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Öğe An economical electrocoagulation process of a hazardous anionic azo dye wastewater with the combination of recycled electrodes and solar energy(Springer Heidelberg, 2023) Akkaya, Gulizar Kurtoglu; Polat, Gokhan; Nalcaci, Gamze; Eker, Yasin RamazanThe energy and electrode costs are the restrictions of applying electrocoagulation (EC) in wastewater treatment and many attempts have been made to decrease these costs. In this study, an economical EC was investigated to treat a hazardous anionic azo dye wastewater (DW) that threatens the environment and human health. Firstly, an electrode for EC process was produced from recycled aluminum cans (RACs) by remelting in an induction melting furnace. The performance of the RAC electrodes in the EC was evaluated for COD, color removal, and the EC operating parameters such as initial pH, current density (CD), and electrolysis time. Response surface methodology which is based on central composite design (RSM-CCD) was used for the optimization of the process parameters which were found to be pH 3.96, CD 15 mA/cm(2), and electrolysis time 45 min. The maximum COD and color removal values were determined as 98.87% and 99.07%, respectively. The characterization of electrodes and the EC sludge was conducted by XRD, SEM, and EDS analyses for the optimum variables. In addition, the corrosion test was conducted to determine the theoretical lifetime of the electrodes. The results showed that the RAC electrodes show an extended lifetime as compared to their counterparts. Secondly, the energy cost required to treat DW in the EC was aimed to decrease by using solar panels (PV), and the optimum number of PV for the EC was determined by the MATLAB/Simulink. Consequently, the EC with low treatment cost was proposed for the treatment of DW. An economical and efficient EC process for waste management and energy policies was investigated in the present study which will be instrumental in the emergence of new understandings.Öğe LONG-TERM WIND POWER AND GLOBAL WARMING PREDICTION USING MARS, ANN, CART, LR, AND RF(Amer Inst Mathematical Sciences-Aims, 2023) Yilmaz, Yavuz; Nalcaci, Gamze; Kanczurzewska, Marta; Weber, Gerhard WilhelmThe modeling of electricity generation plays a crucial role in investment and long-term planning in power systems, primarily due to the significant volatility associated with wind and solar energy sources. Nevertheless, forecasting wind speeds for wind turbines based on weather conditions over an extended period is difficult and not feasible. This study provides long-term projections for wind power generation derived from a 2 MW wind turbine for the upcoming year and subsequent years utilizing the Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), Classification And Regression Tree (CART), Linear Regression (LR) and Random Forest (RF) techniques. The research is carried out in two distinct phases. During Phase 1 all considered predictive methods are compared. The research demonstrates that the MARS algorithm is a robust and efficient predictor for wind based power generation, exhibiting strong competitiveness in its performance. During Phase 2, the MARS algorithm is employed to forecast the future 30 year wind power generation capacity lifespan hourly for nine cities in Texas, USA. It is projected that El Paso and Dallas will witness a mean rise of 8.6% in wind power capacity over three decades, while the remaining seven cities are anticipated to have an average decline of 7.7%. Hence, it is imperative to do a comprehensive and extended evaluation employing the MARS technique compared to ANN, CART, LR and RF before installing a wind turbine. This analysis would serve as a crucial resource for investors, engineers, and researchers involved in decision-making processes on wind turbine projects.