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  • Küçük Resim
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    Micro-Turbine Design, Production and Testing
    (İstanbul Gelişim Üniversitesi Yayınları / Istanbul Gelisim University Press, 2015-12-23) Sogukpınar, Hacı; Bozkurt, İsmail; Baran, M. Fırat; Türkmenler, Harun; Pala, Murat; Engin, K. Emre; Kaya, A. İhsan
    Large scale utilization of electricity started at the end of 19th century with the construction first power plant and there phase current was introduced. Power plant technology evolved rapidly and electricity use has increased rapidly since then. Outbreak of energy crisis in 1970s and threat of the global warming has forced the people to search clean energy resources. Among the renewable energy sources, wind energy has become the most popular case. Development of the necessary technology for wind turbines reached a commercial competence In the 1990s. Turning to wind energy in Turkey began after 2006 and has shown a rapid increase until 2015. When considering the country's wind potential it tends to stay in rapid growth. In this study, 2 kW micro-turbines is designed, manufactured and tested by using local facility. The aim of this study is to design a micro-turbine for use in low wind speed area, create industrial infrastructure related to the production of micro-turbine, and develop different production technologies for local industry.
  • Küçük Resim
    Öğe
    Performance Assessment of Advanced Biological Wastewater Treatment Plants Using Artificial Neural Networks
    (İstanbul Gelişim Üniversitesi Yayınları / Istanbul Gelisim University Press, 2017-09-26) Türkmenler, Harun; Pala, Murat
    In this study, the application of Artificial Neural Network (ANN) techniques was used to predict the performance of wastewater treatment plant. The ANN-based model for prediction of effluent biological oxygen demand (BOD) concentrations was formed using a three-layered feed forward ANN, which used a back propagation learning algorithm. Based on the mean absolute percentage error (MAPE), the sum of the squares error (SSE), the absolute fraction of variance (R2), the root-mean-square (RMS), the coefficient of variation in percent (cov) values, and ANN models predicted effluent BOD concentration. The R2 values were found to be 94.13% and 93.18% for the training and test sets of treatment plant process, respectively. It was found that the ANN model could be employed successfully in estimating the daily BOD in the effluent of wastewater biological treatment plants.