H. Sebnem Düzgün

Prof. Dr. H. Sebnem Düzgün

Department of Mining Engineering, Colorado School of Mines, 1500 Illinois Street Brown Hall Rm: 268, CO 80401, USA
orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
11 November 2017 in Natural Hazards
Verified
Journal Article
Closed access
PDF file
NANazli Y. Aydin
Nazli Y. Aydin
H. Sebnem DüzgünH. Sebnem Düzgün
H. Sebnem Düzgün
FWFriedemann Wenzel
Friedemann Wenzel
HHHans Rudolf Heinimann
Hans Rudolf Heinimann

27 April 2022 in Renewable Energy
Verified
Journal Article
Closed access
JMJaime Moraga
Jaime Moraga
H. Sebnem DüzgünH. Sebnem Düzgün
H. Sebnem Düzgün
MCMahmut Cavur
Mahmut Cavur
HSHilal Soydan
Hilal Soydan

1 December 2021 in Journal of Rock Mechanics and Geotechnical Engineering
Verified
Journal Article
Open access
EIErgin Isleyen
Ergin Isleyen
H. Sebnem DüzgünH. Sebnem Düzgün
H. Sebnem Düzgün
RCR. McKell Carter
R. McKell Carter

5 September 2022 in Energies
Verified
Journal Article
Open access
PDF file
DDDoga Cagdas Demirkan
Doga Cagdas Demirkan
H. Sebnem DüzgünH. Sebnem Düzgün
H. Sebnem Düzgün
AJAditya Juganda
Aditya Juganda
JBJurgen Brune
Jurgen Brune
GBGregory Bogin
Gregory Bogin
Detecting the formation of explosive methane–air mixtures in a longwall face is still a challenging task. Even though atmospheric monitoring systems and computational fluid dynamics modeling are utilized to inspect methane concentrations, they are not sufficient as a warning system in critical regions, such as near cutting drums, in real-time. The long short-term memory algorithm has been established to predict and manage explosive gas zones in longwall mining operations before explosions happen. This paper introduces a novel methodology with an artificial intelligence algorithm, namely, modified long short-term memory, to detect the formation of explosive methane–air mixtures in the longwall face and identify possible explosive gas accumulations prior to them becoming hazards. The algorithm was trained and tested based on CFD model outputs for six locations of the shearer for similar locations and operational conditions of the cutting machine. Results show that the algorithm can predict explosive gas zones in 3D with overall accuracies ranging from 87.9% to 92.4% for different settings; output predictions took two minutes after measurement data were fed into the algorithm. It was found that faster and more prominent coverage of accurate real-time explosive gas accumulation predictions are possible using the proposed algorithm compared to computational fluid dynamics and atmospheric monitoring systems.

12 September 2019 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Verified
Journal Article
Open access
PDF file
MCMahmut Cavur
Mahmut Cavur
H. Sebnem DüzgünH. Sebnem Düzgün
H. Sebnem Düzgün
SERKAN KEMECSERKAN KEMEC
SERKAN KEMEC
DDD. C. Demirkan
D. C. Demirkan
Land use and land cover (LULC) maps in many areas have been used by companies, government offices, municipalities, and ministries. Accurate classification for LULC using remotely sensed data requires State of Art classification methods. The SNAP free software and ArcGIS Desktop were used for analysis and report. In this study, the optical Sentinel-2 images were used. In order to analyze the data, an object-oriented method was applied: Supported Vector Machines (SVM). An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. The overall classification accuracy of 83,64% with the kappa value of 0.802 was achieved using SVM. The study indicated that of SVM algorithms, the proposed framework on Sentinel-2 imagery results is satisfactory for LULC maps.

14 July 2022 in Resources
Verified
Journal Article
Open access
PDF file
Cansu Perdeli DemirkanCansu Perdeli Demirkan
Cansu Perdeli Demirkan
NSNicole M. Smith
Nicole M. Smith
H. Sebnem DüzgünH. Sebnem Düzgün
H. Sebnem Düzgün
Responsible mine closure and repurposing are key to contributing to sustainable development by ensuring successful environmental rehabilitation and reducing socioeconomic risks. However, mine closure has primarily focused on remediation and rehabilitation of mined lands with limited consideration of stakeholder perspectives and the broader social, economic, and cultural impacts of closure. In this paper, we use stakeholder input to evaluate and compare three different repurposing alternatives for the tailings dam area of a mine in the state of Colorado, USA, which is expected to close in the next twenty years. By using multi-attribute utility theory (MAUT), we determine which alternative better reflects stakeholder preferences and results in the most economically, environmentally, and socially sustainable outcome. Our results show that although stakeholder groups have different ideas about what constitutes sustainable development in the context of mine closure and repurposing, it is possible to identify to what extent different scenarios can address these perspectives. We argue that integrating stakeholder views into mine closure design and repurposing can lead to more responsible and sustainable mine closure that is unique to a particular setting and stakeholder needs, and we provide a methodology that mining companies may use to understand stakeholder priorities and preferences.