Engineering › Mechanical Engineering

Mineral Processing and Grinding

Works Count: 54758

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Description

This cluster of papers focuses on the process of comminution in mineral processing, including topics such as mineral liberation analysis, grinding circuits, particle size distribution, DEM modelling, energy efficiency in comminution, stirred media mills, high voltage pulses for ore pre-weakening, ore sorting technologies, and machine learning applications in mineral processing.

Keywords

Comminution; Mineral Liberation Analysis; Grinding Circuits; Particle Size Distribution; DEM Modelling; Energy Efficiency; Stirred Media Mills; High Voltage Pulses; Ore Sorting; Machine Learning

Continuous Processing in the Pharmaceutical Industry

2005-06-01

K. Plumb

In situ leaching of copper: Challenges and future prospects

2015-09-05

Laura Sinclair , John F. Thompson

Mixing in the Process Industries

1992-01-01

N. Harnby , Michaela Edwards , A. W. Nienow

Physical methods in determinative mineralogy

1968-03-01

F. A. Campbell

Some problems in particle technology

1964-08-01

Hugh M. Hulburt , Stacy Katz

The Infrared Spectra of Minerals

1974-01-01

V. C. Farmer

Abstract The principal concern of this book is the use of vibrational spectroscopy as a tool in identifying mineral species and in deriving information concerning the structure, composition and reactions … Abstract The principal concern of this book is the use of vibrational spectroscopy as a tool in identifying mineral species and in deriving information concerning the structure, composition and reactions of minerals and mineral products. This does not mean that the approach is purely empirical; some theoretical understanding of the vibrational spectra of solids is essential to an assessment of the significance of the variations in the spectra that can be found within what is nominally a single mineral species, but which usually includes a range of compositions and defect structures. Theory alone, however, can give only limited support to the mineral spectroscopist, and careful studies of well-characterized families of natural and synthetic minerals have played an essential role in giving concrete structural significance to spectral features. The publication of this book represents a belief that theory and practice have now reached a state of maturitity and of mutual support which justifies a more widespread application of vibrational spectroscopy to the study of minerals and inorganic materials. The wide area of theory and practice that deserves to be covered has required a careful selection of the subject matter to be incorporated in this book. Since elementary vibrational spectroscopy is now regularly included in basic chemistry courses, and since so many books cover the theory and practice of molecular spectroscopy, it has been decided to assume the very basic level of knowledge which will be found, for example, in the elementary introduction of Cross and Jones (1969). With this assumption, it has been possible to concentrate on those aspects that are peculiar to or of particular significance for mineral spectroscopy.

Fluid Mechanical Description of Fluidized Beds. Equations of Motion

1967-11-01

T. B. Anderson , R. Jackson

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTFluid Mechanical Description of Fluidized Beds. Equations of MotionT. B. Anderson and Roy JacksonCite this: Ind. Eng. Chem. Fundamen. 1967, 6, 4, 527–539Publication Date (Print):November 1, 1967Publication … ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTFluid Mechanical Description of Fluidized Beds. Equations of MotionT. B. Anderson and Roy JacksonCite this: Ind. Eng. Chem. Fundamen. 1967, 6, 4, 527–539Publication Date (Print):November 1, 1967Publication History Published online1 May 2002Published inissue 1 November 1967https://pubs.acs.org/doi/10.1021/i160024a007https://doi.org/10.1021/i160024a007research-articleACS PublicationsRequest reuse permissionsArticle Views5716Altmetric-Citations1603LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access options Get e-Alerts

The Status of Population Balances

1985-01-01

Doraiswami Ramkrishna

CRC handbook of physical properties of rocks

1982-01-01

R. Stephen Carmichael

Data on various properties of rocks, minerals, and other related materials are compiled in an organized format which is predominantly tabular for easy use in comparison and referencing. Volume I … Data on various properties of rocks, minerals, and other related materials are compiled in an organized format which is predominantly tabular for easy use in comparison and referencing. Volume I includes the following topics: Mineral composition: chemical composition and properties of rocks, minerals and crystals, pore fluids, ores, coal, petroleum, Earth's crust, meteorites; Electrical properties: resistivity and dielectric constants of minerals, dry and wet rocks, sedimentary rock sequences, Earth's interior; Spectroscopic properties: absorption/transmission, reflectance and emission, and spectral characteristics of minerals and rocks in the visible and infrared range. Volume II covers Seismic velocities: compressional and shear wave velocities for rocks, minerals, marine sediments, Earth's crust, ice, variation with fluid saturation, pressure and temperature; Magnetic properties: properties of magnetic minerals and rocks and their variation with different parameters: Engineering properties: factors, test, and properties relating to rock appraisal, characterization, and assessment of hardness, strength, and deformation. Volume III treats these topics: Density of rocks and minerals: includes histograms of density ranges; Elastic constants of minerals: elastic moduli, thermal properties; Inelastic properties: strength and rheology for rocks and minerals, rock mechanics and friction, and stress-strain relations; Radioactivity: decay constants and heat production of isotope systems in geology; Seismic attenuation: in rocks, minerals, and the Earth, with application to oil exploration and terrestrial studies. -- AATA

DEM modelling of industrial granular flows: 3D case studies and the effect of particle shape on hopper discharge

2002-02-01

Paul W. Cleary , Mark L. Sawley

High-resolution PCB analysis: synthesis and chromatographic properties of all 209 PCB congeners

1984-06-01

Michael D. Mullins , Cynthia M. Pochini , Shelia. McCrindle +3 more

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTHigh-resolution PCB analysis: synthesis and chromatographic properties of all 209 PCB congenersMichael D. Mullins, Cynthia M. Pochini, Shelia. McCrindle, Marjorie. Romkes, Stephen H. Safe, and Lorna M. … ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTHigh-resolution PCB analysis: synthesis and chromatographic properties of all 209 PCB congenersMichael D. Mullins, Cynthia M. Pochini, Shelia. McCrindle, Marjorie. Romkes, Stephen H. Safe, and Lorna M. SafeCite this: Environ. Sci. Technol. 1984, 18, 6, 468–476Publication Date (Print):June 1, 1984Publication History Published online1 May 2002Published inissue 1 June 1984https://pubs.acs.org/doi/10.1021/es00124a014https://doi.org/10.1021/es00124a014research-articleACS PublicationsRequest reuse permissionsArticle Views1135Altmetric-Citations459LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts

Analytical Methods for Coal and Coal Products

1979-01-01

Clarence Karr

Experimental study of transverse bed motion in rotary kilns

1983-06-01

H. Henein , J. K. Brimacombe , A. P. Watkinson

Introduction to the mathematical description of grinding as a rate process

1971-11-01

L.G. Austin

Principles of geostatistics

1963-12-01

G. Matheron

Knowledge of ore grades and ore reserves as well as error estimation of these values, is fundamental for mining engineers and mining geologists. Until now no appropriate scientific approach to … Knowledge of ore grades and ore reserves as well as error estimation of these values, is fundamental for mining engineers and mining geologists. Until now no appropriate scientific approach to those estimation problems has existed: geostatistics, the principles of which are summarized in this paper, constitutes a new science leading to such an approach. The author criticizes classical statistical methods still in use, and shows some of the main results given by geostatistics. Any ore deposit evaluation as well as proper decision of starting mining operations should be preceded by a geostatistical investigation which may avoid economic failures.

