Jörg Benndorf

Making Use of Online Production Data: Sequential Updating of Mineral Resource Models

The information flow along the mining value chain from exploration through resource/reserve estimation, mine planning, operations management and processing generally occurs discontinuously over long time spans. To react to deviations between produced ore and model based expectations, reconciliation exercises are performed adjusting resource models and mine planning assumptions. However, there is often a lag of weeks, months or even years. Developments over the last decade have created a flood of online data about different aspects during the production process. For example, sensor technology enables online characterization of geochemical, mineralogical and physical material characteristics. The ability to fully exploit this additional information and feed it back into resource models will open up new opportunities for improved decision making in short-term planning and operational control. This contribution introduces a new approach for sequential resource model updating utilizing online sensor data. The updating approach is based on differences between model-based predictions and sensor measurements of raw material properties. In this context, one major challenge has to be solved. Raw material streams at sensor stations often occur as a blend of material originating from multiple different extraction locations. A direct allocation of the source of differences between the model-based prediction and sensor measurements is difficult. To overcome this issue, this contribution proposes an adaption of a data assimilation-based approach for sequential model updating. The theoretical description of the method is provided followed by a demonstrative case study that investigates the performance with respect to different mine configurations.

Improving the assessment of uncertainty and risk in the spatial prediction of environmental impacts: a new approach for fitting geostatistical model parameters based on dual Kriging in the presence of a trend

Modern methods of geostatistics deliver an essential contribution to Environmental Impact Assessment (EIA). These methods allow for spatial interpolation, forecast and risk assessment of expected impact during and after mining projects by integrating different sources of data and information. Geostatistical estimation and simulation algorithms are designed to provide both, a most likely forecast as well as information about the accuracy of the prediction. The representativeness of these measures depends strongly on the quality of the inferred model parameters, which are mainly defined by the parameters of the variogram or the covariance function. Available data may be sparse, trend affected and of different data type making the inference of representative geostatistical model parameters difficult. This contribution introduces a new method for best fitting of the geostatistical model parameters in the presence of a trend, which utilizes the empirical and theoretical differences between Universal Kriging and trend-predictions. The method extends well known approaches of cross validation in two aspects. Firstly, the model evaluation is not only limited to sample data locations but is performed on any prediction locations of the attribute in the domain. Secondly, it extends the measure used in cross validation, based on a single point replacement by using error curves. These allow defining rings of influence representing errors resulting from separate variogram lags. By analyzing the different variogram lags the fit of the complete covariance can be assessed and the influence of the several model parameters separated. The use of the proposed method in an EIA context is illustrated in a case study related on the prediction of mining-induced ground movements.

Investigating in situ variability and homogenisation of key quality parameters in continuous mining operations

Abstract The delivery of in-spec qualities out of the mine is essential for an efficient and environmental friendly operation of the subsequent beneficiation process. One goal of the extraction and production process in mining operations is to reduce the deposit inherent grade variability to a usually contractual defined level. The design of blending opportunities along the mining chain plays a key role in homogenising variability and improving the prediction of key parameters, such as the calorific value in the case of coal deposits. Geological conditions of deposits to exploit are becoming more complex. Typical currently exploited coal deposits in Europe are already characterised by multiple seams with multiple splits, each split representing a different variability in seam geometry and quality. Modern methods of conditional simulation in geostatistics allow for generating several spatial models or possible scenarios for large deposits capturing in situ variability. Integrating simulated models with the excavation sequence and transport and blending models of the mining operation leads to valuable insights into the expected performance as a function of the technical design and operational mode. The presented case study uses the technique of conditional simulation in geostatistics to investigate the variability of the calorific value in exploiting a complex lignite deposit. The study analyses the behaviour along the extraction, transportation and blending process in a continuous mining environment. Results provide valuable inside into the performance of a continuous mining system in terms of homogenisation and allow identifying sources for controlling variability. Conclusions can be drawn to adjust the mine design and scheduling of key equipment as well as to adjust the operation mode according to the customer’s requirements in terms of coal quality variability.

Moving towards Real-Time Management of Mineral Reserves – A Geostatistical and Mine Optimization Closed-Loop Framework

The flow of information along the mining value chain from exploration through resource modeling, reserve estimation, mine planning, operations management and beneficiation occurs typically in a discontinuous fashion over long time spans. Due to the uncertain nature of the knowledge about the deposit and its inherent spatial distribution of grades, actual production figures in terms of produced ore grades and quality but as well production efficiency often deviate from expectations. To react to deviations, reconciliation exercises are performed to adjust mineral resource models and mine planning assumptions, however, often with a timely lag of weeks, months or even years.

