Browsing by Author "Aydin L."

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    Filtration and removal performances of membrane adsorbers
    (Elsevier B.V., 2017) Yurekli Y.; Yildirim M.; Aydin L.; Savran M.
    Membrane adsorbers are promising candidates for the efficient and effective removal of heavy metals in waste water due to their unattainable adsorption and filtration capabilities. In the present study, zeolite nanoparticles incorporated polysulfone (PSf10) membrane was synthesized by means of non-solvent induced phase separation technique for the removal of lead and nickel ions in water. PSf10 showed a remarkable sorption capability and after repeated (adsorption/desorption)5 cycles in batch experiments, it preserved 77% and 92% of its initial sorption capacity for the lead and nickel, respectively. Addition of nanoparticles increased the pore radius of the native PSf from 10 to 19 nm, while bovine serum albumin rejection remained unchanged at 98%. Increments in the pore size and enhancement in hydrophilicity caused to increase hydraulic permeability of the native PSf from 23 to 57 L/m2 h bar. Cross-flow filtration studies revealed that the filtrate concentrations were inversely affected by the initial metal concentration and transmembrane pressure due to reaction limited region. Nonlinear rational regression model perfectly described the filtration behavior of the PSf10 within the experimental range and suggested that lower initial metal concentration and pressure with a short filtration time should be selected for the target response to be minimum. © 2017 Elsevier B.V.
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    A new design strategy with stochastic optimization on the preparation of magnetite cross-linked tyrosinase aggregates (MCLTA)
    (Elsevier Ltd, 2020) Polatoğlu İ.; Aydin L.
    In this study, a new design strategy with a systematic optimization process is proposed for the preparation of magnetite cross-linked tyrosinase aggregates (MCLTA) by using the concentration of magnetite nanoparticle, glutaraldehyde and tyrosinase enzyme as design variables. A comprehensive study on multiple non-linear neuro-regression analysis has been performed as a compelling alternative to the insufficient approaches on modeling-design-optimization of MCLTA. For this aim, the experimental process has been modeled with 13 candidate functional structures by using a hybrid method to test the accuracy of their predictions. R2training, R2testing values, and boundedness of the functions have been checked to reveal the realistic ones. Then four different design approaches in terms of three distinct scenarios have been used to optimize the process. The results show that, all models define the process well, depending on R2training. However, only five and nine models are appropriate based on R2testing for the first use activity and residual activity, respectively. On the other hand, depending on to be a realistic value, model TON best describes the “first use activity,” while the best one is FONT for residual activity. It is also concluded that the scenario types and selection of constraints for design variables affect the optimization results. © 2020 Elsevier Ltd