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
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Scopus Author ID
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Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

5

GENDER EQUALITY
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0

Research Products

14

LIFE BELOW WATER
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0

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10

REDUCED INEQUALITIES
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0

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3

GOOD HEALTH AND WELL-BEING
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1

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2

ZERO HUNGER
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0

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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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2

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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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0

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11

SUSTAINABLE CITIES AND COMMUNITIES
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3

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8

DECENT WORK AND ECONOMIC GROWTH
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13

CLIMATE ACTION
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3

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4

QUALITY EDUCATION
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6

CLEAN WATER AND SANITATION
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7

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1

NO POVERTY
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15

LIFE ON LAND
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17

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7

AFFORDABLE AND CLEAN ENERGY
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3

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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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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

WoS h-index

5

Scopus h-index

5

Patents

0

Projects

0

WoS Citations per Publication

18.07

Scopus Citations per Publication

20.50

Open Access Source

5

Supervised Theses

1

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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
Current Page: 1 / 2

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Author: Balku, ŞaziyeSubject: İnorganik ve Nükleer

Scholarly Output Search Results

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  • Thumbnail Image
    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
    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.