Pub Date : 2024-07-26DOI: 10.1177/25726668241263408
Yi Jiang, R. Dimitrakopoulos
A mining complex or mineral value chain is an integrated system composed of mines, stockpiles, waste disposal and tailings facilities, processing destinations and transportation, that leads to generating sellable products delivered to customers and/or the spot market. To deal with such a system, conventional approaches optimise the related components independently and sequentially, while ignoring the related uncertainties. This article extends the simultaneous stochastic optimisation of mining complexes, so as to incorporate equipment uncertainties in addition to supply uncertainty. The inclusion of multiple components and different sources of uncertainty empowers the optimisation to capitalise on the synergies between the different components of a mining complex, while also managing the related technical risk and maximising the net present value. An application at a copper mining complex demonstrates the applied aspects of the proposed approach that jointly considers supply and equipment uncertainty to generate life-of-asset production schedules with a 2% higher net present value, when compared to the results considering only supply uncertainty.
{"title":"Simultaneous stochastic optimisation of mining complexes with equipment uncertainty: Application at an open-pit copper mining complex","authors":"Yi Jiang, R. Dimitrakopoulos","doi":"10.1177/25726668241263408","DOIUrl":"https://doi.org/10.1177/25726668241263408","url":null,"abstract":"A mining complex or mineral value chain is an integrated system composed of mines, stockpiles, waste disposal and tailings facilities, processing destinations and transportation, that leads to generating sellable products delivered to customers and/or the spot market. To deal with such a system, conventional approaches optimise the related components independently and sequentially, while ignoring the related uncertainties. This article extends the simultaneous stochastic optimisation of mining complexes, so as to incorporate equipment uncertainties in addition to supply uncertainty. The inclusion of multiple components and different sources of uncertainty empowers the optimisation to capitalise on the synergies between the different components of a mining complex, while also managing the related technical risk and maximising the net present value. An application at a copper mining complex demonstrates the applied aspects of the proposed approach that jointly considers supply and equipment uncertainty to generate life-of-asset production schedules with a 2% higher net present value, when compared to the results considering only supply uncertainty.","PeriodicalId":518351,"journal":{"name":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","volume":"10 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.1177/25726668241264923
Mohammad Shami-Qalandari, M. Rahmanpour, Hassan Bakhshandeh Amnieh
Optimization of mining projects is often aimed at maximizing the net present value (NPV). Cut-off grade along with production rate determines the quantity and destination of material that is mined and processed. Thus, the cash flows and the NPV of a mining project are directly affected by the cut-off grade, the mineable reserve and the production rate. In order to achieve the maximum NPV, these factors must be evaluated. Block caving is a non-selective mass mining method. In block caving method, as the cut-off grade changes, the amount of mineable reserve, and the correlated mining envelope changes consequently. Determining the optimum cut-off grade and production rate for block cave mining is a complex task, therefore, artificial neural network (ANN) and response surface method (RSM) approaches are utilized in this paper. According to the results, a combination of RSM and ANN models is able to determine the best configuration of cut-off grade and production rate that leads to the maximum NPV.
