Balku, Şaziye

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Profile URL
https://hdl.handle.net/20.500.14411/8421
Name Variants
B., Saziye
B.,Saziye
Ş.,Balku
Balku, Şaziye
Saziye, Balku
Şaziye, Balku
Balku, Saziye
B.,Şaziye
Balku,Ş.
S.,Balku
S., Balku
Balku,S.
Job Title
Doçent Doktor
Email Address
saziye.balku@atilim.edu.tr
Main Affiliation
Energy Systems Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
7
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
3
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
2
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
3
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
3
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
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This researcher does not have a Scopus ID.
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Scholarly Output

14

Articles

13

Views / Downloads

8/0

Supervised MSc Theses

0

Supervised PhD Theses

1

WoS Citation Count

253

Scopus Citation Count

287

Patents

0

Projects

0

WoS Citations per Publication

18.07

Scopus Citations per Publication

20.50

Open Access Source

5

Supervised Theses

1

JournalCount
Celal Bayar Üniversitesi Fen Bilimleri Dergisi1
Civil Engineering Journal1
Energy Conversion and Management1
Gazi University Journal of Science1
Hittite Journal of Science and Engineering1
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Start date: 2010Subject: Analitik

Scholarly Output Search Results

Now showing 1 - 1 of 1
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    Article
    Citation - WoS: 7
    Citation - Scopus: 10
    Ann-Assisted Forecasting of Adsorption Efficiency To Remove Heavy Metals
    (Tubitak Scientific & Technological Research Council Turkey, 2019) Buaısha, Magdi; Balku, Şaziye; Yaman, Şeniz Özalp; Özalp Yaman, Şeniz
    In wastewater treatment, scientific and practical models utilizing numerical computational techniques suchas artificial neural networks (ANNs) can significantly help to improve the process as a whole through adsorption systems.In the modeling of the adsorption efficiency for heavy metals from wastewater, some kinetic models have been used such as pseudo first-order and second-order. The present work develops an ANN model to forecast the adsorption efficiency of heavy metals such as zinc, nickel, and copper by extracting experimental data from three case studies. To do this, we apply trial-and-error to find the most ideal ANN settings, the efficiency of which is determined by mean square error (MSE) and coefficient of determination (R2). According to the results, the model can forecast adsorption efficiency percent (AE%) with a tangent sigmoid transfer function (tansig) in the hidden layer with 10 neurons and a linear transferfunction (purelin) in the output layer. Furthermore, the Levenberg–Marquardt algorithm is seen to be most ideal for training the algorithm for the case studies, with the lowest MSE and high R2 . In addition, the experimental results and the results predicted by the model with the ANN were found to be highly compatible with each other.