Influence of implant length and diameter on stress distribution: A finite element analysis

2004-01-01

Lucie Himmlová , Taťjána Dostálová , Alois Kácovský +1 more

Modeling and Simulation of Mineral Processing Systems

2001-01-01

Rudibert King

The simulation of fabric development during plastic deformation and its application to quartzite: the influence of deformation history

1980-01-01

Gordon Lister , B. E. Hobbs

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Application of Box–Behnken design and response surface methodology for modeling of some Turkish coals

2006-07-22

N. Aslan , Yakup Cebeci

Modern SEM-based mineral liberation analysis

2006-10-06

Rolf Fandrich , Ying Gu , Debra Burrows +1 more

The Chemistry and Technology of Coal

2012-09-04

James G. Speight

The demand for coal use (for electricity generation) and coal products, particularly liquid fuels and chemical feedstocks, is increasing throughout the world. Traditional markets such as North America and Europe … The demand for coal use (for electricity generation) and coal products, particularly liquid fuels and chemical feedstocks, is increasing throughout the world. Traditional markets such as North America and Europe are experiencing a steady increase in demand whereas emerging Asian markets, such as India and China, are witnessing a rapid surge in dema

Medial axis analysis of void structure in three‐dimensional tomographic images of porous media

1996-04-10

W. Brent Lindquist , S.L Lee , David A. Coker +2 more

We introduce the medial axis as a tool in the analysis of geometric structure of void space in porous media. The medial axis traces the fundamental geometry of the void … We introduce the medial axis as a tool in the analysis of geometric structure of void space in porous media. The medial axis traces the fundamental geometry of the void pathways. We describe an algorithm for generating the medial axis of the void structure from digitized three dimensional images of porous media obtained from X ray CAT scans. The medial axis is constructed during an iterative erosion procedure which, at each step, replaces the image of the void structure with a smaller version obtained by eroding its surface layer of voxels. The algorithm is applied to high (5 μm) resolution microtomographic images of two rock chips (Berea sandstone and Danish chalk) and a sample of uniform (100 μm) diameter, packed glass beads. We statistically investigate several geometrical properties of the structure of the medial axes obtained. The first is the distribution of relative volumes in each erosion layer of the void space. We find the distributions to be exponential for the two real rock samples and normal for the packed glass beads. The second property investigated is the distribution of volumes of disconnected segments of the medial axis which are in one‐to‐one correspondence with disconnected void segments of the sample. We find indications for a universal power law behavior governing the distribution of volumes of the smallest disconnected pieces. The final behavior studied is a geometric tortuosity as measured by shortest paths through the medial axis. This tortuosity distribution appears well described by a gamma distribution.

Dynamic recrystallization of quartz: correlation between natural and experimental conditions

2002-01-01

Michael Stipp , Holger Stünitz , Renée Heilbronner +1 more

Abstract Quartz veins in the Eastern Tonale mylonite zone (Italian Alps) were deformed in strike-slip shear. Due to the synkinematic emplacement of the Adamello Pluton, a temperature gradient between 280°C … Abstract Quartz veins in the Eastern Tonale mylonite zone (Italian Alps) were deformed in strike-slip shear. Due to the synkinematic emplacement of the Adamello Pluton, a temperature gradient between 280°C and 700°C was effected across this fault zone. The resulting dynamic recrystallization microstructures are characteristic of bulging recrystallization, subgrain rotation recrystallization and grain boundary migration recrystallization. The transitions in recrystallization mechanisms are marked by discrete changes of grain size dependence on temperature. Differential stresses are calculated from the recrystallized grain size data using paleopiezometric relationships. Deformation temperatures are obtained from metamorphic reactions in the deformed host rock. Flow stresses and deformation temperatures are used to determine the strain rate of the Tonale mylonites through integration with several published flow laws yielding an average rate of approximately 10 −14 s −1 to 10 −12 s −1 . The deformation conditions of the natural fault rocks are compared and correlated with three experimental dislocation creep regimes of quartz of Hirth & Tullis. Linking the microstructures of the naturally and experimentally deformed quartz rocks, a recrystallization mechanism map is presented. This map permits the derivation of temperature and strain rate for mylonitic fault rocks once the recrystallization mechanism is known.

The role of steric repulsive force in the dispersion of cement particles in fresh paste prepared with organic admixture

1997-01-01

Hiroshi Uchikawa , Shunsuke Hanehara , Daisuke Sawaki

XLFRAC: a program for the interactive testing of magmatic differentiation models

1978-01-01

John C. Stormer , J. Nicholls

The impossibility of comminuting small particles by compression

1978-04-01

K. Kendall

Introduction to mineral sciences

1992-01-01

Andrew Putnis

The subject of mineralogy is moving away from the traditional systematic treatment of mineral groups toward the study of the behaviour of minerals in relation to geological processes. A knowledge … The subject of mineralogy is moving away from the traditional systematic treatment of mineral groups toward the study of the behaviour of minerals in relation to geological processes. A knowledge of how minerals respond to a changing geological environment is fundamental to our understanding of many dynamic earth processes. By adopting a materials science approach, An Introduction to Mineral Sciences explains the principles underlying the modern study of minerals, discussing the behaviour of crystalline materials with changes in temperature, pressure and chemical environment. The concepts required to understand mineral behaviour are often complex, but are presented here in simple, non-mathematical terms for undergraduate mineralogy students. After introductory chapters describing the principles of diffraction, imaging and the spectroscopic methods used to study minerals, the structure and behaviour of the main groups of rock-forming minerals are covered, and the role of defects in the deformation and transformation of a mineral are explained. The energy changes and the rate of transformation processes are introduced using a descriptive approach rather than attempting a complete and rigorous treatment of the thermodynamics and kinetics. Examples and case histories from a range of mineral groups are set in an earth science context, such that the emphasis of this book is to allow the student to develop an intuitive understanding of the structural principles controlling the behaviour of minerals.

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Mechanochemistry in Nanoscience and Minerals Engineering

2008-01-01

Peter Baláž

Neural Networks in Materials Science.

