Mining Technology-Transactions of the Institutions of Mining and Metallurgy最新文献

Predicting energy consumption SAG mills through Bayesian generalized linear model and random forest. 利用贝叶斯广义线性模型和随机森林预测SAG轧机能耗。

IF 3.4

Mining Technology-Transactions of the Institutions of Mining and Metallurgy Pub Date : 2025-11-05 eCollection Date: 2025-12-01 DOI: 10.1177/25726668251391540

Zhanbolat Magzumov, Mustafa Kumral

{"title":"Predicting energy consumption SAG mills through Bayesian generalized linear model and random forest.","authors":"Zhanbolat Magzumov, Mustafa Kumral","doi":"10.1177/25726668251391540","DOIUrl":"10.1177/25726668251391540","url":null,"abstract":"The mining industry consumes about 1.7% of the energy generated worldwide, which is expected to increase in the coming decades. Milling is the most energy-intensive process of a typical mining operation. Many variables (e.g., rock characteristics, mineral matrix, and equipment properties) affect energy consumption. This paper proposes that Random Forests and the Generalised Linear Model (GLM) be used to predict the energy consumption of the SAG mill, which significantly contributes to the energy consumption of mining operations. To show the performance of the proposed approach, a case study was applied to a copper mine dataset from South America. The proposed approaches were applied to forecast the SAG mill energy consumption. The outcomes demonstrated that these methods could be used to predict energy consumption. Random Forest can have a high prediction accuracy of 95% but lacks explanatory ability, as shown in R2 at 50%. GLM provided additional insights by showing the feature importances and their relationships with SAG mill energy consumption, along with considering the potential uncertainties and generating posterior probability distributions for the model outcomes. Both models identified key variables as significant predictors identically, with the GLM offering a more comprehensive view of best-case and worst-case energy consumption scenarios.","PeriodicalId":44166,"journal":{"name":"Mining Technology-Transactions of the Institutions of Mining and Metallurgy","volume":"134 4","pages":"289-311"},"PeriodicalIF":3.4,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13131276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic multi-period mixed-integer non-linear programming model for equipment selection in the mining industry. 矿山设备选择的动态多周期混合整数非线性规划模型。

IF 3.4

Mining Technology-Transactions of the Institutions of Mining and Metallurgy Pub Date : 2025-06-10 eCollection Date: 2025-06-01 DOI: 10.1177/25726668251348712

Sena Senses, Mustafa Kumral

{"title":"Dynamic multi-period mixed-integer non-linear programming model for equipment selection in the mining industry.","authors":"Sena Senses, Mustafa Kumral","doi":"10.1177/25726668251348712","DOIUrl":"10.1177/25726668251348712","url":null,"abstract":"Planning an equipment fleet is a complex engineering challenge involving (1) the capacity selection of equipment pieces forming a fleet, (2) the determination of fleet size, (3) strategic timing of equipment acquisitions, and (4) ensuring compatibility between interconnected equipment. Selecting equipment type involves evaluating capacity to meet production requirements effectively while considering operational constraints. It also requires determining the optimal number of units to avoid underutilisation or redundancy. In multi-period projects, the timing of equipment acquisition further adds complexity, as decisions must align with production rates that evolve over time. Moreover, matching equipment types is essential to ensure smooth and efficient workflows. This study develops a dynamic multi-period mixed-integer non-linear programming model to optimise equipment selection, considering capital recovery, operating costs, and equipment availability under match factor and production constraints. The model's effectiveness is demonstrated through a case study encompassing greenfield and brownfield scenarios in open-pit mining operations. The greenfield scenario emphasises phased equipment acquisition to align with production ramp-up and minimising initial costs. The brownfield scenario addresses the integration of aging and new equipment to sustain operational efficiency. The results highlight the model's flexibility and applicability, offering a robust tool for optimising equipment selection while balancing cost and performance.","PeriodicalId":44166,"journal":{"name":"Mining Technology-Transactions of the Institutions of Mining and Metallurgy","volume":"134 2","pages":"143-158"},"PeriodicalIF":3.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13131275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reassessing Janssen's equation for cave stress estimation in block cave mining. 块状洞室开采中洞室应力估算的Janssen方程再评价

