Studying pore networks in rocks is critical for understanding reservoir properties, with mercury intrusion porosimetry (MIP) being a key laboratory method. MIP involves capillary intrusion of ...mercury, with intrusion pressure (P) related to capillary diameter (d) by the Washburn equation, incorporating mercury properties like contact angle (θ) and surface tension (γ). While a constant contact angle of 140° is typically assumed for MIP, it can vary based on rock mineralogy and surface roughness. This study aims to analyze how contact angle variations and surface roughness affect MIP analysis of carbonate rocks. Initially, contact angle data was obtained for calcite, dolomite, and quartz with varying roughness levels. Synthetic rocks were then constructed using these minerals. MIP was performed using the typical 140° contact angle and a calculated contact angle from the Cassie model, which considers surface composition fraction. Natural rock MIP analysis followed, with contact angles varying due to mineral composition and roughness. The study found higher contact angles than the standard 140°, affecting pore size distributions up to 30%. Natural rock pore network curves were adjusted based on synthetic rock data analysis. These findings emphasize the need for accurate contact angle selection in MIP for precise pore distribution analysis and contribute to the further study of carbonate rocks.
Brazil has 95 million tons of Li reserves in the form of pegmatites but produces less than 1% of the global output. Historically Li production in Brazil has been low due to governmental restrictions ...aimed at controlling the exploitation and trade of Li in Brazil. However, as of 2022, these restrictions were revoked. The abundance of untapped pegmatite ores in Brazil complements the soaring demand for Li in energy-storage applications. This study performs process mineralogy studies on 10 samples collected from a Li pegmatite deposit in the southeastern region of Minas Gerais in Brazil. The samples were characterized by combining density separation and SEM-based automated mineralogy processing system allied with XRF, ICP OES, XRD, and LA–ICPMS. The latter was used to determine Li content in micas which allowed determining the Li deportment between Li-bearing minerals. The results show that the samples contain such Li-bearing minerals as muscovite (0.5 wt% Li2O) and lepidolite (3.1 wt% Li2O), in addition to spodumene (8.0 wt% Li2O). According to the characterization of the spodumene concentrate (d = 3.11) by density separation (at d = 2.95), two main trends were observed: (a) low Li deportment in the sink product (approximately 44% wt%) and higher Li2O grade (approximately 6.5 wt%), and (b) higher Li deportment in the sink product (58%) and lower Li2O content (approximately 4.9 wt%). The first trend is associated with higher modal content of mica since it carries Li to the light product. Lower Li grade is related to the presence of Fe-bearing minerals (e.g., epidote and amphibole) as they report to the dense product and do not contain Li. Spodumene has a high degree of liberation in all samples; therefore, it did not influence the deportment results. The findings highlight the benefit of combining scanning electron microscopy-based automated mineralogy with LA–ICPMS and other techniques from process mineralogy studies in mineral processing. In addition to the mineralogy and liberation characteristics, identifying Li-bearing minerals and determining Li deportment is crucial.
The demand for critical chemical elements applied in clean energy technologies is increasing and, although deep-sea mining feasibility still is an open question, it might be a pathway to overcome the ...problem. Among areas of interest for seabed mining, the Rio Grande Rise is an ocean ridge in the southwest Atlantic Ocean known for hosting a large polymetallic mineral deposit containing various critical elements at high concentrations. Therefore, this mineralogical and chemical study characterizes the Rio Grande Rise FeMn crusts and establishes features from a structural-chemical perspective that can guide further studies from various locations. Combining X-ray diffractometry, Raman spectroscopy, chemical analysis, and Scanning Electron Microscopy, it was found that samples are composed by manganese oxides, MnOx; ferryxite or goethite, FeOOH; magnesian calcite, Ca,Mg(CO3)2; and carbonate fluorapatite, Ca5(PO4,CO3)3F. The external portion of analyzed rocks consists of younger crust layers enriched in critical metals, such as cobalt (Co) and nickel (Ni), associated with MnOx – mainly vernadite and asbolane; titanium (Ti), associated with iron oxyhydroxides; and rare earth elements (REE), at distinct bearing minerals. Therefore, the data herein reported show that regions where the FeMn crusts have a thicker young layer host a higher content of critical elements. In addition, the information about the elemental distribution and mineral associations can guide further mineral and metallurgical processing steps.