1999-01-01

H. K. D. H. Bhadeshia

There are difficult problems in materials science where the general concepts might be understood but which are not as yet amenable to scientific treatment. We are at the same time … There are difficult problems in materials science where the general concepts might be understood but which are not as yet amenable to scientific treatment. We are at the same time told that good engineering has the responsibility to reach objectives in a cost and time-effective way. Any model which deals with only a small part of the required technology is therefore unlikely to be treated with respect. Neural network analysis is a form of regression or classification modelling which can help resolve these difficulties whilst striving for longer term solutions. This paper begins with an introduction to neural networks and contains a review of some applications of the technique in the context of materials science.

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Grain detection from 2d and 3d EBSD data—Specification of the MTEX algorithm

2011-09-07

Florian Bachmann , Ralf Hielscher , Helmut Schaeben

Chemical and Fuel Production by Anaerobic Bacteria

1980-10-01

J. G. Zeikus

Human coronavirus (HCoV) infection causes respiratory diseases with mild to severe outcomes. In the last 15 years, we have witnessed the emergence of two zoonotic, highly pathogenic HCoVs: severe acute … Human coronavirus (HCoV) infection causes respiratory diseases with mild to severe outcomes. In the last 15 years, we have witnessed the emergence of two zoonotic, highly pathogenic HCoVs: severe acute respiratory syndrome coronavirus (SARS-CoV) and ...Read More

FLINCOR: A microcomputer program for the reduction and investigation of fluid-inclusion data.

1989-12-01

Philip E. Brown

Basic Linear Geostatistics

1998-01-01

Margaret Armstrong

Practical model for organic richness from porosity and resistivity logs

1991-09-01

Multivariate Geostatistics

1995-01-01

Hans Wackernagel

95/00016 Coal: Typology-Physics-Chemistry-Constitution

1995-01-01

Van Krevelen

Fault and Joint Development in Brittle and Semi-Brittle Rock

1966-01-01

Neville J. Price

Sedimentary carbonate minerals

1973-12-01

F. Lippmann

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Rate Processes of Extractive Metallurgy

1979-01-01

Hong Yong Sohn , M.E. Wadsworth

Efficiency of Bioreactors in the Removal of Heavy Metals in Acidic Metallic Mining-Influenced Water in Ponce Enríquez, Ecuador

2025-06-25

Paola Almeida-Guerra , Paulo Escandón-Panchana , Maribel Aguilar-Aguilar +3 more | International Journal of Environmental Impacts

Multi-rate data-driven model predictive control for ball mill grinding process

2025-06-25

Wei Dai , Qian Tian , Yi‐Zhuo Yang | Minerals Engineering

Identification of NAPL Contamination Occurrence States in Low-Permeability Sites Using UNet Segmentation and Electrical Resistivity Tomography

2025-06-24

Man Gao , Yu Xiao , Xiao-Lei Zhang | Applied Sciences

To address the challenges in identifying NAPL contamination within low-permeability clay sites, this study innovatively integrates high-density electrical resistivity tomography (ERT) with a UNet deep learning model to establish an … To address the challenges in identifying NAPL contamination within low-permeability clay sites, this study innovatively integrates high-density electrical resistivity tomography (ERT) with a UNet deep learning model to establish an intelligent contamination detection system. Taking an industrial site in Shanghai as the research object, we collected apparent resistivity data using the WGMD-9 system, obtained resistivity profiles through inversion imaging, and constructed training sets by generating contamination labels via K-means clustering. A semantic segmentation model with skip connections and multi-scale feature fusion was developed based on the UNet architecture to achieve automatic identification of contaminated areas. Experimental results demonstrate that the model achieves a mean Intersection over Union (mIoU) of 86.58%, an accuracy (Acc) of 99.42%, a precision (Pre) of 75.72%, a recall (Rec) of 76.80%, and an F1 score (f1) of 76.23%, effectively overcoming the noise interference in electrical anomaly interpretation through conventional geophysical methods in low-permeability clay, while outperforming DeepLabV3, DeepLabV3+, PSPNet, and LinkNet models. Time-lapse resistivity imaging verifies the feasibility of dynamic monitoring for contaminant migration, while the integration of the VGG-16 encoder and hyperparameter optimization (learning rate of 0.0001 and batch size of 8) significantly enhances model performance. Case visualization reveals high consistency between segmentation results and actual contamination distribution, enabling precise localization of spatial morphology for contamination plumes. This technological breakthrough overcomes the high-cost and low-efficiency limitations of traditional borehole sampling, providing a high-precision, non-destructive intelligent detection solution for contaminated site remediation.

Energy expenditure during grain grinding using reciprocal quern and rotary quern

2025-06-24

Martin Hora , Vojtěch Mařík , Matěj Dundr +2 more | Journal of Archaeological Science

Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms

2025-06-24

Olga Shiryayeva , Batyrbek Suleimenov , Yelena Kulakova | Technologies

This study presents a sustainable and adaptive approach to mineral processing. A hybrid intelligent control system was developed to beneficiate fine chromite ore in a jigging machine. The objective is … This study presents a sustainable and adaptive approach to mineral processing. A hybrid intelligent control system was developed to beneficiate fine chromite ore in a jigging machine. The objective is to enhance separation efficiency and reduce chromium losses through real-time optimization of process parameters under variable feed conditions. The method addresses ore composition fluctuations by integrating three components: Physical modeling of particle motion, regression analysis, and neural network-based prediction. The jig bed level and pulsation frequency are used as control variables, while the Cr2O3 content in the feed (Cr) is treated as a disturbance. A neural network predicts the Cr2O3 content in the concentrate (Cc) and in the tailings (Ct), representing chromite-rich and gangue fractions, respectively. The optimization is performed using a constrained Interior-Point algorithm. The model demonstrates high predictive accuracy, with a mean squared error (MSE) below 0.01. The proposed control algorithm reduces chromium losses in tailings from 7.5% to 5.5%, while improving concentrate quality by 3–6%. A real-time human–machine interface (HMI) was developed in SIMATIC WinCC for process visualization and control. The hybrid framework can be adapted to other mineral processing systems by adjusting the model structure and retraining the neural network on new ore datasets.

