Extractive industry is an industry where large volumes of waste are generated. Solid waste from mining and quarrying is the second largest stream of waste in European Union. Extractive industry and higher education programs related to it such as mining, mineral engineering, raw materials, and applied earth sciences need to put an emphasis on this context and include this concept in the existing curricula and/or create new study programs or short courses that will include circular economy (CE) approach. In 2020 project CIRCEXTIN funded by the Erasmus+ Strategic partnerships Key Action 2 was started. The objective of this project is to create a strategic partnership between Universities and companies developing a comprehensive training platform that will help to modify existing study programmes related to the extractive industry and knowledge of proper waste management incorporating a circular economy approach. This article presents major assumptions and result of the project as of September 2022.

Satisfying safety issues plays a critical role in mining operations. Although the use of emerging technology became a new trend in preventing powered haulage hazards in the mining industry, these technologies themselves posed new hazards to the problem that are necessary to be identified, assessed, and managed together with common hazards. This study investigates the existing gaps in powered haulage safety to establish a comprehensive framework for conducting risk analysis procedures. To achieve this purpose, a literature search methodology is employed to recognize the most relevant resources and extract the essential information. The most critical hazards in powered haulage operations are identified and classified into main groups. Then, root causes and consequences are designated for these hazards, providing substantial elements for risk analysis, which serves as an effective hazard measurement. Afterward, an overview of popular risk analysis techniques applied in the mining industry is provided to establish a holistic risk analysis framework. Finally, available hazard management strategies are discussed as solutions for mitigating and preventing potential hazards. The study results demonstrated the importance of establishing comprehensive safety protocols, continuously upgrading the advanced technologies, regular training, and continuous risk assessment to mitigate and prevent fatal and non-fatal hazards in mining operations.

Multi-element (ME) datasets provide comprehensive geochemical signatures of an orebody and are commonly used to gain insight into the mineralogy, lithology, alteration patterns and to identify target-pathfinders. However, little effort is made in using these data to explain comminution or recovery characteristics. This paper describes an agglomerative hierarchical clustering approach applied to ME data from the Tropicana Gold Mine, Australia, and investigates the relationship between the resultant classes and run-of-mine comminution and recovery parameters. First, it is demonstrated how an industry scale ME dataset is prepared for clustering. The preparation consists of verifying the absence of interlaboratory and intralaboratory bias between measurements, centred log-ratio transformation (clr), normalisation and principal component analysis (PCA). Afterwards, the first case study indicate that the clustering separation is primarily driven by geochemical differences caused by major rock-forming mineral signatures (felsic vs mafic, alteration vs no alteration, chert or quartz lithologies, unmineralised vs mineralised material). This case study separates the ME dataset into five unmineralised and two Au-mineralised material classes. The second case study continues with the two identified mineralised material classes and further separates these samples into five new classes. These classes are explored geochemically and by using the spatial context (within domains) better matched with metallurgical test results. It is found that domain-related material class proportions assist in interpreting different processing proxies such as the Equotip hardness (Leeb), Bond Work index (BWi), Axb, and processing recovery and reagent consumption. Knowledge of the processing parameters per domain and class composition can be used to infer such characteristics in the absence of standard metallurgical tests. This new approach of gaining insights into comminution and recovery parameters through geochemical analysis demonstrates the benefit of the conceptualised material fingerprinting concept.

Geochemical and mineralogical datasets from Tropicana Gold Mine, Australia, have been used to define Au-mineralised fingerprints. VNIR-SWIR spectral data were represented by four normalised wavelength regions and were clustered to form spectral classes. Sequentially, these spectral class proportions within a block and co-located pXRF data were clustered to discriminate material types (fingerprints). The hardness of each type was further explored using collocated BWi, Axb, Equotip rebound hardness and penetration rate datasets, but also by considering spatial contextual relationships and the within material type variability. The Tropicana orebody example gave a good illustration of how a phengitic-epidote K-feldspar rich domain (schistosity and softer, ∼15–18 kWh/t) separated from a harder (>20 kWh/t), shorter wavelength phengitic plagioclase-rich feldspar dominated domain. Exploring the within material type differences using the white mica composition (wAlOH) and a new w605 spectral feature demonstrated how the effects of shearing were captured within material types. Such findings will ultimately improve the understanding of the constitutive material hardness and have significance for process optimisation and blending strategy design.

Material attributes (e.g., chemical composition, mineralogy, texture) are identified as the causative source of variations in the behaviour of mineral processing. That makes them suitable to act as key characteristics to characterise and classify material. Therefore, vast quantities of collected data describing material attributes could help to forecast the behaviour of mineral processing. This paper proposes a conceptual framework that creates a data-driven link between ore and the processing behaviour through the creation of material “fingerprints”. A fingerprint is a machine learning-based classification of measured material attributes compared to the range of attributes found within the mine’s mineral reserves. The outcome of the classification acts as a label for a machine learning model and contains relevant information, which may identify the root cause of measured differences in processing behaviour. Therefore, this class label can forecast the associated behaviour of mineral processing. Furthermore, insight is given into the confidence of available data originating from different analytical techniques. Taken together, this enhances the understanding of how differences in geology impact metallurgical plant performance. Targeted measurements at low-confidence unit processes and for specific attributes would upgrade the confidence in fingerprints and capabilities to predict plant performance.

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.

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.