474 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
On the other hand, in Figure 7 is presented the graphs
obtained using the XRT measurements for the C and F
fraction, respectively.
As an example, the top 33% of the mass of the speci-
mens that belong to the C fraction, characterized by the
highest calcium carbonate grade based on XRT measure-
ments, will exhibit a grade of 84% and a recovery of 36%
(Figure 7). Whereas the F fraction will possess a calcium
carbonate grade of 83% and a recovery of 36%.
CONCLUSION
A study was undertaken to explore the feasibility of employ-
ing particle sensor-based sorting for the beneficiation of
local calcite. The response of four different sensors—XRT,
XRF, laser, and colorimeter—was analyzed for each speci-
men. The Theory of Sampling was utilized to quantify
heterogeneity, and the resulting CH values indicated no
substantial differences between the heterogeneity of the
two size fractions. Consequently, the size appeared to have
no significant impact on constitutive heterogeneity for the
evaluated material.
SRL and ROC both showed that XRT and XRF were
the sensors with the most impressive performances. The clas-
sification performances of the SRL and ROC approaches
were compared, revealing that ROC outperforms SRL as
the recovery of calcite is significantly higher when using
the ROC method. XRT and XRF emerged as the sensing
technologies demonstrating the best performance for both
C and F fractions. The study concludes that sensor-based
sorting is a technology capable of effectively classifying
material based on its calcite grade with the appropriate sen-
sor, demonstrating the potential of SBS to be applied in
industrial minerals.
REFERENCES
Cernuschi, F. (2014). Minería en Uruguay. Materias primas,
minería y reciclaje en el mundo. Uruguay ciencia, 18.
de Amores, I., Selier, S., Tomey, E., &Sánchez, G. (2023).
Beneficiation of limestone: synergy between sorting
and flotation (Internal report). Montevideo, Uruguay.
Fawcett, T. (2006). An introduction to ROC analysis.
Pattern Recognition Letters, 27(8), 861–874. doi:
10.1016/j.patrec.2005.10.010
Li, G., Klein, B., Sun, C., &Kou, J. (15 de 01 de 2020).
Applying Receiver-Operating-Characteristic (ROC)
to bulk ore sorting using XRF. Minerals Engineering,
146(0892-6875), 106–117. doi: 10.1016/j.mineng
.2019.106117
Modise, E.G., Zungeru, A.M., Mtengi, B., &Ude, A.U.
(2020). Sensor-Based Ore Sorting—A Review of
Current Use of Electromagnetic Spectrum in Sorting.
IEEE Access, 10, 112307–112326. doi: 10.1109/
ACCESS.2022.3216296
Robben, C. a. (2019). Sensor‐Based Ore Sorting Technology
in Mining—Past, Present and Future. Minerals, 9, 523.
doi: 10.3390/min9090523
Smith, P. (2001). A primer for sampling solids, liquids, and
gases. Based on the seven sampling errors of Pierre Gy. doi:
10.1137/1.9780898718478
UPM Biofore. (2023, 06 06). UPM inaugurates its Paso de
los Toros pulp mill in Uruguay. Retrieved from https://
www.upm.com/about-us/for-media/releases/2023/06
/upm-inaugurates-its-paso-de-los-toros-pulp-mill-in
-uruguay/
Wotruba, H. (2015). Sensor sorting technology-is the min-
erals industry missing a chance ?,(pp. 21–29). Aachen,
Germany.
Ordered by assay CaCO
3
grade
Ordered by sensor response
Ordered by assay CaCO
3
grade
Ordered by sensor response
A) B)
Figure 7. Cumulative recovery and cumulative grade of A) the C fraction and B) the F fraction ordered by the XRT response
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Extracted Text (may have errors)

474 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
On the other hand, in Figure 7 is presented the graphs
obtained using the XRT measurements for the C and F
fraction, respectively.
As an example, the top 33% of the mass of the speci-
mens that belong to the C fraction, characterized by the
highest calcium carbonate grade based on XRT measure-
ments, will exhibit a grade of 84% and a recovery of 36%
(Figure 7). Whereas the F fraction will possess a calcium
carbonate grade of 83% and a recovery of 36%.
CONCLUSION
A study was undertaken to explore the feasibility of employ-
ing particle sensor-based sorting for the beneficiation of
local calcite. The response of four different sensors—XRT,
XRF, laser, and colorimeter—was analyzed for each speci-
men. The Theory of Sampling was utilized to quantify
heterogeneity, and the resulting CH values indicated no
substantial differences between the heterogeneity of the
two size fractions. Consequently, the size appeared to have
no significant impact on constitutive heterogeneity for the
evaluated material.
SRL and ROC both showed that XRT and XRF were
the sensors with the most impressive performances. The clas-
sification performances of the SRL and ROC approaches
were compared, revealing that ROC outperforms SRL as
the recovery of calcite is significantly higher when using
the ROC method. XRT and XRF emerged as the sensing
technologies demonstrating the best performance for both
C and F fractions. The study concludes that sensor-based
sorting is a technology capable of effectively classifying
material based on its calcite grade with the appropriate sen-
sor, demonstrating the potential of SBS to be applied in
industrial minerals.
REFERENCES
Cernuschi, F. (2014). Minería en Uruguay. Materias primas,
minería y reciclaje en el mundo. Uruguay ciencia, 18.
de Amores, I., Selier, S., Tomey, E., &Sánchez, G. (2023).
Beneficiation of limestone: synergy between sorting
and flotation (Internal report). Montevideo, Uruguay.
Fawcett, T. (2006). An introduction to ROC analysis.
Pattern Recognition Letters, 27(8), 861–874. doi:
10.1016/j.patrec.2005.10.010
Li, G., Klein, B., Sun, C., &Kou, J. (15 de 01 de 2020).
Applying Receiver-Operating-Characteristic (ROC)
to bulk ore sorting using XRF. Minerals Engineering,
146(0892-6875), 106–117. doi: 10.1016/j.mineng
.2019.106117
Modise, E.G., Zungeru, A.M., Mtengi, B., &Ude, A.U.
(2020). Sensor-Based Ore Sorting—A Review of
Current Use of Electromagnetic Spectrum in Sorting.
IEEE Access, 10, 112307–112326. doi: 10.1109/
ACCESS.2022.3216296
Robben, C. a. (2019). Sensor‐Based Ore Sorting Technology
in Mining—Past, Present and Future. Minerals, 9, 523.
doi: 10.3390/min9090523
Smith, P. (2001). A primer for sampling solids, liquids, and
gases. Based on the seven sampling errors of Pierre Gy. doi:
10.1137/1.9780898718478
UPM Biofore. (2023, 06 06). UPM inaugurates its Paso de
los Toros pulp mill in Uruguay. Retrieved from https://
www.upm.com/about-us/for-media/releases/2023/06
/upm-inaugurates-its-paso-de-los-toros-pulp-mill-in
-uruguay/
Wotruba, H. (2015). Sensor sorting technology-is the min-
erals industry missing a chance ?,(pp. 21–29). Aachen,
Germany.
Ordered by assay CaCO
3
grade
Ordered by sensor response
Ordered by assay CaCO
3
grade
Ordered by sensor response
A) B)
Figure 7. Cumulative recovery and cumulative grade of A) the C fraction and B) the F fraction ordered by the XRT response

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