Multiscale modeling of rock fracture in comminution — A comparative study of FEM accuracy and DEM scalability

2025-06-24

Laura Suarez , Vedad Tojaga , Erik Olsson +4 more | Minerals Engineering

Influence of Ball and Powder Filling Rate on the Evolution Behavior of Particles in Stirred Mills

2025-06-23

G.Z. Wang , Keqi Guo , Yaowen Xing +1 more | Mining Metallurgy & Exploration

A Comparative Evaluation of Threshold Segmentation and LiDAR for Sawmill Residue Volume Estimation

2025-06-22

Carlos Borrego-Núñez , Juan García-Quezada , Leonardo Vásquez-Ibarra +4 more | Forests

The sawn timber production process generates up to 63% of residues during primary processing in sawmills. For this industry, the devaluation and disposal of these residues remain significant challenges; proper … The sawn timber production process generates up to 63% of residues during primary processing in sawmills. For this industry, the devaluation and disposal of these residues remain significant challenges; proper management requires a more accurate quantification of the volume. This study evaluates and compares two indirect methods for estimating the volume of stacked residues: one based on image processing and the other on terrestrial LiDAR technology. Residues of Pinus spp. from a sawmill were used, with their actual volume determined using a xylometer. The image-based method, which uses threshold-based segmentation, achieved a R2 = 0.64 and RMSE = 0.006 m3. In contrast, the LiDAR-based method, which derives measurements directly from 3D reconstruction, obtained an R2 = 0.506 and RMSE = 0.009 m3. Despite these differences, ANOVA testing (p > 0.05) indicated no statistically significant differences between the methods. The results suggest that both approaches may serve as preliminary tools for forest residue quantification and provide a solid foundation for future research aimed at developing field-applicable technological solutions.

Uncertainty evaluation of coal fineness measured by imaging technique: model, validation, and online application

2025-06-22

Qiwen Jin , Lechong Chen , Dian Zhu +5 more | International Journal of Coal Preparation and Utilization

Research on recognition method of liner wear stage based on operation and vibration information of semi-autogenous mil

2025-06-21

Tao Li , Dakuo He , Shui-Qing Yu | Minerals Engineering

Comparative Analysis of Throughput Prediction Models in SAG Mill Circuits: A Geometallurgical Approach

2025-06-20

Madeleine Guillen , Guillermo Kenneth Iriarte Dávila , Hector Montes +2 more | Mining

This study was conducted on a copper porphyry deposit located in Espinar, Cusco (Peru), with the objective of developing and comparing predictive models for processing capacity in SAG grinding circuits. … This study was conducted on a copper porphyry deposit located in Espinar, Cusco (Peru), with the objective of developing and comparing predictive models for processing capacity in SAG grinding circuits. A total of 174 samples were used for the JK Drop Weight Test (JKDWT) and 1172 for the Bond Work Index (BWi), along with 36 months of operational plant data. Three modeling methodologies were evaluated: DWi-BWi, SGI-BWi, and SMC-BWi (Mia, Mib), all integrated into a geometallurgical block model. Validation was performed through reconciliation with actual plant data, considering operational constraints such as transfer size (T80) and maximum throughput (TPH). The model based on SMC parameters and BWi showed the best predictive performance, with a root mean square error (RMSE) of 143 t/h and a mean relative deviation of 1.5%. This approach enables more accurate throughput forecasting, improving mine planning and operational efficiency. The results highlight the importance of integrating geometallurgical and operational data to build robust models that are adaptable to ore variability and applicable to both short- and long-term planning scenarios.

Investigation of Strength Diversity Characterization in Mineral Materials Using Discrete Element Method

2025-06-20

Xiao Wang , Jun Liu , Fang Yang +3 more | Minerals

Accurate modeling of ore materials is fundamental to high-precision simulations in mineral processing and remains a key research focus. To address the modeling challenges arising from the inherent heterogeneity and … Accurate modeling of ore materials is fundamental to high-precision simulations in mineral processing and remains a key research focus. To address the modeling challenges arising from the inherent heterogeneity and strength diversity of ores, this study proposes a novel method based on the bonded particle model (BPM) in the Discrete Element Method (DEM), incorporating multi-sized sub-particle stochastic generation and assembly, as well as bond strength parameter design. The method was applied to model and simulate impact crushing of 30 mm size fraction gold, iron, and copper ores with varying strengths. The resulting particle size distributions of fragmented ores were analyzed. Furthermore, drop weight tests were conducted on ore samples of the same size fraction, and the experimental mass distribution of fragmented particles demonstrated good consistency with simulation results. These findings validate the capability of the proposed method to effectively characterize the strength diversity of natural ores, offering an advanced approach for high-fidelity modeling of mineral materials.

Geological characterisation methods for IOCG samples, resolution comparison and considerations for productive stages of mining

2025-06-19

Fernando Stocker , Pía Lois-Morales , Kimie Suzuki Morales | International Journal of Mining Reclamation and Environment

PREDICTION OF FLOW PROPERTIES OF HIGH-FLUIDITY CONCRETE USING MULTIPLE MACHINE LEARNING MODELS

2025-06-19

R. Miura , Kouta NAMIHIRA , Yoshitomo YAMADA +1 more | AIJ Journal of Technology and Design

Session 7. Oral Presentation for: Improving operational confidence and decision-making in the absence of key reservoir data through utilisation of a machine learning-based coal identification model in coal seam gas wells

2025-06-19

Gonzalo Vázquez | Australian Energy Producers journal.

Presented on 27 May 2025: Session 7 This study investigates the feasibility of using machine learning algorithms to identify the presence of coal (at a scale comparable to wireline logging), … Presented on 27 May 2025: Session 7 This study investigates the feasibility of using machine learning algorithms to identify the presence of coal (at a scale comparable to wireline logging), utilising drilling parameters from coal seam gas (CSG) wells located in the Surat Basin, Queensland, Australia. Generally, during the drilling operation and before wireline logging, the presence of coal lithologies is inferred from elevated gas levels liberated into the drilling mud. However, the reliability of gas sensors can be compromised, necessitating operations geologists to rely on fluctuating drilling parameters to make decisions. To address this, a supervised classification model using the XGBoost algorithm has been developed to predict coal in the absence of reliable gas sensor data, improving data available to the operations geologists for decision-making purposes. Trained on data from over 150 wells, the classification model identifies coal lithologies by analysing typically available, high-resolution drilling parameters. While these parameters vary due to physical changes in the drilled lithology, they are also significantly overprinted by operational factors. Underpinned by iterative exploratory data analysis, the machine learning workflow involved processing a large amount of raw data, defining the predictive target, feature engineering and model development. Traditional machine learning performance metrics, such as F1 score, recall and precision, have been used in conjunction with business-based metrics to compare model iterations. Even though the model faces challenges related to class imbalances, overfitting and variable operational environments, results demonstrate the utility in predicting the location of coal and assisting operational geology workflows in wells where gas readings are unreliable or unavailable. To access the Oral Presentation click the link on the right. To read the full paper click here

GS-YOLO-Seg: A Lightweight Instance Segmentation Method for Low-Grade Graphite Ore Sorting Based on Improved YOLO11-Seg