采矿项目的优化通常以净现值（NPV）最大化为目标。边界品位和生产率决定了开采和加工材料的数量和目的地。因此，采矿项目的现金流和净现值直接受到边界品位、可开采储量和生产率的影响。为了实现最大净现值，必须对这些因素进行评估。块状崩落法是一种非选择性大规模采矿方法。在分块崩落法中，随着边界品位的变化，可开采储量和相关开采包络线也会随之变化。确定块体崩落采矿的最佳边界品位和生产率是一项复杂的任务，因此本文采用了人工神经网络（ANN）和响应面法（RSM）方法。结果表明，RSM 和 ANN 模型的组合能够确定最佳边界品位和生产率配置，从而获得最大净现值。
{"title":"A modified approach for cut-off grade and production rate optimization in block caving projects","authors":"Mohammad Shami-Qalandari, M. Rahmanpour, Hassan Bakhshandeh Amnieh","doi":"10.1177/25726668241264923","DOIUrl":"https://doi.org/10.1177/25726668241264923","url":null,"abstract":"Optimization of mining projects is often aimed at maximizing the net present value (NPV). Cut-off grade along with production rate determines the quantity and destination of material that is mined and processed. Thus, the cash flows and the NPV of a mining project are directly affected by the cut-off grade, the mineable reserve and the production rate. In order to achieve the maximum NPV, these factors must be evaluated. Block caving is a non-selective mass mining method. In block caving method, as the cut-off grade changes, the amount of mineable reserve, and the correlated mining envelope changes consequently. Determining the optimum cut-off grade and production rate for block cave mining is a complex task, therefore, artificial neural network (ANN) and response surface method (RSM) approaches are utilized in this paper. According to the results, a combination of RSM and ANN models is able to determine the best configuration of cut-off grade and production rate that leads to the maximum NPV.","PeriodicalId":518351,"journal":{"name":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","volume":"29 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1177/25726668241255442
Sahar Ghadirianniari, Scott McDougall, Erik Eberhardt, Jovian Varian, Karl Llewelyn, Ryan Campbell, Allan Moss
In cave mines, wet inrushes occur when there is an uncontrolled inflow of fine, wet material from drawpoints. Currently, uncertainty exists regarding the spatial-temporal pattern and severity of inrush incidents. This uncertainty arises from the limited understanding of wet inrush mechanisms within the complex conditions of a cave mine. In this study, the existing gaps in knowledge around the spatial and temporal patterns of inrush incidents were addressed using machine learning techniques. A random forest (RF) model was employed to analyse the inrush database collected at the Deep Ore Zone mine over several years. The conceptual understanding of inrush mechanisms and triggers, along with historical evidence, was employed to establish an initial set of key inrush variables to be used in the RF model. The developed RF model demonstrated promising performance with an accuracy of 85%. The feature importance results indicated that previous inrush history, fragment size, draw rate (short term and long term), differential draw index (short term and long term) and history of inrush at neighbouring drawpoints had the highest impact on inrush susceptibility. The insights gained provide an improved assessment of inrush susceptibility, thereby improving the strategies employed to mitigate inrush risk.
{"title":"Wet inrush susceptibility assessment at the Deep Ore Zone mine using a random forest machine learning model","authors":"Sahar Ghadirianniari, Scott McDougall, Erik Eberhardt, Jovian Varian, Karl Llewelyn, Ryan Campbell, Allan Moss","doi":"10.1177/25726668241255442","DOIUrl":"https://doi.org/10.1177/25726668241255442","url":null,"abstract":"In cave mines, wet inrushes occur when there is an uncontrolled inflow of fine, wet material from drawpoints. Currently, uncertainty exists regarding the spatial-temporal pattern and severity of inrush incidents. This uncertainty arises from the limited understanding of wet inrush mechanisms within the complex conditions of a cave mine. In this study, the existing gaps in knowledge around the spatial and temporal patterns of inrush incidents were addressed using machine learning techniques. A random forest (RF) model was employed to analyse the inrush database collected at the Deep Ore Zone mine over several years. The conceptual understanding of inrush mechanisms and triggers, along with historical evidence, was employed to establish an initial set of key inrush variables to be used in the RF model. The developed RF model demonstrated promising performance with an accuracy of 85%. The feature importance results indicated that previous inrush history, fragment size, draw rate (short term and long term), differential draw index (short term and long term) and history of inrush at neighbouring drawpoints had the highest impact on inrush susceptibility. The insights gained provide an improved assessment of inrush susceptibility, thereby improving the strategies employed to mitigate inrush risk.","PeriodicalId":518351,"journal":{"name":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","volume":"128 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141811324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.1177/25726668241256707
N. Azadi, Hossein Mirzaei-Nasirabad
The method described for production scheduling in this study is a simultaneous use of a clustering algorithm with a genetic algorithm (GA). The aggregating algorithm presented in this study aims to control the concentration of operations and the cluster size, which is evaluated using the Silhouette criterion. The fitness function and the chromosome length in the GA have differences from the usual one. The results showed the number of binary variables in a mixed-integer linear programming model was reduced by 78.5% based on the created clusters. Although the aggregated model's net present value (NPV) is decreased by 7%, the solution time significantly dropped from 3 h to 43.1 s. Also, compared to the non-clustering block model, the aggregated block model's NPV, obtained by GA, was improved.