In a multivariate database, the missing data can be obtained through several imputation techniques, which are particularly useful for data that are difficult to obtain, for any reason, or have high ...uncertainties or scarce variables. A Self-Organizing Maps (SOM) neural network is an effective tool for the analysis of multidimensional data applied for the imputation of data. In this paper, data from drilling were used for training, testing, and validation using the variables: total Au recovery (%), which means gold recovery from a gravity concentration plus hydrometallurgical process, Au (g/t), As (ppm), S (%), Al2O3 (%), CaO (%), K2O (%), and MgO (%). After training, the partial omission of Au content and recovery was carried out, from 10% to 50%, to evaluate the data imputation performance for those variables. The results imputed by the SOM were compared with the original data values and evaluated according to descriptive statistics; the results indicated a determination coefficient of 85% when 50% of the data were omitted and 93% when 10% of the data were omitted. Once demonstrated, the correlation between the original data and SOM imputation analysis can help geologists and metallurgists to obtain results with a high degree of reliability of metallurgical recovery through related chemical variables, making it possible to implement SOM analysis as a powerful tool to input analytical data. One of the practical applications of the proposed model is to produce a pattern of imputed data that can be a good alternative in the construction or generation of a synthetic geometallurgical database with missing data.
Sand scarcity is occurring in different countries all over the world as highlighted by the United Nations Environmental Program, therefore, the development of sustainable alternative materials is ...vital. This paper presents the potential of mineral processing to produce high quality recycled sand by comminuting mixed CDW (Construction and Demolition Waste) into fine fraction at an industrial plant. This methodology represents a change in recycling strategies that proved to be effective for attaining high-quality products that will contribute to enlarge recycled sand market, thus moving toward a circular economy. Samples of mixed CDW were collected from different sources, with different compositions and locations. Primary and secondary crushing were conducted by a jaw crusher, tertiary crushing in a cone crusher and then the material was milled in a rotor impact mill in closed circuit, with 2.0 mm size screening followed by an air classifier to remove the fines (below 0.074 mm). The crushed sand obtained, composed mainly by quartz and feldspar and particles shaped like natural sand, high density (2.71 g/cm3), low water absorption (4.6%) and represents 80% in mass of the recovered fraction. The filler fraction produced, 20% in mass, has a notably higher content of calcium oxide due to the content of cement paste. The sand obtained is particularly distinct from the usually recycled sand generated as a by-product (or mostly tailing), characterized by irregular shape, rough surface and high-water absorption. Appropriate processing is crucial for increasing the waste recycling rates and mitigate the sand scarcity in many world regions.
Information about accessibility is of great relevance for gold recovery studies. Obtaining these variables from machine learning models can greatly assist in quickly determining accessibility. Few ...studies have been published relating the mineralogy of the gold ore process and the application of artificial intelligence, mainly algorithms in predicting variables related to gold recovery and extraction. Accessibility is an important variable for understanding the ability to recover gold from a cyanide solution, which can occur through fractures or some other means that provides access to the solution and consequent leaching of the gold grain. This study aims to present a model capable of predicting the accessibility variable using a data set with 168 characterization results from different ML methods, such as Linear Regression (LR), Random Forest (RF), Sequential Minimum Optimization for Support Vector Machine (SMOreg) and Gaussian Processes (GP). In this context, it was possible to establish that the random forest model performed best by presenting a coefficient of determination R2 (0.77), MAE (11.76), and RMSE (14.48). It was also reported from the SHAP analysis that the Au_grade, exposed_a, and As_grade showed the highest contribution level towards the perdition process of the model.
