Integrated Parametric Graph Closure and Branch-and-Cut Algorithm for Open Pit Mine Scheduling under Uncertainty

Open pit mine production scheduling is a computationally expensive large-scale mixed-integer linear programming problem. This research develops a computationally efficient algorithm to solve open pit production scheduling problems under uncertain geological parameters. The proposed solution approach for production scheduling is a two-stage process. The stochastic production scheduling problem is iteratively solved in the first stage after relaxing resource constraints using a parametric graph closure algorithm. Finally, the branch-and-cut algorithm is applied to respect the resource constraints, which might be violated during the first stage of the algorithm. Six small-scale production scheduling problems from iron and copper mines were used to validate the proposed stochastic production scheduling model. The results demonstrated that the proposed method could significantly improve the computational time with a reasonable optimality gap (the maximum gap is 4%). In addition, the proposed stochastic method is tested using industrial-scale copper data and compared with its deterministic model. The results show that the net present value for the stochastic model improved by 6% compared to the deterministic model.

Hybrid Fuzzy-Analytic Hierarchy Process (AHP) Model for Porphyry Copper Prospecting in Simorgh Area, Eastern Lut Block of Iran

The eastern Lut block of Iran has a high potential for porphyry copper mineralization due to the subduction tectonic regime. It is located in an inaccessible region and has harsh arid conditions for traditional mineral exploration campaigns. The objective of this study is to use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for porphyry copper exploration in Simorgh Area, eastern Lut block of Iran. Hydrothermal alteration zones such as argillic, phyllic and propylitic zones associated with porphyry copper systems in the study were identified using false color composition (FCC), band ratio (BR), principal component analysis (PCA) and minimal noise fraction (MNF). The thematic alteration layers extracted from FCC, BR, PCA and MNF were integrated using hybrid Fuzzy-AHP model to generate a porphyry copper potential map for the study area. Four high potential zones were identified in the central, western, eastern and northeastern of the study area. Fieldwork was used to validate the approach used in this study. This investigation exhibits that the use of hybrid Fuzzy-AHP model for the identification of hydrothermal alteration zones associated with porphyry copper systems that is typically applicable to ASTER data and can be used for porphyry copper potential mapping in many analogous metallogenic provinces.

Practical Use of Measuring the Deflection of Roof Layers in the Assessment of the Stability of Mining Excavations in the Polish Copper Ore Mine “Polkowice-Sieroszowice”

The paper presents the results of long-term continuous measurements of the deflection of the roof layers in the underground copper mine Polkowice-Sieroszowice, Poland belonging to KGHM Polska Miedź S.A. The measurements were performed with the use of the inclinometric method consisting of continuous registration of changes in the angle of inclination of the roof strata. The measurements were carried out using an inclinometer sensor fixed to the end of a rockbolt in the roof. Measurements presented in the article were made in various regions of the underground mine. The monitoring covered: The exploitation front, machinery chamber and the region of the experimental longwall mining of copper ore. The obtained results proved the usefulness of the developed method in the process of the evaluation of the stability of mining excavations. The sensors were highly sensitive and performed the measurements in a simple way; highly accurate and reliable results were obtained.