{"title":"Development of an intelligent evolution algorithm for open pit mines’ long-term production scheduling using the concept of block aggregation","authors":"N. Azadi, Hossein Mirzaei-Nasirabad","doi":"10.1177/25726668241256707","DOIUrl":"https://doi.org/10.1177/25726668241256707","url":null,"abstract":"The method described for production scheduling in this study is a simultaneous use of a clustering algorithm with a genetic algorithm (GA). The aggregating algorithm presented in this study aims to control the concentration of operations and the cluster size, which is evaluated using the Silhouette criterion. The fitness function and the chromosome length in the GA have differences from the usual one. The results showed the number of binary variables in a mixed-integer linear programming model was reduced by 78.5% based on the created clusters. Although the aggregated model's net present value (NPV) is decreased by 7%, the solution time significantly dropped from 3 h to 43.1 s. Also, compared to the non-clustering block model, the aggregated block model's NPV, obtained by GA, was improved.","PeriodicalId":518351,"journal":{"name":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","volume":"45 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141345715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-22DOI: 10.1177/25726668241244930
Zachary Levinson, R. Dimitrakopoulos
Simultaneous stochastic optimisation frameworks provide a method for optimising long-term production schedules in mining complexes that aim to maximise net present value and manage risk related to supply uncertainty. The uncertainty and local variability related to the quality and quantity of material in the mineral deposits are modelled with a set of stochastic orebody simulations, an input into the simultaneous stochastic optimisation framework. Infill drilling provides opportunities to collect additional information associated with the mineral deposits, which can inform future production scheduling decisions. A framework is developed for optimising infill drilling locations with a criterion that seeks areas that directly affect long-term planning decisions and requires the use of geostatistical simulations. Actor-critic reinforcement learning is applied to identify infill drilling locations in a copper mining complex using this criterion. The case study demonstrates that adapting production scheduling decisions given additional information has the potential to improve the associated production and financial forecasts and identifies a stable area for infill drilling.
{"title":"A reinforcement learning approach for selecting infill drilling locations considering long-term production planning in mining complexes with supply uncertainty","authors":"Zachary Levinson, R. Dimitrakopoulos","doi":"10.1177/25726668241244930","DOIUrl":"https://doi.org/10.1177/25726668241244930","url":null,"abstract":"Simultaneous stochastic optimisation frameworks provide a method for optimising long-term production schedules in mining complexes that aim to maximise net present value and manage risk related to supply uncertainty. The uncertainty and local variability related to the quality and quantity of material in the mineral deposits are modelled with a set of stochastic orebody simulations, an input into the simultaneous stochastic optimisation framework. Infill drilling provides opportunities to collect additional information associated with the mineral deposits, which can inform future production scheduling decisions. A framework is developed for optimising infill drilling locations with a criterion that seeks areas that directly affect long-term planning decisions and requires the use of geostatistical simulations. Actor-critic reinforcement learning is applied to identify infill drilling locations in a copper mining complex using this criterion. The case study demonstrates that adapting production scheduling decisions given additional information has the potential to improve the associated production and financial forecasts and identifies a stable area for infill drilling.","PeriodicalId":518351,"journal":{"name":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","volume":"76 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140675387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1177/25726668241242232
Phillip Stothard, Peter Ryan, Takeshi Kurata, Doug Stapleton
Access to persistent computer-generated virtual worlds may provide a powerful tool for conceptualising the mining cycle and managing the domains of exploration, feasibility, planning, design, construction, operations, rehabilitation, decommissioning and closure. Each domain presents a significant challenge to mine operations. Realisation of persistent virtual worlds that can be accessed by many simultaneously may be possible by leveraging Metaverse technologies to produce an ‘always on’ Mining Metaverse based on International Standards and industry collaboration. The realisation of a Mining Metaverse is a complex task because the Metaverse itself has many components and domains that must be managed effectively for it to be sustainable. This article introduces the complexity of the Metaverse components as a taxonomy and was inspired from collaborative work completed by the Standards Australia IT-031 Modelling and Simulation Committee and International Standards Organisation ISO/IEC JTC 1/SC 24 Committee. It is intended as a starting point for the mining industry towards understanding what the Mining Metaverse may be, and effectively embracing and managing this complex emerging technology in the future.