2025-06-19

Zeyang Qiu , Xueyu Huang , Zhaojie Sun +2 more | Sustainability

Efficient identification and removal of low-grade minerals during graphite ore processing is essential for improving product quality, optimizing resource recovery, and promoting sustainable production. To address the limitations of traditional … Efficient identification and removal of low-grade minerals during graphite ore processing is essential for improving product quality, optimizing resource recovery, and promoting sustainable production. To address the limitations of traditional sorting methods and performance bottlenecks in edge devices, this paper proposes a lightweight instance segmentation model, GS-YOLO-seg, for rapid identification and intelligent sorting of low-grade graphite ore in industrial production lines. The model first reduces network depth by adjusting the depth factor. Subsequently, the backbone network adopts the lightweight and efficient GSConv to perform downsampling, while a novel C3k2-Faster architecture is proposed to improve the effectiveness of feature extraction. Finally, the Segment-Efficient segmentation head is optimized to reduce redundant computations, further lowering the model load. On a self-constructed graphite ore image dataset, GS-YOLO-seg achieved comparable segmentation performance to the baseline YOLO11n-seg, while achieving a 30% reduction in FLOPs, 59% fewer parameters, 56% smaller model size, and 8% higher FPS. This method enhances the intelligence of the sorting process, preventing low-grade ores from entering subsequent stages, thus reducing resource waste, energy consumption, and carbon emissions, providing crucial technical support and feasible deployment pathways for building intelligent, green, and sustainable mining systems.

Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms

2025-06-19

Prosper Chimunhu , Roohollah Shirani Faradonbeh , Erkan Topal +2 more | Mining Technology Transactions of the Institutions of Mining and Metallurgy

Unplanned dilution in underground mining is detrimental to the business, as imprecise dilution factors may impair production forecasts for existing operations or the economic evaluation and viability of brownfield expansions … Unplanned dilution in underground mining is detrimental to the business, as imprecise dilution factors may impair production forecasts for existing operations or the economic evaluation and viability of brownfield expansions and greenfield projects. While high prediction accuracy of over 90% has been achieved using machine learning algorithms, particularly artificial neural networks (ANNs), the studies mostly predicted the overall dilution of stopes or included performance-subjective determinants, such as drill and blast factors. These factors compromise the models’ reproducibility for extensional application to cover new mining projects that do not have historical drill and blast input. To address this, the study explores gene expression programming (GEP) and ANN with backpropagation (BPNN) to predict dilution on a per-stope granularity based on geotechnical and design data. A 138-stope sample from a sublevel open stoping gold mine operation in Western Australia was used to generate predictive models. Model and infield results showed that the GEP model performed better, with a coefficient of determination, R 2 , of 0.740 with a root mean square error (RMSE) of 0.361 compared to BPNN's 0.681 and 0.409, respectively. Accordingly, the GEP model is recommended for dilution prediction for mine planning and production scheduling at the prescribed level of accuracy.

Fluorite ore recognition using spectral clustering and smartphone digital images calibrated with a ColorChecker: A case study at the Lújar underground mine, Spain

2025-06-18

Enming Li , Pablo Segarra , José A. Sanchidrián +4 more | Minerals Engineering

Stellar-YOLO: A Graphite Ore Grade Detection Method Based on Improved YOLO11

2025-06-18

Z. Qiu , Xueyu Huang , Sifan Li +1 more | Symmetry

Mineral recognition technology is crucial for improving mining efficiency and advancing smart mining development. To enable the efficient deployment of graphite ore grade detection on edge computing devices, we propose … Mineral recognition technology is crucial for improving mining efficiency and advancing smart mining development. To enable the efficient deployment of graphite ore grade detection on edge computing devices, we propose Stellar-YOLO, a YOLO11-based detection framework with asymmetrical architecture optimizations tailored for real-world conditions. The backbone is replaced by the lightweight StarNet to enhance computational efficiency, while the C3k2-CAS module, integrating convolution and additive attention, is embedded in the neck to improve feature expressiveness. The head incorporates the SEAM module, forming the Detect-SEAM, to boost the recognition of complex mineral details. Moreover, to robustly adapt to real mining environments, we apply simulated data augmentation techniques involving motion blur, dust noise, and low brightness conditions. Stellar-YOLO achieves 93.6% mAP based on a custom-built graphite ore dataset, outperforming the baseline by 4.5% and reducing the FLOPs, parameters, and model size by 27%, 26%, and 23%, respectively. This work explores how asymmetrical architectural innovations and robustness-oriented evaluation contribute to a lightweight and effective approach for computer vision-based mineral quality assessment, demonstrating strong potential for practical applications in real-world industrial environments.

Pressure Pulsation Characteristics and Constitutive Modeling of Filling Paste under Dual-Pump Parallel Operation

2025-06-18

Yue Pan , Xiaolong Zhang , Hao Yuan +2 more | Engineering Research Express

Abstract To address the demand for flow capacity expansion in filling mining systems, employing two parallel paste conveying pumps offers a cost-effective solution. However, as a viscoelastic-plastic fluid, filling paste … Abstract To address the demand for flow capacity expansion in filling mining systems, employing two parallel paste conveying pumps offers a cost-effective solution. However, as a viscoelastic-plastic fluid, filling paste is transported through pipelines under pulsed step excitation from paste pumps. Consequently, paste transportation exhibits distinct characteristics in both material properties and pumping mechanisms. The low efficiency of parallel pumping configurations constrains the practical adoption of dual-pump parallel systems. This study establishes a constitutive model based on the viscoelastic-plastic mechanism of filling paste. Using MATLAB/Simulink, a computational model simulating the constitutive behavior subjected to step-input excitation is developed. Two unbalanced pressure pulsation signals are generated to simulate parallel pump inputs, with systematic adjustments to frequency and phase differences for constitutive model loading. Analysis reveals that parallel pump operation achieves optimal flow continuity and pressure stability when synchronized at identical pulsation frequencies with a 90 0 phase difference. The findings substantially enhance pipeline transport efficiency while mitigating instantaneous impacts, providing critical theoretical guidance for parallel pump system design and operational control. This research establishes a foundation for effective implementation of dual-pump parallel conveying technology in mining applications.

Vanadiferous titanomagnetite ore rock classifier using machine learning from portable X-ray fluorescence spectra

2025-06-18

Jieun Baek , Seongjun Cho , Seungwook Shin | Environmental Earth Sciences

Artificial Intelligence as Analysis Tool of the Circuit Behavior of Mineral Processing Plants

2025-06-17

Jean-Alexander Müller , Julius Ortmanns , Felix Heinicke +1 more | Chemie Ingenieur Technik

Abstract Production key figures of mineral processing plants, often designed as circuits with recirculation of material, are subject to a high number of influencing factors. In order to set up … Abstract Production key figures of mineral processing plants, often designed as circuits with recirculation of material, are subject to a high number of influencing factors. In order to set up plant operation in an optimal way, identifying factors with high significance is important. In this study, an artificial neural network is employed as an additional tool for such processing plant audits by means of feature importance analysis. The presented method is applicable independently of the specific plant design, wherever sufficient process data is available. Furthermore, specific outcomes of the analysis of an exemplary potash compaction circuit are discussed.

