Dry laboratories – Mapping the required instrumentation and infrastructure for online monitoring, analysis, and characterization in the mineral industry

Ghorbani, Yousef; Zhang, Steven E.; Nwaila, Glen T.; Bourdeau, Julie E.; Safari, Mehdi; Hoseinie, Seyed Hadi; Nwaila, Phumzile C.; Ruuska, Jari

doi:10.1016/j.mineng.2022.107971

Cited by 14 publications

(2 citation statements)

References 116 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to cell hydrodynamics, the differences observed in recoveries could also be attributed to mineral surface chemistry and hydrophobicity, as reported by other researchers. This suggests that multiple factors can contribute to the variations in flotation performance, and highlights the importance of a comprehensive understanding of the underlying mechanisms involved [43][44][45][46][47]. Awatey et al [30] found that the critical contact angle required to float coarse sphalerite particles in CF was higher than in HF, and it increased as particle size increased for the flotation of sphalerite (0.250-1.180 mm) at laboratory scale.…”

Section: Hydrofloat Vs Denver Machine Cpf Performancementioning

confidence: 99%

Investigating the Amenability of a PGM-Bearing Ore to Coarse Particle Flotation

et al. 2023

Self Cite

View full text Add to dashboard Cite

Coarse particle flotation (CPF) is one of the strategies employed to reduce energy consumption in mineral-processing circuits. HydrofloatTM (HF) technology has been successfully applied in the coarse flotation of industrial minerals and sulphide middlings. However, this technology has not yet been applied in platinum group minerals (PGMs)’ flotation. In this paper, the amenability of platinum group minerals to CPF was investigated. Extensive flotation testwork was conducted to optimise the hydrodynamic parameters, i.e., bed level, air and water flow rates, in the flotation of coarse PGM feed using Hydrofloat. Mineralogical analysis of the feed and selected flotation products was conducted to understand the reasons for the recovery and loss of the valuable minerals. The results showed that the HF separator could upgrade the PGM ore with particles as coarse as +106 − 300 µm. For the optimised test, a reasonable Pt, Pd and Au recovery of 84% was achieved at a grade of 10 g/t and 16.5% mass pull, despite the platinum group minerals being poorly liberated (4.5 vol% fully liberated). The results demonstrated that HF achieved high recovery efficiencies across the 150–300 microns size fraction. The HF was therefore able to substantially increase the upper particle size that can be successfully treated by flotation in PGM operations. It was found that an increase in bed height, water rate and air flow rate resulted in an increase in recovery to a maximum. A further increase in the hydrodynamic parameters resulted in a decline in recovery. Hydrofloat outperformed the conventional Denver flotation machine across the following size fractions: +106 − 150 µm, +150 − 212 µm, +212 − 250 µm and +250 − 300 µm. The practical implications of the findings on the modification of existing circuits and the design of novel flowsheets for the processing of PGM ores with less water and energy consumption are discussed.

show abstract

Section: Hydrofloat Vs Denver Machine Cpf Performancementioning

confidence: 99%

Investigating the Amenability of a PGM-Bearing Ore to Coarse Particle Flotation

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are two major axes of integration: (1) adoption of transdisciplinary methods (artificial intelligence and data science) into the mineral industry (Karpatne et al, 2018;Bergen et al, 2019;He et al, 2022 and references therein) and (2) geometallurgical integration, of exploration, mining and mineral processing (e.g., Mena Silva et al, 2018). De-risked outcomes (e.g., survey data and prospectivity maps) are expected along both axes of integration and are an evolutionary pressure that is re-shaping the mineral value chain and its supporting disciplines (Mena Silva et al, 2018;Lawley et al, 2021;Zhang et al, 2021aZhang et al, , 2022bDaviran et al, 2022;Ghorbani et al, , 2023Government of Canada, 2022;Shirmard et al, 2022). Geoscientific surveys contain three key stages of the data pipeline (or lifecycle):…”

Section: Introductionmentioning

confidence: 99%

Predictive Geochemical Exploration: Inferential Generation of Modern Geochemical Data, Anomaly Detection and Application to Northern Manitoba

Bourdeau,

Zhang,

Lawley

et al. 2023

Nat Resour Res

Self Cite

View full text Add to dashboard Cite

Geochemical surveys contain an implicit data lifecycle or pipeline that consists of data generation (e.g., sampling and analysis), data management (e.g., quality assurance and control, curation, provisioning and stewardship) and data usage (e.g., mapping, modeling and hypothesis testing). The current integration of predictive analytics (e.g., artificial intelligence, machine learning, data modeling) into the geochemical survey data pipeline occurs almost entirely within the data usage stage. In this study, we predict elemental concentrations at the data generation stage and explore how predictive analytics can be integrated more thoroughly across the data lifecycle. Inferential data generation is used to modernize lake sediment geochemical data from northern Manitoba (Canada), with results and interpretations focused on elements that are included in the Canadian Critical Minerals list. The results are mapped, interpreted and used for downstream analysis through geochemical anomaly detection to locate further exploration targets. Our integration is novel because predictive modeling is integrated into the data generation and usage stages to increase the efficacy of geochemical surveys. The results further demonstrate how legacy geochemical data are a significant data asset that can be predictively modernized and used to support time-sensitive mineral exploration of critical minerals that were unanalyzed in original survey designs. In addition, this type of integration immediately creates the possibility of a new exploration framework, which we call predictive geochemical exploration. In effect, it eschews sequential, grid-based and fixed resolution sampling toward data-driven, multi-scale and more agile approaches. A key outcome is a natural categorization scheme of uncertainty associated with further survey or exploration targets, whether they are covered by existing training data in a spatial or multivariate sense or solely within the coverage of inferred secondary data. The uncertainty categorization creates an effective implementation pathway for future multi-scale exploration by focusing data generation activities to de-risk survey practices.

show abstract

Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data

Jan,

Minallah,

Sher

et al. 2024

Engineering Reports

View full text Add to dashboard Cite

Automating mineral delineation and rock type analysis using remote sensing imaging data is a critical application of machine learning. Traditional machine learning methods often struggle with accuracy and precise map generation. This study aims to enhance performance through a refined deep learning model. In this work, we present a deep learning pipeline to map the mineral deposits in the study area. Initially, we apply a deep convolutional neural network (CNN) to a specialized mineral dataset to map mineral deposits within the study area. Subsequently, we build a hybrid model combining deep CNN layers with a support vector machine (SVM). This merger significantly improves classification accuracy from an initial 92.7% to 95.3%. In our approach, CNN layers function as feature extractors while the SVM serves as the classification model. Moreover, we conduct an evaluation of the SVM using polynomial kernels of degrees 3, 6, 9, and 12. The results indicate that the SVM with a degree of 12 achieved the highest classification accuracy, followed by degrees 9, 6, and 3. Experimental results demonstrate the effectiveness of our proposed method for classifying remote sensing imaging data, showcasing its potential for advancing mineral delineation and rock type analysis.

show abstract

Dry laboratories – Mapping the required instrumentation and infrastructure for online monitoring, analysis, and characterization in the mineral industry

Cited by 14 publications

References 116 publications

Investigating the Amenability of a PGM-Bearing Ore to Coarse Particle Flotation

Investigating the Amenability of a PGM-Bearing Ore to Coarse Particle Flotation

Predictive Geochemical Exploration: Inferential Generation of Modern Geochemical Data, Anomaly Detection and Application to Northern Manitoba

Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data

Contact Info

Product

Resources

About