{"title":"Towards a mining metaverse","authors":"Phillip Stothard, Peter Ryan, Takeshi Kurata, Doug Stapleton","doi":"10.1177/25726668241242232","DOIUrl":"https://doi.org/10.1177/25726668241242232","url":null,"abstract":"Access to persistent computer-generated virtual worlds may provide a powerful tool for conceptualising the mining cycle and managing the domains of exploration, feasibility, planning, design, construction, operations, rehabilitation, decommissioning and closure. Each domain presents a significant challenge to mine operations. Realisation of persistent virtual worlds that can be accessed by many simultaneously may be possible by leveraging Metaverse technologies to produce an ‘always on’ Mining Metaverse based on International Standards and industry collaboration. The realisation of a Mining Metaverse is a complex task because the Metaverse itself has many components and domains that must be managed effectively for it to be sustainable. This article introduces the complexity of the Metaverse components as a taxonomy and was inspired from collaborative work completed by the Standards Australia IT-031 Modelling and Simulation Committee and International Standards Organisation ISO/IEC JTC 1/SC 24 Committee. It is intended as a starting point for the mining industry towards understanding what the Mining Metaverse may be, and effectively embracing and managing this complex emerging technology in the future.","PeriodicalId":518351,"journal":{"name":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","volume":" 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140692315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-13DOI: 10.1177/25726668241241993
Daniel Morales, R. Dimitrakopoulos
Stochastic long-term mine planning has evolved to account for different sources of uncertainty. Typically, the uncertainty and local variability of boundaries in geological domains have been overlooked by experts through their deterministic interpretation of available data. Categorical attributes are used to model geological domains, and their stochastic simulation accounts for the mentioned issues. The ability of two-points simulation methods to reproduce complex patterns or the requirement of a training image in multiple-points simulation methods has limited their implementation in mining environments. The high-order simulation of categorical attributes presents a mathematically consistent framework that overcomes these limitations by using high-order spatial statistics from sample data. The case study at a gold mining complex shows two stochastic mine plans based on two sets of geological realisations: geological domains in the first set are modelled using conventional wireframes, while, in the second, they are simulated through the high-order method. The resulting mine plans are substantially different; while both plans present a similar quantity of metal recovered and lifespan, risk profiles are up to 40% wider, and the expected NPV is 20% higher for the case of simulated geological domains, given the decrease of waste handling costs and the corresponding reduction in environmental footprint.
