Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Scopus Q: search.filters.scopusQuartile.Q3Subject: search.filters.subject.Process Optimization

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  • Article
    Citation - Scopus: 25
    Recursive Cluster Elimination Based Rank Function (SVM-RCE-R) Implemented in KNIME
    (F1000 Research Ltd, 2021-01-05) Yousef, Malik; Bakir-Güngör, Burcu; Jabeer, Amhar; Göy, Gökhan; Qureshi, Rehman A.; C Showe, Louise; C. Showe, Louise
    In our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R. SVM-RCE-R, further enhances the capabilities of SVM-RCE by the addition of a novel user specified ranking function. This ranking function enables the user to stipulate the weights of the accuracy, sensitivity, specificity, f-measure, area under the curve and the precision in the ranking function This flexibility allows the user to select for greater sensitivity or greater specificity as needed for a specific project. The usefulness of SVM-RCE-R is further supported by development of the maTE tool which uses a similar approach to identify MicroRNA (miRNA) targets. We have also now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to applyThe use of SVM-RCE-R in Knime is simple and intuitive and allows researchers to immediately begin their analysis without having to consult an information technology specialist. The input for the Knime implemented tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in SVM-RCE. The results show that the inclusion of the ranking function has a significant impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics. © 2021 Elsevier B.V., All rights reserved.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Optimization of Carbon Dioxide Absorption in a Continuous Bubble Column Reactor Using Response Surface Methodology
    (Wiley, 2023-05-28) Gul, Ayse; Derakhshandeh, Masoud; Un, Umran Tezcan
    Carbon 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.