Cumulative gangue mixing ratio prediction model for image-based in situ coal/gangue identification

2025-06-17

Jinwang Zhang , Jialin Zhao , Geng He +7 more | Computational Particle Mechanics

Coupling Bayesian Neural Network and Linear Regression Approach for High-Strength Rock Fragmentation Energy Optimization

2025-06-17

Blessing Olamide Taiwo , Yewuhalashet Fissha , Esma Kahraman +5 more | Rock mechanics letters.

The utilization of blasting has been prevalent in mining and civil engineering domains due to its cost-effectiveness and affordability as a method for fracturing rock. The achievement of an ideal … The utilization of blasting has been prevalent in mining and civil engineering domains due to its cost-effectiveness and affordability as a method for fracturing rock. The achievement of an ideal blast results in the most effective fragmentation while ensuring safety, cost-effectiveness, and environmental sustainability. In developing blast efficiency model, this study considered uniaxial compressive strength (UCS), spacing, hole depth, stemming length, point load index, powder factor, charge weight and burden. The model was trained by artificial neural network (ANN) and linear multivariate regression (LMVR) using 85 production datasets. After training four-layer ANN architecture 8-4-1 was found to be optimum. The prediction accurateness of two developed models was analysed using mean square error (MSE), performance index (PI), variance accounted for (VAF), root mean square error (RMSE), and co-efficient of determination (R2). The obtained values of performance parameters reveals ANN model to be more accurate as compared to the LMVR model. The ANN and LMVR model have R2 value of 90.9% and 56.3%. The optimized model was employed to achieve the optimal blast design for high strength granite to enhance blast efficiency. The test data result was utilized to optimize the blast parameters, including spacing, stemming length, burden, charge length, and charge. The values chosen for these parameters were 1.8 m, 1.8 m, 1.5 m, 157.17 kg, and 1.35 kg/m3, respectively, based on the optimum model.

Design, Fabrication, and Performance Evaluation of BRRI Compact Rice Mill

2025-06-17

A. K. M. Saiful Islam , Sajid Ullah Khan , Md Golam Kibria Bhuiyan +2 more | Journal of agricultural machinery and bioresources engineering.

In the past, smaller rice mills such as the Engelberg huller mill were frequently utilized. But caused by technological developments, larger rice mills with rubber roll huskers have shown promise, … In the past, smaller rice mills such as the Engelberg huller mill were frequently utilized. But caused by technological developments, larger rice mills with rubber roll huskers have shown promise, however at a high initial cost. The BRRI compact rice mill is designed to improve the rice milling system's efficiency. This single-pass hulling machine processes paddy into polished white rice by sequentially lifting, cleaning, de-husking, polishing, and grading the rice into head and broken categories using rubber rollers, blowers, and a size grader. Additionally, husk and bran are separated during the milling process. The rubber-roller spring system, main body shell, and machine base structure were validated using finite element analysis (FEA). From the simulation study, the machine's maximum displacement experienced 0.13 mm, and its base structure safety factor was 6.56. In addition, the FEA study was carried out to analyze the fatigue life, fatigue damage, safety factor, and fatigue sensitivity for the machine rigidity. The analysis showed a deformation of 2.43 mm and a safety factor of 1.74 for the main body shell, while the spring system demonstrated a safety factor of 2.93. These findings confirm the machine's strength, durability, and safe performance under applied loads. The machine is developed using locally available materials from the Bangladeshi marketplace. Its design is created using computer-aided modeling (3D and 2D) with all necessary technical specifications for future manufacturing. The selected materials ensure optimal performance for the compact rice mill. In power transmission, three B105 belts, each with a pitch length of 2667 mm, were used to transfer mechanical power from the main motor pulley to the polisher pulley; moreover, the adjustable motor base prevented slippage during the operation period. The rice milling machine was working at 0.02 kWh per kg of rice, with an 850 kg hr-1 capacity for hulling. The milling recovery was only 65%, but husking was better at smaller roller clearances. Thus, in the waters of enhanced milling recovery and lesser power consumption exists the potential for the BRRI compact rice mill to be a substitution for the expensive-type rice mills.

Entendiendo los conceptos desplazamiento y separación en fallas geológicas con TruDisp 2.0

2025-06-17

Ángel F. Nieto-Samaniego , Shunshan Xu , Ricardo Nieto Fuentes +1 more | Deleted Journal

En los estudios de campo que involucran fallas geológicas es poco común que se midan o se calculen desplazamientos netos, generalmente se miden separaciones considerándolas una aproximación de los desplazamientos. … En los estudios de campo que involucran fallas geológicas es poco común que se midan o se calculen desplazamientos netos, generalmente se miden separaciones considerándolas una aproximación de los desplazamientos. Esto puede deberse a lo complicado y tedioso que pueden resultar los cálculos, o bien a la falta de claridad sobre las relaciones que guardan los elementos geométricos de una falla. En este trabajo presentamos una breve descripción de los elementos que constituyen una falla haciendo énfasis en el “plano de observación” y la “línea de observación”, los cuales son fundamentales para distinguir entre desplazamientos y separaciones. Las separaciones pueden tener magnitudes y sentidos (desplazamientos aparentes) muy distintos a los desplazamientos reales, eso puede llevar a interpretaciones equivocadas y cálculos con errores muy grandes, especialmente si quien realiza el análisis no está familiarizado con los conceptos aquí analizados. Se hace uso del programa TruDisp 2.0 como herramienta didáctica para mostrar cómo se calcula el desplazamiento neto a partir de separaciones. Presentando ejemplos sintéticos y reales se ilustra el impacto que tienen los cambios de orientación del plano y la línea de observación en los cálculos del desplazamiento neto. Los casos reales que se presentan han sido usados en cursos de licenciatura en Geología.

<i>Pydidas</i>: a tool for automated X-ray diffraction data analysis

2025-06-16

Malte Storm , Peter Staron , Christina Krykwa | Journal of Applied Crystallography

The processing and analysis of X-ray diffraction (XRD) data at synchrotrons is often left to the user groups, which limits the user base to groups with a background in analyzing … The processing and analysis of X-ray diffraction (XRD) data at synchrotrons is often left to the user groups, which limits the user base to groups with a background in analyzing XRD data. Pydidas is a new Python package for processing XRD data. It provides an easy and intuitive interface and versatile processing options with the aim of being accessible to non-experts in XRD analysis. A graphical user interface (GUI) allows users to perform the full pipeline of data browsing, experiment calibration, workflow setup, processing and visualization in a single tool. In addition, pydidas ' logic is decoupled from the GUI and it can be fully used from within scripts or embedded into other processing pipelines. The pydidas processing pipeline is assembled from individual plugins which perform specific processing steps. This modular design allows for very versatile pipelines covering a wide range of applications. To improve the usability even further, custom plugins can be integrated in the pydidas workflow to allow specialized processing steps.