Product Development of a Rock Reinforcing Bolt for Underground Hard Rock Mining

The demand for mineral resources has dramatically increased over the past few decades; this increase directly correlates to an increase in underground mining activity. There are different mining methods for different minerals, and each have their risks. In hard rock mining activities such as mining for gold, rockfalls are the most significant deterrent to obtaining mineral resources. This paper focuses on rock reinforcement systems to prevent fatal rockfalls in underground excavations. Presently, there is a global steel shortage and an increase in prices that has impacted the productivity of the mining operations that support most national economies. The paper’s main objective is to present the improvement of a rock bolt design used to support the roof in underground mining activities and keep working personnel and equipment safe from rockfalls. This study presents two rock bolt designs: a preliminary design and an improved model of the rock bolt. The paper discusses the operation of the rock bolt and provides laboratory test results on the bolt in operation. The principle of operation of the yield bolt is based on the science of radial expansion of hollow tubes in tension, to provide integrity to underground excavations. This functional design of the rock bolt requires less steel and has the same performance as the current rock reinforcement elongates. The research methodology involved interviewing rock mining engineers to determine their desired rock reinforcement device that would adequately meet the unpredictable dynamic and static behavior of underground rocks. The methodology also included experimental tests of a rock bolt design that was aimed at meeting the desired and acceptable performance determined from the interviews. The experimental results were obtained from a 60-ton hydraulic press that simulated seismic activity underground. The experimental results showed several modes of failure for the bolt; however, the improved rock bolt yielded at an average of 200 KN, as designed. During testing of the preliminary bolt design, there were failures that resulted from the manufacturing process of the bolt, such as splitting of the tube due to the welded end components. After a dynamic test, the preliminary bolt tube bent, creating huge forces on the tube which may cause fracture. The coefficient of friction during dynamic testing was lower than during static testing, leading to undesirable results for the preliminary bolt. The optimized bolt design addressed the failures and the low yield tonnage of the preliminary bolt design. It successfully yielded at 20 tons, even during the dynamic event. The bolt had similar alignment issues which caused failure during testing, as can be seen from the results. A guide tube was implemented in the design and the manufacturing process changed; these changes resulted in the bolt having a more reliable performance that met the requirements throughout.

A New Method to Analyze the Mine Liquidation Costs in Poland

Coal mine closure processes are being carried out in the European Union due to the current energy transition. The use of coal-fired power plants has been significantly reduced in recent years. Because of the significant financial outlays, processes of rationalization and minimization of the mine liquidation cost should be carried out. In this paper, a statistical analysis of the liquidation processes in hard coal mines in Poland was carried out. A new tool was developed in order to optimize the mine liquidation costs. The mine liquidation process can be divided into ten different processes, which have been analyzed in detail in this research work. The method of the assessment of the amount of estimated liquidation costs described is based on the analysis of the total liquidation cost. The presented method of signaling deviations of the costs of the liquidation of the mining plant from the average value is a useful tool in the process approach to the issues connected with the restructuring of post-industrial property. The presented cost assessment procedure may facilitate the monitoring of conducted activities in terms of rationalization and minimization of the costs incurred. Finally, the proposed method for assessing the cost of mine liquidation is understandable, simple, and easy to use for applications in preliminary design works and on-going engineering works.

An Autochthonous Acidithiobacillus ferrooxidans Metapopulation Exploited for Two-Step Pyrite Biooxidation Improves Au/Ag Particle Release from Mining Waste

Pyrite bio-oxidation by chemolithotrophic acidophile bacteria has been applied in the mining industry to bioleach metals or to remove pyritic sulfur from coal. In this process, it is desirable to use autochthonous and already adapted bacteria isolated directly from the mining sites where biomining will be applied. Bacteria present in the remnant solution from a mining company were identified through cloning techniques. For that purpose, we extracted total RNA and performed reverse transcription using a novel pair of primers designed from a small region of the 16S gene (V1–V3) that contains the greatest intraspecies diversity. After cloning, a high proportion of individuals of the strains ATCC-23270 (NR_074193.1 and NR_041888.1) and DQ321746.1 of the well-known species Acidithiobacillus ferrooxidans were found, as well as two new wild strains of A. ferrooxidans. This result showed that the acidic remnant solution comprises a metapopulation. We assayed these strains to produce bioferric flocculant to enhance the subsequent pyrite bio-oxidation, applying two-stage chemical–bacterial oxidation. It was shown that the strains were already adapted to a high concentration of endogenous Fe2+ (up to 20 g·L−1), increasing the volumetric productivity of the bioferric flocculant. Thus, no preadaptation of the community was required. We detected Au and Ag particles originally occluded in the old pyritic flotation tailings assayed, but the extraction of Au and Ag by cyanidation resulted in ca. 30.5% Au and 57.9% Ag.