{"title":"High-order simulation of geological domains and effects on stochastic long-erm planning of mining complexes","authors":"Daniel Morales, R. Dimitrakopoulos","doi":"10.1177/25726668241241993","DOIUrl":"https://doi.org/10.1177/25726668241241993","url":null,"abstract":"Stochastic long-term mine planning has evolved to account for different sources of uncertainty. Typically, the uncertainty and local variability of boundaries in geological domains have been overlooked by experts through their deterministic interpretation of available data. Categorical attributes are used to model geological domains, and their stochastic simulation accounts for the mentioned issues. The ability of two-points simulation methods to reproduce complex patterns or the requirement of a training image in multiple-points simulation methods has limited their implementation in mining environments. The high-order simulation of categorical attributes presents a mathematically consistent framework that overcomes these limitations by using high-order spatial statistics from sample data. The case study at a gold mining complex shows two stochastic mine plans based on two sets of geological realisations: geological domains in the first set are modelled using conventional wireframes, while, in the second, they are simulated through the high-order method. The resulting mine plans are substantially different; while both plans present a similar quantity of metal recovered and lifespan, risk profiles are up to 40% wider, and the expected NPV is 20% higher for the case of simulated geological domains, given the decrease of waste handling costs and the corresponding reduction in environmental footprint.","PeriodicalId":518351,"journal":{"name":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","volume":"56 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140707949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-13DOI: 10.1177/25726668241242230
Cristina Penadillo, R. Dimitrakopoulos, Mustafa Kumral
The three main optimisation components of sublevel stoping methods are stope layout, production schedule (or stope sequencing) and access networks. The joint optimisation of these components could further add value to an underground mining project. This potential has not been considered in the literature due to computational difficulties, and the problem was solved sequentially. This paper proposes a new joint optimisation model to integrate these components. In addition, the proposed optimisation model incorporates stochastic simulations to capture uncertainty and variability associated with the grades of the related mineral deposits mined. The optimisation model is based on a two-stage stochastic integer programming (SIP) formulation that maximises the project's net present value (NPV) and minimises the planned dilution. Applying the proposed method at a small copper deposit shows that the SIP outperforms the results obtained from mixed integer programming. For a seven-year mine life, the SIP model generated ∼20% more NPV, demonstrating the importance of developing a joint optimisation formulation and accounting for grade uncertainty and variability.
{"title":"Joint stochastic optimisation of stope layout, production scheduling and access network","authors":"Cristina Penadillo, R. Dimitrakopoulos, Mustafa Kumral","doi":"10.1177/25726668241242230","DOIUrl":"https://doi.org/10.1177/25726668241242230","url":null,"abstract":"The three main optimisation components of sublevel stoping methods are stope layout, production schedule (or stope sequencing) and access networks. The joint optimisation of these components could further add value to an underground mining project. This potential has not been considered in the literature due to computational difficulties, and the problem was solved sequentially. This paper proposes a new joint optimisation model to integrate these components. In addition, the proposed optimisation model incorporates stochastic simulations to capture uncertainty and variability associated with the grades of the related mineral deposits mined. The optimisation model is based on a two-stage stochastic integer programming (SIP) formulation that maximises the project's net present value (NPV) and minimises the planned dilution. Applying the proposed method at a small copper deposit shows that the SIP outperforms the results obtained from mixed integer programming. For a seven-year mine life, the SIP model generated ∼20% more NPV, demonstrating the importance of developing a joint optimisation formulation and accounting for grade uncertainty and variability.","PeriodicalId":518351,"journal":{"name":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","volume":"50 223","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140708138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-11DOI: 10.1177/25726668241243265
Xiaomeng Gu, Nigel J. Cook, Andrew V. Metcalfe, Chris Aldrich
Drill core images offer valuable insights into the texture, structure and mineralogy of ores and their host rocks, which can be used to optimise downstream processes in the mining industry. The impact on downstream processes from particles of similar composition and mineralogy but different textures has been examined by several previous researchers through the application of supervised machine-learning techniques. This study proposes a novel approach for detecting changes in drill core textures through the analysis of optical images. This approach compares three widely used image feature extraction techniques (local binary patterns, grey-level co-occurrence matrix and convolutional neural network), followed by calculation of a uniqueness measure, based on the Hotelling statistic, designed to identify anomalous segments of core. The effectiveness of the uniqueness measure is validated on a test core comprising six sections with different textures. Two drill cores, from the Brukunga test site in South Australia, were selected as case studies. Of the three feature extraction methods, local binary patterns were found to give the strongest signals of change. There exist two main regimes that separate halfway along both drill cores, indicating a change in lithology or the presence of mineralisation.
