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- MakaleAçık Erişim2025Scopus SJRQ1Sol–Gel Synthesized CuFe2O4-Modified Biochar Derived from Tea Waste for Efficient Ni(II) Removal: Adsorption, Regeneration, and ANN Modeling
Duran, Celal; Ozeken, Sengul Tugba; Seker, Serdal; Ozdes, Duygu
In the present research, a novel magnetic adsorbent was developed via the sol–gel method by coating CuFe2O4 nanoparticles on biochar sourced from brewed tea waste. The synthesized adsorbent was utilized for the removal of Ni(II) ions from aqueous media. The adsorption efficiency of Ni(II) ions was assessed under crucial experimental conditions such as initial solution pH, contact time, adsorbent dosage, and initial Ni(II) concentration. The adsorbent exhibited rapid adsorption kinetics, achieving equilibrium in approximately 15 min, and maintained high efficiency across a wide pH range. Adsorption experiments were conducted for Ni(II) solutions at their natural pH (5.6) to minimize chemical usage and enhance process simplicity. An impressive maximum adsorption capacity of 232.6 mg g−1 was recorded, outperforming many previously reported adsorbents. Furthermore, desorption studies demonstrated nearly 100% recovery of Ni(II) ions using 1.0 M HCl solution, indicating excellent regeneration potential of the adsorbent. Additionally, the prediction performance of an artificial neural network (ANN) model was evaluated to predict Ni(II) removal efficiency based on experimental variables, showing strong agreement with experimental data. Isotherm and kinetic models were also applied to the data to estimate the adsorption mechanisms. These findings demonstrate the promise of CuFe2O4-modified tea waste biochar for sustainable water treatment applications.
AtıfScopus4WoS4OpenAlex4İncele - MakaleYalnız künye2022Scopus SJRQ2Simultaneous autonomous system and powerplant design for morphing quadrotors
Sahin, Huseyin; Kose, Oguz; Oktay, Tugrul
Purpose This study aims to optimize autonomous performance (i.e. both longitudinal and lateral) and endurance of the quadrotor type aerial vehicle simultaneously depending on the autopilot gain coefficients and battery weight. Design/methodology/approach Quadrotor design processes are critical to performance. Unmanned aerial vehicle durability is an important performance parameter. One of the factors affecting durability is the battery. Battery weight, energy capacity and discharge rate are important design parameters of the battery. In this study, proper autopilot gain coefficients and battery weight are obtained by using a stochastic optimization method named as simultaneous perturbation stochastic approximation (SPSA). Because there is no direct correlation between battery weight and battery energy density, artificial neural network (ANN) is benefited to obtain battery energy density corresponding to resulted battery weight found from SPSA algorithm. By using the SPSA algorithm optimum performance index is obtained, then obtained data is used for longitudinal and lateral autonomous flight simulations. Findings With SPSA, the best proportional integrator and derivative (PID) coefficients and battery weight, energy efficiency and endurance were obtained in case of morphing. Research limitations/implications It takes a long time to find the most suitable battery values depending on quadrotor endurance. However, this situation can be overcome with the proposed SPSA. Practical implications It is very useful to determine quadrotor endurance, PID coefficients and morphing rate using the optimization method. Social implications Determining quadrotor endurance, PID coefficients and morphing rate using the optimization method provides advantages in terms of time, cost and practicality. Originality/value The proposed method improves quadrotor endurance. In addition, with the SPSA optimization method and ANN, the parameters required for endurance will be obtained faster and more securely. In addition, the energy density according to the battery weight also contributes to the clean environment and energy efficiency.
AtıfWoS40OpenAlex43İncele - BildiriYalnız künye2017Artificial intelligent models for thermodynamic properties of R152a and R134a refrigerants in automobile air conditioning systems
Kalkisim, Ahmet Tahir; Bilen, Kadir; Hasiloglu, Abdussamet; Sobh, T.; Elleithy, K.
With the increase in environmental awareness, the quest for alternative refrigerants for automobile air conditioning systemsis a necessity for the protection of the ozone layer and the prevention of global warming. Therefore, it is important that the refrigerant to be used as an alternative must be at least thermodynamically sufficient and within specified environmental limits. In this study, R152a refrigerant which is an alternative gas for R134a that's use in automobile air conditioning systems will be removed within a certain schedule has been examined thermodynamically. Artificial intelligent model was generated and compared by using data obtained from the examination. Through this model, system was tested with untrained data. The generated model has been very close to test data and successful in high precision.
- MakaleAçık Erişim2014Scopus SJRQ1Dynamic mechanical spectra of selected turkish honeys: Construction of predictive models for complex viscosity using two different nonlinear modeling techniques
Kayacier, Ahmed; Yüksel, Ferhat; Karaman, Safa
Dynamic oscillatory shear rheological characteristics of honey samples from different floral sources were evaluated using frequency sweep test and stress sweep test at three different temperatures (10, 15, and 20°C). All honey samples showed liquid-like behavior because the loss modulus was significantly greater than the elastic modulus. Pine honey showed the highest complex viscosity (86.33 Pa s at 10°C), while the lowest complex viscosity was observed in citrus honey (22.15 Pa s) at the same temperature. Temperature dependency of complex viscosity of honey samples was modeled by Arrhenius model and activation energy values of honeys ranged from 83.13 to 97.46 kJ/mol. An efficient predictive model for complex viscosity values of honeys was constructed using adaptive neuro fuzzy inference system that showed satisfactory prediction performance with high coefficient of determination (0.995) and low root mean square error (0.04) in the validation period compared to artificial neural networks.
AtıfScopus8WoS8OpenAlex9İncele