procedure depending on the casting module and the solidification time.

  AtıfScopus2·WoS2·OpenAlex2

  İncele
• MakaleAçık Erişim2022Scopus SJRQ2/r/bayburt/13395
  Determination of flow distance of the fluid metal due to fluidity in ductile iron casting by artificial neural networks approach

  Teke, Çağatay

  Ductile irons (DIs) have properties such as high strength, ductility, and toughness, as well as a low degree of melting, good fluidity, and good machining. Having these characteristics make them the most preferred among cast irons. The combination of excellent properties, especially in DI castings with a thin section, make it an alternative for steel casting and forging. But in the manufacture of thin-section parts, fluidity characteristics need to be improved and the liquid metal must fill the mold completely. The fluidity of liquid metal is influenced by many factors depending on the casting processes such as material and mold properties, casting temperature, inoculation, globalization, and grain refinement. In this study, an artificial neural network (ANN) model has been developed that allows for determining the flow distance of the liquid metal in the sand mold casting method under changing casting conditions of DI. Thus, the flow distance was estimated depending on the cross-sectional thickness during the sand casting under changing casting conditions. The experimental parameters were determined as casting temperature, liquid metal metallurgy quality, cross-sectional thickness, and filling time. Filling modeling was performed with FlowCast software. When the results were examined, it was seen that the developed ANN model has high success in predicting the flow distances of the liquid metal under different casting conditions. The calculated coefficient of determination (R2) value of 0.986 confirms the high prediction performance of the model.

  AtıfScopus4·WoS4

  İncele
• MakaleAçık Erişim2021Scopus SJRQ3/r/bayburt/13310
  A novel approach to finding optimum operating conditions of design factors for the grinding experiment

  Uçurum, Metin; Özdemir, Akın; Teke, Çağatay; Tekin, İlker

  The efficiency of grinding experiments is an important issue for many industries. In this paper, a central composite design-based methodology was proposed to investigate the four design factors that affect the particle sizes. The four design factors were specified as mill speed (% of Nc), ball filling ratio (fc), powder filling ratio (jb) and grinding time (min). Another important issue was how to obtain an optimum operating condition for four design factors. For this particular purpose, a novel dual response optimization model was proposed using the particle sizes (d 10, d 50, and d 90) and the span value concept. This proposed approach was compared to the desirability function-based optimization concept. The verification study of the experiment was also carried out. The results of the grinding experiment runs showed that the optimum operating conditions were mill speed 73.495% of Nc, ball filling ratio 0.354, powder filling ratio 0.157, and grinding time 70 min. In addition, d 10, d 50, and d 90 were found 3.31 µm, 12µm, and 45.6 µm, respectively. The span value was also found at 3.52.

  AtıfScopus3·WoS3·OpenAlex4

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