{"title":"A machine vision approach for detecting changes in drill core textures using optical images","authors":"Xiaomeng Gu, Nigel J. Cook, Andrew V. Metcalfe, Chris Aldrich","doi":"10.1177/25726668241243265","DOIUrl":"https://doi.org/10.1177/25726668241243265","url":null,"abstract":"Drill core images offer valuable insights into the texture, structure and mineralogy of ores and their host rocks, which can be used to optimise downstream processes in the mining industry. The impact on downstream processes from particles of similar composition and mineralogy but different textures has been examined by several previous researchers through the application of supervised machine-learning techniques. This study proposes a novel approach for detecting changes in drill core textures through the analysis of optical images. This approach compares three widely used image feature extraction techniques (local binary patterns, grey-level co-occurrence matrix and convolutional neural network), followed by calculation of a uniqueness measure, based on the Hotelling statistic, designed to identify anomalous segments of core. The effectiveness of the uniqueness measure is validated on a test core comprising six sections with different textures. Two drill cores, from the Brukunga test site in South Australia, were selected as case studies. Of the three feature extraction methods, local binary patterns were found to give the strongest signals of change. There exist two main regimes that separate halfway along both drill cores, indicating a change in lithology or the presence of mineralisation.","PeriodicalId":518351,"journal":{"name":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","volume":"51 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140713598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-03DOI: 10.1177/25726668241241989
Nadia Bustos, Ernesto Villaescusa, Fernando García
Drawbells are large constructions that allow the flow of the broken ore at the production level within the drawpoints. The results of drawbell construction are crucial for a successful mine plan extraction in a block caving mine. Analysis and integration of all the topics related to a drawbell can improve the performance of the surrounding area of drawbells, particularly the damage associated to induced stress during the different stages of a block caving operation. The improvement of operational aspects related to the drawbells would decrease the risk of failure, particularly in deep mines subject to large stresses, which are more likely to experience sudden violent or progressive failure. A large-scale numerical model was developed to analyse different mining sectors using sub-models. It was found that the geotechnical response is highly correlated to the stress field, which also controlled the resulting seismicity. In a scale of a drawbell, the drawpoint drift roof and the bridge pillar between the drawbell and the undercutting level were found the most critical zones in the design. When geological structures are present, they can be activated in the construction at an earlier stage and, therefore, could easily become critical in the resulting rock mass damage.
{"title":"Analysis of induced stress during construction and production stages of drawbells in block caving mines","authors":"Nadia Bustos, Ernesto Villaescusa, Fernando García","doi":"10.1177/25726668241241989","DOIUrl":"https://doi.org/10.1177/25726668241241989","url":null,"abstract":"Drawbells are large constructions that allow the flow of the broken ore at the production level within the drawpoints. The results of drawbell construction are crucial for a successful mine plan extraction in a block caving mine. Analysis and integration of all the topics related to a drawbell can improve the performance of the surrounding area of drawbells, particularly the damage associated to induced stress during the different stages of a block caving operation. The improvement of operational aspects related to the drawbells would decrease the risk of failure, particularly in deep mines subject to large stresses, which are more likely to experience sudden violent or progressive failure. A large-scale numerical model was developed to analyse different mining sectors using sub-models. It was found that the geotechnical response is highly correlated to the stress field, which also controlled the resulting seismicity. In a scale of a drawbell, the drawpoint drift roof and the bridge pillar between the drawbell and the undercutting level were found the most critical zones in the design. When geological structures are present, they can be activated in the construction at an earlier stage and, therefore, could easily become critical in the resulting rock mass damage.","PeriodicalId":518351,"journal":{"name":"Mining Technology: Transactions of the Institutions of Mining and Metallurgy","volume":"52 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140747987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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