Sakallı, Fatma Nur

Profile URL
https://hdl.handle.net/11147/15897
Name Variants
Job Title
Email Address
Main Affiliation
01. Izmir Institute of Technology
Status
Website
ORCID ID
0000-0002-6100-6655
Scopus Author ID
8240135300
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
ENY-6682-2022

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
Research Products
GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
0
Research Products
GENDER EQUALITY5
GENDER EQUALITY
0
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
1
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
CLIMATE ACTION13
CLIMATE ACTION
1
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
Scopus data could not be loaded because of an error. Please refresh the page or try again later.
WoS data could not be loaded because of an error. Please refresh the page or try again later.

Publication Collaboration

Affiliation Name Count
Trakya University 8
Ondokuz Mayıs University 5
Kocaeli Üniversitesi 5
University of Tartu 2
Tekirdağ Namık Kemal University 1
1 / 2
Data obtained from OpenAlex
Scholarly Output

1

Articles

0

Views / Downloads

829/805

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

0

Scopus Citation Count

0

Patents

0

Projects

0

WoS Citations per Publication

0.00

Scopus Citations per Publication

0.00

Open Access Source

1

Supervised Theses

1

Journals data is not available

Scopus Quartile Distribution

Quartile distribution chart data is not available

Competency Cloud

GCRIS Competency Cloud

Filters

20211

Settings

Author of Publication: search.filters.isAuthorOfPublication.151d4780-9f3b-414b-bb50-18ef6d3d0e62

Scholarly Output Search Results

Now showing 1 - 1 of 1
  • Master Thesis
    Determination of Crucial Parameters in Gasoline Blends by Using Infrared Spectroscopy Coupled With Multivariate Calibration Methods
    (01. Izmir Institute of Technology, 2021) Özdemir, Durmuş; Sakallı, Fatma Nur; Özdemir, Durmuş; 01. Izmir Institute of Technology; 04.01. Department of Chemistry; 04. Faculty of Science
    In petroleum refineries, converting the manual gasoline blending system to an automatic inline blending system provides the most economical blending in gasoline production, increasing efficiency, and reliability. The most important requirement for an automatic inline blending system is the determination of gasoline parameters in a short time with high reliability. For this purpose, fast and simple analytical methods have been developed to determine crucial parameters of gasoline blends by using Fourier Transform Infrared Spectroscopy (FTIR) coupled with multivariate calibration methods which are Partial Least Squares Regression (PLSR) and Genetic Inverse Least Squares Regression (GILS) for this study. Turkey Petroleum Refinery Incorporated Company (TUPRAS) Izmir Refinery collected all gasoline samples and tested them using reference test methods at Quality Control Laboratory. Since commercial product samples were used in this study, the data ranges of the parameters were quite narrow. The Standard Error of Cross-Validation (SECV) and Standard Error of Prediction (SEP) values were acceptable, although the determintion coefficient (R2) value of some parameters was below the expectation. It has been observed that the prediction results of GILS are better in these parameters, whose R2 value is low because the data range is very narrow. In the comparison made with the reproducibility values specified in the reference measurement methods, it was determined that the calibration model results of most parameters were acceptable. Collecting more samples in a longer time interval to expand the data range of the parameters, or preparing a data set with experimental design can improve the prediction performance.