Application of Artificial Neural Network (ANN) for Prediction and Optimization of Blast-Induced Impacts

Drilling and blasting remain the preferred technique used for rock mass breaking in mining and construction projects compared to other methods from an economic and productivity point of view. However, rock mass breaking utilizes only a maximum of 30% of the blast explosive energy, and around 70% is lost as waste, thus creating negative impacts on the safety and surrounding environment. Blast-induced impact prediction has become very demonstrated in recent research as a recommended solution to optimize blasting operation, increase efficiency, and mitigate safety and environmental concerns. Artificial neural networks (ANN) were recently introduced as a computing approach to design the computational model of blast-induced fragmentation and other impacts with proven superior capability. This paper highlights and discusses the research articles conducted and published in this field among the literature. The prediction models of rock fragmentation and some blast-induced effects, including flyrock, ground vibration, and back-break, were detailed investigated in this review. The literature showed that applying the artificial neural network for blast events prediction is a practical way to achieve optimized blasting operation with reduced undesirable effects. At the same time, the examined papers indicate a lack of articles focused on blast-induced fragmentation prediction using the ANN technique despite its significant importance in the overall economy of whole mining operations. As well, the investigation revealed some lack of research that predicted more than one blast-induced impact.

One-Dimensional Convolutional Neural Network for Drill Bit Failure Detection in Rotary Percussion Drilling

Drill bit failure is a prominent concern in the drilling process of any mine, as it can lead to increased mining costs. Over the years, the detection of drill bit failure has been based on the operator’s skills and experience, which are subjective and susceptible to errors. To enhance the efficiency of mining operations, it is necessary to implement applications of artificial intelligence to produce a superior method for drill bit monitoring. This research proposes a new and reliable method to detect drill bit failure in rotary percussion drills using deep learning: a one-dimensional convolutional neural network (1D CNN) with time-acceleration as input data. 18 m3 of granite rock were drilled horizontally using a rock drill and intact tungsten carbide drill bits. The time acceleration of drill vibrations was measured using acceleration sensors mounted on the guide cell of the rock drill. The drill bit failure detection model was evaluated on five drilling conditions: normal, defective, abrasion, high pressure, and misdirection. The model achieved a classification accuracy of 88.7%. The proposed model was compared to three state-of-the-art (SOTA) deep learning neural networks. The model outperformed SOTA methods in terms of classification accuracy. Our method provides an automatic and reliable way to detect drill bit failure in rotary percussion drills.

Prediction of Mining Conditions in Geotechnically Complex Sites

Geotechnical complexity in mining often leads to geotechnical uncertainty which impacts both safety and productivity. However, as mining progresses, particularly for strip mining operations, a body of knowledge is acquired which reduces this uncertainty and can potentially be used by mining engineers to improve the prediction of future mining conditions. In this paper, we describe a new method to support this approach based on modelling and neural networks. A high-level causal model of the mining operations based on historical data for a number of parameters was constructed which accounted for parameter interactions, including hydrogeological conditions, weather, and prior operations. An artificial neural network was then trained on this historical data, including production data. The network can then be used to predict future production based on presently observed mining conditions as mining proceeds and compared with the model predictions. Agreement with the predictions indicates confidence that the neural network predictions are properly supported by the newly available data. The efficacy of this approach is demonstrated using semi-synthetic data based on an actual mine.

Near-Field Analysis of Turbidity Flows Generated by Polymetallic Nodule Mining Tools

The interest in polymetallic nodule mining has considerably increased in the last few decades. This has been largely driven by population growth and the need to move towards a green future, which requires strategic raw materials. Deep-Sea Mining (DSM) is a potential source of such key materials. While harvesting the ore from the deep sea by a Polymetallic Nodule Mining Tool (PNMT), some bed sediment is unavoidably collected. Within the PNMT, the ore is separated from the sediment, and the remaining sediment–water mixture is discharged behind the PNMT, forming an environmental concern. This paper begins with surveying the state-of-the-art knowledge of the evolution of the discharge from a PNMT, in which the discharge characteristics and generation of turbidity currents are discussed. Moreover, the existing water entrainment theories and coefficients are analyzed. It is shown how plumes and jets can be classified using the flux balance approach. Following that, the models of Lee et al. (2013) and Parker et al. (1986) are combined and utilized to study the evolution of both the generated sediment plume and the subsequent turbidity current. The results showed that a smaller sediment flux at the impingement point, where the plume is transformed into a turbidity current, results in a shorter run-out distance of the turbidity current, consequently being more favorable from an environmental point of view.