GCRIS

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Development of New Chemometrics Approaches To Determine Physical and Chemical Properties of Crude Distillation Unit Products Based on Molecular Spectroscopy
(01. Izmir Institute of Technology, 2022) Meşe Sezen, Ayten Ekin; Özdemir, Durmuş
Crude distillation units are the first processing units of crude oils based on fractional distillation. The properties of the petroleum products obtained from refinery units are frequently analyzed to ensure that the off-spec product cannot be obtained and that the process is working under the desired conditions. This study aims to develop a method based on multivariate data analysis to determine physical and chemical properties of petroleum samples as an alternative to time-consuming and conventional analytical methods. Four different petroleum products obtained from CDU for years were selected and used in this study, which are heavy and light diesel, heavy and light straight run naphtha. Four different spectroscopic methods which are UV-Vis, Fluorescence, FT-NIR and FTIR-ATR spectroscopy, were performed and compared. Multivariate calibration models were developed using Partial least Squares (PLS) and Genetic Inverse Least Squares (GILS) algorithms. For heavy and light diesel, predictive performance of three different spectroscopic methods were compared and for heavy diesel UV-Vis spectroscopy, for light diesel FT-NIR spectroscopy was selected for most of the parameters. Developed models by fluorescence analysis of light diesel samples conducted with two different measurement modes and synchronized fluorescence spectral data has resulted in better models compared to total fluorescence spectra. Studies with straight run naphtha samples were obtained from three different refineries and prediction performances were compared. All obtained model results indicates that developed methodology can be used in routine operations instead of conventional analytical methods.
Development of Genetic Algorithm Based Classification and Cluster Analysis Methods for Analytical Data
(Izmir Institute of Technology, 2009) Öztürk, Betül; Özdemir, Durmuş
In this study genetic algorithm based classification and clustering methods were aimed to develop for the spectral data. The developed methods were completely achieved hybridization of nature inspired algorithm (genetic algorithms, GAs) to other classification or clustering methods. The first method was genetic algorithm based principal component analysis (GAPCAD), and the second was genetic algorithm based discriminant analysis (GADA). Both methods were performed to achieve the best discrimination between the olive oil and vegetable oil samples. The classifications of samples were examined directly from their spectral data obtained from using near infrared spectrometry, Fourier transform infrared (FTIR) spectrometry, and spectrofluorometry. The GA was used to optimize the performance of classification or clustering techniques. on training set in order to maximize the correct classification of acceptable and unacceptable samples or samples of dissimilar properties and to reduce the spectral data by wavelength selection. After GA optimization the classification results of training set were controlled by validation set. Lastly, the success of both algorithms was compared to the results of PCA and SIMCA.
Development of Clustering and Classification Strategies for the Determination of Geographical Origin of Honey by Using Atomic and Molecular Spectrometry
(Izmir Institute of Technology, 2011) Yersel, Müşerref; Özdemir, Durmuş
Honey is a natural, nutritious and healthy food produced by honeybees from the nectar of plants. The classification of honey based on geographical origin is of great interest since the quality of honey depends on its chemical composition and geographical origin. In this study, it is aimed to develop classification models using elemental and molecular composition of honey samples via atomic and molecular spectrometry. For this purpose, honey samples from different regions of Turkey were collected from producers and they were scanned with Fourier Transform infrared spectrometer equipped with attenuated total reflectance (FTIR-ATR) accessory, and fluorescence spectrophotometer (synchronous fluorescence mode and 3D excitation emission mode). Afterwards, any clustering of the samples based on their regions was investigated using principal component analysis (PCA) and hierarchical cluster analysis (HCA) and soft independent modeling of class analogies (SIMCA). Finally, inductively coupled plasma mass spectrometry was applied to determine the metal concentrations (Mg, Al, Mn, Fe, Co, Ni, Cu, Zn, Sr, Ba) in honey samples and then the same classification methods were performed to compare the results. In conclusion, molecular spectrometry gave better classification results based on geographical origin compared to the results obtained with atomic spectrometry. Molecular spectrometry is more advantageous for the classification of honey samples in the case of saving time, saving chemicals and ease of usage.

Phd Degree / Doktora

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