SEM-based automated image analysis is one of the most comprehensive tools in mineralogical characterization and plays an important role in the mining sector, mainly due to its statistical robustness, ...reliability of results and rapid analysis compared to analogue methods. Mineralogical ore characterization, such as gold distribution, grain size and mode of occurrence, together with density separation, cyanidation and diagnostic leaching tests, are the key for the appropriate process design of the geometallurgy concept. The present research focuses on the development of a mineralogical characterization of low-grade gold ore (<0.5 g/t) using SEM-based automated image analysis (SEM-IA) to evaluate minerals association, gold exposure and elemental deportment by gold-bearing mineral as a support for the geometallurgy program. A set of 168 samples of low-grade carbonaceous seriticitic phyllite gold ore from an open pit mine located in Minas Gerais State, Brazil, was characterized. The geological units could be defined by the content of arsenic (<1000; 1000–2000; 2000–3000; 3000–4000 and >4000 ppm) due to a unique set of compositional properties, such as grain size, sulphide mineralogy and accessibility, which directly affect the metallurgical performance. The results demonstrated that there is a direct correlation between the arsenic grades and the gold content, as well as an influence of arsenic grades on gold accessibility. Furthermore, high arsenic content in gold grains tends to provide greater accessibility. Although gold grains occur mainly as inclusions in pyrite and arsenopyrite, they are rarely associated with other sulphide (pyrrhotite and galena).
Few studies have been published on the analysis and correlation of data from process mineralogical studies of gold ore employing artificial neural networks (ANNs). This study aimed to analyse and ...investigate the correlations obtained by the technological characterization of auriferous ore using an ANN called self-organizing map (SOM) to support geometallurgical studies. The SOM is a data analysis technique in which patterns and relationships within a database are internally derived and the outputs are visual, assisting in the understanding of data in the representation of 2D maps. In the representation generated, it was possible to establish that the variables of accessibility, exposed perimeter, median gold grain diameter (D50), and SiO2 and arsenic contents have strong positive correlations. Regarding geometallurgy, this study shows that SOM can identify large-scale spatial chemical–mineralogical gold ore patterns, which can help define the most relevant indicator variables for mineral processing.
This paper compares the use of jaw and impact secondary crushing for producing coarse recycled aggregates from concrete wastes, obtained from road pavement and demolished building materials. The ...crushing mechanism interferes directly with recycled aggregate properties at different levels: particle size distribution, aggregate shape, generation of micro-fractures, as well as regarding the detachment of porous hardened cement paste from particle surface in order to recover pure, non-porous natural aggregates. However, crusher selection in the recycling industry is mostly carried out by acquisition and maintenance costs, industry and manufacturer traditional habits, low cost associated with second hand equipment. It also does not consider essential parameters such as the final properties of the desired end-product. Representative samples from two recycling plants were collected after primary impact crusher and secondary crushing were performed in a controlled laboratory condition through jaw and impact crushers. The aggregates attained were characterized, demonstrating similar density, porosity, particle size distribution and content of attached cement paste. Minor observed differences do not justify the common belief in the industry that impact crushers provide an improvement in the quality of recycled aggregates due to the higher detachment of cement paste from aggregates.
Rocks with representative physical and chemical properties are essential to understanding fluid-solid flow behaviors at the pore scale. In this way, studying the pore space characteristics is a key ...point for evaluating and providing petrophysical properties for distinct rock types, such as synthetic rocks, with controlled and representative properties like natural ones. This work studies the petrophysical properties of synthetic carbonate plugs with a novel approach by correlating particle size, particle size fraction, and the morphology of particles with porosity and permeability, which could guide the scientific community to further forming of carbonate rocks with a controlled pore network. Results indicated that particle shape influenced the accommodation of particles in the porous space and, therefore, in the petrophysical properties, where an increase in particle size decreases porosity and increases permeability. Also, the obtained plugs showed the following petrophysical features: gas porosity from 10% to 17%, mercury porosity from 11% to 19%, gas permeability from 0.07 mD to 0.70 mD, and mercury permeability from 0.02 mD to 0.35 mD, providing important insight on controlling pore space in synthetic carbonate rocks.
•Pore space study is vital for evaluating petrophysical properties of rocks, including representative synthetic ones resembling naturals.•A novel approach studied synthetic carbonate plugs′petrophysical properties, correlating particle size, fraction, morphology with porosity and permeability.•Particle shape influenced the accommodation of particles in the porous space and, therefore, in the petrophysical properties. An increase in particle size decreased porosity and increased permeability.•Synthetic carbonate plugs showed gas and mercury porosity 10-17%, 11-19%; gas and mercury permeability 0.07-0.7mD, 0.02-0.35mD, respectively.•These results provide important insights into controlling pore space in synthetic carbonate rocks and could guide the scientific community towards further development of rocks with a controlled pore network.
PDF
