Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Motion-Robust Optical Granulometry for Comminution Circuits: A Multi-Stream Temporal Deep Learning Framework
Hasozdemir, Kursat; Meral, Mert; Kahraman, Muhammet Mustafa
Accurate near-real-time granulometry is critical for optimizing energy efficiency in comminution circuits, yet standard optical monitoring systems fail under the harsh conditions of industrial mining. High conveyor velocities induce motion blur, while heavy dust loads occlude particle boundaries, leading to severe under-counting of fine fractions and an overestimation of product coarseness. This paper proposes a novel tracking framework to overcome these limitations. The method integrates a five-frame temporal rolling buffer with a multi-stream augmentation architecture that decouples motion and texture features. Experimental validation on the feed of a secondary conical crusher at an operational facility demonstrates that the proposed framework recovers significant particle data lost by baseline models, correcting the measured D80 and D50 values of the crushed material. This correction reveals a finer true load, enabling more precise closed-loop control of crusher settings and reducing unnecessary energy consumption.
- MakaleAçık Erişim2021A Data-Driven Approach to Control Fugitive Dust in Mine Operations
Kahraman, Muhammet Mustafa; Erkayaoglu, Mustafa
Particulate matter (PM) is one of the main actors related to air pollution caused by surface mining. Fugitive dust, considered as particulate matter that cannot be collected by conventional measures, is classified by the particle size. The Environmental Protection Agency (EPA) categorizes PM as coarse and fine particles based on the particle size being less than 10 μm (PM10) and less than 2.5 μm (PM2.5). Basic operations of surface mining such as drilling and blasting, loading, haulage, and processing are processes that can potentially generate fugitive dust. Regulations and legislations enforce the mining industry to use environmental monitoring systems, fugitive dust level measured by PM10 level as part of this. Air quality monitors are positioned at different locations around surface coal mines and track air quality levels during production. This study introduces a data-driven methodology to handle air quality issues related to fugitive dust at surface coal mines. Data is sourced from different mine equipment in real-time and they are integrated with air quality monitoring systems to provide information to support decisions for fugitive dust. The method is implemented and demonstrated in a case study at a large surface coal mine.
- MakaleYalnız künye2021Analysis of Mining Lost Time Incident Duration Influencing Factors Through Machine Learning
Kahraman, Muhammet Mustafa
Despite technological advancements and organizational adjustments, lost time accidents are major issues in occupational safety. However, there is very limited work that focuses on variables influencing days lost as a result of occupational accidents. In this study, decision tree and artificial neural network methods were used as machine learning techniques to investigate the impact of factors on accident lost day duration. Degree of injury, worker age, and worker activity were found to be the top three variables impacting loss of time from work. It was also identified that the mining method, location, and nature of injury had a moderate influence on duration lost due to occupational accidents. However, worker experience and ore type did not have any significant impact on the duration, which is an unexpected result. These results confirmed that some accident factors that might have a large influence on the number of mine accidents can be less critical when it comes to accident lost day duration.
- MakaleYalnız künye2018Increasing adherence to mine plan through data integration and process change
Kahraman, Muhammet Mustafa; Dessureault, Sean
This paper focuses on the development of a decision support element to control real-time adherence to mine plan within a control room environment. This system, known as a mine planning screen (MPL), integrates mine planning and production processes. The key inputs are production data tracked by the fleet management system and mine plans entered by mine planners into spreadsheets. The data are integrated with the use of database query languages and MPL charts are displayed in a web environment. The MPL screen displays two types of information: real-time adherence to plan by pit and historical adherence by crews. The objective of the screen is to increase adherence to the overall plan, a key part of a larger real-time blending initiative. This research discusses the technical development of the new tool, its deployment and peer pressure technique for user motivation.