Çimen Mesutoğlu, Özgül

Profile URL:
Job Title:Doç. Dr.
Email Address:ocmesutoglu@ktun.edu.tr
Main Affiliation:01.01. Other Departments
Status: Current Staff
ORCID:ORCID Profile0000-0002-6704-8645
Scopus ID:Scopus Profile57194521918
YÖK Akademik: 65607A7258FC875F
Google Scholar:Google Scholar ProfiledH2jXK8AAAAJ
Web of Science ID:Web of Science ProfileHHN-1399-2022
Name Variants:
Cimen Mesutoglu, Ozgul Çimen Mesutoğlu, Ö.
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2025 - 20262

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Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Article
    Machine Learning-Based Uniaxial Compressive Strength Estimation for Lignite in an Underground Coal Mine
    (Springer, 2026) Mesutoglu, Mehmet; Mesutoglu, Ozgul Cimen; Solak, Ahmet; Ozsen, Hakan; Rodriguez-Dono, Alfonso; Ozkan, Ihsan
    Uniaxial compressive strength (UCS) is one of the most fundamental parameters used in rock mechanics and mining design; however, laboratory UCS testing is often time-consuming, costly, and impractical for continuous field applications. This study aims to develop a rapid and low-cost UCS estimation framework using two easily obtainable indices: Schmidt hammer rebound hardness (SHT) and point load strength (PLT). A total of 114 coal samples collected from the A1 panel of the & Ouml;merler Mine were used to train and evaluate four machine-learning models; multiple linear regression (MLR), regression trees (RT), support vector regression (SVR with linear, polynomial, and RBF kernels), and artificial neural networks (ANN). Model performances were assessed through 5-fold cross-validation and statistically compared using the Friedman and Nemenyi tests. The ANN model achieved the highest predictive accuracy, with an R-2 value exceeding 0.85 and the lowest error metrics among all evaluated algorithms. SVR models also produced competitive results. Statistical rank comparisons confirmed the significant superiority of the ANN model over the RT method. The findings demonstrate that reliable UCS prediction can be achieved using only SHT and PLT, offering a practical and cost-effective alternative for preliminary geotechnical characterization in mining operations. The proposed framework provides field engineers with a fast decision-support tool for strength estimation when laboratory testing is limited or unavailable.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Predictive Modeling of MB Adsorption on Activated Olive Stone Through Artificial Neural Networks
    (Nature Portfolio, 2025-07-11) Mesutoglu, Ozgul Cimen; Çimen Mesutoğlu, Özgül
    The primary objective of this study was to evaluate the potential of activated olive stone (AOS), an organic waste material, for adsorbing Methylene Blue (MB) dye from aqueous solutions and to develop a predictive model using Artificial Neural Networks (ANNs). This research aimed to explore AOS as an eco-friendly and cost-effective adsorbent for wastewater treatment, emphasizing its potential for large-scale applications. Additionally, the study sought to enhance the understanding of how various factors-such as pH, contact time, and adsorbent dosage-affect the adsorption process and to optimize the conditions for maximum dye removal efficiency. The material's structure and functional groups were analyzed using Fourier Transform Infrared (FTIR) spectroscopy. Adsorption experiments conducted in a batch system demonstrated a removal efficiency of 93% under optimal conditions, with a maximum adsorption capacity of 446 mg/g for MB. The optimal conditions were identified as pH 7, a contact time of 30 min, 10 g/L of AOS, and an MB concentration of 250 mg/L. To better understand the influence of various parameters on MB adsorption, an ANN model was developed. The model analysis revealed a strong correlation coefficient (R2) of 91%, indicating that the model could reliably predict MB removal. Overall, the study highlights the promising potential of AOS as an adsorbent for wastewater treatment and demonstrates the effectiveness of ANN models for optimizing adsorption processes.

Research Topics

Domains
Physical Sciences
Fields
Environmental ScienceComputer Science
Subfields
Water Science and TechnologyIndustrial and Manufacturing EngineeringArtificial IntelligencePollution
Specific Research Areas
Adsorption and biosorption for pollutant removal
Water Quality Monitoring Technologies
Water Quality Monitoring and Analysis
Neural Networks and Applications
Heavy metals in environment

Sustainable Development Goals

SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
1
Research Products
Documents

7

Citations

73

h-index

4

Go to Scopus profile
Documents

8

Citations

71

Go to WoS profile

Publication Collaboration

Affiliation Name Count
Aksaray University 14
Konya Technical University 2
Waters (United States) 1
Dokuz Eylül University 1
Sustainability Institute 1
1 / 2
Data obtained from OpenAlex
JournalCount
Environmental Earth Sciences1
Scientific Reports1
Current Page: 1 / 1
Scholarly Output

2

Articles

2

Views / Downloads

4/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

1

Scopus Citation Count

1

Patents

0

Projects

0

WoS Citations per Publication

0.50

Scopus Citations per Publication

0.50

Open Access Source

1

Supervised Theses

0

Scopus Quartile Distribution

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