A consistency evaluation quantification method for ball mill digital twin model based on multidimensional analysis

2025-06-16

Wei Guan , Shuai Wang , Guoqiang Wang +5 more | Minerals Engineering

Optimizing HY Wet Ball Mill Efficiency Using Response Surface Methodology

2025-06-16

Peng Chen , Ying Hou , Hua Lv +1 more | JOM

Abstract This study uses response surface methodology (RSM) with a central composite design to optimize the HY wet ball mill work index. Key operational parameters—ore quantity, water addition, and media … Abstract This study uses response surface methodology (RSM) with a central composite design to optimize the HY wet ball mill work index. Key operational parameters—ore quantity, water addition, and media proportion—were systematically investigated. Variance analysis identified these factors as statistically significant ( p < 0.0001), with ore quantity exhibiting the strongest influence ( F value = 82.67). Optimal conditions (water addition: 1083.880 mL, media filling rate: 40.362%, ore quantity: 1827.130 g) achieved a minimum work index of 10.195 kW h t −1 . Validation tests confirmed the model’s accuracy, yielding an average work index of 10.168 kW h t −1 (deviation: 0.027 kW h t −1 ). Compared to single-factor tests, RSM optimization reduced energy consumption by 3.76% (0.3975 kW h t −1 ), translating to annual savings of $214,000 for a mid-sized processing plant. This demonstrates RSM’s efficacy in bridging the gap between laboratory-scale experiments and industrial grinding optimization.

Insights into Ball Milling for the Production of Highly Active Zeolites for Catalytic Cracking of VGO

2025-06-16

P. S. Kuznetsov , V. V. Malyavin , K. I. Dement’ev | Catalysts

This research systematically investigates the influence of high-energy ball-milling (BM) parameters on the acidic and textural properties of zeolite Y. Among the BM parameters, the milling time (MT) exerted a … This research systematically investigates the influence of high-energy ball-milling (BM) parameters on the acidic and textural properties of zeolite Y. Among the BM parameters, the milling time (MT) exerted a more significant influence on the zeolite degradation than milling speed (MS), primarily affecting particle size and crystallinity. Milling produced nanozeolites with particle sizes ranging from 210 to 430 nm, and their activity was tested in the catalytic cracking of vacuum gas oil (VGO). The highest catalytic activity was observed for the zeolite with a particle size of 397 nm and a crystallinity of 75.9%: the VGO conversion was 69.0%, and the gasoline fraction yield was 33.9%, compared to the parent zeolite’s 62.7% and 22.1%, respectively. It was found that the activity of milled zeolites in catalytic cracking is determined by the accessibility of acid sites, which can be controlled by forming an optimal micro-mesoporous structure.

STATISTICAL JUSTIFICATION OF A MANAGEMENT DECISION ON FIXED ASSET ACQUISITION: A CASE STUDY OF A MINING AND METALLURGICAL ENTERPRISE

2025-06-16

Alona Khmelyuk , Yevhen Herasimov | ECONOMIC BULLETIN OF THE DNIPROVSK STATE TECHNICAL UNIVERSITY

The article examines a real production situation involving a managerial decision on the feasibility of acquiring fixed assets at a mining and metallurgical enterprise. Emphasis is placed on the use … The article examines a real production situation involving a managerial decision on the feasibility of acquiring fixed assets at a mining and metallurgical enterprise. Emphasis is placed on the use of statistical analysis in combination with financial modeling. The study substantiates the advisability of switching from leasing equipment to purchasing it, through the construction of a financial model based on statistical methods of cost analysis, cost growth dynamics, and payback period calculation. The object of the study is one of the largest metallurgical enterprises in Ukraine, which has not ceased operations even once during the wartime period. Budgeting of alternative scenarios was carried out, taking into account the consistent load on machine and tractor units. Cumulative costs of leasing versus purchasing were graphically visualized. Conclusions were made regarding the economic efficiency of the investments, and tools of internal cost control within the enterprise management system were recommended. As a result of the conducted research, a practical case was formed for making a managerial decision based on cost budgeting and statistical analysis in the context of a mining and metallurgical enterprise. The study confirmed the feasibility of applying a combined approach—integrating economic modeling and the analysis of dynamic indicators–when making decisions about investing in fixed assets. The calculations demonstrate that, under conditions of stable production volume and equipment utilization, the shift from leasing to ownership allows for significant cost reductions in the long term. This is particularly evident in the case of the bulldozer, where the payback period remains within an acceptable range. In contrast, investment in the excavator requires additional conditions and more in-depth economic evaluation. Thus, the implementation of cost budgeting in conjunction with statistical analysis should become an integral part of the internal control system and strategic management of industrial enterprises aiming to improve resource efficiency and resilience to economic risks.

A Comprehensive Review of Various AI-Based Segmentation Algorithms for Multiscale Rocks: Principles, Evaluations, Simple Applications and Future Directions

2025-06-16

Zhi Zhao , Xiaoping Zhou | Rock Mechanics and Rock Engineering

Performance improvement of medium-speed coal mills based on response surface method

2025-06-16

Hyo‐Eon Jin , Yiming Li , Biaobiao Lin +2 more | International Journal of Coal Preparation and Utilization

Insights into gangue reagent interactions using process mineralogy

2025-06-16

N.J. Shackleton , V. Malysiak , L. Moshabeshe +1 more | Minerals Engineering

Study of Rare Earth Elements of Brown Coal of the Southern Region of Kyrgyzstan

2025-06-15

N. Erkinbaeva , K. Shakirbaev | Bulletin of Science and Practice

The results of X-ray fluorescence analysis of brown coal from the southern region of Kyrgyzstan are presented. It was found that coal from the Min-Kush and Bel-Alma deposits contains from … The results of X-ray fluorescence analysis of brown coal from the southern region of Kyrgyzstan are presented. It was found that coal from the Min-Kush and Bel-Alma deposits contains from 0.007% to 15.12% Ir, 0.174% Zr, 0.198% Rb, from 0.232% to 0.242% In, from 1.106% to 1.188% Pd and 58.97% Os.

