Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 3Citation - Scopus: 3Optimization of Carbon Dioxide Absorption in a Continuous Bubble Column Reactor Using Response Surface Methodology(Wiley, 2023-05-28) Gul, Ayse; Derakhshandeh, Masoud; Un, Umran TezcanCarbon dioxide absorption using amine based solvents is a well-known approach for carbon dioxide removal. Especially with the increasing concerns about greenhouse gas emissions, there is a need for an optimization approach capable of multifactor calibration and prediction of interactions. Since conventional methods based on empirical relations are not efficiently applicable, this study investigates use of Response Surface Methodology as a strong optimization tool. A bubble column reactor was used and the effect of solvent concentration (10.0, 20.0 and 30.0 vol%), flow rate (4.0, 5.0 and 6.0 L min-1), diffuser pore size (0.5, 1.0 and 1.5 mm) and temperature (20.0, 25.0 and 30.0 degrees C) on the absorption capacity and also overall mass transfer coefficient was evaluated. The optimization results for maintaining maximum capacity and overall mass transfer coefficient revealed that different optimization targets led to different tuned operational factors. Overall mass transfer coefficient decreased to 34.7 min-1 when the maximum capacity was the desired target. High reaction rate along with the highest absorption capacity was set as desirable two factor target in this application. As a result, a third scenario was designed to maximize both mass transfer coefficient and absorption capacity simultaneously. The optimized condition was achieved when a gas flow rate of 5.9 L min-1, MEA solution of 29.6 vol%, diffuser pore size of 0.5 mm and temperature of 20.6 degrees C was adjusted. At this condition, mass transfer coefficient reached a maximum of 38.4 min-1, with a forecasted achievable absorption capacity of 120.5 g CO2 per kg MEA.Article Comprehensive Optimization of Shot Peening Intensity Using a Hybrid Model With AI-Based Techniques via Almen Tests(Walter de Gruyter Gmbh, 2025-06-03) Karaveli, Kadir Kaan; Bal, BurakShot peening is a crucial surface treatment technique that significantly improves the mechanical properties of metallic components, particularly their fatigue resistance and ability to withstand corrosion cracking. This study aims to optimize the shot peening process for aviation applications by evaluating and comparing various mathematical modeling and optimization techniques. Seven mathematical models were analyzed using a neuro-regression method (NRM), among which the second-order trigonometric non-linear (SOTN) model exhibited the highest reliability, achieving R2 values of 0.93 and 0.90 for training and testing datasets, respectively. To improve the model's robustness, four optimization algorithms - differential evolution (DE), simulated annealing (SA), Nelder-Mead (NM), and random search (RS) - were applied to the SOTN model. Although each technique offered valuable insights, performance fluctuations across different intensity ranges necessitated the development of a hybrid optimization model that combines the strengths of all four methods. The hybrid model achieved a mean error of approximately 2.69 %, outperforming individual approaches and demonstrating strong potential for reliable shot peening optimization across a wide range of target intensities. These findings provide a comprehensive methodology for AI-based optimization of surface treatment processes in engineering applications.