Promotie C. Yüksel: mijnbouw
13 december 2017 10:00 - Locatie: Aula, TU Delft - Door: webredactie
Real-Time Resource Model Updating in Continuous Mining Environment Utilizing Online Sensor Data. Promotor 1: Prof.dr.ir. J.D. Jansen (CiTG); Promotor 2: Prof.dr. J. Benndorf (TU Bergakademie Freiberg).
In mining, modelling of the deposit geology is the basis for many actions to be taken in the future, such as predictions of quality attributes, mineral resources and ore reserves, as well as mine design and long-term production planning. The essential knowledge about the raw material product is based on this model-based prediction, which comes with a certain degree of uncertainty. This uncertainty causes one of the most common problems in the mining industry, predictions on a small scale such as a train load or daily production are exhibiting strong deviations from reality. Some of the most important challenges faced by the lignite mining industry are impurities located in the lignite deposit. Most of the times, these high ash values cannot be captured completely by exploration data and in the predicted deposit models. This lack of information affects the operational process.
The current way of predicting coal quality attributes is using geostatistical interpolation or simulation methods to create resource models based on exploration data, which are very precise but separated by large distances and represent extremely small volumes. Mining companies have lately started to benefit from the recent developments in information technology, including online-sensor technologies for the characterization of materials, measuring the equipment efficiencies or defining the location of the equipment. Online-sensor measurements provide two different measurement systems that have recently been introduced to assess the components of the produced lignite. The precision of the data is lower than exploration data, which are analyzed in laboratories. However, these data are much more dense than exploration data and provide additional information about the coal attributes.
To benefit from this available dense data, a closed-loop concept for mining has recently been introduced. To enable fast online interpretation of online sensor data combined with an automated near-real time updating of the resource model, a new algorithmic approach was developed. This extends current practice in lignite mining, where data are analyzed off-line in a laboratory. Reconciliation exercises to integrate these data are done regularly, however the current practice is still intermittent involving time laps often exceeding weeks or months.
The proposed new concept offers to continuously fuse the online-sensor data measured from the production line into the resource or grade/quality control model and continuously provides locally more accurate estimates. The concept has been applied in two industrial coal mines with the aim of identifying local impurities in a coal seam and to improve the prediction of coal quality attributes in neighbouring blocks. This dissertation focuses on the development, validation and application of the real-time resource model updating framework in a real mining environment.
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