Yazar "Bayramoglu, Mahmut" seçeneğine göre listele

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    Mineralization of o-tolidine by electrooxidation with BDD, Ti/Pt and MMO anodes
    (Desalination Publ, 2019) Can, Orhan Taner; Bayramoglu, Mahmut; Sozbir, Mustafa; Aras, Omur
    The present study deals with the electrooxidative mineralization of o-tolidine from aqueous solution using various Ti/Pt, mixed metal oxide (MMO) and boron doped diamond (BDD) anodes. The experiments were carried out in two phases. In the first phase, the effect of anode type on the total organic carbon (TOC) removal efficiency was investigated at various pH levels. Furthermore, the second phase was carried out with the most effective anode, to investigate the effects of various operating parameters on the TOC removal efficiency, such as current density, stirring speed, inter electrode distance, concentrations of o-tolidine and the electrolyte. Also, specific energy consumption (SEC) based on the amount of electricity consumed for TOC removal was estimated. The results showed that BDD anode was much more efficient than Ti/Pt and MMO anodes for the mineralization of o-tolidine. The current density and stirring speed were the most effective parameters. With BDD anode, TOC removal efficiency realized as 54.6% and 79.9% for the current density of 25 mA/cm(2) and 125 mA/cm(2) respectively, at 150 min of processing time. On the other hand, TOC removal efficiency realized as 51.5% and 79.1% at stirring speeds of 0 and 1000 rpm at 150 min.
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    Optimization of ultrasound-assisted extraction of phenolic compounds from grapefruit (Citrus paradisi Macf.) leaves via D-optimal design and artificial neural network design with categorical and quantitative variables
    (Wiley, 2018) Cigeroglu, Zeynep; Aras, Ömür; Pinto, Carlos A.; Bayramoglu, Mahmut; Kirbaslar, Sismail; Lorenzo, Jose M.
    BACKGROUND: The extraction of phenolic compounds from grapefruit leaves assisted by ultrasound-assisted extraction (UAE) was optimized using response surface methodology (RSM) by means of D-optimal experimental design and artificial neural network (ANN). For this purpose, five numerical factors were selected: ethanol concentration (0-50%), extraction time (15-60 min), extraction temperature (25-50 degrees C), solid:liquid ratio (50 - 100 gL(-1)) and calorimetric energy density of ultrasound (0.25-0.50 kW L-1), whereas ultrasound probe horn diameter (13 or 19 mm) was chosen as categorical factor. RESULTS: The optimized experimental conditions yielded by RSM were: 10.80% for ethanol concentration; 58.52 min for extraction time; 30.37 degrees C for extraction temperature; 52.33 g L-1 for solid:liquid ratio; 0.457 kW L-1 for ultrasonic power density, with thick probe type. Under these conditions total phenolics content was found to be 19.04 mg gallic acid equivalents g dried leaf. CONCLUSION: The same dataset was used to train multilayer feed-forward networks using different approaches via MATLAB, with ANN exhibiting superior performance to RSM (differences included categorical factor in one model and higher regression coefficients), while close values were obtained for the extraction variables under study, except for ethanol concentration and extraction time. (C) 2018 Society of Chemical Industry