Time Series Prediction Method of Clean Coal Ash Content in Dense Medium Separation Based on the Improved EMD-LSTM Model

2025-06-15

Kai Cheng , Xiaokang Zhang , Keping Zhou +5 more | Big Data and Cognitive Computing

Real-time ash content control in dense medium coal separation is challenged by time lags between detection and density adjustment, along with nonlinear/noisy signals. This study proposes a hybrid model for … Real-time ash content control in dense medium coal separation is challenged by time lags between detection and density adjustment, along with nonlinear/noisy signals. This study proposes a hybrid model for clean coal ash content in dense medium separation by integrating empirical mode decomposition, long short-term memory networks, and sparrow search algorithm optimization. A key innovation lies in removing noise-containing intrinsic mode functions (IMFs) via EMD to ensure clean signal input to the LSTM model. Utilizing production data from a Shanxi coal plant, EMD decomposes ash content time series into intrinsic mode functions (IMFs) and residuals. High-frequency noise-containing IMFs are selectively removed, while LSTM predicts retained components. SSA optimizes LSTM parameters (learning rate, hidden layers, epochs) to minimize prediction errors. Results demonstrate the EMD-IMF1-LSTM-SSA model achieves superior accuracy (RMSE: 0.0099, MAE: 0.0052, MAPE: 0.047%) and trend consistency (NSD: 12), outperforming baseline models. The study also proposes the novel “Vector Value of the Radial Difference (VVRD)” metric, which effectively quantifies prediction trend accuracy. By resolving time-lag issues and mitigating noise interference, the model enables precise ash content prediction 16 min ahead, supporting automated density control, reduced energy waste, and eco-friendly coal processing. This research provides practical tools and new metrics for intelligent coal separation in the context of green mining.

Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence Technologies

2025-06-15

Saleh Ghadernejad , Kamran Esmaeili , Mariano P. Consens | Remote Sensing

Rock hardness significantly impacts comminution efficiency, one of mining’s most energy-intensive processes. Accurate, rapid, and non-invasive hardness characterization can enhance mine-to-mill optimization and energy management. This study investigates sensor-based technologies, … Rock hardness significantly impacts comminution efficiency, one of mining’s most energy-intensive processes. Accurate, rapid, and non-invasive hardness characterization can enhance mine-to-mill optimization and energy management. This study investigates sensor-based technologies, hyperspectral imaging, and portable X-ray fluorescence (pXRF) integrated with machine learning (ML) algorithms for characterizing rock hardness in open-pit gold mining contexts. A total of 159 rock samples from two Canadian open-pit gold mines were analyzed through Leeb rebound hardness (LRH), short-wave infrared (SWIR) hyperspectral imaging, and a pXRF analyzer for chemical characterization. The most critical spectral features of SWIR images were extracted using a novel and automated feature extraction approach and further refined by applying a recursive feature elimination (RFE) algorithm to reduce the dimensionality of the spectral feature space. Three ML algorithms, including Random Forest Regressor (RFR), Adaptive Boosting (AdaBoost), and Multivariate Linear Regression (MLR), were applied to develop predictive hardness models considering three scenarios: using chemical features, using refined spectral features, and their combination. The findings underscore the potential of advanced sensor integration and analytics in remotely characterizing rock hardness, which could contribute to enhancing efficiency and sustainability in modern mining operations.

Artificial neural network integrated SHapley Additive exPlanations modeling for sodium dichromate formation

2025-06-14

M.J. Mvita , N. G. Zulu , B.M. Thethwayo | Engineering Applications of Artificial Intelligence

Optimization of key operating parameters to enhance performance and energy efficiency of a hammer mill for corn grinding

2025-06-13

Maher Al-Eid , Ahmad Qabatty , Rayad Kubaisi +1 more | Deleted Journal

Comminution Flowsheet Energy Requirements of a New Narrow-Vein Mining Method

2025-06-13

Judith George , Allan Cramm , Stephen Butt | Energies

Narrow-vein deposits have historically been valuable in producing gold, tin, copper, silver, lead, and zinc. Developing these mineral resources is sometimes challenging due to economic and safety concerns. Given the … Narrow-vein deposits have historically been valuable in producing gold, tin, copper, silver, lead, and zinc. Developing these mineral resources is sometimes challenging due to economic and safety concerns. Given the small to medium scale of production, narrow-vein mining could be labor-intensive with increased exposure of the miners to hazardous conditions. A safe, mechanized, efficient, and sustainable method can be invaluable to operators looking to develop narrow-vein mineral resources. The comminution circuit (consisting of crushing and grinding) is downstream of most mineral resources’ extraction processes. Comminution is significantly energy-intensive, consuming almost half of the energy supplied to a mineral-processing activity. Thus, several engineers have investigated the continued development of sustainable narrow-vein mining and comminution technologies. This journal article focuses on a developed innovative, safe, mechanized, and continuous narrow-vein mining technology that has further made accessing narrow-vein deposits more economically feasible and efficient while reducing dilution of ores. The article also extensively presents the impact of this new mining approach on the daily production of the operation and the observed particle size distributions of the day-to-day operational output. Subsequently, the article evaluates and presents the impact of the new procedure of mineral extraction on the resultant size of the cuttings generated as well as the expected energy input of the comminution process downstream of the mining operation. The novelty of the mining method upon which this work is based is improved capital expenditure and reduced dilution. With the new mining method, otherwise-uneconomic narrow-vein deposits can be accessed.

CEMENT KILN DUST (CKD) AS A POTENTIAL STABILIZER TO MITIGATE THE SWELLING OF CLAYS

2025-06-13

Yahia E.-A. Mohamedzein , Mohamed Al-Aghbari , Zuweina Al Kindi | Jurnal Teknologi

Utilizing industrial waste, like CKD (cement kiln dust) to stabilize expansive soils is of paramount importance in reducing the environmental impact of waste and in alleviating the expansive soil-induced structural … Utilizing industrial waste, like CKD (cement kiln dust) to stabilize expansive soils is of paramount importance in reducing the environmental impact of waste and in alleviating the expansive soil-induced structural damage. More studies are needed to demonstrate that CKD is beneficial in lowering the potential of expansive soils to swell. Therefore, this paper investigates how CKD affects the swelling of the CKD-treated soil as well as other engineering properties like compaction, Atterberg limits, mineral compositions, and microfabric. Soil stabilization was achieved by adding CKD in amounts of 6 to 30% of soil mass. The study revealed that CKD reduced the liquid limit from 85 % to 75 % and the plasticity index from 46 % to 16 % and thus, improved the plasticity and workability of the soil. Moreover, CKD decreased the swelling potential from 11.2 % to 3.4%, and the swelling pressure from 108 kPa to 17.5 kPa. XRD patterns demonstrated the usefulness of CKD for reduction in the intensity of palygorskite, montmorillonite, and illite swelling minerals. The success of the stabilization mechanism was also confirmed by the SEM micrographs where condensed soil particles with a few numbers of small pores were observed.

Application of harmonic state estimation for identification of illegal cryptocurrency mining farms

2025-06-12

Amir-Saeed Es’Haghi , Abbas Marini , Maziar Karimi +1 more | Electrical Engineering

Exploring an alternative flowsheet for the recovery of magnetite from magnetite rich tailings

2025-06-12

Niyoshaka Lameck , Athule Ngqalakwezi , Vincent Sithole +3 more | Minerals Engineering