This paper examines a problem related to dispatching materials to spreaders in coal (lignite) mines operated under the paradigm of continuously excavated material flow. In the considered particular case, complexity arises from different material types of overburden to be placed on the dump site in pre-defined patterns that guarantee geotechnical safety. These types include wet, semi-wet and dry material, which are accessed on the excavation site according to the geological deposition and the mining plan. Controlling of the dispatch system has to take into account the extraction sequence and geological stratification on the excavation site and the available dump space per material on the dumping site. With eight excavators on the excavation site and seven spreaders on the dump site the problem is already complex, having not stated yet that random breakdowns may make some options temporarily unavailable. To optimize the dispatch system in terms of minimum idle time due to unavailability of dumping space, a new multi-stage simulation-based optimization approach is proposed. This approach consists of running alternatingly a deterministic optimization model and a stochastic simulation model. It combines simulation and algorithms to solve a transportation problem and a job-shop scheduling problem. The proposed approach is tested on a large continuous mine under given different dumping sequences, and results are reported. The merits and limitations of the proposed approach as pinpointed and farsighted operations management are discussed.

The flow of information and consequently the decision making along the chain of mining from exploration to beneficiation typically occurs in a discontin- uous fashion over long time spans. In addition, due to the uncertain nature of the knowledge about the deposit and its inherent spatial distribution of material char- acteristics actual production performance in terms of produced ore grades and quantity and extraction process efficiency often deviate from expectations. Reconciliation exercises to adjust mineral reserve models and planning assumptions are performed with timely lags of weeks, months or even years. With the devel- opment of modern Information and Communication Technology over the last decade, literally a flood of data about different aspects of the production process is available in a real-time manner. For example, sensor technology enables online characterisation of geochemical, mineralogical and physical material characteristics on conveyor belts or at working faces. The ability to utilise the value of this additional information and feed it back into reserve block models and planning assumptions opens up new opportunities to continuously control the decisions made in production planning to increase resource recovery and process efficiency. This leads to a change in paradigm from a discontinuous to a near real-time reserve reconciliation and model updating, which calls for suitable modelling and optimi- sation methodologies to quantify prior knowledge in the reserve model, to process and integrate information from different sensor-sources and accuracy, back prop- agate the gain in information into reserve models and efficiently optimise opera- tional decisions real-time. This contribution introduces the concept of an integrated closed-loop framework for Real-Time Reserve management (RTRM) incorporating

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.

The mining industry continuously struggles to keep produced tonnages and grades aligned with targets derived from model-based expectations. Deviations often result from the inability to characterise short-term production units accurately based on sparsely distributed exploration data. During operation, the characterisation of short-term production units can be significantly improved when deviations are monitored and integrated back into the underlying grade control model. A previous contribution introduced a novel simulation-based geostatistical approach to repeatedly update the grade control model based on online data from a production monitoring system. The added value of the presented algorithm results from its ability to handle inaccurate observations made on blended material streams originating from two or more extraction points. This contribution further extends previous work studying the relation between system control parameters and algorithm performance. A total of 125 experiments are conducted to quantify the effects of variations in measurement volume, blending ratio and sensor precision. Based on the outcome of the experiments, recommendations are formulated for optimal operation of the monitoring system, guaranteeing the best possible algorithm performance.

Material management in opencast mines is concerned with planning, organizing, and control of the flow of materials from their extraction points to destinations. It can be strongly affected by operational decisions that have to be made during the production process. To date, little research has focused on the application of simulation modeling as a powerful supportive tool for decision making in such systems. Practical experiences from implementing a simulation model of a mine for the operational support on an industrial scale are not known to the authors. This paper presents the extension of a developed stochastic simulation model by the authors from a conceptual stage (TRL4) to a new Technology Readiness Level (TRL 6) by implementing it in an industrially relevant environment. A framework for modeling, simulation, and validation of the simulation model applied to two large opencast lignite mines is presented in detail. Operational implementation issues, experiences, and challenges in practical applications are discussed. Furthermore, the strength of applying the simulation modeling as an operational decision support for material management in coal mining is demonstrated. Results of the case studies are used to describe the details of the framework, and to illustrate the strength and limitations of its application.

An efficient resource model updating framework concept was proposed aiming for the improvement of raw material quality control and process efficiency in any type of mining operation. The concept integrates sensor data measured online on the production line into the resource or grade/quality control model and continuously provides locally more accurate estimates. The concept has been applied in a lignite field with the aim of identifying local impurities in a coal seam and to improve the prediction of coal quality attributes in neighbouring blocks. A significant improvement was demonstrated which led to better coal quality management. So far, the proposed concept and the application in coal mining was limited to a case where online measurements were unambiguously trackable due to a single extraction face being the point of origin for the material. This contribution presents an extension to the case, where characteristics from blended material, originating from two or three simultaneously operating extraction faces, are measured. The challenge tackled in this contribution is the updating of local coal quality estimates in different production benches based on measurements of a blended material stream. For a practical application of the updating concept, which is based on the Ensemble Kalman Filter, a simple method for generating prior ensemble members based on block geometries defined in the short-term model and the variogram, is discussed. This method allows for a fast, semi-automated and rather simple generation of prior models instead of generating a fully simulated deposit model using conditional simulation in geostatistics. It should foster operational implementation in an industrial environment. The main purpose of this article is to investigate the applicability of the developed framework with a simplified prior resource model. In addition to this any model improvements due to the integration of sensor data obtained by observing a blend of coal from multiple extraction faces is investigated.

Advanced data acquisition and process modelling technology provide ‘real-time’ data and decision support capacity for different aspects of the resource extraction process. Closed-loop approaches have recently been applied to utilize information extracted from these data in combination with advanced computing technology for improved production control in mineral resource extraction. Similar techniques have been developed in the petroleum industry combining computer-assisted model updating with model-based production optimization. This contribution reviews recent developments and methods applied, highlights differences and assesses the potential value addition for both application domains. The focus here is on the two main constituents of closed-loop concepts, data assimilation and optimization. Technological readiness of the constituents is assessed, and gaps for further technology development are identified. The value added is illustrated by means of selected cases.

One of the main challenges of the mining industry is to ensure that produced tonnages and grades are aligned with targets derived from model-based expectations. Unexpected deviations, resulting from large uncertainties in the grade control model, often occur and strongly impact resource recovery and process efficiency. During operation, local predictions can be significantly improved when deviations are monitored and integrated back into the grade control model. This contribution introduces a novel realization-based approach to real-time updating of the grade control model by utilizing online data from a production monitoring network. An algorithm is presented that specifically deals with the problems of an operating mining environment. Due to the complexity of the material handling process, it is very challenging to formulate an analytical approximation linking each sensor observation to the grade control model. Instead, an application-specific forward simulator is built, translating grade control realizations into observation realizations. The algorithm utilizes a Kalman filter-based approach to link forward propagated realizations with real process observations to locally improve the grade control model. Differences in the scale of support are automatically dealt with. A literature review, following a detailed problem description, presents an overview of the most recent approaches to solving some of the practical problems identified. The most relevant techniques are integrated and the resulting mathematical framework is outlined. The principles behind the self-learning algorithm are explained. A synthetic experiment demonstrates that the algorithm is capable of improving the grade control model based on inaccurate observations on blended material streams originating from two extraction points.