Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2021Investigation of the removal of sulfamethoxazole drug waste from aqueous solutions under the effect of zinc oxide/montmorillonite nanocomposite by photocatalytic ozonation process
Özyürek, Işıl Nihan; Kıranşan, Murat; Karaca, Semra
In this study, the synergistic effect of photocatalysis and ozonation processes to degrade the sulfamethoxazole (SMX), one antibiotic pharmaceutical compound, has been studied through the influence of main operational variables such as zinc oxide/montmorillonite (ZnO/MMT) dos-age, SMX initial concentration, ozone flow rate, pH and the addition of organic and inorganic scavengers. Synthesized ZnO nanoparticles were immobilized on the montmorillonite surface to prepare ZnO/MMT catalyst as a photocatalyst. The catalyst was characterized by X-ray fluo-rescence, X-ray diffraction, scanning electron microscope, high-resolution transmission electron microscope and N2 adsorption/desorption. The highest SMX degradation efficiency (80.84% for 30 min of reaction time) was obtained for the photocatalytic ozonation process compared to adsorption, single ozonation, catalytic ozonation, and photolysis. ZnO/MMT was more effective photocatalyst according to ZnO, indicating its usability as a promising photocatalyst for the removal of organic contaminants in aqueous solutions. The reduced degradation efficiency with the addition of organic and inorganic scavengers showed the important role of the photo-produced radicals and holes as well as direct ozonation in SMX degradation. A mechanism is also proposed for photocatalytic ozonation degradation by using the gas chromatography-mass spectroscopy (GC-MS) analysis method. Finally, several by-products were identified by GC-MS analysis, which allowed to depict a possible mechanism for the sulfamethoxazole degradation.
- MakaleAçık Erişim2021Satellite-derived shallow wetland bathymetry using different classification algorithms and datasets
Dervisoglu, Adalet; Yagmur, Nur; Bilgilioglu, Burhan Baha
Remote sensing (RS) effectively identifies, analyzes, and monitors wetlands. In addition to these two-dimensional studies, RS is used with several techniques in determining shallow water depths. The primary purpose of this study is to obtain shallow wetland bathymetry utilizing spectral reflections obtained at different water depths by field study and satellite images. Machine learning (ML) algorithms, which are widely used in remote sensing, are used in this study. Four algorithms were selected as random forest (RF), support vector machine (SVM), Neural Networks (NN), and Maximum Likelihood Classification (MLC). Since machine learning algorithms use training samples/datasets, the classification accuracy is directly related to selecting these data. The effect of pixel counts on classification was investigated by using two different training data set also. Duden (Kulu) Lake, which is a shallow wetland, was chosen as the study area. The Iterative Self-Organizing Data Analysis Technique (ISODATA) classification algorithm divided into as many clusters as possible was applied on Sentinel-2 multispectral images. All classes were rede-fined using measured spectral signatures and were created a bathymetric map. This map was used as reference data in creating training sets and the accuracy assessment of ML algorithms. When the water surface areas obtained from algorithms were compared with the bathymetric map and Normalized Difference Water Index, the best result was obtained with RF. According to the accuracy assessment results, it was seen that the number of training data affects the accuracy, and the best results were obtained with SVM and RF algorithms with training data containing more pixels (overall accuracy 93.87% and 92.64, kappa 0.89 and 0.87, respectively).
- MakaleAçık Erişim2021Experimental and numerical studies on predicting and improving the full-scale wastewater treatment plant hydrodynamics
Şibil, Rahim; Aras, Egemen; Kankal, Murat
This paper presents the results of both experimental and numerical studies on biological wastewater treatment plant (WWTP), oxidation ditches (OD) hydrodynamics to design and develop the desired flow conditions of the WWTP. Computational fluid dynamics (CFD) models were developed using CFD software, ANSYS Fluent (V13) with the three-dimensional, steady, incompressible flow based on the Reynolds-Averaged Navier-Stokes equations for flow field calculations in the combined ODs. Also, three different turbulence models [standard k–ε (ske), renormalization group k–ε (RNG), and realizable k–ε (real)] were performed for a comparative study. The numerical model was verified based on the experimental data in the relative errors for ske, RNG, and real 13%, 17%, and 18%, respec-tively. According to the parametric studies, the hydrodynamic characteristics of the existing WWTP were investigated. The maximum wastewater velocity occurred at the inlet and outlet, affecting the flow field in ODs. Moreover, the water velocity decreased as it moved away from the inlet and outlet locations at vertical and horizontal. It can also be noted that there was no homogeneous flow field distribution in ODs. Because the current OD model needs improvement hydrodynamically, a new original OD geometry was presented to eliminate the hydraulic weakness of existing WWTP by CFD analysis. The new original geometry provides a more homogeneous flow field in ODs that mean it will also help treatment efficiency and energy saving according to the operating principles of this facility.
- DergiDesalination and Water Treatment