Browsing by Author "Aydin, L"

Now showing 1 - 5 of 5
  • No Thumbnail Available
    Item
    Quantum Dots for Bioelectrochemical Applications
    Polatoglu, I; Eroglu, E; Aydin, L
  • No Thumbnail Available
    Item
    A new design strategy with stochastic optimization on the preparation of magnetite cross-linked tyrosinase aggregates (MCLTA)
    Polatoglu, I; 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 neuroregression analysis has been performed as a compelling alternative to the insufficient approaches on modelingdesign-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. R-training(2), R-testing(2) 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 R-training(2). However, only five and nine models are appropriate based on R-testing(2) 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.
  • No Thumbnail Available
    Item
    A Novel Approach for the Optimal Design of a Biosensor
    Polatoglu, I; Aydin, L; Nevruz, BÇ; Özer, S
    A novel design optimization strategy is proposed to enhance the analytical performance of a biosensor by taking into consideration the constructional and experimental parameters as design variables. A detailed study on multiple nonlinear neuro-regression analysis has been performed methodically in order to overcome the insufficient approaches on modeling-design-optimization of a biosensor. For this aim, the data were selected from a literature study. A hybrid method is used to test the accuracy of the predictions of 12 candidate functional structures that were proposed for modeling the data. The boundedness of the candidate models is checked after the calculation of R-training(2) and R-testing(2) values to reveal whether the model is realistic or not. Then appropriate models were optimized by using the four different optimization algorithms in terms of three different optimization scenarios. The results show that all the models express the process well regarding R-training(2). However, only four models are appropriate based on R-testing(2), and two of them were selected as the objective function depending on to be a realistic value. This novel optimization approach is also feasible for another modeling-design-optimization problem in analytical applications.
  • No Thumbnail Available
    Item
    A new hybrid approach in selection of optimum establishment location of the biogas energy production plant
    Ceylan, AB; Aydin, L; Nil, M; Mamur, H; Polatoglu, I; Sözen, H
    In this study, a new hybrid modeling optimization approach is presented for choosing the best installation location of a biogas power plant. This approach was evaluated in a case study for Manisa province in Turkey. First, the animal waste potential in Manisa was determined. By examining the biogas potential in Manisa, the mathematical model of the process is identified with the neuro-regression approach. Comparisons were made with the traditional and hybrid models, and it was seen that the values of the hybrid model based on the introduced approach were at more acceptable levels. Depending on this model, the most appropriate district where the power plant can be installed was calculated by considering the potentials in the environment. The single-objective and multi-objective approaches were considered to acquire the optimum design for the system. The modified versions of the optimization methods differential evolution (MDE), Nelder-Mead (MNM), simulated annealing (MSA), and random search (MRS) algorithms were used to solve problems. Thanks to the calculations and optimizations, it was concluded that it would be more appropriate to establish a biogas plant around Golmarmara, Salihli, and Ahmetli triangle in Manisa. It was determined that when this installation takes place, 68 GWh of electrical energy can be produced annually. This study is a pioneering study for the installation locations of bioenergy power plants in terms of the methods and approaches.
  • No Thumbnail Available
    Item
    Filtration and removal performances of membrane adsorbers
    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 (adsorptionidesorption)(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/m(2) 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. (C) 2017 Elsevier B.V. All rights reserved.