Recent Developments in Closed-Loop Approaches for Real-Time Mining and Petroleum Extraction

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.

Planning for Reliable Coal Quality Delivery Considering Geological Variability: A Case Study in Polish Lignite Mining

The aim of coal quality control in coal mines is to supply power plants daily with extracted raw material within certain coal quality constraints. On the example of a selected part of a lignite deposit, the problem of quality control for the runof-mine lignite stream is discussed. The main goal is to understand potential fluctuations and deviations from production targets dependent on design options before an investment is done. A single quality parameter of the deposit is selected for this analysis—the calorific value of raw lignite. The approach requires an integrated analysis of deposit inherent variability, the extraction sequence, and the blending option during material transportation. Based on drill-hole data models capturing of spatial variability of the attribute of consideration are generated. An analysis based on two modelling approaches, Kriging and sequential Gaussian simulation, reveals advantages and disadvantages lead to conclusions about their suitability for the control of raw material quality. In a second step, based on a production schedule, the variability of the calorific value in the lignite stream has been analysed. In a third step the effect of different design options, multiple excavators and a blending bed, was investigated.

Optimizing of Long-Term Mine Planning in Large Lignite Deposits

The aim of long-term mine planning is to define an optimal extraction sequence with respect to economic, technical and legal aspects. Mine planning has to meet increasing requirements. This is mainly due to technical advancements in power plant engineering and increased geological complexity of deposits currently exploited.

Cut-off Grade Based Sublevel Stope Mine Optimisation

Research in the field of cut-off grade optimisation has shown a relationship between cut-off grade, project life and Net Present Value. Lane’s theory demonstrates that cut-off grades can be optimised in order to maximise project profitability. Although the theory forms the basis for many open pit mining projects, application of the theory in underground mining remains limited to-date. The main reason for this is the complex interaction between all processes in underground mine planning which makes it difficult to apply Lane’s mathematical optimisation approach. Recently a new Stope Optimiser product was released. The AMS Stope Optimiser automates the design of underground stopes at user defined cut-off grades and allows for rapid evaluation of mine designs at different cut-off grades. Using this software, an optimisation approach was developed and validated on an underground gold deposit in northern Sweden. Potential project NPV increased by approximately 30% when using this new approach. Spatial grade uncertainty in mineral resources was identified to be a major risk in underground stope design. The optimisation approach was further extended to account for grade risk using estimated and stochastic simulated resource models. The resulting optimisation process accounts for grade risk early in the design process and reduces the risk of a stope not meeting the cut-off grade with subsequent financial loss.

An Integrated Approach to Simulate and Validate Orebody Realizations with Complex Trends: A Case Study in Heavy Mineral Sands

Characterization of spatial variability in earth science commonly requires random fields which are stationary within delineated domains. This contribution presents an alternative approach for simulating attributes in combination with a non-stationary first-order moment. A new procedure is presented to unambiguously decompose the observed behaviour into a deterministic trend and a stochastic residual, while explicitly controlling the modelled uncertainty. The practicality of the approach resides in a straightforward and objective inference of the variogram model and neighborhood parameters. This method does not require a prior removal of the trend. The inference principle is based on minimizing the deviation between empirical and theoretical errors calculated for increasingly distant neighborhood shells. Further, the inference is integrated into a systematic simulation framework and accompanying validation guidelines are formulated. The effort results in a characterization of the resource uncertainty of an existing heavy mineral sand deposit.

Integrating geological uncertainty into planning and modelling for modern and efficient dewatering concepts in large surface mines

The dewatering of subsurface unconsolidated rock strata is a critical prerequisite for many mining operations. The uncertainty in knowledge about the aquifer geometry is critical for both, the planning of directional dewatering drilling and the hydrological planning. This contribution focuses on investigating the influence of geological uncertainty on modern mining-related dewatering measures, namely horizontal directional dewatering wells. The traditional approach of drilling, planning and groundwater modelling on the basis of interpolated geological models is extended to integrate geological uncertainty. Using conditional simulation allows for more robust decisions in planning and subsequently increased efficiency. A case study in a large open pit operation demonstrates the benefit of using such methods. For a specified level of confidence, proposed methods provide a means of determining the number of samples required for the optimal placement of the filter section. The results also provide the means of quantifying uncertainty in predicting filter-well output.