IF 2.2

Mining Technology-Transactions of the Institutions of Mining and Metallurgy Pub Date : 2025-04-29 eCollection Date: 2025-06-01 DOI: 10.1177/25726668251337293

Yalin Li, Davide Elmo

{"title":"Reassessing Janssen's equation for cave stress estimation in block cave mining.","authors":"Yalin Li, Davide Elmo","doi":"10.1177/25726668251337293","DOIUrl":"https://doi.org/10.1177/25726668251337293","url":null,"abstract":"Cave mines operating at greater depths are faced with higher draw column heights, typically ranging from several hundred metres to over one kilometre. This results in significantly higher cave stress on the production level, making reliable stress estimation critical for assessing long-term stability. The current approach to estimating cave stresses in the draw column relies on Janssen's equation, developed initially using the Method of Differential Slices (MDS) and based on laboratory observations of corn and bulk solids in storage silos. This article first reviews the MDS and the assumptions used to derive Janssen's equation. The article also discusses how the limitations of the analogues (e.g. corn material and full-to-empty silo condition) used to develop the equation do not represent conditions equivalent to those observed in cave mining (e.g. fragmented rock blocks undergoing fragmentation and no-column to full-column condition as cave propagates). These mechanistic differences may lead to erroneous estimations of cave stresses. While Janssen's equation is a valuable initial tool for estimating cave stresses, practitioners must carefully assess its assumptions and limitations before applying it to engineering designs.","PeriodicalId":44166,"journal":{"name":"Mining Technology-Transactions of the Institutions of Mining and Metallurgy","volume":"134 2","pages":"95-104"},"PeriodicalIF":2.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13131274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementing Gaussian process modelling in predictive maintenance of mining machineries. 高斯过程建模在矿山机械预测维修中的应用。

IF 3.4

Mining Technology-Transactions of the Institutions of Mining and Metallurgy Pub Date : 2024-08-30 eCollection Date: 2024-12-01 DOI: 10.1177/25726668241275434

Zhixuan Shao, Mustafa Kumral

{"title":"Implementing Gaussian process modelling in predictive maintenance of mining machineries.","authors":"Zhixuan Shao, Mustafa Kumral","doi":"10.1177/25726668241275434","DOIUrl":"10.1177/25726668241275434","url":null,"abstract":"Mining machinery constitutes essential assets for a mining corporation. Due to economies of scale, technological innovations and stringent quality and safety requirements, the size, complexity, functionality and diversity of industrial machinery have expanded markedly over the last two decades. This growth has increased sensitivity to machine availability and reliability. Mining operations install comprehensive maintenance units tasked with inspection, repair, replacement and inventory management for the machines in use. Leveraging the proliferation of sensor technologies integrated within the machines, maintenance units obtain rich data streams synchronously disclosing machine health and performance metrics, which enables a predictive maintenance programme. This programme performs prognostic detections of anomalies and permits timely intervention to avert catastrophic breakdowns. However, such sensor-driven predictive maintenance scheme for machinery in the mining sector is limited. The present paper utilises the Gaussian process, a powerful predictive modelling technique, to show its potential in addressing this challenge. The efficacy of this approach is validated through three case studies. Each case study is equipped with sensor data and represents a typical predictive maintenance task for mining assets. The developed Gaussian process models successfully capture meaningful temporal patterns in sensor data and generate credible predictions across all three tasks: temporal prediction of sensor data degradation trends, remaining useful lifespan prediction and simultaneous monitoring and prediction of multiple machine conditions. Furthermore, the models offer uncertainty estimates to the prediction outcomes, potentially facilitating maintenance decision-making process.","PeriodicalId":44166,"journal":{"name":"Mining Technology-Transactions of the Institutions of Mining and Metallurgy","volume":"133 4","pages":"348-368"},"PeriodicalIF":3.4,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13131273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital twins in the minerals industry – a comprehensive review 矿业中的数字孪生——全面回顾

