Washability Analysis of Coal Using RhoVol: A Novel 3D Image-based Method

Botlhoko, Shirley; Campbell, Q.P.; Roux, M. Le; Nakhaei, Fardis

doi:10.1080/08827508.2022.2027769

Cited by 4 publications

(9 citation statements)

References 25 publications

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“…Moreover, it is impossible to capture the impact of porosity and cracks on the volume of coal samples obtained from 3D images. The difference between the RhoVol measurements and sink and float data is not necessarily constant over all particles (Botlhoko et al, 2021;2022). This mismatch is termed the heterogeneity error in this paper.…”

Section: Rhovol Repeatability and Accuracymentioning

confidence: 96%

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Estimation of coal density using a 3D imaging system: RhoVol

Nakhaei

Campbell

Roux

et al. 2022

J. S. Afr. Inst. Min. Metall.

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The results of coal sink and float tests are plotted as washability curves. Analysis of these curves indicates the most effective method by which the coal can be cleaned. Sink and float experiments are sampling-dependent, destructive, and use toxic chemicals. The present research introduces an alternative method based on a 3D imaging system called RhoVol, in which the density of individual particles is determined from mass and volume measurements. A quantitative error analysis was conducted on coal sample density measurements obtained by RhoVol, and the main factors influencing the errors identified and investigated. The results show that the drawback of this technique is related to its not being able to detect hidden concavities in the particle, resulting in an overestimation of particle volume. Furthermore, it is difficult to capture the impact of porosity and cracks on the volume of coal particles obtained from 3D silhouette images. This mismatch between the RhoVol data and sink and float test results has led to attempts to apply neural network (NN) and linear regression techniques to produce a fully reliable model for correction and estimation of coal density. The NN approach offers superior predictive capability over linear regression, and the estimated density distribution is in line with the sink and float analysis. The root mean square error in estimation of density using the NN model was less than 0.05 g/cm3.

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Section: Rhovol Repeatability and Accuracymentioning

confidence: 96%

“…Coal washability data is vital for assessing the performance of coal preparation plants. The washability curves are expressed under optimal conditions in sink and float experiments in a graphic format (Korkmaz and Bentli, 2017;Botlhoko et al, 2021;2022).…”

Section: Introductionmentioning

confidence: 99%

Estimation of coal density using a 3D imaging system: RhoVol

Nakhaei

Campbell

Roux

et al. 2022

J. S. Afr. Inst. Min. Metall.

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“…All parameters except mass were calculated directly from the 3D images. A moving funnel finds the most suitable bin, and the particle then falls into the bin [16,18].…”

Section: Figure 1 Classification Chart Based On Roundness Andmentioning

confidence: 99%

“…In the last decade, with the development of new computer technologies, the number of publications on the successful implementation of the three-dimensional (3D) imaging systems in mineral processing has been growing fast [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, several techniques have been introduced to capture the 3D particle geometries by various 3D imaging systems, including RhoVol, stereo photography, X-ray CT, 3D laser scanning, white light scanning [19][20][21][22]. Some application of 3D imaging techniques in mineral processing is summarized as follows (Table 1): -crack density, rock characteristics -intergranular fracture and crack surface area during multiphase particle breakage in comminution operations [25, 26] [27, 28] Quantitative characterization of pore size and structural features [29] -Mineral liberation and grade grade-recovery curves -Exposed grain surface area [17,30,31] [32, 33] Density [16,22] This paper summarizes the publications and findings from 2015 to 2023 regarding the advances and applications of 3D imaging technology in the field of mineral processing. The existing technologies, such as high-speed scanning and image analysis techniques to measure significant particle characteristics, such as size, shape, volume, density and composition are introduced.…”

Section: Introductionmentioning

confidence: 99%

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Developments and applications of 3D imaging systems in mineral processing

Nakhaei,

Jovanović

2023

J Min & Metal A Mining

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Three-dimensional (3D) surface imaging systems have gained popularity in monitoring the status and condition of separation processes by characterizing the internal and external structures of particles such as size, shape, density and composition. This review article mainly focuses on demonstrating the state of the art of 3D imaging systems in mineral processing based of the recent growth in 3D sensors. The structure of this manuscript comprises an overview of the two 3D imaging systems, including RhoVol and X-ray computed tomography, based on the basic principles. Their applications in mineral processing are then explained. By integrating with other imaging tools and the 3D printing technology, 3D surface imaging systems will play an important role in the automation and control of mineral processing in the future.

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