A primary concern of the mining industry is to meet production targets, which are required and defined by customers. Deviations from these targets, in terms of quality and quantity, highly affect the economical aspect. Recently, an efficient resource model updating framework concept has been proposed aiming for the improvement of raw material quality control and process efficiency in any type of mining operation. The concept integrates online sensor measurements, obtained during production, into the resource model. In this way, due to the spatial variability, quality attributes of the blocks that will be produced in the next days or weeks are being updated based on real-time measurements. The concept has been applied in a lignite field with the aim of identifying local impurities in a lignite seam and to improve the prediction of coal quality attributes in neighbouring blocks. This paper investigates the added value of using the resource model updating framework by using the value of information analysis. The expected benefit of additional information (integration of the online sensor measurements into the resource model) is compared to a case where there is no additional information integrated into the process. These benefits are evaluated based on the economic impact determined by applying the resource model updating framework in mine planning.

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Recently an efficient updating framework was proposed aiming to improve the raw material quality control and process efficiency in any type of mining operation. The concept integrates sensor data measured on the production line into the resource model and continuously provides locally more accurate resource models. A demonstration in lignite production is applied in order to identify the impurities (marine and fluvial sands) in the coal seams to lead better coal quality management. The updating algorithm applies different algorithmic parameters. This study aims to investigate the sensitivity of the performance with respect to different parameters for optimal application. Main parameters include the ensemble size, the localization and neighborhood strategies, and the sensor precision. The results should assist in future applications by determining the impact of the different parameters.@en

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 materialproduct is based on this model-based prediction, which comes with a certaindegree 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.@en

In recent years a real-time resource model updating concept has proven to increase the material quality control and process efficiency in geostatistics. The real-time resource model updating concept integrates online-sensor data, measured fromthe production line, into the resourcemodel. This integration quickly improves the accuracy of the resource model. The aim of this contribution is to adapt this concept into coal production and to apply the developed framework on an industrial case. The result of this study will provide an additional improvement to coal quality management, by mainly focusing on the ash content in the deposit. This includes high ash values in coal seams, which are caused by sand intrusions and are greatly affecting the operational process. A tailored Ensemble Kalman Filter approach, specifically applicable in coal production, is presented after a detailed literature review. For validation, a 2D case study is performed in a fully controllable environment. Further, the approach is benchmarked against an alternative proven approach. To demonstrate the value added a full scale industrial application is performed focusing on improving the lignite quality control in the production process. The results of integrating online measurement data into the resource model indicate a significant improvement (in the order of 70%) in coal quality production. @en