Mining Technology-Transactions of the Institutions of Mining and Metallurgy Pub Date : 2023-09-20 DOI: 10.1080/25726668.2023.2257479

Juncong Qu, Mehmet S. Kizil, Mohsen Yahyaei, Peter F. Knights

{"title":"Digital twins in the minerals industry – a comprehensive review","authors":"Juncong Qu, Mehmet S. Kizil, Mohsen Yahyaei, Peter F. Knights","doi":"10.1080/25726668.2023.2257479","DOIUrl":"https://doi.org/10.1080/25726668.2023.2257479","url":null,"abstract":"ABSTRACTDigital Twins (DT) have become popular in various industries to solve complex problems. A true DT allows for bidirectional real-time data flows between the physical and virtual worlds.The term DT was often overused, leading to confusion about its capabilities therefore this paper provides a review of the current state of DT and its applications in the minerals industry. Existing literature shows a slow adoption of DTs in the minerals industry, which is largely due to challenges associated with geological unpredictability, legacy system integration, cost, skill gaps, cybersecurity, and lack of standards.Combining immersive visualisation with real-time spatial computer graphics could improve the usability and acceptance of DTs in the minerals industry which has been used successfully in training while underexplored for operational scenarios. The study concludes by highlighting the need for further research to understand the effectiveness of such an approach in mineral operations.KEYWORDS: Digital twinsmining and mineral processingIndustry 4.0internet of thingsdigitalisationsystem integrationextended realitycorporate Metaverse Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported by University of Queensland.","PeriodicalId":44166,"journal":{"name":"Mining Technology-Transactions of the Institutions of Mining and Metallurgy","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136313438","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}

引用次数: 0

Mining Metaverse – a future collaborative tool for best practice mining 挖掘Metaverse——一个用于最佳实践挖掘的未来协作工具

IF 1.1

Mining Technology-Transactions of the Institutions of Mining and Metallurgy Pub Date : 2023-07-03 DOI: 10.1080/25726668.2023.2235155

P. Stothard

引用次数: 1

Reliability evaluation of CAN-bus connectors with tailored testing 基于定制测试的can总线连接器可靠性评估

IF 1.1

Mining Technology-Transactions of the Institutions of Mining and Metallurgy Pub Date : 2023-07-03 DOI: 10.1080/25726668.2023.2235496

P. Ojala, J. Rämö, Juha Miettinen, M. Vilkko

引用次数: 0

Sustainable open pit fleet management system: integrating economic and environmental objectives into truck allocation 可持续露天矿车队管理系统:将经济和环境目标整合到卡车分配中

IF 1.1

Mining Technology-Transactions of the Institutions of Mining and Metallurgy Pub Date : 2023-07-03 DOI: 10.1080/25726668.2023.2233230

Matin Ghasempour Anaraki, A. M. Afrapoli

引用次数: 0

A Genetic algorithm scheme for large scale open-pit mine production scheduling 大型露天矿生产调度的遗传算法方案

IF 1.1

Mining Technology-Transactions of the Institutions of Mining and Metallurgy Pub Date : 2023-06-30 DOI: 10.1080/25726668.2023.2228071

N. Azadi, Hossein Mirzaei-Nasirabad, Amin Mousavi

引用次数: 0

Development of site specific blasting index parameters based on single hole blast test cratering 基于单孔爆破试验坑的现场特定爆破指标参数的制定

IF 1.1

Mining Technology-Transactions of the Institutions of Mining and Metallurgy Pub Date : 2023-06-14 DOI: 10.1080/25726668.2023.2219128

J. Aubertin, M. Wimmer, Masoud Sedghi

引用次数: 0