Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2026/r/gumushane/12113
  Motion-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.

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• MakaleAçık Erişim2021/r/gumushane/14796
  A 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.

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• BildiriYalnız künye2021/r/gumushane/13325
  Sequential Pattern Mining Method for Predictive Maintenance of Large Mining Trucks

  Kahraman, Abdulgani; Kantardzic, Mehmed; Kahraman, M. Mustafa; Kotan, Muhammed

  In recent decades, various solutions had been sought for reducing operating costs while increasing the production of minerals in mining operations. Equipment health monitoring technologies had been used for monitoring and increasing the availability of machines. However, the data obtained from these technologies had only been used for monitoring the equipment health, and not for the prediction of failures. In this paper, it was relied on alarms and signals collected through real-time health monitoring technologies for predicting crucial mining truck failures. Sequential Pattern Mining (SPM) Method for Predictive Maintenance had been developed and implemented as a methodology to discover which group of alarms and signals might be related to specific truck failures. The results indicate that the SPM method is able to detect machine failures of trucks with high accuracy with an average 96%. The proposed methodology may reduce the maintenance time, and the expenditures caused by truck breakdowns in the mining industry.

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• MakaleYalnız künye2021/r/gumushane/12881
  Analysis 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.

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• MakaleAçık Erişim2021/r/gumushane/12873
  A data-driven multi-regime approach for predicting energy consumption

  Kahraman, Abdulgani; Kantardzic, Mehmed; Kahraman, Mustafa; Kotan, Muhammed

  There has been increasing interest in reducing carbon footprints globally in recent years. Hence increasing share of green energy and energy efficiency are promoted by governments. There-fore, optimizing energy consumption is becoming more critical for people, companies, industries, and the environment. Predicting energy consumption more precisely means that future energy management planning can be more effective. To date, most research papers have focused on predicting residential building energy consumption; however, a large portion of the energy is consumed by industrial machines. Prediction of energy consumption of large industrial machines in real time is challenging due to concept drift, in which prediction performance deteriorates over time. In this research, a novel data-driven method multi-regime approach (MRA) was developed to better predict the energy consumption for industrial machines. Whereas most papers have focused on finding an excellent prediction model that contradicts the no-free-lunch theorem, this study concentrated on adding potential concept drift points into the prediction process. A real-world dataset was collected from a semi-autonomous grinding (SAG) mill used as a data source, and a deep neural network was utilized as a prediction model for the MRA method. The results proved that the MRA method enables the detection of multi-regimes over time and provides a highly accurate prediction performance, thanks to the dynamic model approach.

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• MakaleYalnız künye2020/r/gumushane/13279
  Bottleneck identification and ranking model for mine operations

  Kahraman, M. Mustafa; Rogers, W. Pratt; Dessureault, Sean

  Mining has a long history of production and operations management. Considering the increasing complexity of reserves, declining metal grades, and worldwide price volatilities, mines have to operate more efficiently than ever. In recent years, companies have been adopting innovative solutions and technologies to reduce costs and remain competitive in the global market. Specifically, management of bottlenecks has been in the focus of the manufacturing industry for decades now. The mining industry has made significant advancements in process improvement tools using equipment, human, and asset tracking capabilities. However, to date, there is no holistic approach or system to identify and manage the bottleneck in the entire value chain of mining. This research proposes a new method to identify and rank the bottlenecks in a mine value chain. The method is applied in a coal mine and the results are presented in the case study.

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• MakaleAçık Erişim2019/r/gumushane/15147
  Formal Assessment and Measurement of Data Utilization and Value for Mines

  Rogers, W. Pratt; Kahraman, M. Mustafa; Dessureault, Sean

  Most contemporary mines have considerable amounts of data. Structured and unstructured large data sets are colloquially named big data. Much of the data in mining operations come from a variety of systems, each with different databases and reporting environments. Standard technology deployments create a “silo-ification” of data leading to poor system benefit. Through modern server monitoring and systematic approach, data utilization and value can quantifiably be measured. The Data Utilization and Value Index (DUVI) can quantify business intelligence best practices and user interaction. This study seeks to provide a data management tool to measure data utilization across the process of converting data into action.

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• BildiriYalnız künye2019/r/gumushane/15064
  Measuring and improving operator performance on large mining equipment

  Kahraman, M.M.

  Mining as an industry is in the middle of commodity price upheaval that is wiping out many mining firms. Only the companies that have adapted right technology and data driven decision making culture will survive after each commodity price cycle. Latest technological solutions made it possible to track materials and equipment in every step of mining; from the face to plant to facilitate decision making process. Mines are investing in equipment monitoring technology with the expectation of gaining large increases in equipment productivity and reliability. Simply buying the technology does not result in achieving the promised benefits. Only a holistic approach can achieve these benefits, considering the people (analytical skills and culture), the process (deliberate means of using the data), and the platform (analytical technology). One of the principle methods for achieving this advantage is through increasing productivity and equipment health of major production equipment. This paper presents a case study using equipment monitoring technology as part of an operator performance system for a major machine asset: a dragline.

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• Derleme MakaleYalnız künye2019/r/gumushane/11366
  Automation in the Mining Industry: Review of Technology, Systems, Human Factors, and Political Risk

  Rogers, W. Pratt; Kahraman, M. Mustafa; Drews, Frank A.; Powell, Kody; Haight, Joel M.; Wang, Yaxue; Baxla, Kritika; Sobalkar, Mohit

  In the opinion of these authors, technology alone can never give an organization an edge over competitors or provide an industry step change. However, history has shown that technology applied with correct logistics and strategy makes a significant difference. Never has the minerals industry faced such daunting challenges and been in such need of a step change. Application of autonomous systems has been steadily increasing in the minerals industry. Automation technology has changed the mining value chain from exploration to mineral processing and refining. The future state will only see additional in-roads of automation into more processes and the whole system design. To better understand the near-future potential of automation, it is useful to explore and review the current state of automation in minerals development. A review of the state of automation research is given. The review will consider three critical areas: automation technology, system’s engineering and management processes around automation, and the role of human factors engineering in automated and semi-automated systems. The review ends with a discussion on the social and political risks of automation in terms of shared value and sustainable development.

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• MakaleYalnız künye2019/r/gumushane/11352
  Exploring the value of using data: a case study of continuous improvement through data warehousing

  Rogers, W. Pratt; Kahraman, M. Mustafa; Dessureault, Sean

  The mining industry is adapting data-rich technology. Data management processes and infrastructure require additional investment which, can be difficult to justify. A valuation model for data management infrastructure would necessitate modelling the ‘use’ of data and quantifying the value and the likelihood of success of improvement initiatives. A model is presented which can assess the impacts of a data-driven initiative. The initiatives are shown to be the foundation of improved shift coordination resulting in significant and sustained operational improvements. This paper aims to illustrate the value of using data from source systems in mining operations.

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